Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers

Toward Effective Educational Supervision in... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/14-956" }, "headline": "Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of...", "datePublished": "2025-09-22T09:50:16", "dateModified": "2026-02-09T12:30:33", "author": [ { "@type": "Person", "name": "Riyadh Ghaleb A. Alshameri" }, { "@type": "Person", "name": "Adel A. Nasser" }, { "@type": "Person", "name": "Abdul Hakim Abdullah" }, { "@type": "Person", "name": "Abed Saif Ahmed Alghawli" }, { "@type": "Person", "name": "Amani A. K. Elsayed" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Objectives Comprehensive educational supervision is essential for ensuring quality teaching, fostering professional development, and supporting institutional capacity building. However, its implementation encounters numerous structural, technical, and human resource challenges. This study aimed to identify, validate, rank, and cluster the technical barriers affecting comprehensive educational supervision in Amanat Al Asimah, Yemen. This aligns with national reform goals by offering strategic insights to improve supervisory systems, thereby enhancing teaching quality, institutional performance, and educational resilience in fragile contexts Methods This study employed a three-phase mixed-methods approach. Initially, a literature review identified 11 key barriers to effective supervision. These were validated using the Fuzzy Delphi Method (FDM), involving 16 experienced educational supervisors to assess the consensus and suitability of the items. Subsequently, a quantitative survey targeting 370 teachers was conducted to evaluate their perceived severity. Fuzzy set theory was used to aggregate and defuzzify the responses, generating crisp scores for prioritization. Finally, K-means clustering was applied to segment the barriers based on their impacts. Results FDM analysis confirmed the validity of all 11 identified barriers, with a domain-level threshold of 0.093 and an average expert consensus of 90%, indicating strong agreement. The fuzzy set-based evaluation highlighted three top-priority challenges: weak supervisory competencies, limited ability to develop effective supervisory plans, and poor supervisor-teacher relationships. K-means clustering grouped the barriers into three segments: one high-priority barrier, seven moderate-priority concerns, and three low-priority issues. Notably, weak supervisory competencies emerged as the most critical barrier, isolated in a high-priority cluster. Conclusion These findings provide evidence-based guidance for policy and strategic interventions aimed at enhancing the effectiveness of supervision systems in fragile educational settings. The study concludes with recommendations for strengthening supervisory competencies, improving resource allocation, and fostering trust-based supervisor-teacher relationships, thereby contributing to the quality of education and institutional resilience in Yemen. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-956/v1", "name": "Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy..." } } ] } Home Browse Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Alshameri RGA, Nasser AA, Abdullah AH et al. Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :956 ( https://doi.org/10.12688/f1000research.167498.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] Riyadh Ghaleb A. Alshameri 1 , Adel A. Nasser https://orcid.org/0000-0001-5456-1303 2,3 , Abdul Hakim Abdullah 1 , Abed Saif Ahmed Alghawli https://orcid.org/0000-0002-6586-2141 4 , Amani A. K. Elsayed 4 Riyadh Ghaleb A. Alshameri 1 , Adel A. Nasser https://orcid.org/0000-0001-5456-1303 2,3 , [...] Abdul Hakim Abdullah 1 , Abed Saif Ahmed Alghawli https://orcid.org/0000-0002-6586-2141 4 , Amani A. K. Elsayed 4 PUBLISHED 22 Sep 2025 Author details Author details 1 Department of Education, Sultan Zainal Abidin University, Kuala Terengganu, Terengganu, Malaysia 2 Department of Information Systems and Computer Science, Sa’adah University, Sa'adah, Sa'adah, Yemen 3 Department of Artificial Intelligence, Modern Specialized University, Sana'a, Sana'a, Yemen 4 Department of Computer Science, College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al Kharj, Riyadh Province, Saudi Arabia Riyadh Ghaleb A. Alshameri Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Visualization, Writing – Original Draft Preparation Adel A. Nasser Roles: Conceptualization, Formal Analysis, Methodology, Software, Writing – Original Draft Preparation Abdul Hakim Abdullah Roles: Formal Analysis, Supervision, Writing – Review & Editing Abed Saif Ahmed Alghawli Roles: Formal Analysis, Funding Acquisition, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Amani A. K. Elsayed Roles: Investigation, Resources, Validation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Objectives Comprehensive educational supervision is essential for ensuring quality teaching, fostering professional development, and supporting institutional capacity building. However, its implementation encounters numerous structural, technical, and human resource challenges. This study aimed to identify, validate, rank, and cluster the technical barriers affecting comprehensive educational supervision in Amanat Al Asimah, Yemen. This aligns with national reform goals by offering strategic insights to improve supervisory systems, thereby enhancing teaching quality, institutional performance, and educational resilience in fragile contexts Methods This study employed a three-phase mixed-methods approach. Initially, a literature review identified 11 key barriers to effective supervision. These were validated using the Fuzzy Delphi Method (FDM), involving 16 experienced educational supervisors to assess the consensus and suitability of the items. Subsequently, a quantitative survey targeting 370 teachers was conducted to evaluate their perceived severity. Fuzzy set theory was used to aggregate and defuzzify the responses, generating crisp scores for prioritization. Finally, K-means clustering was applied to segment the barriers based on their impacts. Results FDM analysis confirmed the validity of all 11 identified barriers, with a domain-level threshold of 0.093 and an average expert consensus of 90%, indicating strong agreement. The fuzzy set-based evaluation highlighted three top-priority challenges: weak supervisory competencies, limited ability to develop effective supervisory plans, and poor supervisor-teacher relationships. K-means clustering grouped the barriers into three segments: one high-priority barrier, seven moderate-priority concerns, and three low-priority issues. Notably, weak supervisory competencies emerged as the most critical barrier, isolated in a high-priority cluster. Conclusion These findings provide evidence-based guidance for policy and strategic interventions aimed at enhancing the effectiveness of supervision systems in fragile educational settings. The study concludes with recommendations for strengthening supervisory competencies, improving resource allocation, and fostering trust-based supervisor-teacher relationships, thereby contributing to the quality of education and institutional resilience in Yemen. READ ALL READ LESS Keywords educational supervision; Fuzzy Delphi Method; Yemen; fuzzy set theory; K-means ; clustering; supervisory competencies Corresponding Author(s) Adel A. Nasser ( [email protected] ) Close Corresponding author: Adel A. Nasser Competing interests: No competing interests were disclosed. Grant information: This study was supported via funding from Deanship of Scientific Research, Prince Sattam bin Abdulaziz University [project number: PSAU/2025/R/1446]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Alshameri RGA et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Alshameri RGA, Nasser AA, Abdullah AH et al. Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :956 ( https://doi.org/10.12688/f1000research.167498.1 ) First published: 22 Sep 2025, 14 :956 ( https://doi.org/10.12688/f1000research.167498.1 ) Latest published: 09 Feb 2026, 14 :956 ( https://doi.org/10.12688/f1000research.167498.2 )  There is a newer version of this article available. Suppress this message for one day. 1. Introduction The current global shifts in science and education have significantly impacted various educational dimensions, including systems and supervision processes. These transformations have compelled educators to critically examine educational structures and reaffirm the teacher’s role as a central pillar within the system. 1 Within this context, educational supervision has emerged as a pivotal component of educational management, fundamental to the broader educational framework that encompasses both teaching and learning. 2 Educational supervision serves as a cornerstone for ensuring educational quality and promoting continuous improvement. It plays a vital role in enhancing teacher performance by offering professional guidance, fostering reflective practice, and aligning teaching with curriculum standards. 3 Historically limited to classroom observation, the scope of supervision has expanded to include mentorship, collaborative problem-solving, and the development of targeted professional growth plans. 4 In today’s dynamic educational landscape—shaped by digital transformation and evolving pedagogical expectations—supervisors are instrumental in helping educators integrate technology, maintain digital ethics, and implement innovative teaching strategies suited to 21st-century learners. 5 Beyond instructional support, effective supervision also nurtures teachers’ emotional well-being and professional development, ensuring that education remains inclusive, adaptive, and student-centered. 6 Modern supervisory models now emphasize collaborative learning environments and constructive feedback mechanisms, thereby cultivating a culture of continuous improvement. 7 As Okafor et al. note, effective supervision not only enhances instructional practices but also strengthens institutional effectiveness by fostering an environment conducive to both teacher and student success. 8 Given its multifaceted nature, educational supervision continues to evolve—requiring ongoing research and innovation to meet the complex demands of contemporary education. The success of educational supervision hinges on several critical indicators. Supervisors must offer clear directives and align instructional practices with institutional objectives. 9 Emphasis should be placed on implementing quality assurance mechanisms that align with global standards while addressing local educational contexts. 9 Continuous professional development is essential in fostering a culture that supports quality teaching and lifelong learning. 10 , 11 Moreover, supervision becomes most effective when teachers actively engage with feedback, thereby promoting a culture of accountability and improvement. 12 Effective planning is equally important—supervisors must ensure that their approach is strategic, goal-oriented, and systematic. 13 , 14 Competent supervisors are characterized by strong instructional leadership, effective communication, accurate observation, and constructive feedback delivery. 15 , 16 Building trust-based professional relationships between supervisors and teachers further enhances collaboration and facilitates open communication. 17 , 18 Additionally, the integration of modern technology and the availability of financial and learning resources significantly contribute to the success of supervision practices. 19 These factors collectively support a dynamic, responsive, and impactful supervision system. Ultimately, educational supervision focuses on actively involving teachers in the learning environment to enhance teaching methods and improve student achievement. It plays a central role in overseeing the educational process by integrating instructional activities and continuously evaluating institutional outcomes. 2 , 20 Through identifying challenges and working collaboratively to resolve them, supervision ensures the implementation of appropriate actions that drive educational advancement. Numerous studies indicate that educational supervision, in its current state, faces significant challenges that hinder its effectiveness. Including inadequate training programs, 10 limited planning, 21 weak supervisory competencies, 15 low teacher acceptance of modern methods, 22 strained relationships, 18 technological gaps, 19 and insufficient financial, infrastructural, and learning resources. 2 , 23 To overcome these barriers, studies recommend structured professional development, strategic planning, competence-based supervisor selection, 16 fostering collaborative cultures, 24 integrating digital tools, 25 securing adequate funding, 26 and providing supportive environments to strengthen supervision’s impact. Comprehensive educational supervision is a fundamental approach employed in schools to assess and respond to their challenges, needs, and educational and social realities. It involves the continuous presence of educational supervisors within school environments to gain firsthand insights into the teaching and learning processes both inside and outside the classroom. Through this approach, supervisors are able to diagnose the real conditions of the educational system—including the roles of teachers, learners, school leadership, and the broader learning environment. However, in Yemen, the ability to implement effective supervision has been severely undermined by ongoing conflict and political instability. The education system, which once showed progress with enrollment rates increasing from 71.3% to 97.5% between 1999 and 2013, has since experienced significant setbacks. By the 2015/2016 academic year, around 1.8 million children were out of school, and over 1,100 schools had been either destroyed or repurposed for emergency use. 27 These structural disruptions have weakened the foundation of educational supervision. Furthermore, the quality of education has declined significantly, with reports of curriculum manipulation for ideological purposes and widespread issues such as the absence of teacher training and salary payments. 28 In response to these profound challenges, Yemen’s National Vision for Building the Modern Yemeni State and the national research priorities emphasize the urgent need for comprehensive education reform. In line with these priorities, this study aims to analyze the technical challenges facing educational supervision in schools located in Amanat Al Asimah and explore viable strategies to improve the efficiency of educational processes. The focus is on supporting national goals by modernizing supervisory practices and ensuring they contribute meaningfully to sustainable development and institutional capacity building. The study seeks to identify, rank, and cluster these challenges in schools within Amanat Al Asimah, Yemen, while exploring viable strategies to enhance the efficiency of educational supervision processes. The significance of this study lies in its alignment with Yemen’s National Vision for Building a Modern Yemeni State and national research priorities. It contributes to modernizing supervisory practices in a conflict-affected context, supports sustainable development, and enhances institutional capacity in Yemen’s education sector. Additionally, it provides insights into addressing teacher-related challenges and improving the quality of education. Methodologically, the study employs a mixed-methods approach that includes a literature review to identify key barriers to effective supervision, the Fuzzy Delphi Method to validate and prioritize these barriers, a quantitative survey of teachers to assess their perceptions of supervisory challenges, and fuzzy set theory and K-means clustering analysis to categorize and prioritize the identified barriers. The findings of this study could inform targeted policy interventions to enhance supervisory effectiveness, guide resource allocation for improving educational supervision, support capacity-building initiatives for supervisors, and contribute to developing a responsive supervision framework aligned with national development objectives. 2. Methods Multi-Criteria Decision-Making (MCDM) methods have emerged as vital analytical tools in a wide range of fields, including health, sustainability, tourism, healthcare, engineering, education and financial risk assessment. 29 – 39 The integration of MCDM methods with fuzzy logic and clustering techniques provides a robust framework for addressing complex decisions under uncertainty. 29 – 33 Fuzzy-based MCDM approaches effectively capture expert ambiguity and contextual nuances, making them particularly suitable for fields such as health, education, and sustainability. 35 , 38 – 40 The Fuzzy Delphi Method (FDM), enhanced by fuzzy set theory, improves expert consensus and facilitates clearer selection and prioritization of variables. 35 , 38 , 41 , 42 Meanwhile, machine learning (ML) has significantly advanced real-world decision-making through predictive modeling, risk analysis, and pattern recognition. 43 – 48 Its applications span various domains, including healthcare, 43 big data analytics, 44 manufacturing quality control, 45 and infrastructure forecasting. 46 ML also supports academic evaluation through web mining 47 and Industry 4.0 applications. 48 Recent research increasingly integrates MCDM, fuzzy logic, and unsupervised ML techniques—particularly K-means clustering—to enhance decision-making by segmenting barriers and identifying strategic priorities. 29 – 33 This hybrid approach has been successfully applied in health security, 29 – 33 ecotourism, 39 urban resilience assessment, 49 and education, 40 , 50 offering scalable, context-sensitive solutions to complex policy and planning challenges. Building on these methodological advancements and empirical applications, the present study adopts a similar integrative approach to explore and prioritize barriers in the context of educational supervision in Yemen. In the present study, the Fuzzy Delphi Method (FDM) was employed as a preliminary phase to identify and validate the most contextually relevant barriers to effective educational supervision in Yemen. The primary purpose of the FDM was not to evaluate or resolve the barriers but to systematically screen and refine a set of candidate variables based on expert consensus for use in subsequent analyses. This was followed by a quantitative teacher survey assessing perceptions of supervisory challenges and the application of fuzzy set theory and K-means clustering to categorize and prioritize the identified barriers. Our analysis was conducted through a structured, three-phase process, with each stage clearly outlined and supported by a rationale for the chosen methodology. 2.1 Technical challenges in comprehensive educational supervision – Literature review A substantial body of research underscores the significance of educational supervision in enhancing instructional quality, fostering professional development, and aligning teaching practices with institutional and national objectives. Despite its critical role, several technical challenges impede the effectiveness of educational supervision, particularly in fragile contexts such as Yemen. A key issue identified in the literature is teachers’ lack of interest in the instructions and directives provided by supervisors. This disinterest undermines efforts to implement improvements in teaching practices and reflects broader issues of disengagement and accountability. 9 , 12 , 51 , 52 This is due to the weak capacity of some educational supervisors to develop effective supervisory plans, which are essential for guiding systematic, goal-oriented oversight. 13 , 14 , 53 , 54 Without proper planning, supervision can become inconsistent, fragmented, and reactive. Another challenge is the limited ability to develop and implement effective training programs, which are vital for equipping teachers with up-to-date skills and aligning instructional methods with evolving standards. 10 , 21 The literature also highlights the lack of supervisory competencies among some educational supervisors, including deficiencies in instructional leadership, communication, and feedback delivery. 15 , 16 , 19 Such gaps diminish the credibility and impact of the supervision. Equally critical is the resistance among some teachers to modern supervision methods such as collaborative evaluation and peer feedback. This resistance can limit the effectiveness of reforms intended to make supervision more participatory and data-driven. 22 , 24 , 55 – 57 Moreover, weak or distant professional relationships between supervisors and teachers hinder open communication, reduce trust, and lead to ineffective feedback cycles. 17 , 18 , 58 , 59 Technological and infrastructural deficiencies also pose significant obstacles. The absence or inadequacy of modern technologies and digital tools, such as learning management systems, video conferencing platforms, and data analytics software, limits the efficiency and adaptability of supervision practices. 19 , 25 , 59 , 60 Furthermore, the lack of financial resources allocated to supervision hampers planning, professional development, transportation, and the acquisition of the necessary tools. 8 , 23 , 61 A closely related issue is the inadequacy of supportive learning resources in schools, such as libraries, science laboratories, Internet access, and computers. These are essential for both effective teaching and meaningful supervisory assessment. 19 , 62 – 64 Additionally, the lack of dedicated training and rehabilitation halls limits opportunities for continuous professional development, and the absence of a specialized office for managing supervision compromises the coordination and institutionalization of supervisory activities. 2 2.2 Phase 2-assessing the suitability of research variables using the fuzzy delphi method. The Fuzzy Delphi Method (FDM) is a widely used technique for assessing the suitability of research indicators in measuring specific phenomena within defined environments because of its ability to combine expert judgment with the flexibility of fuzzy logic. 35 , 39 , 40 Often, determining the relevance of indicators requires subjective input from domain experts, whose perspectives may vary or be influenced by uncertainties arising from complex contextual conditions. 65 FDM addresses this challenge by allowing experts to provide assessments using linguistic terms, which are then converted into fuzzy numbers, enabling the method to handle ambiguity and imprecision more effectively. 35 , 65 Compared to the traditional Delphi method, 41 which often requires multiple rounds of consultation to reach a consensus and may lead to misinterpretation or loss of expert interest due to repetitive rounds, FDM enhances efficiency by minimizing the number of iterations needed. It also reduces the risk of bias introduced through forced modifications of expert opinions and lowers the overall cost and time required for data collection. 35 , 39 Additionally, the FDM increases survey response rates, improves the reliability of expert feedback, and effectively resolves issues related to non-consensus cases. This helps make evaluations more consistent and clear, keeping expert opinions genuine while encouraging agreement and realism in finding relevant indicators. The method further provides a systematic approach to filter and validate indicators by applying a consensus threshold, ensuring the retention of only the most relevant and context-appropriate variables. Owing to its adaptability across domains and environments, FDM enables experts to assess indicator suitability based on theoretical soundness and practical relevance to the target context. 35 , 39 , 42 Overall, by merging expert knowledge, fuzzy logic, and a structured consensus mechanism, the Fuzzy Delphi Method (FDM) enhances the robustness, credibility, and contextual relevance of selected indicators, making it particularly valuable in complex evaluation frameworks, such as the assessment of educational barriers in low-income countries and other context-sensitive settings. In this study, the following FDM procedural steps were implemented 42 , 65 : • Step 1: Identification of Key Evaluation Barriers through Literature Review Based on the literature reviewed in Section 2.1, 11 critical technical barriers impacting the effectiveness of educational supervision were identified. These include (B1) teachers’ disinterest in supervisors’ directives, (B2) Limited capability of certain educational supervisors in creating effective supervisory plans, (B3) Limited capability of certain educational supervisors in creating and implementing effective supervisory training programs, (B4) Weak competencies of educational supervisors in supervision, (B5) limited teacher acceptance of modern supervisory methods, (B6) poor relationships between supervisors and teachers, (B7) lack of access to modern technologies, (B8) inadequate financial allocations for supervision activities, (B9) insufficient availability of learning resources in schools, (B10) Absence of training and rehabilitation halls, and (B11) lack of dedicated offices for educational supervision. These barriers represent the foundational factors proposed for expert evaluation, as addressing them is essential to improve the quality of comprehensive educational supervision and ensure its alignment with Yemen’s broader educational development objectives. • Step 2: Expert Selection and Data Collection In accordance with FDM guidelines, this study purposively selected a panel of experts to ensure homogeneity, as recommended in previous research. 39 A total of 16 educational supervisors were chosen based on their direct involvement in educational supervision and extensive experience—each with over 10 years in the field. A structured questionnaire was used to collect data, consisting of 11 barriers to effective supervision. Fifteen printed questionnaires were distributed and collected manually. Experts were asked to rate the relevance and contextual suitability of each barrier within the Yemeni educational environment using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). The collected responses (see Table 1 ) were then converted into fuzzy numbers using the conversion scale proposed by. 42 , 65 ( Table 2 ). • Step 3: Fuzzy Data Analysis ○ Step 3.1: Aggregation of Fuzzy Ratings Table 1. Experts’ scores using a five-point likert scale. Expert No B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 E1 4 5 4 5 4 4 5 5 5 4 4 E2 4 5 5 5 5 5 5 4 5 3 4 E3 4 5 5 5 5 5 4 5 5 4 4 E4 4 5 4 5 4 4 5 5 5 4 4 E5 4 4 5 4 5 5 5 5 5 3 4 E6 5 4 4 4 5 5 4 5 5 4 4 E7 4 5 4 5 4 4 5 5 5 4 4 E8 4 5 5 5 5 5 5 5 5 4 4 E9 5 5 4 5 5 5 5 5 5 4 3 E10 4 5 4 5 4 5 5 5 5 4 4 E11 4 4 5 4 5 5 5 5 5 4 4 E12 5 5 4 5 4 4 4 5 5 4 3 E13 4 5 4 5 4 5 5 5 5 3 4 E14 4 5 5 5 5 4 5 5 5 4 4 E15 4 5 4 5 4 4 5 5 5 4 4 E16 4 5 5 5 5 5 5 5 5 4 4 Table 2. Mapping likert to fuzzy scoring scale. Likert scale scoring Linguistic variable Fuzzy Scale scoring m1 m2 m3 5 Highly Agree 0.6 0.8 1 4 Agree 0.4 0.6 0.8 3 Moderately/Not sure 0.2 0.4 0.6 2 Not Agree 0 0.2 0.4 1 Highly Not Agree 0 0 0.2 In fuzzy set theory, each element is characterized by a pair: the value and its degree of membership in the fuzzy set, defined within the [0,1] interval. A membership function quantifies this degree, with values closer to 1 indicating a stronger inclusion in the fuzzy set. As detailed in Table 2 , fuzzy values were used to represent the uncertain expert responses (see Table 3 ). Table 3. Experts’ scores using a fuzzy rating scale. Expert No B1 B2 … . B11 m1 m2 m3 m1 m2 m3 … . m1 m2 m3 E1 0.4 0.6 0.8 0.6 0.6 1 … . 0.4 0.6 0.8 E2 0.4 0.6 0.8 0.6 0.8 1 … . 0.4 0.6 0.8 E3 0.4 0.6 0.8 0.6 0.8 1 … . 0.4 0.6 0.8 E4 0.4 0.6 0.8 0.6 0.8 1 … . 0.4 0.6 0.8 E5 0.4 0.6 0.8 0.4 0.6 0.8 … . 0.4 0.6 0.8 E6 0.6 0.8 1 0.4 0.6 0.8 … . 0.4 0.6 0.8 E7 0.4 0.6 0.8 0.6 0.8 1 … . 0.4 0.6 0.8 E8 0.4 0.6 0.8 0.6 0.8 1 … . 0.4 0.6 0.8 E9 0.6 0.8 1 0.6 0.8 1 … . 0.2 0.4 0.6 E10 0.4 0.6 0.8 0.6 0.8 1 … . 0.4 0.6 0.8 E11 0.4 0.6 0.8 0.4 0.6 0.8 … . 0.4 0.6 0.8 E12 0.6 0.8 1 0.6 0.8 1 … . 0.2 0.4 0.6 E13 0.4 0.6 0.8 0.6 0.8 1 … . 0.4 0.6 0.8 E14 0.4 0.6 0.8 0.6 0.8 1 … . 0.4 0.6 0.8 E15 0.4 0.6 0.8 0.6 0.8 1 … . 0.4 0.6 0.8 E16 0.4 0.6 0.8 0.6 0.8 1 … . 0.4 0.6 0.8 AFR 0.438 0.638 0.838 0.563 0.750 0.963 … . 0.375 0.575 0.775 CS 0.638 0.758 … . 0.575 The average fuzzy rating (AFR) for each variable (barrier z) was then calculated across all n experts using the following formula: (1) w z ~ = ( a z . b z . c z ) = ( 1 n ∑ r = 1 n a rz . 1 n ∑ r = 1 n b rz . 1 n ∑ r = 1 n c rz ) ○ Step 3.2: Defuzzification The fuzzy values for each variable were then converted into crisp scores (CS) (See Table 3 ) using the centroid method (center of gravity), defined as: (2) D z = ( a z . b z . c z ) 3 • Step 4: Evaluation of Domain Acceptability ○ Step 4.1: Distance Calculation As shown in Table 4 , the deviation between each expert’s fuzzy rating and the aggregated fuzzy score was computed using the following formula: (3) D rz = ( a z − a rz ) 2 + ( b z − b rz ) 2 + ( c z − c rz ) 2 3 ○ Step 4.2: Threshold Value of Each barrier Table 4. Deviation calculation of expert fuzzy ratings versus aggregated scores. Expert No B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 E1 0.1 0.2 0.1 0.1 0.2 0.2 0.1 0.0 0.0 0.1 0.0 E2 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.3 0.0 0.2 0.0 E3 0.1 0.1 0.2 0.1 0.1 0.1 0.2 0.0 0.0 0.1 0.0 E4 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.0 0.0 0.1 0.0 E5 0.1 0.2 0.2 0.2 0.1 0.1 0.1 0.0 0.0 0.2 0.0 E6 0.2 0.2 0.1 0.2 0.1 0.1 0.2 0.0 0.0 0.1 0.0 E7 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.0 0.0 0.1 0.0 E8 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.0 0.0 0.1 0.0 E9 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.0 0.1 0.3 E10 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.0 0.0 0.1 0.0 E11 0.1 0.2 0.2 0.2 0.1 0.1 0.1 0.0 0.0 0.1 0.0 E12 0.2 0.1 0.1 0.1 0.2 0.2 0.2 0.0 0.0 0.1 0.3 E13 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.0 0.0 0.2 0.0 E14 0.1 0.1 0.2 0.1 0.1 0.2 0.1 0.0 0.0 0.1 0.0 E15 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.0 0.0 0.1 0.0 E16 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.0 0.0 0.1 0.0 Threshold Value 0.093 0.104 0.150 0.093 0.150 0.143 0.093 0.036 0.000 0.093 0.067 Domain-Level Threshold 0.093 < 0.2 For each variable, a threshold value was calculated by averaging these distances: (4) Th z = 1 n ∑ r = 1 n D rz ○ Step 4.3: Domain-Level Threshold The overall threshold for the entire domain of variables was then determined by averaging the threshold values of all factors: (5) Th domain = 1 n ∑ z = 1 m Th z A domain and its variables were considered valid if their threshold values were ≤ 0.2. 42 , 65 Barriers with threshold values below this criterion were deemed acceptable for further analysis. In this study, As shown in Table 4 , the overall domain threshold was calculated as 0.093, while all individual variable threshold values were found to be less than 0.2. • Step 5: Assessing Expert Consensus To determine expert consensus on each factor, the number of experts (n) whose ratings were within a fuzzy distance of ≤ 0.2 from the average fuzzy rating ( E z ) was calculated (see Table 5 ). The percentage of these experts EA z was defined as: (6) EA z = E z n % Table 5. Determining expert consensus through fuzzy distance metrics. Expert n B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 Number of experts, d ≤ 0.2 13 13 16 13 16 16 13 15 16 13 14 Percentage of Experts (%), d ≤ 0.2 81 81 100 81 100 100 81 94 100 81 88 Average 90% Consensus was measured by assessing the proportion of experts whose ratings deviated from the group average by less than or equal to 0.2, as indicated in Equation (6) . A consensus threshold of 75% was adopted. All 11 barriers met or exceeded this threshold. Subsequently, the final crisp scores were used to rank the barriers in terms of priority, with any factor scoring below 0.4 excluded from further consideration. • Step 6: Final Evaluation and Variable Selection After confirming the validity of the evaluation domain and identifying variables with sufficient expert consensus, the final step involved analyzing the crisp scores of the accepted barriers. These scores were used to rank the barriers in order of priority, with any variable scoring below 0.4 excluded from further consideration. However, as presented in the results section, all 11 barriers achieved scores above the threshold and were therefore accepted and retained for further analysis. 2.3 Barrier prioritization in educational supervision via fuzzy set theory and cluster segmentation To investigate the technical barriers impacting comprehensive educational supervision in schools in Sana’a, Yemen, a structured questionnaire was developed. The items in this instrument were derived from key barriers previously identified and validated through the Fuzzy Delphi Method. The questionnaire aimed to capture teachers’ perceptions of the supervisory challenges within their institutional contexts. Step 1: Sample of the Study A descriptive quantitative approach was employed, aligning with the study’s objective to assess systemic supervisory barriers from the teachers’ perspective. The study population comprised approximately 7,000 teachers working in schools that implemented comprehensive educational supervision in the capital city. Following Krejcie and Morgan’s sampling guidelines, a sample of 370 teachers was selected, surpassing the recommended minimum of 364 for a population of this size. This sample size ensured representativeness and allowed for statistical generalization to the broader population. Step 2: Research Instrument Design To gather the necessary data, a structured questionnaire was designed based on the study objectives, relevant literature, validated prior instruments, and the Fuzzy Delphi procedures outlined in Section 2.2. The instrument consisted of 11 items, each corresponding to a specific technical barrier affecting the effectiveness of comprehensive educational supervision. A five-point Likert scale was used to measure participants’ perceptions of each barrier, with response options ranging from “Very Low” (1 point) to “Very High” (5 points). The full scale included very low (1), low (2), moderate (3), high (4), and very high (5). This structure enabled respondents to express their views clearly and allowed for the consistent quantification of data for analysis. Step 3: Data Collection Procedures The questionnaire was distributed across four educational districts in Amanat Al-asima Sana’a, where comprehensive educational supervision is currently being practiced. These districts were selected from among the ten official educational zones in the city of Amanat Al-Asima, Yemen. Teachers from private schools that implemented comprehensive supervision practices were also included. Completed questionnaires were collected through school administrations and returned directly to the researcher. Step 4: Aggregation and Defuzzification of Responses The collected responses were converted into fuzzy numbers using the predefined scale (see Table 1 ), enabling the representation of uncertainty in respondents’ judgments. The average fuzzy rating for each barrier was calculated across all respondents using Equation 1 . These fuzzy values were then defuzzified into crisp scores using the centroid (center of gravity) method, as described in Equation 2 (see Table 6 ). Table 6. Respondents’ scores using a fuzzy rating scale. Respondent No B1 B2 … . B11 m1 m2 m3 m1 m2 m3 … . m1 m2 m3 R1 0 0 0.2 0.6 0.6 1 … . 0 0.2 0.4 R2 0 0.2 0.4 0 0.6 0.4 … . 0 0.2 0.4 R3 0.6 0.8 1 0.6 0.8 1 … . 0.2 0.4 0.6 R4 0.2 0.4 0.6 0.2 0.4 0.6 … . 0 0 0.2 R5 0 0 0.2 0.4 0.4 0.8 … . 0 0 0.2 R6 0.4 0.6 0.8 0 0.2 0.4 … . 0.4 0.6 0.8 R7 0.2 0.4 0.6 0.4 0.4 0.8 … . 0.6 0.8 1 R8 0.4 0.6 0.8 0.4 0.4 0.8 … . 0.4 0.6 0.8 R9 0.2 0.4 0.6 0.2 0.4 0.6 … . 0.2 0.4 0.6 R10 0.2 0.4 0.6 0.2 0.6 0.6 … . 0.2 0.4 0.6 R11 0.2 0.4 0.6 0.2 0.4 0.6 … . 0.4 0.6 0.8 R12 0.4 0.6 0.8 0.4 0.4 0.8 … . 0.4 0.6 0.8 R13 0 0 0.2 0 0 0.2 … . 0 0 0.2 … .. … .. … .. … .. … .. … .. … .. … .. … .. … .. … .. R369 0.4 0.6 0.8 0.4 0.2 0.8 … . 0.4 0.6 0.8 R370 0.6 0.8 1 0.6 0.6 1 … . 0.6 0.8 1 AFR 0.235 0.415 0.615 0.257 0.421 0.648 … . 0.256 0.426 0.626 CS 0.422 0.442 … . 0.436 Following the evaluation of the barriers and the computation of the average score for each based on respondents’ input, the aggregated scores were used to rank and categorize the barriers according to their impact on the effectiveness of comprehensive educational supervision. Step 5. Clustering Analysis and Barrier Prioritization Using K-Means The aim of the clustering process was to categorize the identified barriers into distinct groups based on their combined impact. This approach allows for a deeper understanding of the relative effects of these factors on the success of comprehensive educational supervision and enables the targeted prioritization of interventions. Clustering is a fundamental unsupervised machine learning technique that organizes unlabeled data into distinct clusters based on inherent similarities, such as proximity or density metrics. 29 – 32 Unlike supervised learning, which relies on labeled outputs for training, clustering reveals hidden patterns without predefined categories, making it essential for exploratory analysis. By uncovering latent structures, clustering aids in hypothesis generation and provides valuable insights into strategic decision-making. 66 Among the various clustering techniques, K-means is particularly favored for its simplicity, computational efficiency, and versatility across disciplines. 29 , 30 The algorithm partitions n data points into k clusters by minimizing the variance within each cluster. It follows an iterative process 29 – 33 : (1) randomly initializing k centroids; (2) assigning each data point to the nearest centroid; (3) recalculating centroids based on updated cluster memberships; and (4) repeating these steps until the model converges or reaches a predetermined iteration limit. To determine the optimal number of clusters, the Elbow Method was employed using the Total Sum of Squared Errors (SSE) as a performance metric (see Figure 1 ). The SSE was computed for k values ranging from one to five. A significant reduction in SSE was observed between k = 1 (0.0055724) and k = 3 (0.0006534), indicating an improved model fit. Beyond k = 3, further reductions in SSE were minimal, suggesting diminishing returns. The inflection point at k = 3, known as the “elbow,” indicates an optimal balance between simplicity and explanatory power. Consequently, k = 3 was selected as the ideal number of clusters. Figure 1. Elbow Method results for determining the optimal number of clusters. The plot illustrates the correlation between the within-cluster sum of squares (WCSS) and the number of clusters (k). The final clustering model classified the barriers into a three-tier system based on their aggregated impacts. Segment 3 represents a high-priority danger zone, encompassing barriers with high average ratings from respondents, indicating significant concerns and severity. Segment 2 reflects a moderate level of concern, where the perceived impact of the barriers is notable but less critical. Segment 1 comprises low-priority or minimal-risk barriers, characterized by relatively low average ratings and limited influence on the effectiveness of comprehensive education supervision. This structured grouping enhances the interpretability of the results and provides a practical framework for prioritizing interventions and developing targeted policy responses. 2.4. Ethical approval and consent to participate This study adhered to the principles of the Declaration of Helsinki and the national guidelines for research involving human participants. Official permission to conduct the study was secured from the Office of Education, Bani-Alhareth, Amanat Al Asimah, Republic of Yemen (Approval Reference No.: [72-9-24), authorizing data collection from participating schools. Participants received clear information about the study’s objectives and procedures and were assured that their participation was entirely voluntary, with the right to withdraw at any time, without consequences. Written informed consent was obtained from all participants before data collection, and no minors were included in this study. 3. Results 3.1 Fuzzy delphi method: Domain acceptability and expert agreement The evaluation of domain acceptability aimed to assess the consistency of expert responses across all the identified barriers. The process began by calculating the distance between each expert’s fuzzy rating and the aggregated fuzzy score for each barrier. This deviation indicates how closely an individual expert’s judgment aligns with the group’s average perception. The mean of these deviations for each barrier was then computed to establish the threshold value. The results in Table 4 show that all 11 barriers had threshold values below the established cutoff of 0.2, ranging from 0.000 (for B9) to 0.150 (for B3 and B5). These findings suggest a high level of agreement among the experts for each variable. Furthermore, the domain-level threshold, derived by averaging the individual barrier thresholds, was calculated as 0.093, which is well below the acceptable upper limit. This confirmed the internal consistency of the expert evaluations and validated the overall domain of barriers for further analysis using the Fuzzy Delphi Method. Following the confirmation of domain validity, the focus shifted to assessing the expert consensus. This was achieved by identifying the number of experts whose ratings deviated from the group average by no more than 0.2 for each barrier. The percentage of experts who met this condition was calculated. The results in Table 5 reveal that all 11 barriers met or exceeded the minimum consensus threshold of 75%, with agreement levels ranging from 81% to 100%. Specifically, barriers B3, B5, B6, and B9 achieved full consensus (100%), while the lowest consensus rate was 81% for barriers B1, B2, B4, B7, and B10. The overall average expert agreement across all barriers was 90%, indicating a robust level of shared perceptions among the panel. Based on these results, all 11 barriers were accepted and retained for the final prioritization phase using defuzzified crisp scores. In the final stage of the fuzzy Delphi analysis, the validated barriers were assessed and ranked according to their defuzzified crisp scores ( Table 7 ). These scores reflect the central tendency of expert judgments converted from fuzzy numbers into precise quantitative values using the centroid method. A threshold score of 0.4 was established to filter out low-priority barriers; any variable scoring below this cutoff was omitted from the final list. The results indicated that all 11 barriers achieved crisp scores well above the minimum threshold, underscoring their significance in the context of educational supervision challenges in Yemen’s medical schools. Among them, the barrier “insufficient availability of learning resources in schools” (B9) was ranked highest, with a crisp score of 0.800, followed by “inadequate financial allocations for supervision” (B8) at 0.788, and both “weak competencies of educational supervisors” (B4) and “lack of access to modern technologies” (B7) tied with a score of 0.763. The lowest-ranking yet accepted variables included “lack of dedicated offices for educational supervision” (B11) and “absence of training and rehabilitation halls” (B10), scoring 0.575 and 0.563, respectively. This ranking provides a clear prioritization of the most pressing barriers and serves as a critical reference point for targeted policy interventions and resource allocation to enhance the effectiveness of comprehensive educational supervision. Table 7. Defuzzified crisp scores and ranking of educational supervision barriers based on expert evaluation. Barrier The average fuzzy rating for each variable Rank Decision B1 0.638 9 Accepted B2 0.758 5 Accepted B3 0.688 8 Accepted B4 0.763 3 Accepted B5 0.713 7 Accepted B6 0.725 6 Accepted B7 0.763 3 Accepted B8 0.788 2 Accepted B9 0.800 1 Accepted B10 0.563 11 Accepted B11 0.575 10 Accepted 3.2. Assessment, ranking and clustering of technical supervision barriers The sample included a diverse group of educators varying in gender, academic qualifications, years of experience, and school type. Specifically, 30% of the respondents (n = 111) were male and 70% (n = 259) were female. Regarding educational attainment, 40.8% (n = 151) held a high school diploma, 57.8% (n = 214) had a bachelor’s degree, and 1.4% (n = 5) had a master’s diploma or higher. In terms of professional experience, 32.4% (n = 120) had 1–5 years of teaching experience, 24.1% (n = 89) had 6–10 years, and 43.5% (n = 161) had over 10 years. The questionnaire was distributed almost evenly across school types, with 51.4% of respondents (n = 190) from public schools and 48.6% (n = 180) from private ones. These demographic characteristics ensured that the collected data reflected a broad range of perspectives, enhancing the credibility and depth of the subsequent analysis of the perceived technical barriers. The demographic data of the sample ( Table 8 ) indicate a predominantly female teaching workforce, with most holding at least a bachelor’s degree. A significant portion of respondents (43.5%) had more than ten years of teaching experience, suggesting that the dataset included insights from educators with substantial professional exposure. Additionally, the nearly equal distribution of respondents across public and private schools enhances the generalizability of the findings, allowing comparisons between different institutional contexts. Table 8 presents the full distribution of demographic variables among the sample: Table 8. Demographic distribution of the study sample. Variable Category Frequency Percentage Gender Male 111 30.00% Female 259 70.00% Qualification High School 151 40.80% Bachelor’s Degree 214 57.80% Master’s Degree 5 1.40% Experience 1–5 years 120 32.40% 6–10 years 89 24.10% More than 10 years 161 43.50% School Type Public 190 51.40% Private 180 48.60% Table 9 presents the average fuzzy ratings for the various barriers impacting the efficacy of comprehensive educational supervision, along with their respective ranks. This evaluation, derived from the respondents’ input, offers a thorough overview of the challenges encountered in educational supervision. Table 10 illustrates the distribution of these barriers, which are categorized into three segments based on their perceived severity and impact. A total of 11 barriers were assessed, culminating in an overall sum of 100.0%. The table also includes the Sum of Squared Errors (SSE) for each segment. This distribution underscores the varying levels of concern associated with each segment, providing a clear overview of the challenges that require future attention. Segment 3, containing only one barrier, accounted for 9.1% of the total and reflected high-priority concerns perceived as critical threats to effective field supervision. In contrast, Segment 2 comprises seven barriers, representing 63.6% of the total, highlighting moderate-impact issues that significantly affect the supervision process of the internship. Segment 1 included three barriers, accounting for 27.3% of the total, indicating low-priority or minimal risk concerns that required less immediate attention. These barriers are characterized by relatively low average ratings and limited influence on the effectiveness of comprehensive educational supervision. Table 9. Ranking of barriers impacting the effectiveness of educational supervision based on respondents’ input. Barrier The average fuzzy rating for each variable Rank Segment B1 0.422 6 2 B2 0.442 2 2 B3 0.425 5 2 B4 0.467 1 3 B5 0.398 9 1 B6 0.440 3 2 B7 0.389 11 1 B8 0.391 10 1 B9 0.417 8 2 B10 0.421 7 2 B11 0.436 4 2 Table 10. Distribution and segmentation of barriers impacting educational supervision. Segment Number of barriers Percentage % SSE/Segment Segment 1 3 27.3% 0.000047 Segment 2 7 63.6% 0.000607 Segment 3 1 9.1% 0.000000 TOTAL 11 100.0% 0.00065 4. Discussion The Fuzzy Delphi Method (FDM) process confirmed the appropriateness and relevance of all 11 barriers to effective educational supervision. Each barrier met the necessary consensus and threshold criteria, demonstrating that the selected variables were not only theoretically grounded in the literature but also validated by expert opinion as reflective of the real-world challenges facing Yemen’s supervision system. The high level of consensus among experts (average agreement of 90%) and a low domain-level threshold (0.093) indicate a consistent and strong agreement across the expert panel. These outcomes enhance the methodological rigor and credibility of barrier selection processes. Notably, the high crisp scores obtained for several barriers, particularly those related to resource limitations (e.g., insufficient learning materials and inadequate financial support), highlight their practical significance. These challenges are not just perceived concerns but are deeply embedded in the operational dynamics of the educational supervision system. Even barriers with relatively lower average ratings, such as the lack of training halls or dedicated office space, were retained in the final model, emphasizing their contextual importance despite being perceived as less urgent issues. The use of FDM in this study served as an effective filtering and validation mechanism, ensuring that only the most contextually relevant and expert-validated barriers were included in subsequent analytical phases. This is especially critical in complex decision-making environments, where aligning empirical evidence with expert insights enhances the focus, reliability, and applicability of advanced evaluation models, such as MCDM techniques or policy simulations. By incorporating expert judgment under conditions of uncertainty and applying structured fuzzy logic principles, the method strengthens the foundation for deeper investigation and prioritization [39,42,65]. Following FDM validation, ranking and clustering analyses ( Tables 9 and 10 ) provided further insights into how these barriers vary in severity and impact. Segmentation into three groups enabled the strategic categorization of challenges, offering a clearer roadmap for targeted interventions and policy reform. The analysis identified a critical barrier in educational supervision: the weak competencies of educational supervisors (B4). This barrier, despite representing only 9.1% of total concerns, received the highest average score (0.467) and ranked first among all barriers, emphasizing its importance. Consistent with the existing literature, 67 – 69 this finding highlights the vital role of competent supervision in teacher development, instructional improvement, and overall school enhancement. Competent supervisors possess essential skills in instructional leadership, communication, observation, feedback delivery, and the ability to analyze teaching practices and design appropriate interventions. 15 , 16 Weak supervisory competencies can undermine the entire supervisory process, leading to unclear guidance, ineffective support, and hindered teacher development, 70 potentially compromising educational quality and impeding systemic improvement. This analysis emphasizes the need for immediate and sustained action to enhance supervisory competencies, as they form the foundation of effective educational supervision and improve teaching and learning outcomes. 16 – 18 To address this critical issue, policymakers and educational institutions must prioritize comprehensive professional development programs for supervisors, focusing on continuous training, mentorship programs, tailored performance evaluation systems, and the development of instructional leadership capabilities. 15 , 17 , 18 , 68 , 69 Strengthening supervisory competencies is likely to have far-reaching effects, potentially mitigating the interconnected barriers within the educational system. Furthermore, segment 2, comprising seven barriers and representing the largest portion (63.6%), included various moderate-impact issues influencing daily supervision operations. The most significant barrier, B2: Limited capability in creating effective supervisory plans (average score: 0.442), highlights the necessity of enhanced strategic planning skills among supervisors. This aligns with the understanding that well-developed supervisory plans are essential for effective supervision, providing structure, clear goals, and direction. 53 , 54 Effective supervisory plans ensure a systematic rather than a reactive process, aligning supervision with instructional improvement. These plans must be goal-oriented, realistic, adaptable, time-bound, and developed collaboratively with the teachers. The ability to create and implement strong supervisory plans is central to ensuring purposeful and focused supervision aligned with educational goals. 13 , 14 Conversely, poorly planned supervision can be inconsistent or unclear, limiting its impact on teacher development and student learning. Equally concerning is B6: Poor relationships between supervisors and teachers (0.440), which points to underlying communication and trust issues. Effective supervision relies not only on technical competence but also on interpersonal relationships that foster a supportive and collaborative school environment. A professional relationship built on trust and communication encourages teacher openness, responsiveness to feedback, and collaboration. 18 , 59 In contrast, weak relationships or lack of trust may result in resistance and reduce the impact of supervisory efforts. Additional barriers in this segment include B11: Lack of dedicated offices for educational supervision (0.436) and B3: Limited capability in implementing training programs (0.425). These barriers indicate both infrastructural deficiencies and limited pedagogical leadership, suggesting a need for resource investment and capacity building. Well-designed and timely training programs directly enhance teachers’ instructional skills, promote consistency in instructional quality, and foster a culture of professional growth. 10 , 11 The distribution of barriers in Segment 2 underscores the need for a comprehensive improvement strategy that addresses both relational and structural challenges. Enhancing supervisors’ abilities across three key dimensions—planning, relational engagement, and training delivery—is foundational to improving the overall effectiveness of supervision systems. This approach would involve the socioeconomic status: Strengthening planning capabilities to improve the quality, consistency, and outcomes of the educational supervision process; developing meaningful relationships between supervisors and teachers to create a supportive and collaborative environment is essential; improving the design and implementation of impactful training programs to enhance teacher performance and professional development is crucial. By addressing these interconnected aspects, supervisors can provide more meaningful feedback, manage their time effectively, and identify key areas for improvement. 13 This comprehensive approach would significantly enhance the quality and impact of educational supervision, ultimately benefiting the development of teachers and student learning outcomes. Finally, segment 1, comprising three barriers and representing 27.3% of the total, included concerns that were considered low-priority or minimal risk. These barriers are marked by relatively low average ratings and limited impact on the effectiveness of comprehensive educational supervision. Despite being rated as lower priority, the barriers in Segment 1 highlight structural limitations that could negatively affect the supervision process if not addressed. For example, limited teacher acceptance of modern supervisory methods can stifle innovation and diminish the effectiveness of such interventions. When teachers resist collaborative evaluation, peer feedback, or data-informed coaching, it undermines the culture of trust and continuous improvement that is essential for effective supervision. 22 Similarly, inadequate financial allocations for supervision activities restrict supervisors’ ability to conduct school visits, attend professional development programs, or acquire necessary materials, resulting in poorly planned and under-resourced supervision systems. 8 , 23 Additionally, the lack of access to modern technologies limits the use of digital tools for classroom observation, feedback, training and communication. Without these tools, supervision becomes less interactive, data-informed, and responsive. 19 , 60 While these barriers may not have the most immediate impact, their cumulative effect over time can impede broader reform efforts, stifle innovation, and exacerbate other challenges in educational supervision. Therefore, continuous monitoring and proactive mitigation of lower-ranked issues are crucial for sustaining long-term improvements. The low average ratings and limited influence of these barriers indicate that resources and efforts should primarily focus on addressing more critical issues in comprehensive educational supervision. Nevertheless, it remains important to monitor these low-priority barriers, as they could potentially affect the overall effectiveness of supervision if neglected for an extended period. In summary, the integrated FDM and clustering analysis offered a multilayered understanding of the barriers hindering educational supervision in Yemen. The findings emphasize the urgency of enhancing supervisory competencies and highlight the need to address moderate-impact barriers related to planning, relationships, infrastructure, and training. Low-priority issues should not be ignored, as their future impact may become more significant in a changing educational landscape. Collectively, these insights provide a strategic framework for policymakers, educational leaders, and stakeholders to allocate resources, design interventions, and improve supervisory effectiveness, ultimately contributing to more robust and equitable educational outcomes 5. Conclusion This study systematically investigated the technical barriers undermining the effectiveness of comprehensive educational supervision in Amanat Al Asimah, Yemen, using a robust mixed-methods framework. Eleven key challenges were identified, validated through the Fuzzy Delphi Method, and prioritized using fuzzy set theory and K-means clustering. The Fuzzy Delphi results demonstrated strong expert consensus, with a domain-level threshold of 0.093 and an average agreement of 90%, affirming the contextual relevance of the selected barriers. Through clustering analysis, the barriers were segmented into three priority levels. Segment 3, comprising a single high-priority issue—weak supervisory competencies—emerged as the most critical concern, underscoring the central role of professional capacity in enabling effective supervision. Segment 2 contained seven moderate-priority barriers, including planning deficiencies, strained supervisor–teacher relationships, and inadequate training infrastructure. Segment 1 grouped three low-priority barriers, such as resistance to modern supervision methods and technological or financial constraints, which, though less urgent, could accumulate into significant challenges if left unaddressed. These findings carry important implications for policymakers, educational leaders, and planners operating in fragile and resource-limited contexts. The study offers a data-driven roadmap for prioritizing reforms, emphasizing the urgent need to invest in supervisory training, institutional planning, and resource allocation. The segmentation of barriers provides a practical framework for designing targeted, phased interventions. Additionally, the approach used in this study can be replicated or adapted to other conflict-affected or low-income settings, contributing to broader efforts in educational reform and capacity building. However, the study also faces several limitations. It was geographically restricted to Amanat Al Asimah, potentially limiting generalizability across Yemen’s diverse regions. Furthermore, it focused primarily on teachers’ and supervisors’ perspectives, excluding voices from students, principals, and policymakers. Also, it concentrated on technical barriers, leaving out sociopolitical or cultural dimensions that may influence supervisory effectiveness. Future research should expand geographically, incorporate multiple stakeholders, and explore non-technical factors influencing educational supervision. Longitudinal and experimental designs could also evaluate how barriers evolve and how proposed solutions perform over time. Comparative studies across similar contexts could provide valuable benchmarks and inform regional or global supervisory strategies. In summary, this study highlights the urgency of strengthening supervisory competencies and institutional frameworks to enhance the quality and effectiveness of educational supervision in Yemen. By integrating expert validation, teacher feedback, and advanced clustering techniques, it presents a scalable and evidence-based framework that can inform strategic planning, policy development, and educational resilience in post-crisis and low-resource environments Ethics and consent This study adhered to the principles of the Declaration of Helsinki and the national guidelines for research involving human participants. Official permission to conduct the study was secured from the Office of Education, Bani-Alhareth, Amanat Al Asimah, Republic of Yemen (Approval Reference No.: [72-9-24), authorizing data collection from participating schools. Participants received clear information about the study’s objectives and procedures and were assured that their participation was entirely voluntary, with the right to withdraw at any time, without consequences. Written informed consent was obtained from all participants before data collection, and no minors were included in this study. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used [DeepSeek v3, Paperpal, Quillbot, and ChatGPT] for language refinement and structure. After using this tools, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. Data availability statement Underlying data Not applicable Extended data Figshare: Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers. Doi: https://doi.org/10.6084/m9.figshare.29483738.v4 . 71 The project contains the following underlying data: - Supplementary material – Toward Effective Educational Supervision in Yemen.xlsx. All data, and processing results related to this study are presented in this file. This file integrates the application of the Fuzzy Delphi method to assess the consensus and suitability of the items, validated by 16 experienced educational experts. It also contains the collected and processed data from a quantitative survey targeting 370 teachers, along with the Fuzzy Set Theory processes for data aggregation. Additionally, it illustrates the K-means clustering results for segmenting the barriers based on their impacts. All this data is merged into a single Excel-based tool. This source also includes the values behind the results reported. This file also includes the values behind the measures reported in all analysis and discussion sections, as well as the values used to construct tables and figures. This supplementary resource also provides detailed support for replicating the study’s methods and results. - Supplementary files (copy of Ethical approval, Consent Form for participants and Questionnaires for data collection) (Original Arabic Version) - Supplementary Files – English Translation of Original Documents This data are publicly available and can be accessed through the following repository ( https://figshare.com/articles/dataset/Supplementary_material_-_Toward_Effective_Educational_Supervision_in_Yemen-A_Hybrid_Fuzzy_Delphi_ and_Clustering_Analysis_of_Technical_Barriers/29483738?file=56000291 ) and archived via [ https://doi.org/10.6084/m9.figshare.29483738.v4 ] 71 ). Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgments Not applicable. References 1. Al-Ajeely SA, Alkhawaldeh MA, Khasawneh MAS: Developing curricula standards in general education in the light of international standards. Migration Letters. 2023; 20 (S3): 1090–1104. 2. Alazzam NMA, Mohammad I: Challenges facing the educational supervision methods of the Ministry of Education in light of Saudi Vision 2030. JETT. 2022; 13 (4): 390–401. Publisher Full Text 3. Sandyawati NS, Handoko R, Su AD: Integration Of School Administration System And Academic Supervision As An Efforts To Improve The Quality Of Education. J. Inter. Multi. Res. Liter. 2022; 1 (1): 71–85. Publisher Full Text 4. Duisenbayev A, Shavaliyeva Z, Khassanova I, et al. : The Study of Effective Methods and Strategies for the Professional Development of Educators: Mentorship, Collaboration, and Self-Organization Skills. International Journal of Education in Mathematics. Sci. Technol. 2023; 12 (2): 450–469. Publisher Full Text 5. Rasdiana, Wiyono BB, Imron A, et al. : Elevating Teachers’ Professional Digital Competence: Synergies of Principals’ Instructional E-Supervision, Technology Leadership and Digital Culture for Educational Excellence in Digital-Savvy Era. Educ. Sci. 2024; 14 (3): 266. Publisher Full Text 6. Cordis GM: Role of Supportive Leadership and Teacher-School Head Relationships in Cabuyao District 4 Elementary Schools. Educational Research (IJMCER). 2025; 7 (3): 438–468. Reference Source 7. Ramos JL, Cattaneo AAP, de Jong FPCM , et al. : Pedagogical models for the facilitation of teacher professional development via video-supported collaborative learning. A review of the state of the art. J. Res. Technol. Educ. 2021; 54 (5): 695–718. Publisher Full Text 8. Okafor RN, Obona EE, Ngene AN, et al. : School supervision and inspection: Enhancing educational quality, accountability, and student social welfare in Nigerian secondary schools. UNIJIK J. Educ. Res. Pol. Stud. 2024; 18 (3). 9. Asiyai RI: Best practices for quality assurance in higher education: implications for educational administration. Int. J. Leadersh. Educ. 2020; 25 (5): 843–854. Publisher Full Text 10. Ameli IL, Ayaga G, Ouda JB: Effectiveness of Teacher Supervision Practices in the Implementation of the Early Years Education Program. Social Education Research. 2024; 302–318. Publisher Full Text 11. Karim A, Kartiko A, Daulay DE, et al. : The Effect of The Supervision of The Principal and The Professional Competency of Teachers on Teacher Performance in Private MI in Pacet District. Nidhomul Haq: Jurnal Manajemen Pendidikan Islam. 2021; 6 (3): 497–512. Publisher Full Text 12. Prasetia I, Akrim A, Sulasmi E: Developing Collaborative-Based Supervision Model Which Accentuates Listening and Responding Skills. AL-ISHLAH: Jurnal Pendidikan. 2022; 14 (1): 709–720. Publisher Full Text 13. Lestari TY, Bedi F: Supervision Strategies for Improving Learning Outcomes in Islamic-Based General Education Institutions. Journal of Islamic Education Research. 2025; 6 (1). Publisher Full Text 14. Esin Kancan O, Altinay F, Altinay Z, et al. : The Role of Supervisor to Develop Strategic Planning for the Future of Education. J. Chin. Hum. Resour. Manag. 2023; 14 (3): 70–83. Publisher Full Text 15. Naguit J: Instructional Leadership Practices of School Heads and Performance of Teachers in Instructional Supervision. Int. J. Adv. Multidiscip. Res. Stud. 2024; 4 (7): 193–207. Reference Source 16. Hasanah C, Sururi S: Principal’s Supervision Strategy to Improve Teachers’ Performance Based on Professional Competence. JMKSP (Jurnal Manajemen, Kepemimpinan, Dan Supervisi Pendidikan). 2025; 10 (1): 289–309. Publisher Full Text 17. Jardí A, Webster R, Petreñas C, et al. : Building successful partnerships between teaching assistants and teachers: Which interpersonal factors matter?. Teach. Teach. Educ. 2022; 109 : 103523. Publisher Full Text 18. Çoban Ö, Özdemir N, Bellibaş MŞ: Trust in principals, leaders’ focus on instruction, teacher collaboration, and teacher self-efficacy: testing a multilevel mediation model. Ed. Mgt. Adm. & Ldrshp. 2020; 51 (1): 95–115. Publisher Full Text 19. Kabariah S, Adiyono A: Efforts to use technology effectively in supporting the implementation of educational supervision. Indonesian Journal of Education (INJOE). 2023; 3 (1): 63–78. 20. Yahya Ilma WS, Hidayati D, Martaningsih S: Addressing Challenges in School-Based Management: Planning for Better Learning and Resource Management. Journal of Education and Teaching (JET). 2024; 5 (3): 247–263. Publisher Full Text 21. Amelia C, Aprilianto A, Supriatna D, et al. : The Principal’s Role as Education Supervisor in Improving Teacher Professionalism. Nidhomul Haq: Jurnal Manajemen Pendidikan Islam. 2022; 7 (1): 144–155. Publisher Full Text 22. Alajmi H: Modern supervisory methods for educational supervisors in Kuwait schools and their impact on their performance. Res. Humanit. Soc. Sci. 2017; 7 (18): 58–65. Reference Source 23. Imolong OM, Adesola OM, Nwakaego EC: Addressing Accountability Challenges in Higher Education Management for Sustainable National Development. Int. J. Soc. Sci. Excep. Res. 2025; 4 (3): 43–49. Reference Source 24. Hamamra B, Qabaha A, Daragmeh A: Online education and surveillance during COVID-19 pandemic in Palestinian universities. Int. Stud. Sociol. Educ. 2022; 31 (4): 446–466. Publisher Full Text 25. Walid A, Sutiah S, Shodiq J: EFFECT OF EFFECTIVE SUPERVISION ON IMPROVING THE QUALITY OF EDUCATION IN THE ERA OF DISRUPTION. International Journal Multidisciplinary (IJMI). 2024; 1 (2): 70–76. Publisher Full Text 26. Katureebe Y, Turyamureeba S, Rahim A, et al. : The The Effect of Financial Management on the Learning Ability of Students in Government-Aided Primary Schools in Ibanda Municipality Uganda. International Journal of Humanities, Management and Social Science (IJ-HuMaSS). 2023; 6 (2): 109–118. Publisher Full Text 27. Yemen Watcher: Education crisis deepens in Yemen.2024, May 20. Reference Source 28. Alsharif R, Alhaimi YM, Alroba’ee A, et al. : Diagnostic tools for improving education policy planning: a case study on yemen girls dropout in primary education in emergencies. KIX EAP Learning Cycle Case Study, March 2023.2023. Reference Source 29. Aljober MN, Nasser AA, Alghawli ASA, et al. : Health Security inequalities in Non-EU European Countries: A Cross-National Comparative Assessment Using an Integrated MCDM-Machine Learning Approach. F1000Res. 2025; 14 : 462. Publisher Full Text 30. Nasser AA, Al-Samawi YA, Alghawli ASA, et al. : Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework. F1000Res. 2025; 14 : 600. PubMed Abstract | Publisher Full Text | Free Full Text 31. Nasser AA, Alghawli ASA: Evaluation and clustering of health security performance in Africa: A comparative analysis through the entropy-TOPSIS-K-means approach. African Security Review. 2024; 33 (3): 330–348. Publisher Full Text 32. Nasser AA, Alghawli ASA, Saleh S, et al. : Health security disparities in the Eastern Mediterranean Region: A comparative analysis using an integrated MCDM and clustering approach. J. Biosaf. Biosecur. 2025; 7 (1): 38–51. Publisher Full Text 33. Nasser AA, Alghawli ASA, Saleh S, et al. : Income-Based analysis of health security in Western Asia through an integrated GHSI, MCDM, and Clustering Model. F1000Res. 2025; 14 : 43. PubMed Abstract | Publisher Full Text | Free Full Text 34. Nasser AA, Saeed MM, Aljober MN: Application of Selected MCDM Methods for Developing a Multi-Functional Framework for Eco-Hotel Planning in Yemen. Int. J. Comput. Sci. Eng. 2021; 9 (10): 7–18. Publisher Full Text 35. Alghawli ASA, Nasser AA, Aljober MN: A Fuzzy MCDM Approach for Structured Comparison of the Health Literacy Level of Hospitals. Int. J. Adv. Comput. Sci. Appl. 2021; 12 (7). Publisher Full Text 36. Nasser AA, et al. : Assessing Equity in Healthcare Facility Resource Allocation in Yemen: An Entropy-TOPSIS Analysis. Int. J. Recent Innov. Trends Comput. Commun. 2023; 11 (9): 1598–1609. Publisher Full Text 37. Al-Khulaidi AAG, Nasser AA, Al-ashwal MHY, et al. : Investigating information security risk management in Yemeni banks: An CILOS-TOPSIS approach. Multidiscip. Sci. J. 2024; 6 (9): 2024175. Publisher Full Text 38. Nasser AA, Al-Ashwal MM, Al-Khulaidi AA, et al. : A hybrid business-technical model for evaluating IoT platforms’ functionality, reliability, and usability. Int. J. Eng. Trends Technol. 2023; 71 (10): 39–59. Publisher Full Text 39. Said MM, Nasser AA, Alkhulaidi AA: Prioritization of the eco-hotels performance criteria in Yemen using fuzzy Delphi method. Int. J. app. Inf. Syst. 2021; 12 (36): 20–29. Reference Source 40. Al-Amari A, Abdulqader B, Nasser A, et al. : Prioritizing research management planning strategies for digital repository development: a case study in Yemeni universities. Seybold Report. 2023; 18 (10): 1563–1577. 41. Dalkey N, Helmer O: An experimental application of the Delphi method to the use of experts. Manag. Sci. 1963; 9 (3): 458–467. Publisher Full Text 42. Manakandan SK, Rosnah I, Mohd RJ, et al. : Pesticide applicators questionnaire content validation: A fuzzy delphi method. Med J Malaysia. 2017; 72 (4): 228–235. Reference Source 43. Ataallah Muhammed S, Flaih RL: Predictive Modeling in Healthcare: A Survey of Data Mining Applications. 5TH International conference on communication engineering and computer science (CIC-COCOS’24). 2024; 1–11. Publisher Full Text 44. Alhegami AS, Alsaeedi HA: A framework for incremental parallel mining of interesting association patterns for big data. Int. J. Comput. 2020; 106–117. Publisher Full Text 45. Flaih LR, Baha al_Deen SA, Ayoobkhan MUA: Analysis of Surface Quality Measurement With Classification Approach. J. Phys. Conf. Ser. 2020; 1712 (1): 012027. Publisher Full Text 46. Al-Hegami AS, Al-Hddad NSA, Alsaeedi HA: A Web Mining Approach for Evaluation of Quality Assurance at University of Science and Technology. Journal of Engineering and Technological Sciences - JOEATS. 2025; 3 (1): 146–158. Publisher Full Text 47. Balamurugan E, Flaih LR, Yuvaraj D, et al. : Use Case of Artificial Intelligence in Machine Learning Manufacturing 4.0. 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). 2019; 656–659. Publisher Full Text 48. Mahmoodzadeh A, Nejati HR, Flaih LR, et al. : A Rigorous Examination of Twelve Cutting-Edge Machine-Learning Techniques for Predicting Time and Cost in Tunneling Projects. J. Constr. Eng. Manag. 2024; 150 (12). Publisher Full Text 49. Moradpour N, Pourahmad A, Ziari K, et al. : Downscaling urban resilience assessment: A spatiotemporal analysis of urban blocks using the fuzzy Delphi method and K-means clustering. Build. Environ. 2024; 263 : 111898. Publisher Full Text 50. Deb K, Banerjee S, Chatterjee R, et al. : Educational Website Ranking Using Fuzzy Logic and K-Means Clustering Based Hybrid Method. Ingénierie Des Systèmes d Information. 2019; 24 (5): 497–506. Publisher Full Text 51. Gading SJL: Instructional Leadership Practices of the School Heads to Improve Teachers Performance. United Int. J. Res. Technol. 2024; 5 (6): 89–119. Reference Source 52. Jusoh R, Md Dasuki N, Shu Q, et al. : Sustainable Leadership: Encouraging Teacher Performance and Classroom Excellence. International Journal of Academic Research in Business and Social Sciences (IJ-ARBSS). 2024; 14 (11). Publisher Full Text 53. Simon DJ, Swerdlik ME: Characteristics of Effective Supervisory Relationships. Supervision in School Psychology. 2022; 47–65. Publisher Full Text 54. Crowley G, Denney ML, Mehay R, et al. : Effective educational supervision. The essential handbook for GP training and education. CRC Press; 2021; pp. 378–393. 55. Pruett AL: Administrators’ Perceptions of Their Roles in Facilitating DataDriven Classroom Instruction in Elementary Schools. Walden University; 2025. (Doctoral dissertation). 56. Vadivel B, Namaziandost E, Saeedian A: Progress in English Language Teaching Through Continuous Professional Development—Teachers’ Self-Awareness, Perception, and Feedback. Frontiers in Education. 2021; 6 . Publisher Full Text 57. Rahmadani S, Harahap RN, Sembiring SBB: Strategies for Overcoming Teachers’ Resistance to Change in 21st Century Education: Teachers’ Perspectives. Sintaksis: Publikasi Para ahli Bahasa dan Sastra Inggris. 2025; 3 (1): 123–131. Publisher Full Text 58. Faustyna F: Crisis Communications Related to Student Learning Implementation in Public Relations Marketing Event Course. International Journal Reglement & Society (IJRS). 2023; 4 (2): 126–133. Reference Source 59. Gholaminejad R: When the evil pops in: exploring the unheard voices of teachers working in private language schools in Iran concerning supervisory observation. Int. J. Leadersh. Educ. 2020; 26 (1): 100–123. Publisher Full Text 60. Sumampow ZF, Tudus MV: Transformation of the Role of Educational Supervisors in the Digital Era: Adaptation Strategies and Supervision Innovations. Jurnal Masyarakat Digital. 2024; 1 (1): 30–36. Reference Source 61. Viano S, Shahrokhi F, Hunter SB: Improvement science and school leadership: the precarious path to dynamic school improvement. Frontiers in Education. 2024; 9 . Publisher Full Text 62. Bawaneh AK, Rababh BS, Al-Salman SM: Distance Education: The Satisfaction Level of Science and Mathematics Teachers towards the Electronic Educational Supervision. International Journal of Education and Humanities (IJEH). 2023; 3 (4): 300–315. Publisher Full Text 63. Eyenaka FD, Nsit A, Umoren FA, et al. : Laboratory and library facilities: investigating their potencies in promoting student interest and performance in science subject. Inf. Sci. 2024; 6 (1). Reference Source 64. Olajide O, Zinn S: The Role of School Libraries in Supporting Inquiry-Based Methods for Teaching Science in Nigerian Schools: Challenges and Possibilities. Libri. 2021; 71 (2): 171–182. Publisher Full Text 65. Alghawli ASA, Al-khulaidi AA, Nasser AA, et al. : Application of the fuzzy Delphi method to identify and prioritize the socialhealth family disintegration indicators in Yemen. Int. J. Adv. Comput. Sci. Appl. 2022; 13 (5): 680–691. Publisher Full Text 66. Gao CX, Dwyer D, Zhu Y, et al. : An Overview of Clustering Methods with Guidelines for Application in Mental Health Research. Psychiatry Res. 2023; 327 : 115265. PubMed Abstract | Publisher Full Text 67. Falender CA, Kaslow NJ, Goodyear R, et al. : Defining competencies in psychology supervision: a consensus statement. J. Clin. Psychol. 2004; 60 (7): 771–785. PubMed Abstract | Publisher Full Text 68. Kelley C, Dikkers S: Framing Feedback for School Improvement Around Distributed Leadership. Educ. Adm. Q. 2016; 52 (3): 392–422. Publisher Full Text 69. Williams J: Teacher Educator Professional Learning in the Third Space. J. Teach. Educ. 2014; 65 (4): 315–326. Publisher Full Text 70. Liu S, Hallinger P: Teacher development in rural China: how ineffective school leadership fails to make a difference. Int. J. Leadersh. Educ. 2017; 21 (6): 633–650. Publisher Full Text 71. Alshameri RGA, Nasser AA, Abdullah AH, et al. : Supplementary material - Toward Effective Educational Supervision in Yemen-A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers. Dataset. figshare. 2025. Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 22 Sep 2025 ADD YOUR COMMENT Comment Author details Author details 1 Department of Education, Sultan Zainal Abidin University, Kuala Terengganu, Terengganu, Malaysia 2 Department of Information Systems and Computer Science, Sa’adah University, Sa'adah, Sa'adah, Yemen 3 Department of Artificial Intelligence, Modern Specialized University, Sana'a, Sana'a, Yemen 4 Department of Computer Science, College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al Kharj, Riyadh Province, Saudi Arabia Riyadh Ghaleb A. Alshameri Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Visualization, Writing – Original Draft Preparation Adel A. Nasser Roles: Conceptualization, Formal Analysis, Methodology, Software, Writing – Original Draft Preparation Abdul Hakim Abdullah Roles: Formal Analysis, Supervision, Writing – Review & Editing Abed Saif Ahmed Alghawli Roles: Formal Analysis, Funding Acquisition, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Amani A. K. Elsayed Roles: Investigation, Resources, Validation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This study was supported via funding from Deanship of Scientific Research, Prince Sattam bin Abdulaziz University [project number: PSAU/2025/R/1446]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 09 Feb 2026, 14:956 https://doi.org/10.12688/f1000research.167498.2 version 1 Published: 22 Sep 2025, 14:956 https://doi.org/10.12688/f1000research.167498.1 Copyright © 2025 Alshameri RGA et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Alshameri RGA, Nasser AA, Abdullah AH et al. Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :956 ( https://doi.org/10.12688/f1000research.167498.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 22 Sep 2025 Views 0 Cite How to cite this report: AL-Hagree S. Reviewer Report For: Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :956 ( https://doi.org/10.5256/f1000research.184615.r435827 ) The direct URL for this report is: https://f1000research.com/articles/14-956/v1#referee-response-435827 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 20 Jan 2026 Salah AL-Hagree , Department of Computer Sciences, Sana'a University, Sanaa, Yemen Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.184615.r435827 Correcting the values ​​in Table 5, Percentage of Experts (%) or Average 90% ( Average 89.73 ) - Percentage of Experts, d ≤ 0.2 81.25% 81.25% 100.00% 81.25% 100.00% 100.00% 81.25% 93.75% 100.00% 81.25% 87.50% - Percentage of ... Continue reading READ ALL Correcting the values ​​in Table 5, Percentage of Experts (%) or Average 90% ( Average 89.73 ) - Percentage of Experts, d ≤ 0.2 81.25% 81.25% 100.00% 81.25% 100.00% 100.00% 81.25% 93.75% 100.00% 81.25% 87.50% - Percentage of Experts, d ≤ 0.2 81% 81% 100% 81% 100% 100% 81% 94% 100% 81% 88% Based on the previous correction, the following is corrected: 3. Results The overall average expert agreement across all barriers was 90%, 4. Discussion The high level of consensus among experts (average agreement of 90%) 5. Conclusion - and an average agreement of 90%, affirming the contextual relevance of the selected barriers. - Future research should expand the use of other classification techniques, such as the technique in the reference: https://zenodo.org/records/17251460 -The method should clearly incorporate the concept of Hybrid : A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers - References related to Yemen 1- Alqasemi, F., Salah, A. H., Aqlan, A., Alalayah, K. M., Almotwakl, Z., & Hadwan, M. (2021, March). Education Data Mining For Yemen Regions Based On Hierarchical Clustering Analysis. In 2021 International Conference of Technology, Science and Administration (ICTSA) (pp. 1-4). IEEE. https://doi.org/10.1109/ICTSA52017.2021.9406544 ‏ 2- Al, S., & Alwan, S. M. (2025). Neural Network Analysis of Factors Influencing Forgotten Financial Remittances in Yemen . Journal of Economic Cooperation and Development, 46(2), 71-102. https://doi.org/10.5281/zenodo.17251460 ‏ Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Natural Language Processing, Data Science, Machine Learning, Deep Learning, and Artificial Neural Networks I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT AL-Hagree S. Reviewer Report For: Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :956 ( https://doi.org/10.5256/f1000research.184615.r435827 ) The direct URL for this report is: https://f1000research.com/articles/14-956/v1#referee-response-435827 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 09 Feb 2026 Adel A. Nasser , Department of Information Systems and Computer Science, Sa’adah University, Sa'adah, Yemen 09 Feb 2026 Author Response Responses to Peer Review Report of Assis. Prof. Salah AL-Hagree, Department of Computer Sciences, Sana'a University, Sanaa, Yemen Dear Prof. Salah AL-Hagree, Thank you for your thoughtful review and for the ... Continue reading Responses to Peer Review Report of Assis. Prof. Salah AL-Hagree, Department of Computer Sciences, Sana'a University, Sanaa, Yemen Dear Prof. Salah AL-Hagree, Thank you for your thoughtful review and for the insightful comments provided on our manuscript. Your feedback has been invaluable in guiding our revisions. Comments 1,2: Correcting the values ​​in Table 5, Percentage of Experts (%) or Average 90% ( Average 89.73 ) - Percentage of Experts, d ≤ 0.2 81.25% 81.25% 100.00% 81.25% 100.00% 100.00% 81.25% 93.75% 100.00% 81.25% 87.50% - Percentage of Experts, d ≤ 0.2 81% 81% 100% 81% 100% 100% 81% 94% 100% 81% 88% Response: The authors acknowledge the reviewer's correct observation regarding the precision of the data in Table 5. The specified percentages for the row "Percentage of Experts, d ≤ 0.2" have been revised to two decimal places as suggested: 81.25%, 81.25%, 100.00%, 81.25%, 100.00%, 100.00%, 81.25%, 93.75%, 100.00%, 81.25%, 87.50%. The summary value in the text has been correspondingly updated to "Average 89.73%." We are grateful for this correction. . Comments 3-5: Updating the referenced average value throughout the manuscript: Results section: “The overall average expert agreement across all barriers was 90%”, Discussion section: “The high level of consensus among experts (average agreement of 90%) Conclusion - and an average agreement of 90%, affirming the contextual relevance of the selected barriers. Response: We thank the reviewer for their consistency in pointing out the related text. Following the correction to the data in Table 5, we have also updated the referenced average value throughout the manuscript In the Results section, the sentence now reads: "The overall average expert agreement across all barriers was 89.73%." In the Discussion section, the text has been amended to: "The high level of consensus among experts (average agreement of 89.73%)..." In the Conclusion section, the text has been amended to: "and an average agreement of 89.77%, ...” In the Abstract section, the sentence now reads” and an average expert consensus of 89.77%, indicating strong agreement Comment 6 : - Future research should expand the use of other classification techniques, such as the technique in the reference: https://zenodo.org/records/17251460 Response: We appreciate this suggestion and will explore other classification techniques. The revised manuscript now incorporates a discussion of the suggested reference in the Literature Review/Future Work section. Comment 7 : The method should clearly incorporate the concept of Hybrid: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers Response: Response: Thank you for this insightful recommendation. We have explored this integration in the revised manuscript. The methodology section now clearly illustrates the two-tiered hybrid analytical framework that integrates methodological approaches for barrier validation and data-driven prioritization. The sequential steps of the framework are now explicitly described. Comment 8 : - References related to Yemen 1- Alqasemi, F., Salah, A. H., Aqlan, A., Alalayah, K. M., Almotwakl, Z., & Hadwan, M. (2021, March). Education Data Mining For Yemen Regions Based On Hierarchical Clustering Analysis. In 2021 International Conference of Technology, Science and Administration (ICTSA) (pp. 1-4). IEEE. https://doi.org/10.1109/ICTSA52017.2021.9406544 ‏ 2- Al, S., & Alwan, S. M. (2025). Neural Network Analysis of Factors Influencing Forgotten Financial Remittances in Yemen. Journal of Economic Cooperation and Development, 46(2), 71-102. https://doi.org/10.5281/zenodo.17251460 Response: We acknowledge the importance of citing the relevant literature related to Yemen. We appreciate the references provided, and the revised manuscript now includes five new relevant references (cited as references 28, 29, 49, 54, and 74). We hope that our responses and the corresponding revisions adequately address your concerns and have improved our manuscript. Thank you again for your valuable time and insights Responses to Peer Review Report of Assis. Prof. Salah AL-Hagree, Department of Computer Sciences, Sana'a University, Sanaa, Yemen Dear Prof. Salah AL-Hagree, Thank you for your thoughtful review and for the insightful comments provided on our manuscript. Your feedback has been invaluable in guiding our revisions. Comments 1,2: Correcting the values ​​in Table 5, Percentage of Experts (%) or Average 90% ( Average 89.73 ) - Percentage of Experts, d ≤ 0.2 81.25% 81.25% 100.00% 81.25% 100.00% 100.00% 81.25% 93.75% 100.00% 81.25% 87.50% - Percentage of Experts, d ≤ 0.2 81% 81% 100% 81% 100% 100% 81% 94% 100% 81% 88% Response: The authors acknowledge the reviewer's correct observation regarding the precision of the data in Table 5. The specified percentages for the row "Percentage of Experts, d ≤ 0.2" have been revised to two decimal places as suggested: 81.25%, 81.25%, 100.00%, 81.25%, 100.00%, 100.00%, 81.25%, 93.75%, 100.00%, 81.25%, 87.50%. The summary value in the text has been correspondingly updated to "Average 89.73%." We are grateful for this correction. . Comments 3-5: Updating the referenced average value throughout the manuscript: Results section: “The overall average expert agreement across all barriers was 90%”, Discussion section: “The high level of consensus among experts (average agreement of 90%) Conclusion - and an average agreement of 90%, affirming the contextual relevance of the selected barriers. Response: We thank the reviewer for their consistency in pointing out the related text. Following the correction to the data in Table 5, we have also updated the referenced average value throughout the manuscript In the Results section, the sentence now reads: "The overall average expert agreement across all barriers was 89.73%." In the Discussion section, the text has been amended to: "The high level of consensus among experts (average agreement of 89.73%)..." In the Conclusion section, the text has been amended to: "and an average agreement of 89.77%, ...” In the Abstract section, the sentence now reads” and an average expert consensus of 89.77%, indicating strong agreement Comment 6 : - Future research should expand the use of other classification techniques, such as the technique in the reference: https://zenodo.org/records/17251460 Response: We appreciate this suggestion and will explore other classification techniques. The revised manuscript now incorporates a discussion of the suggested reference in the Literature Review/Future Work section. Comment 7 : The method should clearly incorporate the concept of Hybrid: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers Response: Response: Thank you for this insightful recommendation. We have explored this integration in the revised manuscript. The methodology section now clearly illustrates the two-tiered hybrid analytical framework that integrates methodological approaches for barrier validation and data-driven prioritization. The sequential steps of the framework are now explicitly described. Comment 8 : - References related to Yemen 1- Alqasemi, F., Salah, A. H., Aqlan, A., Alalayah, K. M., Almotwakl, Z., & Hadwan, M. (2021, March). Education Data Mining For Yemen Regions Based On Hierarchical Clustering Analysis. In 2021 International Conference of Technology, Science and Administration (ICTSA) (pp. 1-4). IEEE. https://doi.org/10.1109/ICTSA52017.2021.9406544 ‏ 2- Al, S., & Alwan, S. M. (2025). Neural Network Analysis of Factors Influencing Forgotten Financial Remittances in Yemen. Journal of Economic Cooperation and Development, 46(2), 71-102. https://doi.org/10.5281/zenodo.17251460 Response: We acknowledge the importance of citing the relevant literature related to Yemen. We appreciate the references provided, and the revised manuscript now includes five new relevant references (cited as references 28, 29, 49, 54, and 74). We hope that our responses and the corresponding revisions adequately address your concerns and have improved our manuscript. Thank you again for your valuable time and insights Competing Interests: No competing interests were disclosed Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 09 Feb 2026 Adel A. Nasser , Department of Information Systems and Computer Science, Sa’adah University, Sa'adah, Yemen 09 Feb 2026 Author Response Responses to Peer Review Report of Assis. Prof. Salah AL-Hagree, Department of Computer Sciences, Sana'a University, Sanaa, Yemen Dear Prof. Salah AL-Hagree, Thank you for your thoughtful review and for the ... Continue reading Responses to Peer Review Report of Assis. Prof. Salah AL-Hagree, Department of Computer Sciences, Sana'a University, Sanaa, Yemen Dear Prof. Salah AL-Hagree, Thank you for your thoughtful review and for the insightful comments provided on our manuscript. Your feedback has been invaluable in guiding our revisions. Comments 1,2: Correcting the values ​​in Table 5, Percentage of Experts (%) or Average 90% ( Average 89.73 ) - Percentage of Experts, d ≤ 0.2 81.25% 81.25% 100.00% 81.25% 100.00% 100.00% 81.25% 93.75% 100.00% 81.25% 87.50% - Percentage of Experts, d ≤ 0.2 81% 81% 100% 81% 100% 100% 81% 94% 100% 81% 88% Response: The authors acknowledge the reviewer's correct observation regarding the precision of the data in Table 5. The specified percentages for the row "Percentage of Experts, d ≤ 0.2" have been revised to two decimal places as suggested: 81.25%, 81.25%, 100.00%, 81.25%, 100.00%, 100.00%, 81.25%, 93.75%, 100.00%, 81.25%, 87.50%. The summary value in the text has been correspondingly updated to "Average 89.73%." We are grateful for this correction. . Comments 3-5: Updating the referenced average value throughout the manuscript: Results section: “The overall average expert agreement across all barriers was 90%”, Discussion section: “The high level of consensus among experts (average agreement of 90%) Conclusion - and an average agreement of 90%, affirming the contextual relevance of the selected barriers. Response: We thank the reviewer for their consistency in pointing out the related text. Following the correction to the data in Table 5, we have also updated the referenced average value throughout the manuscript In the Results section, the sentence now reads: "The overall average expert agreement across all barriers was 89.73%." In the Discussion section, the text has been amended to: "The high level of consensus among experts (average agreement of 89.73%)..." In the Conclusion section, the text has been amended to: "and an average agreement of 89.77%, ...” In the Abstract section, the sentence now reads” and an average expert consensus of 89.77%, indicating strong agreement Comment 6 : - Future research should expand the use of other classification techniques, such as the technique in the reference: https://zenodo.org/records/17251460 Response: We appreciate this suggestion and will explore other classification techniques. The revised manuscript now incorporates a discussion of the suggested reference in the Literature Review/Future Work section. Comment 7 : The method should clearly incorporate the concept of Hybrid: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers Response: Response: Thank you for this insightful recommendation. We have explored this integration in the revised manuscript. The methodology section now clearly illustrates the two-tiered hybrid analytical framework that integrates methodological approaches for barrier validation and data-driven prioritization. The sequential steps of the framework are now explicitly described. Comment 8 : - References related to Yemen 1- Alqasemi, F., Salah, A. H., Aqlan, A., Alalayah, K. M., Almotwakl, Z., & Hadwan, M. (2021, March). Education Data Mining For Yemen Regions Based On Hierarchical Clustering Analysis. In 2021 International Conference of Technology, Science and Administration (ICTSA) (pp. 1-4). IEEE. https://doi.org/10.1109/ICTSA52017.2021.9406544 ‏ 2- Al, S., & Alwan, S. M. (2025). Neural Network Analysis of Factors Influencing Forgotten Financial Remittances in Yemen. Journal of Economic Cooperation and Development, 46(2), 71-102. https://doi.org/10.5281/zenodo.17251460 Response: We acknowledge the importance of citing the relevant literature related to Yemen. We appreciate the references provided, and the revised manuscript now includes five new relevant references (cited as references 28, 29, 49, 54, and 74). We hope that our responses and the corresponding revisions adequately address your concerns and have improved our manuscript. Thank you again for your valuable time and insights Responses to Peer Review Report of Assis. Prof. Salah AL-Hagree, Department of Computer Sciences, Sana'a University, Sanaa, Yemen Dear Prof. Salah AL-Hagree, Thank you for your thoughtful review and for the insightful comments provided on our manuscript. Your feedback has been invaluable in guiding our revisions. Comments 1,2: Correcting the values ​​in Table 5, Percentage of Experts (%) or Average 90% ( Average 89.73 ) - Percentage of Experts, d ≤ 0.2 81.25% 81.25% 100.00% 81.25% 100.00% 100.00% 81.25% 93.75% 100.00% 81.25% 87.50% - Percentage of Experts, d ≤ 0.2 81% 81% 100% 81% 100% 100% 81% 94% 100% 81% 88% Response: The authors acknowledge the reviewer's correct observation regarding the precision of the data in Table 5. The specified percentages for the row "Percentage of Experts, d ≤ 0.2" have been revised to two decimal places as suggested: 81.25%, 81.25%, 100.00%, 81.25%, 100.00%, 100.00%, 81.25%, 93.75%, 100.00%, 81.25%, 87.50%. The summary value in the text has been correspondingly updated to "Average 89.73%." We are grateful for this correction. . Comments 3-5: Updating the referenced average value throughout the manuscript: Results section: “The overall average expert agreement across all barriers was 90%”, Discussion section: “The high level of consensus among experts (average agreement of 90%) Conclusion - and an average agreement of 90%, affirming the contextual relevance of the selected barriers. Response: We thank the reviewer for their consistency in pointing out the related text. Following the correction to the data in Table 5, we have also updated the referenced average value throughout the manuscript In the Results section, the sentence now reads: "The overall average expert agreement across all barriers was 89.73%." In the Discussion section, the text has been amended to: "The high level of consensus among experts (average agreement of 89.73%)..." In the Conclusion section, the text has been amended to: "and an average agreement of 89.77%, ...” In the Abstract section, the sentence now reads” and an average expert consensus of 89.77%, indicating strong agreement Comment 6 : - Future research should expand the use of other classification techniques, such as the technique in the reference: https://zenodo.org/records/17251460 Response: We appreciate this suggestion and will explore other classification techniques. The revised manuscript now incorporates a discussion of the suggested reference in the Literature Review/Future Work section. Comment 7 : The method should clearly incorporate the concept of Hybrid: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers Response: Response: Thank you for this insightful recommendation. We have explored this integration in the revised manuscript. The methodology section now clearly illustrates the two-tiered hybrid analytical framework that integrates methodological approaches for barrier validation and data-driven prioritization. The sequential steps of the framework are now explicitly described. Comment 8 : - References related to Yemen 1- Alqasemi, F., Salah, A. H., Aqlan, A., Alalayah, K. M., Almotwakl, Z., & Hadwan, M. (2021, March). Education Data Mining For Yemen Regions Based On Hierarchical Clustering Analysis. In 2021 International Conference of Technology, Science and Administration (ICTSA) (pp. 1-4). IEEE. https://doi.org/10.1109/ICTSA52017.2021.9406544 ‏ 2- Al, S., & Alwan, S. M. (2025). Neural Network Analysis of Factors Influencing Forgotten Financial Remittances in Yemen. Journal of Economic Cooperation and Development, 46(2), 71-102. https://doi.org/10.5281/zenodo.17251460 Response: We acknowledge the importance of citing the relevant literature related to Yemen. We appreciate the references provided, and the revised manuscript now includes five new relevant references (cited as references 28, 29, 49, 54, and 74). We hope that our responses and the corresponding revisions adequately address your concerns and have improved our manuscript. Thank you again for your valuable time and insights Competing Interests: No competing interests were disclosed Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Prasetia I. Reviewer Report For: Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :956 ( https://doi.org/10.5256/f1000research.184615.r417161 ) The direct URL for this report is: https://f1000research.com/articles/14-956/v1#referee-response-417161 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 Oct 2025 Indra Prasetia , Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.184615.r417161 1. Is the work clearly and accurately presented and does it cite the current literature? Comment: This work is presented clearly and accurately. However, there are outdated data reviews in uncovering research issues. (e.g. The education system, ... Continue reading READ ALL 1. Is the work clearly and accurately presented and does it cite the current literature? Comment: This work is presented clearly and accurately. However, there are outdated data reviews in uncovering research issues. (e.g. The education system, which once showed progress with enrollment rates increasing from 71.3% to 97.5% between 1999 and 2013, has since experienced significant setbacks. By the 2015/2016 academic year, around 1.8 million children were out of school, and over 1,100 schools had been either destroyed or repurposed for emergency use. 27 ). More updated data support is needed to reinforce current issues. 2. Is the study design appropriate and is the work technically sound? [Please consider whether appropriate controls have been included] Comment: This study uses an integrative approach to explore and prioritise obstacles in the context of education monitoring in Yemen. My analysis shows that this integrative approach generally refers to research using mixed methods. If this is a mixed design, it needs to be explained in detail with clear references (a diagram of the stages also needs to be provided). 3. Are sufficient details of methods and analysis provided to allow replication by others? [Please consider whether the authors have adhered to relevant reporting guidelines] Comment: The methods and analysis have been sufficiently explained, but there are a few shortcomings. The stages of theory-based data analysis (literature review) have been applied in the research, and a number of important themes or variables such as B1, B2, … B11 have been identified. In my opinion, this is content analysis that produces themes (is my opinion correct?). If this is correct, then the literature data and analysis need to be presented, leading to the conclusion of the themes found (B1, B2...B11), so that these themes are supported by accurate data and analysis. 4. If applicable, is the statistical analysis and its interpretation appropriate? Comment: Already in accordance 5. Are all the source data underlying the results available to ensure full reproducibility? [We require that all data underlying figures and tables are provided (unless there are issues of confidentiality) as part of the article or deposited in a public repository. The data should be described in a usable and accessible format] Comment: The data based on the literature reviewed in Section 2.1 needs to be presented. (I suggest presenting it in a simple table format.) 6. Are the conclusions drawn adequately supported by the results? [Please consider whether any limitations are discussed in sufficient detail] Comment: Very adequate. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: education (education manajemen and leadership) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Prasetia I. Reviewer Report For: Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :956 ( https://doi.org/10.5256/f1000research.184615.r417161 ) The direct URL for this report is: https://f1000research.com/articles/14-956/v1#referee-response-417161 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 09 Feb 2026 Adel A. Nasser , Department of Information Systems and Computer Science, Sa’adah University, Sa'adah, Yemen 09 Feb 2026 Author Response Responses to Peer Review Report of Prof. Indra Prasetia, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia Dear Prof. Indra Prasetia, We sincerely thank the reviewers for their insightful comments and constructive feedback, ... Continue reading Responses to Peer Review Report of Prof. Indra Prasetia, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia Dear Prof. Indra Prasetia, We sincerely thank the reviewers for their insightful comments and constructive feedback, which have greatly improved the quality and clarity of this manuscript. Below, we address each point in detail and describe the corresponding revisions made Comment 1: This work is presented clearly and accurately. However, there are outdated data reviews in uncovering research issues. (e.g. The education system, which once showed progress with enrollment rates increasing from 71.3% to 97.5% between 1999 and 2013, has since experienced significant setbacks. By the 2015/2016 academic year, around 1.8 million children were out of school, and over 1,100 schools had been either destroyed or repurposed for emergency use.27). More updated data support is needed to reinforce current issues. Response: Thank you for your valuable and constructive feedback regarding the use of outdated data in the introduction. We agree that current statistics are essential for accurately framing the research problem within Yemen's severe and ongoing humanitarian crisis. In direct response to your comment, we have thoroughly revised the introduction by connecting the outdated with the most current, authoritative data available from major UN and humanitarian organizations (2024-2025) (Two references were added [28, 29]). Comment 2: This study uses an integrative approach to explore and priorities obstacles in the context of education monitoring in Yemen. My analysis shows that this integrative approach generally refers to research using mixed methods. If this is a mixed design, it needs to be explained in detail with clear references (a diagram of the stages also needs to be provided). Response: Thank you for your constructive feedback. In response, we have revised the Methodology section. New paragraphs describing the framework were added, a new diagram was provided, and the entire section was restructured to explain the detailed steps of the proposed framework in a clear sequence, supported by appropriate references. Comment 3: The methods and analysis have been sufficiently explained, but there are a few shortcomings. The stages of theory-based data analysis (literature review) have been applied in the research, and a number of important themes or variables such as B1, B2, … B11 have been identified. In my opinion, this is content analysis that produces themes (is my opinion correct?). If this is correct, then the literature data and analysis need to be presented, leading to the conclusion of the themes found (B1, B2...B11), so that these themes are supported by accurate data and analysis. Response: Thank you for your insightful observation. In response, we have revised the section to explicitly frame this methodological step. The 11 core barriers (B1–B11) are now clearly presented as direct outcomes of the analysis, with each theme succinctly defined and immediately supported by its foundational citations from the literature. This directly links the conclusions to the supporting data, ensuring the themes are evidence-based. To maintain a logical flow and avoid duplication, the detailed interpretation and synthesis of these findings are elaborated in the dedicated Discussion. Comment 4: If applicable, is the statistical analysis and its interpretation appropriate? Comment: Already in accordance Response: Thank you Comment 5: The data based on the literature reviewed in Section 2.1 needs to be presented. (I suggest presenting it in a simple table format.). Response: Thank you for your insightful observation. A new table (Table 1) has been added to provide a concise, at-a-glance summary of all identified barriers, reinforcing the output of the content analysis. Comment 6: Are the conclusions drawn adequately supported by the results? Comment: Very adequate. Response: Thank you. We hope that these responses adequately address your concerns and further strengthen our manuscript. Thank you again for your valuable insights! Responses to Peer Review Report of Prof. Indra Prasetia, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia Dear Prof. Indra Prasetia, We sincerely thank the reviewers for their insightful comments and constructive feedback, which have greatly improved the quality and clarity of this manuscript. Below, we address each point in detail and describe the corresponding revisions made Comment 1: This work is presented clearly and accurately. However, there are outdated data reviews in uncovering research issues. (e.g. The education system, which once showed progress with enrollment rates increasing from 71.3% to 97.5% between 1999 and 2013, has since experienced significant setbacks. By the 2015/2016 academic year, around 1.8 million children were out of school, and over 1,100 schools had been either destroyed or repurposed for emergency use.27). More updated data support is needed to reinforce current issues. Response: Thank you for your valuable and constructive feedback regarding the use of outdated data in the introduction. We agree that current statistics are essential for accurately framing the research problem within Yemen's severe and ongoing humanitarian crisis. In direct response to your comment, we have thoroughly revised the introduction by connecting the outdated with the most current, authoritative data available from major UN and humanitarian organizations (2024-2025) (Two references were added [28, 29]). Comment 2: This study uses an integrative approach to explore and priorities obstacles in the context of education monitoring in Yemen. My analysis shows that this integrative approach generally refers to research using mixed methods. If this is a mixed design, it needs to be explained in detail with clear references (a diagram of the stages also needs to be provided). Response: Thank you for your constructive feedback. In response, we have revised the Methodology section. New paragraphs describing the framework were added, a new diagram was provided, and the entire section was restructured to explain the detailed steps of the proposed framework in a clear sequence, supported by appropriate references. Comment 3: The methods and analysis have been sufficiently explained, but there are a few shortcomings. The stages of theory-based data analysis (literature review) have been applied in the research, and a number of important themes or variables such as B1, B2, … B11 have been identified. In my opinion, this is content analysis that produces themes (is my opinion correct?). If this is correct, then the literature data and analysis need to be presented, leading to the conclusion of the themes found (B1, B2...B11), so that these themes are supported by accurate data and analysis. Response: Thank you for your insightful observation. In response, we have revised the section to explicitly frame this methodological step. The 11 core barriers (B1–B11) are now clearly presented as direct outcomes of the analysis, with each theme succinctly defined and immediately supported by its foundational citations from the literature. This directly links the conclusions to the supporting data, ensuring the themes are evidence-based. To maintain a logical flow and avoid duplication, the detailed interpretation and synthesis of these findings are elaborated in the dedicated Discussion. Comment 4: If applicable, is the statistical analysis and its interpretation appropriate? Comment: Already in accordance Response: Thank you Comment 5: The data based on the literature reviewed in Section 2.1 needs to be presented. (I suggest presenting it in a simple table format.). Response: Thank you for your insightful observation. A new table (Table 1) has been added to provide a concise, at-a-glance summary of all identified barriers, reinforcing the output of the content analysis. Comment 6: Are the conclusions drawn adequately supported by the results? Comment: Very adequate. Response: Thank you. We hope that these responses adequately address your concerns and further strengthen our manuscript. Thank you again for your valuable insights! Competing Interests: No competing interests were disclosed Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 09 Feb 2026 Adel A. Nasser , Department of Information Systems and Computer Science, Sa’adah University, Sa'adah, Yemen 09 Feb 2026 Author Response Responses to Peer Review Report of Prof. Indra Prasetia, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia Dear Prof. Indra Prasetia, We sincerely thank the reviewers for their insightful comments and constructive feedback, ... Continue reading Responses to Peer Review Report of Prof. Indra Prasetia, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia Dear Prof. Indra Prasetia, We sincerely thank the reviewers for their insightful comments and constructive feedback, which have greatly improved the quality and clarity of this manuscript. Below, we address each point in detail and describe the corresponding revisions made Comment 1: This work is presented clearly and accurately. However, there are outdated data reviews in uncovering research issues. (e.g. The education system, which once showed progress with enrollment rates increasing from 71.3% to 97.5% between 1999 and 2013, has since experienced significant setbacks. By the 2015/2016 academic year, around 1.8 million children were out of school, and over 1,100 schools had been either destroyed or repurposed for emergency use.27). More updated data support is needed to reinforce current issues. Response: Thank you for your valuable and constructive feedback regarding the use of outdated data in the introduction. We agree that current statistics are essential for accurately framing the research problem within Yemen's severe and ongoing humanitarian crisis. In direct response to your comment, we have thoroughly revised the introduction by connecting the outdated with the most current, authoritative data available from major UN and humanitarian organizations (2024-2025) (Two references were added [28, 29]). Comment 2: This study uses an integrative approach to explore and priorities obstacles in the context of education monitoring in Yemen. My analysis shows that this integrative approach generally refers to research using mixed methods. If this is a mixed design, it needs to be explained in detail with clear references (a diagram of the stages also needs to be provided). Response: Thank you for your constructive feedback. In response, we have revised the Methodology section. New paragraphs describing the framework were added, a new diagram was provided, and the entire section was restructured to explain the detailed steps of the proposed framework in a clear sequence, supported by appropriate references. Comment 3: The methods and analysis have been sufficiently explained, but there are a few shortcomings. The stages of theory-based data analysis (literature review) have been applied in the research, and a number of important themes or variables such as B1, B2, … B11 have been identified. In my opinion, this is content analysis that produces themes (is my opinion correct?). If this is correct, then the literature data and analysis need to be presented, leading to the conclusion of the themes found (B1, B2...B11), so that these themes are supported by accurate data and analysis. Response: Thank you for your insightful observation. In response, we have revised the section to explicitly frame this methodological step. The 11 core barriers (B1–B11) are now clearly presented as direct outcomes of the analysis, with each theme succinctly defined and immediately supported by its foundational citations from the literature. This directly links the conclusions to the supporting data, ensuring the themes are evidence-based. To maintain a logical flow and avoid duplication, the detailed interpretation and synthesis of these findings are elaborated in the dedicated Discussion. Comment 4: If applicable, is the statistical analysis and its interpretation appropriate? Comment: Already in accordance Response: Thank you Comment 5: The data based on the literature reviewed in Section 2.1 needs to be presented. (I suggest presenting it in a simple table format.). Response: Thank you for your insightful observation. A new table (Table 1) has been added to provide a concise, at-a-glance summary of all identified barriers, reinforcing the output of the content analysis. Comment 6: Are the conclusions drawn adequately supported by the results? Comment: Very adequate. Response: Thank you. We hope that these responses adequately address your concerns and further strengthen our manuscript. Thank you again for your valuable insights! Responses to Peer Review Report of Prof. Indra Prasetia, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia Dear Prof. Indra Prasetia, We sincerely thank the reviewers for their insightful comments and constructive feedback, which have greatly improved the quality and clarity of this manuscript. Below, we address each point in detail and describe the corresponding revisions made Comment 1: This work is presented clearly and accurately. However, there are outdated data reviews in uncovering research issues. (e.g. The education system, which once showed progress with enrollment rates increasing from 71.3% to 97.5% between 1999 and 2013, has since experienced significant setbacks. By the 2015/2016 academic year, around 1.8 million children were out of school, and over 1,100 schools had been either destroyed or repurposed for emergency use.27). More updated data support is needed to reinforce current issues. Response: Thank you for your valuable and constructive feedback regarding the use of outdated data in the introduction. We agree that current statistics are essential for accurately framing the research problem within Yemen's severe and ongoing humanitarian crisis. In direct response to your comment, we have thoroughly revised the introduction by connecting the outdated with the most current, authoritative data available from major UN and humanitarian organizations (2024-2025) (Two references were added [28, 29]). Comment 2: This study uses an integrative approach to explore and priorities obstacles in the context of education monitoring in Yemen. My analysis shows that this integrative approach generally refers to research using mixed methods. If this is a mixed design, it needs to be explained in detail with clear references (a diagram of the stages also needs to be provided). Response: Thank you for your constructive feedback. In response, we have revised the Methodology section. New paragraphs describing the framework were added, a new diagram was provided, and the entire section was restructured to explain the detailed steps of the proposed framework in a clear sequence, supported by appropriate references. Comment 3: The methods and analysis have been sufficiently explained, but there are a few shortcomings. The stages of theory-based data analysis (literature review) have been applied in the research, and a number of important themes or variables such as B1, B2, … B11 have been identified. In my opinion, this is content analysis that produces themes (is my opinion correct?). If this is correct, then the literature data and analysis need to be presented, leading to the conclusion of the themes found (B1, B2...B11), so that these themes are supported by accurate data and analysis. Response: Thank you for your insightful observation. In response, we have revised the section to explicitly frame this methodological step. The 11 core barriers (B1–B11) are now clearly presented as direct outcomes of the analysis, with each theme succinctly defined and immediately supported by its foundational citations from the literature. This directly links the conclusions to the supporting data, ensuring the themes are evidence-based. To maintain a logical flow and avoid duplication, the detailed interpretation and synthesis of these findings are elaborated in the dedicated Discussion. Comment 4: If applicable, is the statistical analysis and its interpretation appropriate? Comment: Already in accordance Response: Thank you Comment 5: The data based on the literature reviewed in Section 2.1 needs to be presented. (I suggest presenting it in a simple table format.). Response: Thank you for your insightful observation. A new table (Table 1) has been added to provide a concise, at-a-glance summary of all identified barriers, reinforcing the output of the content analysis. Comment 6: Are the conclusions drawn adequately supported by the results? Comment: Very adequate. Response: Thank you. We hope that these responses adequately address your concerns and further strengthen our manuscript. Thank you again for your valuable insights! Competing Interests: No competing interests were disclosed Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 22 Sep 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 09 Feb 26 read read Version 1 22 Sep 25 read read Indra Prasetia , Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia Salah AL-Hagree , Sana'a University, Sanaa, Yemen Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Prasetia I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 13 Feb 2026 | for Version 2 Indra Prasetia , Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia 0 Views copyright © 2026 Prasetia I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I have read the proposed revisions to the manuscript, and it has undergone significant improvements as I suggested. My decision: The manuscript is acceptable for indexing. Competing Interests No competing interests were disclosed. Reviewer Expertise education (education manajemen and leadership) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Prasetia I. Peer Review Report For: Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :956 ( https://doi.org/10.5256/f1000research.195822.r456883) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-956/v2#referee-response-456883 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 AL-Hagree S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 Feb 2026 | for Version 2 Salah AL-Hagree , Department of Computer Sciences, Sana'a University, Sanaa, Yemen 0 Views copyright © 2026 AL-Hagree S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I have no further comments to make. Competing Interests No competing interests were disclosed. Reviewer Expertise Natural Language Processing, Data Science, Machine Learning, Deep Learning, and Artificial Neural Networks I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) AL-Hagree S. Peer Review Report For: Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :956 ( https://doi.org/10.5256/f1000research.195822.r456884) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-956/v2#referee-response-456884 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 AL-Hagree S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 20 Jan 2026 | for Version 1 Salah AL-Hagree , Department of Computer Sciences, Sana'a University, Sanaa, Yemen 0 Views copyright © 2026 AL-Hagree S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Correcting the values ​​in Table 5, Percentage of Experts (%) or Average 90% ( Average 89.73 ) - Percentage of Experts, d ≤ 0.2 81.25% 81.25% 100.00% 81.25% 100.00% 100.00% 81.25% 93.75% 100.00% 81.25% 87.50% - Percentage of Experts, d ≤ 0.2 81% 81% 100% 81% 100% 100% 81% 94% 100% 81% 88% Based on the previous correction, the following is corrected: 3. Results The overall average expert agreement across all barriers was 90%, 4. Discussion The high level of consensus among experts (average agreement of 90%) 5. Conclusion - and an average agreement of 90%, affirming the contextual relevance of the selected barriers. - Future research should expand the use of other classification techniques, such as the technique in the reference: https://zenodo.org/records/17251460 -The method should clearly incorporate the concept of Hybrid : A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers - References related to Yemen 1- Alqasemi, F., Salah, A. H., Aqlan, A., Alalayah, K. M., Almotwakl, Z., & Hadwan, M. (2021, March). Education Data Mining For Yemen Regions Based On Hierarchical Clustering Analysis. In 2021 International Conference of Technology, Science and Administration (ICTSA) (pp. 1-4). IEEE. https://doi.org/10.1109/ICTSA52017.2021.9406544 ‏ 2- Al, S., & Alwan, S. M. (2025). Neural Network Analysis of Factors Influencing Forgotten Financial Remittances in Yemen . Journal of Economic Cooperation and Development, 46(2), 71-102. https://doi.org/10.5281/zenodo.17251460 ‏ Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Natural Language Processing, Data Science, Machine Learning, Deep Learning, and Artificial Neural Networks I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 09 Feb 2026 Adel A. Nasser, Department of Information Systems and Computer Science, Sa’adah University, Sa'adah, Yemen Responses to Peer Review Report of Assis. Prof. Salah AL-Hagree, Department of Computer Sciences, Sana'a University, Sanaa, Yemen Dear Prof. Salah AL-Hagree, Thank you for your thoughtful review and for the insightful comments provided on our manuscript. Your feedback has been invaluable in guiding our revisions. Comments 1,2: Correcting the values ​​in Table 5, Percentage of Experts (%) or Average 90% ( Average 89.73 ) - Percentage of Experts, d ≤ 0.2 81.25% 81.25% 100.00% 81.25% 100.00% 100.00% 81.25% 93.75% 100.00% 81.25% 87.50% - Percentage of Experts, d ≤ 0.2 81% 81% 100% 81% 100% 100% 81% 94% 100% 81% 88% Response: The authors acknowledge the reviewer's correct observation regarding the precision of the data in Table 5. The specified percentages for the row "Percentage of Experts, d ≤ 0.2" have been revised to two decimal places as suggested: 81.25%, 81.25%, 100.00%, 81.25%, 100.00%, 100.00%, 81.25%, 93.75%, 100.00%, 81.25%, 87.50%. The summary value in the text has been correspondingly updated to "Average 89.73%." We are grateful for this correction. . Comments 3-5: Updating the referenced average value throughout the manuscript: Results section: “The overall average expert agreement across all barriers was 90%”, Discussion section: “The high level of consensus among experts (average agreement of 90%) Conclusion - and an average agreement of 90%, affirming the contextual relevance of the selected barriers. Response: We thank the reviewer for their consistency in pointing out the related text. Following the correction to the data in Table 5, we have also updated the referenced average value throughout the manuscript In the Results section, the sentence now reads: "The overall average expert agreement across all barriers was 89.73%." In the Discussion section, the text has been amended to: "The high level of consensus among experts (average agreement of 89.73%)..." In the Conclusion section, the text has been amended to: "and an average agreement of 89.77%, ...” In the Abstract section, the sentence now reads” and an average expert consensus of 89.77%, indicating strong agreement Comment 6 : - Future research should expand the use of other classification techniques, such as the technique in the reference: https://zenodo.org/records/17251460 Response: We appreciate this suggestion and will explore other classification techniques. The revised manuscript now incorporates a discussion of the suggested reference in the Literature Review/Future Work section. Comment 7 : The method should clearly incorporate the concept of Hybrid: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers Response: Response: Thank you for this insightful recommendation. We have explored this integration in the revised manuscript. The methodology section now clearly illustrates the two-tiered hybrid analytical framework that integrates methodological approaches for barrier validation and data-driven prioritization. The sequential steps of the framework are now explicitly described. Comment 8 : - References related to Yemen 1- Alqasemi, F., Salah, A. H., Aqlan, A., Alalayah, K. M., Almotwakl, Z., & Hadwan, M. (2021, March). Education Data Mining For Yemen Regions Based On Hierarchical Clustering Analysis. In 2021 International Conference of Technology, Science and Administration (ICTSA) (pp. 1-4). IEEE. https://doi.org/10.1109/ICTSA52017.2021.9406544 ‏ 2- Al, S., & Alwan, S. M. (2025). Neural Network Analysis of Factors Influencing Forgotten Financial Remittances in Yemen. Journal of Economic Cooperation and Development, 46(2), 71-102. https://doi.org/10.5281/zenodo.17251460 Response: We acknowledge the importance of citing the relevant literature related to Yemen. We appreciate the references provided, and the revised manuscript now includes five new relevant references (cited as references 28, 29, 49, 54, and 74). We hope that our responses and the corresponding revisions adequately address your concerns and have improved our manuscript. Thank you again for your valuable time and insights View more View less Competing Interests No competing interests were disclosed reply Respond Report a concern AL-Hagree S. Peer Review Report For: Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :956 ( https://doi.org/10.5256/f1000research.184615.r435827) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-956/v1#referee-response-435827 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Prasetia I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 Oct 2025 | for Version 1 Indra Prasetia , Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia 0 Views copyright © 2025 Prasetia I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. Is the work clearly and accurately presented and does it cite the current literature? Comment: This work is presented clearly and accurately. However, there are outdated data reviews in uncovering research issues. (e.g. The education system, which once showed progress with enrollment rates increasing from 71.3% to 97.5% between 1999 and 2013, has since experienced significant setbacks. By the 2015/2016 academic year, around 1.8 million children were out of school, and over 1,100 schools had been either destroyed or repurposed for emergency use. 27 ). More updated data support is needed to reinforce current issues. 2. Is the study design appropriate and is the work technically sound? [Please consider whether appropriate controls have been included] Comment: This study uses an integrative approach to explore and prioritise obstacles in the context of education monitoring in Yemen. My analysis shows that this integrative approach generally refers to research using mixed methods. If this is a mixed design, it needs to be explained in detail with clear references (a diagram of the stages also needs to be provided). 3. Are sufficient details of methods and analysis provided to allow replication by others? [Please consider whether the authors have adhered to relevant reporting guidelines] Comment: The methods and analysis have been sufficiently explained, but there are a few shortcomings. The stages of theory-based data analysis (literature review) have been applied in the research, and a number of important themes or variables such as B1, B2, … B11 have been identified. In my opinion, this is content analysis that produces themes (is my opinion correct?). If this is correct, then the literature data and analysis need to be presented, leading to the conclusion of the themes found (B1, B2...B11), so that these themes are supported by accurate data and analysis. 4. If applicable, is the statistical analysis and its interpretation appropriate? Comment: Already in accordance 5. Are all the source data underlying the results available to ensure full reproducibility? [We require that all data underlying figures and tables are provided (unless there are issues of confidentiality) as part of the article or deposited in a public repository. The data should be described in a usable and accessible format] Comment: The data based on the literature reviewed in Section 2.1 needs to be presented. (I suggest presenting it in a simple table format.) 6. Are the conclusions drawn adequately supported by the results? [Please consider whether any limitations are discussed in sufficient detail] Comment: Very adequate. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise education (education manajemen and leadership) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 09 Feb 2026 Adel A. Nasser, Department of Information Systems and Computer Science, Sa’adah University, Sa'adah, Yemen Responses to Peer Review Report of Prof. Indra Prasetia, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia Dear Prof. Indra Prasetia, We sincerely thank the reviewers for their insightful comments and constructive feedback, which have greatly improved the quality and clarity of this manuscript. Below, we address each point in detail and describe the corresponding revisions made Comment 1: This work is presented clearly and accurately. However, there are outdated data reviews in uncovering research issues. (e.g. The education system, which once showed progress with enrollment rates increasing from 71.3% to 97.5% between 1999 and 2013, has since experienced significant setbacks. By the 2015/2016 academic year, around 1.8 million children were out of school, and over 1,100 schools had been either destroyed or repurposed for emergency use.27). More updated data support is needed to reinforce current issues. Response: Thank you for your valuable and constructive feedback regarding the use of outdated data in the introduction. We agree that current statistics are essential for accurately framing the research problem within Yemen's severe and ongoing humanitarian crisis. In direct response to your comment, we have thoroughly revised the introduction by connecting the outdated with the most current, authoritative data available from major UN and humanitarian organizations (2024-2025) (Two references were added [28, 29]). Comment 2: This study uses an integrative approach to explore and priorities obstacles in the context of education monitoring in Yemen. My analysis shows that this integrative approach generally refers to research using mixed methods. If this is a mixed design, it needs to be explained in detail with clear references (a diagram of the stages also needs to be provided). Response: Thank you for your constructive feedback. In response, we have revised the Methodology section. New paragraphs describing the framework were added, a new diagram was provided, and the entire section was restructured to explain the detailed steps of the proposed framework in a clear sequence, supported by appropriate references. Comment 3: The methods and analysis have been sufficiently explained, but there are a few shortcomings. The stages of theory-based data analysis (literature review) have been applied in the research, and a number of important themes or variables such as B1, B2, … B11 have been identified. In my opinion, this is content analysis that produces themes (is my opinion correct?). If this is correct, then the literature data and analysis need to be presented, leading to the conclusion of the themes found (B1, B2...B11), so that these themes are supported by accurate data and analysis. Response: Thank you for your insightful observation. In response, we have revised the section to explicitly frame this methodological step. The 11 core barriers (B1–B11) are now clearly presented as direct outcomes of the analysis, with each theme succinctly defined and immediately supported by its foundational citations from the literature. This directly links the conclusions to the supporting data, ensuring the themes are evidence-based. To maintain a logical flow and avoid duplication, the detailed interpretation and synthesis of these findings are elaborated in the dedicated Discussion. Comment 4: If applicable, is the statistical analysis and its interpretation appropriate? Comment: Already in accordance Response: Thank you Comment 5: The data based on the literature reviewed in Section 2.1 needs to be presented. (I suggest presenting it in a simple table format.). Response: Thank you for your insightful observation. A new table (Table 1) has been added to provide a concise, at-a-glance summary of all identified barriers, reinforcing the output of the content analysis. Comment 6: Are the conclusions drawn adequately supported by the results? Comment: Very adequate. Response: Thank you. We hope that these responses adequately address your concerns and further strengthen our manuscript. Thank you again for your valuable insights! View more View less Competing Interests No competing interests were disclosed reply Respond Report a concern Prasetia I. Peer Review Report For: Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :956 ( https://doi.org/10.5256/f1000research.184615.r417161) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-956/v1#referee-response-417161 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Toward Effective Educational Supervision...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/14-956/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/14-956/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/14-956/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Alshameri RGA et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/14-956/v1/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/14-956", templates : { twitter : "Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy.... Alshameri RGA et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/14-956/v1" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/167498/184615") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "184615"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "417158": 0, "417159": 0, "435842": 0, "435841": 0, "417166": 0, "420558": 0, "423886": 0, "417167": 0, "420559": 0, "423887": 0, "417164": 0, "420556": 0, "423884": 0, "417165": 0, "420557": 0, "423885": 0, "417162": 0, "420554": 0, "423882": 0, "417163": 0, "420555": 0, "423883": 0, "417160": 0, "423880": 0, "417161": 41, "423881": 0, "420562": 0, "420563": 0, "420560": 0, "423888": 0, "420561": 0, "423889": 0, "435822": 0, "435823": 0, "435821": 0, "435828": 0, "456884": 7, "435826": 0, "435827": 21, "456883": 16, "435824": 0, "435825": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "ae94e12e-a106-4d75-a5e6-cb040d911c13"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });