Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study

Exploring the Mediating Role of... | 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/14-1395" }, "headline": "Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education:...", "datePublished": "2025-12-12T14:44:08", "dateModified": "2025-12-12T14:44:08", "author": [ { "@type": "Person", "name": "Titus Kurra" }, { "@type": "Person", "name": "Syarifuddin ." }, { "@type": "Person", "name": "Ervi Novitasari" }, { "@type": "Person", "name": "Lativa Mursyida" }, { "@type": "Person", "name": "Khaidir Rahman" }, { "@type": "Person", "name": "Retyana Wahrini" }, { "@type": "Person", "name": "Andry Tanggu Mara" } ], "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": "The integration of digital technology in vocational education has redefined instructional delivery and learner engagement. However, the extent to which technology enhances learning depends largely on the quality of teacher–student interaction. This study investigates the mediating role of Teacher–Student Interaction (TSI) in the relationship between the Technology-Enhanced Learning Environment (TEL) and Learning Engagement (LE). Drawing on the Community of Inquiry (CoI) and Technological Pedagogical Content Knowledge (TPACK) frameworks, this research tested a structural model to explain how interaction bridges technological and pedagogical processes in vocational learning contexts. A total of 362 valid responses were collected from vocational students across Indonesia using a stratified sampling approach. The data were analyzed through Structural Equation Modelling (SEM) using SmartPLS 4 with 5,000 bootstrap samples. The results demonstrated that TEL positively influenced TSI (β = 0.71, p < .001) and LE (β = 0.39, p < .01). Moreover, TSI significantly predicted LE (β = 0.53, p < .001). The indirect pathway from TEL to LE through TSI was also significant (β = 0.37, p < .001), indicating partial mediation with a Variance Accounted for (VAF) of 48.7%. The model achieved an excellent fit (χ2/df = 2.31, CFI = 0.953, TLI = 0.947, SRMR = 0.046, RMSEA = 0.048) and explained 55% of the variance in TSI and 68% in LE. These findings affirm that technology integration enhances learning engagement primarily when mediated by active teacher–student interaction. The study underscores that digital transformation in vocational education must emphasize pedagogical presence and communicative interaction to sustain engagement, particularly in remote and under-resourced (3T) regions." } { "@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/14-1395", "name": "Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced..." } } ] } Home Browse Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Kurra T, . S, Novitasari E et al. Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1395 ( https://doi.org/10.12688/f1000research.173549.1 ) 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 Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study [version 1; peer review: 1 approved, 1 approved with reservations] Titus Kurra 1 , Syarifuddin . 1 , Ervi Novitasari 1,2 , [...] Lativa Mursyida 1,3 , Khaidir Rahman 1 , Retyana Wahrini 1,4 , Andry Tanggu Mara https://orcid.org/0009-0007-5981-6847 1 Titus Kurra 1 , Syarifuddin . 1 , [...] Ervi Novitasari 1,2 , Lativa Mursyida 1,3 , Khaidir Rahman 1 , Retyana Wahrini 1,4 , Andry Tanggu Mara https://orcid.org/0009-0007-5981-6847 1 PUBLISHED 12 Dec 2025 Author details Author details 1 Technology and Vocational Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 2 Agricultural Technology Education, Universitas Negeri Makassar, Makassar, South Sulawesi, Indonesia 3 Electronic Engineering, Universitas Negeri Padang, Padang, West Sumatra, Indonesia 4 Electronic Engineering, State University of Makassar Faculty of Engineering, Makassar, South Sulawesi, Indonesia Titus Kurra Roles: Funding Acquisition, Investigation, Methodology, Supervision, Writing – Original Draft Preparation Syarifuddin . Roles: Formal Analysis, Methodology, Project Administration, Resources, Visualization Ervi Novitasari Roles: Conceptualization, Formal Analysis, Project Administration, Validation, Writing – Review & Editing Lativa Mursyida Roles: Data Curation, Investigation, Methodology, Validation Khaidir Rahman Roles: Conceptualization, Formal Analysis, Resources, Software, Supervision Retyana Wahrini Roles: Data Curation, Funding Acquisition, Project Administration, Visualization Andry Tanggu Mara Roles: Data Curation, Software, Supervision, Visualization, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract The integration of digital technology in vocational education has redefined instructional delivery and learner engagement. However, the extent to which technology enhances learning depends largely on the quality of teacher–student interaction. This study investigates the mediating role of Teacher–Student Interaction (TSI) in the relationship between the Technology-Enhanced Learning Environment (TEL) and Learning Engagement (LE). Drawing on the Community of Inquiry (CoI) and Technological Pedagogical Content Knowledge (TPACK) frameworks, this research tested a structural model to explain how interaction bridges technological and pedagogical processes in vocational learning contexts. A total of 362 valid responses were collected from vocational students across Indonesia using a stratified sampling approach. The data were analyzed through Structural Equation Modelling (SEM) using SmartPLS 4 with 5,000 bootstrap samples. The results demonstrated that TEL positively influenced TSI (β = 0.71, p < .001) and LE (β = 0.39, p < .01). Moreover, TSI significantly predicted LE (β = 0.53, p < .001). The indirect pathway from TEL to LE through TSI was also significant (β = 0.37, p < .001), indicating partial mediation with a Variance Accounted for (VAF) of 48.7%. The model achieved an excellent fit (χ 2 /df = 2.31, CFI = 0.953, TLI = 0.947, SRMR = 0.046, RMSEA = 0.048) and explained 55% of the variance in TSI and 68% in LE. These findings affirm that technology integration enhances learning engagement primarily when mediated by active teacher–student interaction. The study underscores that digital transformation in vocational education must emphasize pedagogical presence and communicative interaction to sustain engagement, particularly in remote and under-resourced (3T) regions. READ ALL READ LESS Keywords Technology-Enhanced Learning, Teacher–Student Interaction, Learning Engagement, Vocational Education, SmartPLS, Mediation Model, Digital Pedagogy Corresponding Author(s) Andry Tanggu Mara ( [email protected] ) Close Corresponding author: Andry Tanggu Mara Competing interests: No competing interests were disclosed. Grant information: The authors gratefully acknowledge the financial and institutional support provided by the Indonesian Education Scholarship (BPI), Doctoral Scholarship Program for Indonesian Lecturers (PDDI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Science and Technology of the Republic of Indonesia, and the Indonesian Endowment Fund for Education (LPDP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Kurra T 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. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. How to cite: Kurra T, . S, Novitasari E et al. Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1395 ( https://doi.org/10.12688/f1000research.173549.1 ) First published: 12 Dec 2025, 14 :1395 ( https://doi.org/10.12688/f1000research.173549.1 ) Latest published: 12 Dec 2025, 14 :1395 ( https://doi.org/10.12688/f1000research.173549.1 ) 1. Introduction The integration of digital technologies into vocational education has become a central pillar in advancing learning innovation, engagement, and employability in the era of Industry 4.0. Vocational institutions worldwide are increasingly embedding technology-enhanced learning (TEL) strategies to cultivate both technical competencies and digital literacy, which are crucial for sustainable workforce development ( Richard et al., 2023 ; Pan and Jiang, 2024 ). Such integration is not only about the adoption of technology but also about how it transforms pedagogical practices, communication, and the socio-psychological aspects of learning ( Shi et al., 2024 ). In this context, the interaction between teachers and students emerges as a crucial factor that bridges technological affordances with meaningful learning experiences. Recent studies have emphasized that the success of technology integration in vocational settings depends on learners’ engagement and satisfaction, both of which are significantly influenced by pedagogical and interpersonal variables ( Zhang, Qian, & Chen, 2024 ). Teacher–student interaction (TSI), in particular, plays a pivotal role in fostering engagement and maintaining psychological connectedness in digitally mediated learning environments ( Shi et al., 2024 ). In vocational education, where experiential and practice-based learning are fundamental, such interaction provides cognitive guidance, emotional support, and motivation for skill mastery ( Richard et al., 2023 ). Despite growing research on technology-enhanced learning, empirical understanding of how TSI mediates the relationship between technology use and learning outcomes in vocational contexts remains limited, creating a need for further investigation grounded in robust theoretical and statistical models. Building upon prior research, Pan and Jiang (2024) argued that effective technology integration in vocational education must be evaluated not merely by system adoption, but by its pedagogical and interactive impacts on learners. Similarly, Zhang et al. (2024) demonstrated that digital technology enhances student satisfaction primarily through the mediating effects of learning experience and engagement. These findings suggest that interaction dynamics—such as feedback, presence, and communication—may serve as a critical mediating mechanism linking technological environments with educational outcomes. Furthermore, insights from higher education research have revealed that teachers’ readiness, emotional factors, and perceptions of technology-enhanced activities significantly shape learning effectiveness ( Zhao, 2025 ; Li & Li, 2025 ). However, these dimensions have rarely been explored in the context of vocational education, especially using multivariate techniques like Structural Equation Modelling (SEM) to validate mediating relationships. This study adopts the Community of Inquiry (CoI) framework to conceptualize teacher–student interaction as a mediating variable that connects technology-enhanced learning environments with students’ engagement and perceived learning outcomes. Within the CoI model, TSI embodies both teaching and social presence, fostering a sense of belonging and sustained cognitive engagement in digital spaces. By applying SEM, this research aims to provide empirical evidence on the mediating role of TSI in technology-enhanced vocational education, aligning with recent methodological advances in technology integration studies ( Li & Li, 2024 ; Jiang et al., 2025 ). The use of SEM enables the identification of both direct and indirect pathways, offering a nuanced understanding of how interaction quality mediates the influence of technology on learning effectiveness. In summary, this study contributes to the growing discourse on technology-enhanced vocational education in three important ways. First, it extends the theoretical applicability of the CoI framework to vocational contexts, which remain underrepresented in digital pedagogy research. Second, it empirically examines the mediating role of teacher–student interaction using a validated SEM model, addressing a critical gap in understanding the mechanisms of engagement. Third, it offers practical implications for educators and policymakers to design interactive, psychologically supportive, and pedagogically rich technology-mediated environments that enhance vocational learning outcomes. Through these contributions, the study aligns with ongoing efforts to promote sustainable and human-centered digital transformation in vocational education ( Alyoussef & Omer, 2023 ; Shi et al., 2024 ). 2. Method 2.1 Research design This study adopted a quantitative research design employing Structural Equation Modelling (SEM) to investigate the mediating role of Teacher–Student Interaction (TSI) in technology-enhanced vocational education. SEM was selected due to its strength in simultaneously analyzing latent constructs and testing mediating relationships, providing both measurement and structural validity ( Ahmmed, Saha, & Tamal, 2022 ; Abdurrahman & Mulyana, 2022 ). The conceptual framework was grounded in the Community of Inquiry (CoI) model and constructivist learning theory, emphasizing the interaction between technological affordances, social relationships, and engagement in learning ( Lee et al., 2024 ; Pan, 2022 ). Specifically, this model proposed that technology-enhanced learning environments (TEL) influence learning engagement (LE) both directly and indirectly through teacher–student interaction (TSI) as a mediating variable. The hypothesized model and direction of relationships were later analyzed using SEM to test both direct and indirect effects ( Jiang et al., 2025 ; Gurer & Akkaya, 2022 ). 2.2 Participants and context The participants comprised 362 vocational college students from three public polytechnic institutions in Indonesia, representing diverse disciplines including engineering, hospitality, and business management. These institutions had adopted technology-enhanced and blended learning systems as part of national TVET digitalization initiatives. The participants were chosen using stratified random sampling to ensure representativeness across study programs and gender ( Sadam & Al Mamun, 2024 ). Most students had experience using Learning Management Systems (LMS), virtual labs, and collaborative online platforms for coursework and skill-based projects. The study context aligned with the increasing emphasis on digital pedagogy and collaborative learning in vocational settings, which reflect global trends toward technology-supported competency development ( Lee et al., 2024 ). Participation was voluntary, and informed consent was obtained prior to data collection. To maintain research integrity, anonymity and confidentiality were strictly observed. 2.3 Instrumentation A structured questionnaire was developed to measure three latent constructs: a. Technology-Enhanced Learning Environment (TEL), b. Teacher–Student Interaction (TSI), and c. Learning Engagement (LE). All constructs were measured on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The instrument was adapted and validated from prior studies related to technology-enhanced learning and constructivist pedagogy ( Pan, 2022 ; Rosli & Saleh, 2023 ; Lee et al., 2024 ). TEL measured students’ perceptions of accessibility, usability, and the pedagogical integration of digital technologies (e.g., “Digital tools help me understand course materials more effectively”). TSI assessed the quality of communication, feedback, and teacher presence in online or hybrid settings (e.g., “My teacher interacts actively and provides timely feedback in online activities”). LE measured students’ cognitive, emotional, and behavioral engagement in learning (e.g., “I actively participate in discussions and collaborative digital projects”). The instrument underwent expert validation by three scholars in educational technology and vocational pedagogy. A pilot test with 40 students indicated high internal consistency, with Cronbach’s α ranging from 0.84 to 0.92, consistent with prior studies using similar constructs ( Jiang et al., 2025 ; Wang, 2025 ). The current study and the research hypotheses The present study builds upon the theoretical foundation of the Community of Inquiry (CoI) framework and constructivist learning theory, emphasizing the interplay between technological affordances, social interaction, and cognitive engagement within technology-enhanced vocational education (TEVE). Prior research has demonstrated that technology integration alone does not guarantee improved learning outcomes; instead, its success depends on the quality of teacher–student interaction (TSI), which serves as a pedagogical and socio-emotional bridge linking technology use with meaningful learning experiences ( Pan & Jiang, 2024 ; Zhang et al., 2024 ). Within this conceptual framework, TEL represents the perceived accessibility, usability, and pedagogical integration of technology in learning environments; TSI reflects the quality of feedback, communication, and teacher presence; and LE embodies students’ cognitive, emotional, and behavioral engagement. Furthermore, self-efficacy and pedagogical readiness are posited as internal teacher factors that shape the quality of interaction and engagement, while institutional and individual digital competences serve as contextual moderators that enhance the overall technology–learning linkage. Accordingly, the following hypotheses were formulated to guide the empirical investigation: a. H1: Technology-Enhanced Learning Environment (TEL) positively influences Teacher–Student Interaction (TSI). b. H2: Technology-Enhanced Learning Environment (TEL) positively influences Learning Engagement (LE). c. H3: Teacher–Student Interaction (TSI) positively influences Learning Engagement (LE). d. H4: Teacher–Student Interaction (TSI) mediates the relationship between Technology-Enhanced Learning Environment (TEL) and Learning Engagement (LE). 2.4 Data collection procedures Data were collected from January to March 2025 through both online and face-to-face surveys. Respondents from blended-learning programs completed the survey via Google Forms, while those in traditional programs completed a paper-based version administered during class sessions. A total of 378 questionnaires were distributed, and 362 valid responses were retained after screening for missing data and response bias. Participants were briefed on the research purpose, procedures, and ethical considerations before completing the questionnaire. To minimize bias, respondents were assured of the confidentiality of their responses and informed that participation was voluntary ( Wang, 2025 ). 2.5 Data analysis Data were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) via SmartPLS 4.0, which is particularly suitable for exploratory models and moderate sample sizes ( Abdurrahman & Mulyana, 2022 ; Gurer & Akkaya, 2022 ). Following standard procedures in SEM studies, the analysis was conducted in two phases: a. Measurement Model Assessment – To evaluate the reliability and validity of constructs. Cronbach’s α and Composite Reliability (CR) values above 0.70, and Average Variance Extracted (AVE) above 0.50, indicated acceptable reliability and convergent validity. Discriminant validity was confirmed using the Fornell–Larcker criterion. b. Structural Model Assessment – To test the hypothesized relationships and mediating effects using bootstrapping (5,000 resamples). The model’s predictive relevance (Q 2 ) and explanatory power (R 2 ) were assessed to evaluate the robustness of the findings ( Jiang et al., 2025 ). Additionally, multicollinearity was examined using the Variance Inflation Factor (VIF), ensuring values remained below the threshold of 5.0. Model fit indices, including SRMR and NFI, were also reported following current SEM reporting standards ( Ahmmed et al., 2022 ). All constructs demonstrated satisfactory factor loadings and internal reliability (α > 0.88, CR > 0.90). Detailed item-level loadings and confirmatory factor analysis results are presented in Appendix A Table 1 and Table 2 . Table 1. Descriptive statistics, reliability, and normality indices. Construct Mean (M) SD Cronbach’s α CR Skewness Kurtosis Technology-Enhanced Learning Environment (TEL) 4.18 0.57 0.91 0.93 -0.482 -0.914 Teacher–Student Interaction (TSI) 4.02 0.63 0.88 0.91 -0.315 -1.021 Learning Engagement (LE) 4.11 0.59 0.92 0.94 -0.267 -0.878 Table 2. Summary of reliability and validity results. Construct Cronbach’s α CR AVE TEL 0.91 0.93 0.68 TSI 0.88 0.91 0.64 LE 0.92 0.94 0.74 2.6 Ethical considerations Ethical approval for this research was obtained from the Institutional Research Ethics Committee of the host university as evidenced by the official research permit and ethical clearance issued by Universitas Negeri Yogyakarta, with document number B/2795/UN34.17/LT/2025 ( Tanggu Mara, 2025 ). The study adhered to the ethical principles outlined in the Declaration of Helsinki , ensuring participants’ privacy and the confidentiality of all collected data ( Sadam & Al Mamun, 2024 ). 3. Result 3.1 Preliminary data analysis Prior to testing the hypothesized model, preliminary analyses were conducted to assess data normality, reliability, and potential multicollinearity. All constructs demonstrated acceptable values of skewness (|<1.5|) and kurtosis (|<2.0|), indicating normal data distribution. Variance Inflation Factor (VIF) values ranged between 1.25 and 2.42, showing no multicollinearity issues. Table 1 presents the descriptive statistics, reliability indices, and normality measures for the three latent constructs included in the study: Technology-Enhanced Learning Environment (TEL), Teacher–Student Interaction (TSI), and Learning Engagement (LE). The mean scores for all constructs (M = 4.02–4.18) indicate high levels of positive perception among vocational students regarding the integration of technology, interaction quality, and engagement in learning. All constructs demonstrate excellent internal consistency, with Cronbach’s α values ranging from 0.88 to 0.92 and Composite Reliability (CR) values exceeding 0.90, surpassing the recommended threshold of 0.70 ( Hair et al., 2021 ). These results confirm the reliability and internal coherence of the measurement instruments. Additionally, the skewness (−0.482 to −0.267) and kurtosis (−1.021 to −0.878) values fall within acceptable ranges (|skewness| < 1.5, |kurtosis| <2.0), indicating that the data are normally distributed and suitable for Structural Equation Modelling (SEM). Overall, the results suggest that the measurement model exhibits strong psychometric properties and reflects consistent student perceptions of technology-enhanced vocational learning. Descriptive analysis revealed that students reported high perceptions of technology-enhanced learning (TEL) (M = 4.18, SD = 0.57), strong teacher–student interaction (TSI) (M = 4.02, SD = 0.63), and positive learning engagement (LE) (M = 4.11, SD = 0.59). These values suggest that students were generally motivated and satisfied with the use of technology in their learning environment, similar to patterns observed in prior studies on technology adoption and engagement ( Dubey & Sahu, 2021 ; Ikram et al., 2025 ). 3.2 Measurement model evaluation Table 2 show that the measurement model was tested using Confirmatory Factor Analysis (CFA) to verify construct validity and reliability. All standardized factor loadings exceeded 0.74, indicating strong item reliability. The Cronbach’s α coefficients ranged from 0.88 to 0.93, and the Composite Reliability (CR) values were all above 0.90, meeting internal consistency criteria. The Average Variance Extracted (AVE) for all latent constructs ranged between 0.68 and 0.74, confirming convergent validity ( Tai et al., 2024 ). Discriminant validity was established using the Fornell–Larcker criterion and HTMT ratio, both of which satisfied threshold conditions (HTMT < 0.85). These results confirm that the latent variables in this study were statistically distinct and reliable measures of their intended constructs ( Salleh et al., 2021 ; Yang, 2023 ). 3.3 Structural model assessment The structural model was evaluated using SEM to test the hypothesized relationships among constructs. The model fit indices indicated an excellent fit: χ 2 /df = 2.31, CFI = 0.953, TLI = 0.947, SRMR = 0.046, RMSEA = 0.048. The R 2 values showed that the model explained 55% of the variance in Teacher–Student Interaction (TSI) and 68% of the variance in Learning Engagement (LE). This level of explained variance is considered substantial in educational SEM research ( Yang, 2023 ; Boadu & Boateng, 2024 ). Figure 1 illustrates the main output from the SmartPLS analysis, displaying model fit indices, standardized path coefficients, and bootstrapping results. The model fit indicators (SRMR = 0.046, d_ULS = 0.288, d_G = 0.139) meet the recommended thresholds (SRMR < 0.08), confirming the adequacy of the model fit. The path coefficients show that the Technology-Enhanced Learning Environment (TEL) positively influences both Teacher–Student Interaction (TSI) (β = 0.71, t = 14.23, p < .001) and Learning Engagement (LE) (β = 0.39, t = 4.98, p < .001). In addition, TSI significantly predicts LE (β = 0.53, t = 10.02, p < .001). Figure 1. SmartPLS structural model output. Path analysis indicated the following significant relationships: a. TEL → TSI: β = 0.71, t = 14.23, p < .001 b. TSI → LE: β = 0.53, t = 10.02, p < .001 c. TEL → LE: β = 0.39, t = 4.98, p < .01 These results demonstrate that technology integration positively affects both direct student engagement and the quality of interaction between teachers and students. The finding is consistent with Panakaje et al. (2024) and Dubey & Sahu (2021) , who found that technology integration enhances satisfaction and collaborative learning when accompanied by effective pedagogical facilitation. 3.4 Mediation testing The mediating effect of Teacher–Student Interaction (TSI) between Technology-Enhanced Learning (TEL) and Learning Engagement (LE) was examined using bootstrapping with 5,000 samples. Results confirmed a significant partial mediation, as shown below: a. Indirect Effect (TEL → TSI → LE): β = 0.37, p < .001 b. Direct Effect (TEL → LE): β = 0.39, p < .01 c. Total Effect: β = 0.76, p < .001 d. VAF (Variance Accounted For) = 48.7% This indicates that nearly half of the total influence of technology on engagement is transmitted through teacher–student interaction. The finding supports the argument that pedagogical interaction amplifies the benefits of technological adoption, a trend similarly observed in Ruth et al. (2024) and Rosli & Saleh (2024) where digital self-efficacy and technology acceptance were mediated by interactive and motivational factors. Such partial mediation suggests that while technology offers a structural foundation for engagement, teacher involvement remains a vital socio-emotional catalyst that drives meaningful participation and satisfaction in technology-based vocational learning environments ( Yang, 2023 ; Tai et al., 2024 ). Correlation and mediation results Table 3 presents the Pearson correlation coefficients among the three latent constructs: Technology-Enhanced Learning Environment (TEL), Teacher–Student Interaction (TSI), and Learning Engagement (LE). The correlations reveal significant and positive relationships between all variables (p < .001), suggesting that students who perceived higher levels of technological support and usability in their learning environments also experienced stronger teacher–student interaction and engagement. The strongest correlation was observed between TSI and LE (r = 0.73, p < .001), indicating that effective interaction with teachers is strongly associated with students’ cognitive, emotional, and behavioral engagement in learning. This result aligns with previous findings by Pan and Jiang (2024) and Zhang et al. (2024) , which emphasized the critical role of interactive teaching presence in maintaining engagement within digitally mediated environments. Similarly, TEL demonstrated a strong positive correlation with TSI (r = 0.71, p < .001), implying that when digital tools and platforms are well-integrated into instruction, teachers and students interact more frequently and meaningfully. The relationship between TEL and LE (r = 0.68, p < .001) further confirms that technology integration not only improves accessibility and flexibility but also enhances student motivation and participation through interactive channels. These high intercorrelations provided a robust foundation for testing the mediating effects of TSI in the subsequent SEM analysis. Consistent with the proposed conceptual model, the mediation analysis confirmed that Teacher–Student Interaction partially mediates the relationship between Technology-Enhanced Learning Environment and Learning Engagement (β = 0.37, p < .001). This finding indicates that approximately half of the total influence of TEL on LE is transmitted through TSI (VAF = 48.7%), highlighting the centrality of pedagogical interaction in technology-enhanced vocational learning. The findings also mirror trends observed in related studies (e.g., Ikram et al., 2025 ; Hashmi et al., 2025 ; Rosli & Saleh, 2024 ) that underscore the mediating power of social and instructional presence in determining learners’ satisfaction and engagement levels in digital or blended vocational contexts. Collectively, these results affirm that successful digital transformation in vocational education depends not solely on the technological infrastructure but on the quality of pedagogical interactions that sustain engagement and learning continuity. Table 3. Correlation matrix of the variables included in the model (Pearson’s correlations). Variable 1 2 3 1. Technology-Enhanced Learning Environment (TEL) — 2. Teacher–Student Interaction (TSI) r = 0.71, p < .001 — 3. Learning Engagement (LE) r = 0.68, p < .001 r = 0.73, p < .001 — 3.5 Summary of hypothesis testing Table 4 presents the results of the hypothesis testing derived from the Structural Equation Modelling (SEM) analysis using SmartPLS. All four proposed hypotheses (H1–H4) were statistically supported, confirming the robustness of the theoretical model. H1 (TEL → TSI) was supported with a strong standardized path coefficient (β = 0.71, t = 14.23, p < .001), indicating that the Technology-Enhanced Learning Environment (TEL) has a significant positive influence on Teacher–Student Interaction (TSI). This finding suggests that well-integrated technological environments foster richer communication, collaboration, and feedback processes between teachers and students. H2 (TSI → LE) was also supported (β = 0.53, t = 10.02, p < .001), demonstrating that higher levels of teacher–student interaction are associated with greater Learning Engagement (LE). The result reinforces the pedagogical importance of social and instructional presence within digital learning environments. H3 (TEL → LE) showed a significant direct effect (β = 0.39, t = 4.98, p < .01), implying that technology integration independently enhances student engagement by providing flexible access, interactive content, and personalized feedback mechanisms. H4 (TEL → TSI → LE) confirmed a significant indirect pathway (β = 0.37, t = 6.87, p < .001), indicating partial mediation. This means that part of the impact of TEL on learning engagement operates through teacher–student interaction, while another portion exerts a direct effect. The Variance Accounted For (VAF = 48.7%) confirms that nearly half of TEL’s total influence on LE is transmitted via TSI. Table 4. Summary of hypothesis testing. Hypothesis Path β t p Lower (95% CI) Upper (95% CI) % of Total effect (VAF) Result H1 TEL → TSI 0.71 14.23 < .001 0.61 0.79 — Supported H2 TSI → LE 0.53 10.02 < .001 0.44 0.63 — Supported H3 TEL → LE 0.39 4.98 < .01 0.25 0.54 — Supported H4 TEL → TSI → LE (Indirect) 0.37 6.87 < .001 0.28 0.48 48.7% Supported (Partial Mediation) Figure 2 presents the structural model illustrating the direct and indirect relationships among the three latent variables: Technology-Enhanced Learning Environment (TEL), Teacher–Student Interaction (TSI), and Learning Engagement (LE). The standardized path coefficients, model fit indices, and coefficient of determination (R 2 ) values are displayed in the diagram to represent the strength and significance of each relationship within the proposed mediation model. As shown in the figure, TEL exerts a strong positive effect on TSI (β = 0.71), indicating that well-integrated technology environments significantly enhance teacher–student communication, feedback, and instructional presence. In turn, TSI positively predicts LE (β = 0.53), suggesting that effective pedagogical interaction fosters higher levels of student motivation, participation, and cognitive engagement. Additionally, TEL also has a direct positive influence on LE (β = 0.39), implying that technology integration independently contributes to engagement through flexible access and interactive learning features. The R 2 values indicate that 55% of the variance in TSI and 68% of the variance in LE are explained by the model, reflecting substantial explanatory power according to SEM standards. The model fit indices (SRMR = 0.046, CFI = 0.953, TLI = 0.947, RMSEA = 0.048) confirm an excellent model fit, demonstrating that the hypothesized relationships align well with the empirical data. Figure 2. Structural model of TEL, TSI, and LE relationships. 3.6 Interpretation of findings The results validate the hypothesized structural relationships and highlight the mediating importance of human interaction in digital learning environments. Similar to the findings of Boadu and Boateng (2024) , student engagement in technology-mediated settings is driven not only by access to digital tools but also by the social-emotional connection between instructors and learners. These results further confirm that teacher presence and collaborative digital design enhance satisfaction and engagement ( Panakaje et al., 2024 ; Ikram et al., 2025 ). Therefore, even in a technology-rich vocational context, pedagogical relationships remain the central mechanism through which technology translates into effective learning outcomes. 4. Discussion The primary purpose of this study was to examine the mediating role of Teacher–Student Interaction (TSI) in the relationship between the Technology-Enhanced Learning Environment (TEL) and Learning Engagement (LE) in the context of vocational education. The results obtained through Structural Equation Modelling (SEM) and bootstrapping confirm all four proposed hypotheses, demonstrating that digital learning environments and teacher–student interactions jointly determine students’ engagement levels. 4.1 The influence of technology-enhanced learning environment on teacher–student interaction The results reveal a strong positive effect of TEL on TSI (β = 0.71, t = 14.23, p < .001). This finding indicates that digital tools and platforms play a central role in improving communication, feedback, and the sense of presence between teachers and students. When digital resources are integrated purposefully, teachers become more capable of fostering active interaction and dialogue that enhance understanding and motivation. This is consistent with Bowman et al. (2022) , who demonstrated that teachers’ exposure to professional development and their technology-related value beliefs significantly enhance the quality of instructional technology use. Similarly, Tefera et al. (2022) found that the adoption of educational ICT in developing countries strongly depends on instructors’ technological readiness and institutional support. In the vocational context, effective TEL implementation supports interactive and collaborative exchanges that reduce transactional distance, thereby increasing engagement and comprehension. This result aligns with Puspitosari and Lokananta (2021) , who reported that digital communication media reshape teacher–student interaction dynamics, emphasizing immediacy, accessibility, and sustained engagement. 4.2 The direct effect of technology on learning engagement The direct relationship between TEL and LE (β = 0.39, t = 4.98, p < .01) indicates that digital learning environments independently enhance students’ emotional and cognitive involvement. When technology offers flexibility, interactive content, and instant feedback, students become more autonomous and intrinsically motivated. This supports the findings of Pandita and Kiran (2023) , who demonstrated that technology use significantly influences student engagement and sustainable satisfaction. Likewise, Yavuzalp and Bahcivan (2021) emphasized that students’ readiness for e-learning positively predicts self-regulation, satisfaction, and achievement—suggesting that effective TEL integration can directly stimulate engagement even without intermediary factors. In vocational settings, where learning tasks are often practice-oriented, digital environments provide real-time feedback and simulations that make learning more meaningful. This supports the argument by Iqbal et al. (2022) that digital curriculum delivery enhances applied skills through the mediation of ICT knowledge, bridging the gap between classroom content and industry-relevant competencies. 4.3 The role of teacher–student interaction in enhancing engagement The positive and significant path between TSI and LE (β = 0.53, t = 10.02, p < .001) confirms that effective teacher–student communication is essential for sustaining engagement in online and blended learning environments. Regular, supportive, and dialogic interaction strengthens students’ emotional connection and self-regulatory behaviors. Hashmi et al. (2025) similarly found that online learning interactions enhance self-regulated learning through the mediation of technology proficiencies, underscoring how communication quality is integral to student engagement. This result also reflects Suryono et al. (2022) and BANTUL & Wijayanto (n.d.) , who observed that interpersonal teacher behavior and student self-efficacy jointly shape learning motivation and engagement outcomes. Interactional presence thus operates as both a pedagogical and psychological mechanism that sustains learner focus and persistence, especially in vocational contexts requiring continuous feedback and practice. 4.4 The mediating role of teacher–student interaction The mediation analysis confirmed that TSI significantly mediates the relationship between TEL and LE (β_indirect = 0.37, t = 6.87, p < .001), with a Variance Accounted For (VAF) of 48.7%, indicating partial mediation. This means that nearly half of the total effect of TEL on learning engagement occurs indirectly through enhanced teacher–student interaction. The result provides strong empirical support for the Technology–Pedagogy Interaction Model proposed by Qinglin and Hidayat (2025) , where teachers’ technological and pedagogical competencies jointly determine how technology translates into effective engagement. The partial mediation suggests that while technology contributes directly to engagement, its full potential is realized when teachers employ technology to facilitate communication, guidance, and emotional connection. This aligns with Sang et al. (2023) , who found that instructors’ digital competence increases work engagement via effort expectancy, illustrating that human-centered interaction amplifies technology’s benefits. Similarly, Fahrina et al. (2020) and Maro’ah & Surjanti (2020) emphasized that creative pedagogical practices are the key to transforming technological disruption into meaningful educational experiences. 5. Conclusion and implications 5.1 Conclusion This study investigated the mediating role of teacher–student interaction (TSI) in technology-enhanced vocational education using Structural Equation Modelling (SEM). The empirical findings confirmed that technology integration positively influences students’ learning engagement and satisfaction, but these effects are significantly mediated by the quality of interaction between teachers and students. In other words, the presence of technology alone is not sufficient to enhance learning outcomes; rather, it is the meaningful pedagogical interaction that transforms technology into a catalyst for deeper learning. The results extend previous works emphasizing readiness, self-regulation, and satisfaction in digital learning environments ( Yavuzalp & Bahcivan, 2021 ). Specifically, in vocational education contexts, teacher–student interaction emerges as a social and instructional bridge that translates digital engagement into measurable learning gains. This study aligns with Zhang and Huang’s (2023) argument that instructors’ communicative enthusiasm and emotional presence enhance learners’ enjoyment and group interaction in online learning. Furthermore, it supports Lee and Hwang’s (2022) notion that technology-enhanced learning ecosystems—such as those integrating virtual reality and metaverse tools—require strong pedagogical and emotional scaffolding to sustain effective engagement. In line with Ngah et al. (2022) , the findings highlight that learners’ willingness to continue technology-mediated education depends on sequential mediators, including social connectedness, perceived teacher support, and learning satisfaction. From this perspective, teacher–student interaction serves as a pivotal psychological and pedagogical factor shaping learners’ long-term motivation and persistence in technology-rich environments. Additionally, the mediating mechanism identified in this study resonates with the technological pedagogical content knowledge (TPACK) and social cognitive frameworks proposed by Dikmen and Demirer (2022) , suggesting that effective integration of digital tools is closely tied to teachers’ self-efficacy, pedagogical adaptability, and understanding of students’ affective needs. 5.2 Implications for vocational education The findings have several implications for vocational education systems. First, institutional investment in technology must be matched by programs that enhance teachers’ interactional and facilitative skills. Bowman et al. (2022) stressed that teachers’ beliefs and competencies mediate the effectiveness of professional development in using educational technology. Thus, equipping teachers with technological pedagogical readiness is essential to translate infrastructure into active learning engagement. Second, policy frameworks should prioritize capacity building and institutional support, echoing Tefera et al. (2022) , who highlighted that educational ICT adoption in developing contexts depends on systemic enablers. For vocational schools in 3T regions, this means integrating technology with strategies that maintain teacher presence, motivation, and responsiveness across digital platforms. Finally, sustained engagement requires reinforcing both technological and interpersonal dimensions of learning. Instructors must employ digital tools not merely for content delivery but as a means to nurture reflection, collaboration, and socio-emotional support—ensuring that the digital transformation of vocational education remains human-centered. 5.3 Theoretical and practical contributions This study enriches existing literature by empirically validating a mediating mechanism that links technology integration , pedagogical interaction, and student engagement in vocational education. It extends prior frameworks such as the Community of Inquiry (CoI) and the Technological Pedagogical Content Knowledge (TPACK) model, illustrating that teacher–student interaction is the key conduit through which technology translates into engagement and performance. The findings complement those of Hashmi et al. (2025) and Sang et al. (2023) by confirming that digital competence and interaction quality jointly sustain self-regulated and engaged learning behaviors. 5.4 Limitations and future research directions Despite its contributions, this study acknowledges certain limitations. The use of a cross-sectional design restricts causal inference, and future longitudinal studies are encouraged to examine dynamic changes in engagement and interaction over time. Further research could also incorporate constructs such as teacher self-efficacy, institutional support , and students’ digital competence as proposed by Bowman et al. (2022) and Tefera et al. (2022) to expand the explanatory power of the model. Comparative studies between vocational and higher education contexts could further validate the universality of the mediating mechanism identified here. Ethical approval and consent statement In this study, informed consent was obtained verbally from all participants prior to data collection. The survey was conducted in both online (Google Forms) and face-to-face formats. Before completing the questionnaire, participants were verbally informed about the study’s purpose, procedures, voluntary nature, confidentiality protections, and data usage. For online respondents, verbal consent was provided through a recorded information statement delivered by the course instructor during class meetings before the survey link was distributed. For face-to-face respondents, researchers delivered a standardized verbal explanation in the classroom, after which students verbally agreed to participate. Verbal consent was selected instead of written consent because (1) no identifying personal data were collected, (2) the study posed minimal risk, and (3) verbal consent is permissible in classroom-based survey contexts when anonymity is guaranteed ( Sadam & Al Mamun, 2024 ). The Institutional Ethics Committee of Universitas Negeri Yogyakarta approved the use of verbal informed consent as part of the research protocol (Ethics Approval No.: B/2795/UN34.17/LT/2025) ( Tanggu Mara, 2025 ). Data availability statement The dataset underlying the research have been deposited in Zenodo and are accessible at: https://doi.org/10.5281/zenodo.17589828 ( CC BY 4.0 ) ( Tanggu Mara, 2025 ), include all supplementary files: Supplementary Figure 1: SmartPLS Structural Model Output Supplementary Figure 2: Structural Model of TEL, TSI, and LE Relationships Supplementary Table 1: Descriptive Statistics, Reliability, and Normality Indices Supplementary Table 2: Summary of Reliability and Validity Results Supplementary Table 3: Correlation Matrix of the Variables Included in the Model (Pearson’s Correlations) Supplementary Table 4: Summary of Hypothesis Testing Acknowledgements The authors gratefully acknowledge the financial and institutional support provided by the Indonesian Education Scholarship (BPI), Doctoral Scholarship Program for Indonesian Lecturers (PDDI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Science and Technology of the Republic of Indonesia, and the Indonesian Endowment Fund for Education (LPDP). References Abdurrahman L, Mulyana R: Pemodelan nilai teknologi informasi menggunakan structural equation modeling (SEM). JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika). 2022; 7 (2): 469–477. Publisher Full Text Ahmmed S, Saha J, Tamal MA: An empirical study for determining the quality indicators for the primary and secondary school of Bangladesh: A structural equation modeling approach. Heliyon. 2022; 8 (10): e10870. PubMed Abstract | Publisher Full Text | Free Full Text Alyoussef IY, Omer OMA: Investigating student satisfaction and adoption of technology-enhanced learning to improve educational outcomes in Saudi higher education. Sustainability. 2023; 15 (19): 14617. Publisher Full Text Bantul P, Wijayanto W: PENGARUH PERILAKU INTERPERSONAL GURU DAN NILAI UJIAN NASIONAL SMP TERHADAP EFIKASI DIRI AKADEMIK SISWA KELAS X PROGRAM KEAHLIAN TEKNIK INSTALASI TENAGA LISTRIK SMKN. Boadu SK, Boateng FO: Enhancing students’ achievement in mathematics education in the 21st century through technology integration, collaborative learning, and student motivation: The mediating role of student interest. Eurasia Journal of Mathematics, Science and Technology Education. 2024; 20 (11): em2534. Publisher Full Text Bowman MA, Vongkulluksn VW, Jiang Z, et al. : Teachers’ exposure to professional development and the quality of their instructional technology use: The mediating role of teachers’ value and ability beliefs. J. Res. Technol. Educ. 2022; 54 (2): 188–204. Publisher Full Text Dikmen CH, Demirer V: The role of technological pedagogical content knowledge and social cognitive variables in teachers’ technology integration behaviors. Participatory Educational Research. 2022; 9 (2): 398–415. Publisher Full Text Dubey P, Sahu KK: Students’ perceived benefits, adoption intention and satisfaction to technology-enhanced learning: examining the relationships. Journal of Research in Innovative Teaching & Learning. 2021; 14 (3): 310–328. Publisher Full Text Fahrina A, Amelia K, Zahara CR: Minda Guru Indonesia: Pandemi Corona, Disrupsi Pendidikan, dan Kreativitas Guru. Syiah Kuala University Press; 2020; vol. 153 . . Gurer MD, Akkaya R: The influence of pedagogical beliefs on technology acceptance: a structural equation modeling study of pre-service mathematics teachers. J. Math. Teach. Educ. 2022; 25 (4): 479–495. Publisher Full Text Hair JF, Astrachan CB, Moisescu OI, et al. : Executing and interpreting applications of PLS-SEM: Updates for family business researchers. J. Fam. Bus. Strategy. 2021; 12 (3): 100392. Hashmi ZF, Iqbal J, Asghar MZ, et al. : The influence of online learning interactions on self-regulated learning: mediating role of technology proficiencies among higher education students. Open Learning: The Journal of Open, Distance and e-Learning. 2025; 1–26. Publisher Full Text Ikram M, Kenayathulla HB, Saleem SMU: Unlocking the potential of technology usage in fostering education quality and students’ satisfaction: a case of Pakistani higher education. Kybernetes. 2025; 54 (3): 1938–1965. Publisher Full Text Iqbal J, Yi X, Ashraf MA, et al. : How curriculum delivery translates into entrepreneurial skills: The mediating role of knowledge of information and communication technology. Plos one. 2022; 17 (5): e0265880. PubMed Abstract | Publisher Full Text | Free Full Text Jiang H, Chugh R, Turnbull D, et al. : Exploring the effects of technology-related informal mathematics learning activities: A structural equation modeling analysis. Int. J. Sci. Math. Educ. 2025; 23 (1): 49–69. Publisher Full Text Lee H, Hwang Y: Technology-enhanced education through VR-making and metaverse-linking to foster teacher readiness and sustainable learning. Sustainability. 2022; 14 (8): 4786. Publisher Full Text Lee HY, Musa N, Wong YS, et al. : Communication Technology-Enhanced Collaborative and Constructivism Learning in Improving Students’ Skills on Artworks Production: A Structural Equation Modelling Approach. PaperASIA. 2024; 40 (4b): 202–225. Publisher Full Text Li M, Li B: Unravelling the dynamics of technology integration in mathematics education: A structural equation modelling analysis of TPACK components. Educ. Inf. Technol. 2024; 29 (17): 23687–23715. Publisher Full Text Li M, Li Y: Investigating the interrelationships of technological pedagogical readiness using PLS-SEM: A study of primary mathematics teachers in China. J. Math. Teach. Educ. 2025; 1–23. Publisher Full Text Maro’ah S, Surjanti J: Titik Balik “Pendidikan” Kisah Tiada Akhir. DeePublish; 2020. Ngah AH, Kamalrulzaman NI, Mohamad MFH, et al. : The sequential mediation model of students’ willingness to continue online learning during the COVID-19 pandemic. Res. Pract. Technol. Enhanc. Learn. 2022; 17 (1): 13. PubMed Abstract | Publisher Full Text | Free Full Text Pan X: Exploring the multidimensional relationships between educational situation perception, teacher support, online learning engagement, and academic self-efficacy in technology-based language learning. Front. Psychol. 2022; 13 : 1000069. PubMed Abstract | Publisher Full Text | Free Full Text Pan T, Jiang L: Tech-Enhanced Learning: Evaluating General Education in Vocational Colleges Through Technology Integration. Asia Pac. Educ. Res. 2024; 1–11. Panakaje N, Ur Rahiman H, Parvin SR, et al. : Revolutionizing pedagogy: navigating the integration of technology in higher education for teacher learning and performance enhancement. Cogent Education. 2024; 11 (1): 2308430. Publisher Full Text Pandita A, Kiran R: The technology interface and student engagement are significant stimuli in sustainable student satisfaction. Sustainability. 2023; 15 (10): 7923. Publisher Full Text Puspitosari R, Lokananta AC: Peran media komunikasi digital pada pola komunikasi guru dan murid. Avant Garde. 2021; 9 (1): 100. Publisher Full Text Qinglin L, Hidayat R: The Mediating Role of Self-Efficacy and Belief Between Technological Pedagogical Content Knowledge and Modes of Technology use among Elementary School Teacher in China. International Journal of Educational Reform. 2025; 10567879251345810. Publisher Full Text Richard GS, Joseph A, Elikem K, et al. : Technology integration in technical and vocational education and training (TVET): The role of the art teacher. Journal of Technical Education and Training. 2023; 15 (4): 73–88. Publisher Full Text Rosli MS, Saleh NS: Technology enhanced learning acceptance among university students during Covid-19: Integrating the full spectrum of Self-Determination Theory and self-efficacy into the Technology Acceptance Model. Curr. Psychol. 2023; 42 (21): 18212–18231. PubMed Abstract | Publisher Full Text | Free Full Text Rosli MS, Saleh NS: Predicting the acceptance of metaverse for educational purposes in universities: A structural equation model and mediation analysis of the extended technology acceptance model. SN Computer Science. 2024; 5 (6): 688. Publisher Full Text Ruth AO, Meddour H, Majid AHA: Unleashing work engagement: Sighting the influence of technology self-efficacy and the mediating role of ICT adoption. Multidisciplinary Science Journal. 2024; 6 (9): 2024089–2024089. Publisher Full Text Sadam NYS, Al Mamun MA: Polytechnic students’ perceived satisfaction of using technology in the learning process: The context of Bangladesh TVET. Heliyon. 2024; 10 (16): e35977. PubMed Abstract | Publisher Full Text | Free Full Text Salleh SM, Musa J, Jaidin JH, et al. : Development of TVET Teachers’ Beliefs about Technology Enriched Instruction through Professional Development Workshops: Application of the Technology Acceptance Model. Journal of Technical Education and Training. 2021; 13 (2): 25–33. Publisher Full Text Sang G, Wang K, Li S, et al. : Effort expectancy mediate the relationship between instructors’ digital competence and their work engagement: evidence from universities in China. Educ. Technol. Res. Dev. 2023; 71 (1): 99–115. PubMed Abstract | Publisher Full Text | Free Full Text Shi J, Narasuman S, Grigoryan G, et al. : Enhancing student engagement through technology integration in vocational education: A psychological perspective. INTERNATIONAL JOURNAL OF MENTAL HEALTH NURSING. 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY; 2024, November; Vol. 33 . : pp. 34–34. Suryono S, Bastian A, Oemar F: Pengaruh Kebijakan Pembelajaran pada Masa Pandemi Covid-19 terhadap Efikasi Diri dan Perilaku Belajar Siswa. Lectura: Jurnal Pendidikan. 2022; 13 (1): 120–135. Publisher Full Text Tai G, Nasir M, Binti N: A Study on Technology Integration Practices in Arts Instructors: Exploring the Mediating role of Value and Ability Beliefs. South Asian Journal of Social Sciences & Humanities. 2024; 5 (5): 66–90. Publisher Full Text Tanggu Mara A: Supplementary Data For Article Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study. [Data set]. Zenodo. 2025. Publisher Full Text Tefera BF, Elen J, Van Petegem W, et al. : A structural equation model for determinants of instructors’ educational ICT use in higher education in developing countries: Evidence from Ethopia. Comput. Educ. 2022; 188 (October 2022): 1–14. Wang G: The Effect of Teacher-Student Relationships on Innovative Teaching From the Teachers’ Perspective: A Moderated Mediation Model of Teacher Self-Efficacy and Teacher Collaboration. Psychol. Sch. 2025; 62 : 3019–3030. Publisher Full Text Yang Y: Impact of organizational support on students’ information and communication technology self-efficacy, engagement, and satisfaction in a blended learning environment: An empirical study. SAGE Open. 2023; 13 (4): 21582440231216527. Publisher Full Text Yavuzalp N, Bahcivan E: A structural equation modeling analysis of relationships among university students’ readiness for e-learning, self-regulation skills, satisfaction, and academic achievement. Res. Pract. Technol. Enhanc. Learn. 2021; 16 (1): 15. Publisher Full Text Zhang H, Huang F: Perceived teachers’ enthusiasm and willingness to communicate in the online class: The mediating role of learning enjoyment and group interaction for Chinese as a second language. Lang. Teach. Res. 2023; 13621688231216199. Zhang X, Qian W, Chen C: The effect of digital technology usage on higher vocational student satisfaction: the mediating role of learning experience and learning engagement. Frontiers in Education. Frontiers Media SA; 2024, December; Vol. 9 . : 1508119. Zhao G: The Mediating Role of Teachers’ Perceptions of Technology-Enhanced Activities and Their Personal Traits on Teachers’ Emotions and Their Psychological Well-Being. Eur. J. Educ. 2025; 60 (2): e70123. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 12 Dec 2025 ADD YOUR COMMENT Comment Author details Author details 1 Technology and Vocational Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 2 Agricultural Technology Education, Universitas Negeri Makassar, Makassar, South Sulawesi, Indonesia 3 Electronic Engineering, Universitas Negeri Padang, Padang, West Sumatra, Indonesia 4 Electronic Engineering, State University of Makassar Faculty of Engineering, Makassar, South Sulawesi, Indonesia Titus Kurra Roles: Funding Acquisition, Investigation, Methodology, Supervision, Writing – Original Draft Preparation Syarifuddin . Roles: Formal Analysis, Methodology, Project Administration, Resources, Visualization Ervi Novitasari Roles: Conceptualization, Formal Analysis, Project Administration, Validation, Writing – Review & Editing Lativa Mursyida Roles: Data Curation, Investigation, Methodology, Validation Khaidir Rahman Roles: Conceptualization, Formal Analysis, Resources, Software, Supervision Retyana Wahrini Roles: Data Curation, Funding Acquisition, Project Administration, Visualization Andry Tanggu Mara Roles: Data Curation, Software, Supervision, Visualization, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The authors gratefully acknowledge the financial and institutional support provided by the Indonesian Education Scholarship (BPI), Doctoral Scholarship Program for Indonesian Lecturers (PDDI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Science and Technology of the Republic of Indonesia, and the Indonesian Endowment Fund for Education (LPDP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 12 Dec 2025, 14:1395 https://doi.org/10.12688/f1000research.173549.1 Copyright © 2025 Kurra T 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. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. 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 Kurra T, . S, Novitasari E et al. Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1395 ( https://doi.org/10.12688/f1000research.173549.1 ) 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 12 Dec 2025 Views 0 Cite How to cite this report: Özelçi SY. Reviewer Report For: Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1395 ( https://doi.org/10.5256/f1000research.191379.r466602 ) The direct URL for this report is: https://f1000research.com/articles/14-1395/v1#referee-response-466602 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 28 Mar 2026 Serap Yılmaz Özelçi , Necmettin Erbakan Üniversitesi, Konya, Turkey Approved VIEWS 0 https://doi.org/10.5256/f1000research.191379.r466602 This study examines the mediating role of Teacher-Student Interaction (TSI) in the relationship between Technology-Supported Learning Environments (TSL) and Learning Participation (LP) within the context of vocational education. Data was collected appropriately for the purpose. Hypotheses, formulated in parallel ... Continue reading READ ALL This study examines the mediating role of Teacher-Student Interaction (TSI) in the relationship between Technology-Supported Learning Environments (TSL) and Learning Participation (LP) within the context of vocational education. Data was collected appropriately for the purpose. Hypotheses, formulated in parallel with the literature, were tested using structural equation modeling. Mediating values ​​were calculated using the Bootstrap method. According to the analysis results, digital learning environments and teacher-student interactions together determine the level of student participation. This finding is consistent with current literature. Current studies in the field were referenced in the research. In this context, it is a current and contributing research in the field using an Indonesian sample. 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: Critical thinking, teacher training 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. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Özelçi SY. Reviewer Report For: Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1395 ( https://doi.org/10.5256/f1000research.191379.r466602 ) The direct URL for this report is: https://f1000research.com/articles/14-1395/v1#referee-response-466602 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 Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Kolho P. Reviewer Report For: Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1395 ( https://doi.org/10.5256/f1000research.191379.r462654 ) The direct URL for this report is: https://f1000research.com/articles/14-1395/v1#referee-response-462654 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 10 Mar 2026 Piia Kolho , Jyväskylän ammattikorkeakoulu, Jyväskylä, Finland Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.191379.r462654 Dear writers, Thank you for the opportunity to read your article manuscript. Your study makes a valuable contribution to understanding the role of teacher–student interaction in technology‑enhanced vocational education. Overall, the article is clearly written, theoretically well-grounded, and methodologically ... Continue reading READ ALL Dear writers, Thank you for the opportunity to read your article manuscript. Your study makes a valuable contribution to understanding the role of teacher–student interaction in technology‑enhanced vocational education. Overall, the article is clearly written, theoretically well-grounded, and methodologically thoughtful. Below I provide feedback on the strengths of your work, followed by a few suggestions for further refinement. I hope you find these comments constructive and helpful as you continue to strengthen your article. Major strengths of the article are 1. Good theoretical background. The study draws effectively on the Community of Inquiry framework and TPACK/constructivist perspectives, providing a coherent rationale linking TEL, TSI, and LE. 2. High-quality measurement model. Reliability and validity indicators (Cronbach’s α, CR, AVE, HTMT, Fornell–Larcker) are well established and show robust psychometric properties. 3. Transparent mediation testing. The use of bootstrapping and clear reporting of direct, indirect, and total effects strengthen the credibility of the findings. The substantial R² values for TSI and LE further underline the explanatory power of the model. 4. Relevance and ethical clarity. The study addresses an important issue in vocational education and provides practical implications for technology‑enhanced teaching, including in under‑resourced regions. Ethical approval and open‑access data contribute to transparency. My recommendations to clarify or correct the following points (1-4), thank you. 1) Clarify the estimation framework (PLS‑SEM vs. CB‑SEM indices). The manuscript states that SmartPLS (PLS‑SEM) was used, but CB‑SEM indices also appear. To avoid methodological confusion, please either explain explicitly that a parallel CB‑SEM analysis was run (including software and estimator), and why both sets of indices are reported or remove CB‑SEM indices and report PLS‑appropriate model evaluation only. 2) Report predictive relevance (Q²). The methods section (p. 5) states that Q² would be used to assess predictive relevance, but these values are not reported. Please include Q² for TSI and LE (and PLSpredict if available) and briefly interpret whether Q² > 0 indicates meaningful predictive capacity. 3) Improve precision in the Abstract and Conclusion. To maintain alignment with the measured constructs, I encourage a slight refinement of the concluding statements (on p. 10). For example you write: “ The empirical findings confirmed that technology integration positively influences students’ learning engagement and satisfaction , but these effects are significantly mediated by the quality of interaction between teachers and students. In other words, the presence of technology alone is not sufficient to enhance learning outcomes ; rather, it is the meaningful pedagogical interaction that transforms technology into a catalyst for deeper learning." This accurately reflects the model without extending to constructs such as satisfaction or learning outcomes, which were not included in the study. 4) Streamline and tighten the literature narrative. Both the Introduction and Discussion sections are citation‑dense and occasionally repetitive. A more synthesised presentation would improve readability. Potential improvements include: Organising related findings thematically. Highlighting more clearly the specific gap your study addresses in vocational education. Reducing repeated references that do not add new conceptual value. A more concise narrative will help your theoretical contribution stand out even more strongly. Overall, this is a well‑designed and well‑argued study. The suggested refinements, most of which are relatively small adjustments, will further enhance methodological clarity, transparency, and readability. Thank you for this interesting research. 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? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: utilisation of technology in teaching, quantitative research, teacher competence, interaction in teaching, vocational education 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 Kolho P. Reviewer Report For: Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1395 ( https://doi.org/10.5256/f1000research.191379.r462654 ) The direct URL for this report is: https://f1000research.com/articles/14-1395/v1#referee-response-462654 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 Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 12 Dec 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 1 12 Dec 25 read read Piia Kolho , Jyväskylän ammattikorkeakoulu, Jyväskylä, Finland Serap Yılmaz Özelçi , Necmettin Erbakan Üniversitesi, Konya, Turkey 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 Özelçi 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. 28 Mar 2026 | for Version 1 Serap Yılmaz Özelçi , Necmettin Erbakan Üniversitesi, Konya, Turkey 0 Views copyright © 2026 Özelçi 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 This study examines the mediating role of Teacher-Student Interaction (TSI) in the relationship between Technology-Supported Learning Environments (TSL) and Learning Participation (LP) within the context of vocational education. Data was collected appropriately for the purpose. Hypotheses, formulated in parallel with the literature, were tested using structural equation modeling. Mediating values ​​were calculated using the Bootstrap method. According to the analysis results, digital learning environments and teacher-student interactions together determine the level of student participation. This finding is consistent with current literature. Current studies in the field were referenced in the research. In this context, it is a current and contributing research in the field using an Indonesian sample. 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 Critical thinking, teacher training 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) Özelçi SY. Peer Review Report For: Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1395 ( https://doi.org/10.5256/f1000research.191379.r466602) 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/14-1395/v1#referee-response-466602 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Kolho P. 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 Mar 2026 | for Version 1 Piia Kolho , Jyväskylän ammattikorkeakoulu, Jyväskylä, Finland 0 Views copyright © 2026 Kolho P. 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 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 Dear writers, Thank you for the opportunity to read your article manuscript. Your study makes a valuable contribution to understanding the role of teacher–student interaction in technology‑enhanced vocational education. Overall, the article is clearly written, theoretically well-grounded, and methodologically thoughtful. Below I provide feedback on the strengths of your work, followed by a few suggestions for further refinement. I hope you find these comments constructive and helpful as you continue to strengthen your article. Major strengths of the article are 1. Good theoretical background. The study draws effectively on the Community of Inquiry framework and TPACK/constructivist perspectives, providing a coherent rationale linking TEL, TSI, and LE. 2. High-quality measurement model. Reliability and validity indicators (Cronbach’s α, CR, AVE, HTMT, Fornell–Larcker) are well established and show robust psychometric properties. 3. Transparent mediation testing. The use of bootstrapping and clear reporting of direct, indirect, and total effects strengthen the credibility of the findings. The substantial R² values for TSI and LE further underline the explanatory power of the model. 4. Relevance and ethical clarity. The study addresses an important issue in vocational education and provides practical implications for technology‑enhanced teaching, including in under‑resourced regions. Ethical approval and open‑access data contribute to transparency. My recommendations to clarify or correct the following points (1-4), thank you. 1) Clarify the estimation framework (PLS‑SEM vs. CB‑SEM indices). The manuscript states that SmartPLS (PLS‑SEM) was used, but CB‑SEM indices also appear. To avoid methodological confusion, please either explain explicitly that a parallel CB‑SEM analysis was run (including software and estimator), and why both sets of indices are reported or remove CB‑SEM indices and report PLS‑appropriate model evaluation only. 2) Report predictive relevance (Q²). The methods section (p. 5) states that Q² would be used to assess predictive relevance, but these values are not reported. Please include Q² for TSI and LE (and PLSpredict if available) and briefly interpret whether Q² > 0 indicates meaningful predictive capacity. 3) Improve precision in the Abstract and Conclusion. To maintain alignment with the measured constructs, I encourage a slight refinement of the concluding statements (on p. 10). For example you write: “ The empirical findings confirmed that technology integration positively influences students’ learning engagement and satisfaction , but these effects are significantly mediated by the quality of interaction between teachers and students. In other words, the presence of technology alone is not sufficient to enhance learning outcomes ; rather, it is the meaningful pedagogical interaction that transforms technology into a catalyst for deeper learning." This accurately reflects the model without extending to constructs such as satisfaction or learning outcomes, which were not included in the study. 4) Streamline and tighten the literature narrative. Both the Introduction and Discussion sections are citation‑dense and occasionally repetitive. A more synthesised presentation would improve readability. Potential improvements include: Organising related findings thematically. Highlighting more clearly the specific gap your study addresses in vocational education. Reducing repeated references that do not add new conceptual value. A more concise narrative will help your theoretical contribution stand out even more strongly. Overall, this is a well‑designed and well‑argued study. The suggested refinements, most of which are relatively small adjustments, will further enhance methodological clarity, transparency, and readability. Thank you for this interesting research. 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? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise utilisation of technology in teaching, quantitative research, teacher competence, interaction in teaching, vocational education 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 (0) Kolho P. Peer Review Report For: Exploring the Mediating Role of Teacher–Student Interaction in Technology-Enhanced Vocational Education: Evidence from a Structural Equation Modelling Study [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :1395 ( https://doi.org/10.5256/f1000research.191379.r462654) 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/14-1395/v1#referee-response-462654 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 = "Exploring the Mediating Role of Teacher\–Student...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/14-1395/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/14-1395/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/14-1395/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Kurra T 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/14-1395/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/14-1395", templates : { twitter : "Exploring the Mediating Role of Teacher\–Student Interaction.... Kurra T et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/14-1395/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/173549/191379") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "191379"); $(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 = { "460551": 0, "460550": 0, "460549": 0, "460548": 0, "460547": 0, "460546": 0, "460545": 0, "460544": 0, "460553": 0, "460552": 0, "466599": 0, "466598": 0, "466597": 0, "466596": 0, "466595": 0, "466604": 0, "466603": 0, "466602": 2, "466601": 0, "466600": 0, "462655": 0, "462654": 8, "462653": 0, "462652": 0, "462651": 0, "462650": 0, "462659": 0, "462658": 0, "462657": 0, "462656": 0, "456030": 0, "456031": 0, "456028": 0, "456029": 0, "456026": 0, "456027": 0, "456025": 0, "456034": 0, "456032": 0, "456033": 0, "452470": 0, "452471": 0, "452468": 0, "452469": 0, "452476": 0, "452477": 0, "452474": 0, "452475": 0, "452472": 0, "452473": 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 = "078ab061-8d73-4672-9627-f377e8dbee8e"; 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(",","/")); } }); });