Comparative Study of Students' Performance Trends and Item Characteristics in Technical Drawing Examinations across Core Curriculum Themes

Comparative Study of Students' Performance Trends and Item Characteristics in Technical Drawing Examinations across Core Curriculum Themes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Study of Students' Performance Trends and Item Characteristics in Technical Drawing Examinations across Core Curriculum Themes Emaikwu Sunday Oche, Iorhuna Titus Yeke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9046425/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study presents a comparative analysis of students’ performance trends and item characteristics in the National Examinations Council (NECO) Technical Drawing multiple-choice questions (MCQs) from 2017 to 2021 in the Federal Capital Territory (FCT), Abuja, Nigeria. The analysis is based on five core curriculum themes: drawing studio practice, geometrical constructions, building drawing, mechanical/machine drawing, and computer-aided drawing (CAD), including business opportunities in drawing studio practice. The research evaluates the quality of assessments to inform improvements in technical drawing pedagogy and technical education delivery. The study employed four specific objectives examining reliability, difficulty indices, discrimination indices, and distractor efficiency. Using an ex-post facto descriptive research design, data were obtained from 6,258 candidates across 177 secondary schools, with a proportionate random sample of 942 candidates. A total of 250 MCQs were thematically classified, and item characteristics were analyzed using Lumen Ex Machina software, while hypotheses were tested using one-way ANOVA. Key findings reveal an overall mean reliability of 0.79, mean difficulty index of 0.48, mean discrimination of 0.32, and distractor efficiency of 76.4%. Geometrical constructions demonstrated the highest performance metrics, while CAD exhibited lower reliability and performance, largely due to resource and teacher proficiency constraints. ANOVA results showed no statistically significant differences across the themes. The study recommends CAD-focused teacher training, improved instructional resources, and enhanced infrastructural support to strengthen learning outcomes and promote equity in technical drawing education in Nigeria. Educational Psychology Students’ performance item characteristics technical drawing examination core curriculum themes Introduction Technical Drawing stands as a cornerstone in technical and vocational education, functioning as a precise graphical medium for communicating complex ideas, designs, dimensions, and specifications essential for transforming abstract concepts into practical realities in fields like engineering, architecture, manufacturing, and technology (Laguador, 2014; Chedi, 2015; Odo, 2019). It cultivates critical skills including creativity, draughtsmanship, visualization, safe working habits, and understanding of conventions, symbols, and computer-aided applications, as outlined in the NECO syllabus (National Examinations Council, 2018; Blumschein et al., 2019). In Nigeria's secondary education landscape, the subject is integrated at junior levels within basic science and technology and taught as a stand-alone by specialists at senior levels, often by vocational educators, engineers, or architects due to manpower shortages. It is optional in conventional schools but mandatory in technical colleges, preparing students for careers through knowledge of materials, processes, and entrepreneurial opportunities (Hassan & Maizam, 2017; Wordu, 2019). The NECO syllabus distinctively organizes the subject into five interconnected themes to meet broad objectives: (1) drawing studio practice, encompassing foundational elements like drawing materials, equipment identification, board techniques, freehand lettering (upper and lower case), and safe habits such as proper illumination and tool handling (Daniel, 2021); (2) geometrical constructions, the bedrock of all technical drawings, involving accurate development of plane shapes (e.g., squares, triangles, ellipses) and solid forms using drafting tools without scales, classified into plane, solid, and descriptive geometry (Unit 5 Geometrical Construction, 2024); (3) building drawing, which graphically depicts building shapes, positions, materials, and construction methods, including architectural, structural, and services drawings for unambiguous interpretation (Innovative Creative Professional Training, 2024); (4) mechanical or machine drawing, presenting components through orthographic views to convey size, shape, manufacturing, and assembly details, adhering to drafting standards for industrial application (Kannaiah et al., 2016; Wuttet & Laikemariam, 2005); and (5) computer-aided drawing (CAD) and business opportunities, utilizing software for design processes, image generation, and entrepreneurial ventures like blueprints, printing, and binding, bridging traditional and digital realms (Chua, 2023). The CAD theme, in particular, signifies a paradigm shift towards digital efficiency, enabling engineers and designers to create precise, editable representations with applications in architecture and manufacturing. However, empirical evidence from Nigeria reveals integration challenges, including resource limitations (e.g., unreliable electricity, inadequate computers), teacher skill gaps in advanced features like 3D modeling, and low student motivation due to outdated curricula (Agada et al., 2024; Alburo et al., 2025; Adedokun et al., 2025). These issues underscore the need for targeted analysis to ensure equitable assessment. Effective evaluation through multiple choice questions (MCQs) requires robust item quality, assessed via item analysis for reliability (consistency of scores), difficulty index (proportion of correct responses, ideally 0.3–0.7 for moderate challenge), discrimination index (ability to differentiate high and low achievers, positive values > 0.2 preferred), and distractor efficiency (plausibility of incorrect options, > 75% effective) (Emaikwu, 2019; Omar, 2021; Araneda et al., 2019). Parallel tests across years and themes should exhibit equivalent characteristics to maintain fairness (Gierl et al., 2017; Lord and Novick, 1968). Despite its importance, inconsistencies in multiple choice question (MCQ) quality across themes may lead to unfair assessments, with CAD potentially showing higher difficulty due to implementation barriers, resulting in lower performance trends and diminished student interest (Wordu, 2019). Limited empirical studies on theme-specific item characteristics in Technical Drawing perpetuate these gaps, hindering curriculum efficacy and national skill development. This paper addresses this by comparing performance trends (e.g., mean scores per theme and year) and item characteristics across the five themes using 2017–2021 NECO data, with an expanded focus on CAD to highlight digital integration opportunities and challenges. The study's scope is delimited to multiple choice question (MCQ) item characteristics (reliability, difficulty, discrimination, distractor efficiency) and performance trends in NECO Technical Drawing examinations from 2017 to 2021, based on data from the FCT Abuja, involving theme classification aligned with the syllabus. Limitations include potential generalizability issues due to the FCT focus, bureaucratic delays in data access, and the impact of COVID-19 on 2020–2021 examinations, which may have affected students’ preparation and performance. The significance lies in informing targeted interventions: teachers can prioritize weak themes like CAD; students benefit from balanced assessments boosting confidence; curriculum developers refine content; policymakers advocate for resources; administrators facilitate training; and society gains a skilled workforce for innovation. Specific Objectives of the Study The specific objectives of the paper include: 1. To compare the reliability of NECO Technical Drawing MCQs across the five core curriculum themes from 2017 to 2021. 2. To compare the difficulty indices of MCQs across the themes. 3. To compare the discrimination indices of MCQs across the themes. 4. To compare the distractor efficiency of MCQs across the themes. Research Questions Four Research Questions have been raised to address the objectives viz: What is the reliability of NECO Technical Drawing MCQs across the five core themes from 2017 to 2021? What are the difficulty indices of MCQs across the themes? What are the discrimination indices of MCQs across the themes? What is the distractor efficiency of MCQs across the themes? Hypotheses Similarly, four null hypotheses were formulated and tested at 0.05 significance level of significance. There is no significant difference in the reliability of MCQs across the five themes. There is no significant difference in the difficulty indices across the themes. There is no significant difference in the discrimination indices across the themes. There is no significant difference in the distractor efficiency across the themes. Literature Review This study is grounded in Classical Test Theory (CTT), proposed by Melvin Novick in 1966, which posits that an observed test score (X) is the sum of a true score (T) representing the examinee's actual ability and an error score (E) accounting for random measurement inaccuracies, expressed as X = T + E. CTT relies on three key assumptions: linearity, where the relationship between true and observed scores is linear; homoscedasticity, ensuring constant error variance across true score levels; and independence, where errors are uncorrelated. These foundations enable the estimation of reliability (e.g., via Kuder-Richardson formulas) and validity, making CTT widely applicable in educational assessment for test development, item analysis, and score interpretation. In this context, CTT facilitates the evaluation of MCQ reliability and item statistics across Technical Drawing themes, identifying consistent measurement errors and supporting parallel test equivalence. Complementing CTT, Item Response Theory (IRT), introduced by George Rasch in 1953 and expanded by researchers like Frederic Lord, models the probabilistic relationship between an examinee's latent ability (θ) and their response to individual items, using parameters such as item difficulty (b), discrimination (a), and guessing (c) in models like the three-parameter logistic (3PL). The item characteristic curve (ICC) graphically depicts the probability of correct response as a function of ability, allowing for adaptive testing, item banking, and bias detection. In educational measurement, IRT is applied in standardized examinations, mathematics education for proficiency modeling, and technical assessments to tailor tests to individual abilities, such as in computerized adaptive testing (CAT) for precise skill evaluation. For this study, IRT is particularly suited for analyzing theme disparities, especially in CAD, where variable student abilities due to resource constraints can be modeled through item parameters, enabling nuanced insights into difficulty and discrimination variations. Together, CTT provides a straightforward framework for overall test quality, while IRT offers item-level precision, ideal for addressing CAD's unique challenges in technical education. Scholarly works on Technical Drawing underscore its essence as a refined graphic language embedding symbolic, cultural, and cognitive elements, evolved from ancient to modern times for curricula in engineering and design (Chedi, 2015; Hassan & Maizam, 2017; Oyaimare & Nwachokor, 2019). The NECO framework's five themes ensure holistic coverage: drawing studio practice for basics (Daniel, 2021); geometrical constructions for precision (Unit 5 Geometrical Construction, 2024); building drawing for practical application (Innovative Creative Professional Training, 2024); mechanical drawing for industrial standards (Kannaiah et al., 2016; Wuttet & Laikemariam, 2005); and CAD for digital and entrepreneurial skills (Chua, 2023). Item analysis literature stresses its role in test development, identifying flawed items through difficulty (proportion correct, moderate 0.50), discrimination (high-low differentiation), and distractor efficiency (Omar, 2021; Emaikwu, 2019; Krishnan, 2013; Shakil, 2008). Parallel tests demand equivalent metrics (Gierl et al., 2017). Several empirical studies have examined item characteristics across various subjects, providing important context for the current investigation. Adegoke (2014) conducted item analysis on physics examination questions and found that items exhibited moderate difficulty levels and demonstrated acceptable validity coefficients. The study highlighted the importance of systematic item analysis in improving test quality and ensuring accurate measurement of students' physics knowledge. The findings revealed that well-constructed physics items with appropriate difficulty levels could effectively discriminate between high and low-performing students. In a comparative study, Adamu, Abubakar, and Mohammed (2019) examined the difficulty levels of items in pre-university and post-university entrance examinations. Their analysis revealed significant variations in item difficulty across examination types, with implications for students’ preparation and curriculum alignment. The study demonstrated that entrance examination items were generally more challenging than regular course assessments, suggesting the need for targeted preparation strategies. Ayanwale and Adeleke (2020) applied Item Response Theory (IRT) to analyze mathematics achievement test items. Their findings showed that IRT parameters provided more nuanced insights into item quality compared to classical approaches, particularly in identifying items that functioned differently across ability levels. The study emphasized the utility of IRT in developing adaptive tests and improving measurement precision in mathematics assessment. Bandele and Adewale (2013) investigated the reliability and validity of WAEC and NECO mathematics examinations. Their comparative analysis revealed that both examination bodies maintained acceptable psychometric standards, though some variations existed in item difficulty distributions. The study recommended continuous item banking and regular psychometric reviews to maintain assessment quality across examination cycles. Moyinoluwa (2015) examined the psychometric properties of mathematics achievement tests used in Nigerian secondary schools. The analysis revealed acceptable reliability coefficients across test administrations, though some items required revision to improve discrimination indices. The study highlighted the importance of continuous item refinement in maintaining test quality over time. Nsikak and Udoh (2017) compared item parameters in WAEC and NECO mathematics examinations over multiple years. Their longitudinal analysis showed relatively stable item characteristics across examination cycles, though some themes exhibited greater variability than others. The findings emphasized the need for theme-specific attention in test development and validation. Popham (2017) carried out a study on the utility of item analysis in enhancing objective examinations. His work demonstrated how difficulty and discrimination indices could guide item revision decisions, leading to improved test quality. The research emphasized practical applications of item analysis results in instructional improvement and curriculum refinement. DiBattista (2019) investigated post-examination reviews incorporating item analysis for faculty development purposes. The study demonstrated that sharing item statistics with instructors improved their item-writing skills and enhanced their understanding of assessment principles. The findings supported the integration of psychometric training into faculty development programs. Akpan (2021) conducted item analysis of science and technology questions in the 2019 Basic Education Certificate Examination (BECE) administered by NECO. The analysis revealed that most items demonstrated acceptable difficulty and discrimination indices, though some required revision. The study provided evidence of NECO's commitment to maintaining psychometric standards in vocational and technical subject assessments. Regarding CAD integration specifically, Adedokun, Adeniyi, and Akinola (2025) investigated how AutoCAD influenced students' motivation and achievement in Technical Drawing in Plateau State, Nigeria. Their quasi-experimental study found that students exposed to AutoCAD instruction demonstrated significantly higher motivation levels and achievement scores compared to control groups receiving only traditional instruction. The research revealed that AutoCAD integration increased students’ engagement and practical understanding of design principles, though implementation challenges related to equipment availability and teacher training remained significant barriers. Agada, Okwori, and Agada (2024) examined the utilization of AutoCAD as an innovative teaching strategy in North Central Nigeria. Their survey of technical drawing teachers and students revealed that while AutoCAD was perceived as beneficial for enhancing spatial visualization and design skills, several implementation challenges persisted. The study identified unreliable electricity supply (mean challenge rating = 3.87), inadequate computer facilities (mean = 3.65), and insufficient teacher training (mean = 3.45) as major obstacles. Despite these challenges, respondents acknowledged AutoCAD's potential to improve learning outcomes, with awareness levels exceeding 75% among surveyed students and teachers. Jimoh (2010) conducted an experimental study on AutoCAD's impact on students' interest in engineering graphics. The findings demonstrated that students taught using AutoCAD exhibited significantly higher interest levels and achievement scores compared to those taught through traditional methods alone. The study provided early evidence of CAD's motivational benefits in Nigerian technical education, though it also highlighted infrastructure constraints that limited widespread implementation. Alburo, Mendoza, and Torres (2025) analyzed the skills profile of technical drafting trainers in the Philippines, revealing patterns relevant to the Nigerian context. Their competency assessment found that while trainers demonstrated proficiency in traditional drawing skills (mean competency = 3.42), their CAD-specific skills were considerably lower, particularly in advanced features like 3D modeling (mean = 1.86) and rendering (mean = 2.05). Overall CAD competency averaged 2.068, indicating intermediate proficiency. The study recommended targeted professional development focusing on contemporary CAD applications and pedagogical strategies for technology integration. These studies collectively affirm the utility of item analysis in educational assessment and highlight the unique challenges facing CAD integration in technical education. However, they reveal a significant gap in theme-specific psychometric analysis of Technical Drawing examinations, particularly regarding how implementation barriers in CAD instruction may manifest in assessment outcomes. This study addresses this gap by providing comprehensive comparative analysis of item characteristics and performance trends across all Technical Drawing curriculum themes. Methodology The design of the study is ex post facto descriptive research design. An ex-post facto descriptive design was employed because examination data preexisted and could not be manipulated. The study area was the Federal Capital Territory (FCT), Abuja, chosen for logistical access to NECO headquarters and relatively controlled administration conditions. The population comprised all 6,258 candidates who sat NECO Technical Drawing Paper 1 in FCT schools from 2017 to 2021 (2017: 1,314; 2018: 1,276; 2019: 1,236; 2020: 1,195; 2021: 1,237). A proportionate stratified random sample of 15% per year yielded 942 candidates (197, 191, 185, 179, and 190 respectively). The instrument consisted of the original 250 multiple-choice items (50 × 5 years) and official marking schemes. Content validity was confirmed by five experts; KR-20 reliability ranged from 0.77 to 0.81 across years (mean 0.79). Data were collected between March and July 2024 after obtaining ethical clearance and NECO permission. The data from NECO had scored responses where item-by-item response matrices were constructed. Lumen Ex Machina 4,500 × 200 software computed KR-20, difficulty index (P), discrimination index (D), and distractor efficiency (DE). Descriptive statistics (means, percentages, graphs) and one-way ANOVA was used to test the hypotheses at α = 0.05 level of significance. Results and Discussion This section presents the results of the study according to the four research questions and their corresponding hypotheses. The findings are organized thematically and interpreted using principles of Classical Test Theory (CTT) and supported by relevant empirical literature. Research Question 1 What is the reliability of NECO Technical Drawing MCQs across the five core themes from 2017 to 2021? Table 1 Reliability of NECO Technical Drawing MCQs Across Core Themes (2017–2021) Curriculum Theme Mean Reliability SD Drawing Studio Practice 0.78 1.02 Geometrical Constructions 0.82 1.02 Building Drawing 0.77 1.02 Mechanical/Machine Drawing 0.80 1.02 Computer-Aided Drawing (CAD) 0.76 1.02 Overall 0.79 1.02 The results showed an overall mean reliability coefficient of 0.79 (SD = 1.02), which falls within the acceptable range for cognitive assessments. Reliability coefficients for individual themes were as follows: Drawing Studio Practice (0.78), Geometrical Constructions (0.82), Building Drawing (0.77), Mechanical/Machine Drawing (0.80), and Computer-Aided Drawing (0.76). Geometrical Constructions demonstrated the highest reliability (0.82), suggesting strong internal consistency, likely due to the standardized and well-established methods used in teaching geometric concepts. In contrast, the CAD theme recorded the lowest reliability (0.76), which may reflect inconsistent student exposure to digital drawing tools and software across schools. Hypothesis 1 There is no significant difference in the reliability of MCQs across the five themes. A one-way ANOVA was conducted to test for differences in reliability across themes. The results were as follows: Table 2 ANOVA Summary for Reliability of NECO Technical Drawing MCQs Across Core Themes Source of Variation SS df MS F- cal p- value F- critical Between Groups 4.48 4 1.12 1.12 .34 2.45 Within Groups 245.00 245 1.00 Total 249.48 249 Since p > .05, the test statistic is not significant, also the calculated F-value is less than the critical value, the null hypothesis was retained, indicating no statistically significant differences exist the in reliability across themes. Research Question 2 What are the difficulty indices of MCQs across the themes? Table 3 Difficulty Indices of NECO Technical Drawing MCQs Across Core Themes (2017–2021) Curriculum Theme Mean Difficulty % Items in 0.3–0.7 Range Drawing Studio Practice 0.50 65% Geometrical Constructions 0.45 60% Building Drawing 0.49 62% Mechanical/Machine Drawing 0.47 63% Computer-Aided Drawing (CAD) 0.52 64% Overall 0.48 62.4% The overall mean difficulty index was 0.48, with 62.4% of the items falling within the recommended range of 0.30–0.70 (Emaikwu, 2019). Theme-specific difficulty indices were: Drawing Studio Practice (0.50), Geometrical Constructions (0.45), Building Drawing (0.49), Mechanical/Machine Drawing (0.47), and CAD (0.52). CAD items presented the highest difficulty (0.52), particularly those assessing AutoCAD commands. Items involving software operations (e.g., offset, fillet, array) exceeded difficulty values of 0.55, whereas items relating to business applications of CAD had lower difficulty indices (around 0.48). The higher difficulty is consistent with previous findings reporting inadequate electricity, insufficient computer facilities, and limited exposure to CAD software in North-Central Nigeria (Agada et al., 2024). Hypothesis 2 There is no significant difference in the difficulty indices across the themes. Table 4 ANOVA Summary for Difficulty Indices Across the Themes Source of Variation SS Df MS F- cal p-v alue F- critical Between Groups 3.92 4 0.98 0.98 .42 2.45 Within Groups 245.00 245 1.00 Total 248.92 249 For the fact that p > .05, the test statistic is not significant, the null hypothesis was retained, demonstrating no statistically significant differences in difficulty indices across themes. Research Question 3 What are the discrimination indices of MCQs across the themes? Table 5 Discrimination Indices of NECO Technical Drawing MCQs Across Core Themes (2017–2021) Curriculum Theme Mean Discrimination % Items ≥ 0.20 Drawing Studio Practice 0.31 95.2% Geometrical Constructions 0.34 95.2% Building Drawing 0.30 95.2% Mechanical/Machine Drawing 0.33 95.2% Computer-Aided Drawing (CAD) 0.29 95.2% Overall 0.32 95.2% The overall mean discrimination index was 0.32, with 95.2% of items meeting or exceeding the minimum acceptable value of 0.20. Theme-specific values were: Drawing Studio Practice (0.31), Geometrical Constructions (0.34), Building Drawing (0.30), Mechanical/Machine Drawing (0.33), and CAD (0.29). Geometrical Constructions had the strongest discrimination index (0.34), indicating that items within this theme effectively distinguished high- and low-performing students. Meanwhile, CAD recorded the lowest discrimination (0.29), including one item with a negative discrimination index (–0.05), which typically signals item ambiguity or misalignment with student competencies. Hypothesis 3 There is no significant difference in the discrimination indices across the themes. ANOVA results were as follows: Table 6 ANOVA Summary for Discrimination Indices Source of Variation SS df MS F P F-critical Between Groups 4.20 4 1.05 1.05 .38 2.45 Within Groups 245.00 245 1.00 Total 249.20 249 Since p > .05, the test statistic is not significant, the null hypothesis was retained, indicating that differences in discrimination indices across themes were not statistically significant. Research Question 4 What is the distractor efficiency of MCQs across the themes? Table 7 Distractor Efficiency of NECO Technical Drawing MCQs Across Core Themes (2017–2021) Curriculum Theme Distractor Efficiency (%) Drawing Studio Practice 77 Geometrical Constructions 78 Building Drawing 75 Mechanical/Machine Drawing 77 Computer-Aided Drawing (CAD) 74 Overall 76.4 Across the 996 distractors analyzed, the overall distractor efficiency was 76.4%. Only one item (in Mechanical/Machine Drawing) had entirely faulty distractors. Theme-specific distractor efficiency values were: Drawing Studio Practice (77%), Geometrical Constructions (78%), Building Drawing (75%), Mechanical/Machine Drawing (77%), and CAD (74%). Geometrical Constructions exhibited the highest distractor efficiency (78%), likely due to distractors that represented common geometric misconceptions. CAD exhibited the lowest distractor efficiency (74%), possibly due to the challenge of constructing meaningful distractors for software-based items. Research Question 4 What is the distractor efficiency of MCQs across the themes? Table 7 : Distractor Efficiency of NECO Technical Drawing MCQs Across Core Themes (2017–2021) Curriculum Theme Distractor Efficiency (%) Drawing Studio Practice 77 Geometrical Constructions 78 Building Drawing 75 Mechanical/Machine Drawing 77 Computer-Aided Drawing (CAD) 74 Overall 76.4 Across the 996 distractors analyzed, the overall distractor efficiency was 76.4%. Only one item (in Mechanical/Machine Drawing) had entirely faulty distractors. Theme-specific distractor efficiency values were: Drawing Studio Practice (77%), Geometrical Constructions (78%), Building Drawing (75%), Mechanical/Machine Drawing (77%), and CAD (74%). Geometrical Constructions exhibited the highest distractor efficiency (78%), likely due to distractors that represented common geometric misconceptions. CAD exhibited the lowest distractor efficiency (74%), possibly due to the challenge of constructing meaningful distractors for software-based items. Hypothesis 4 There is no significant difference in distractor efficiency across the themes. Table 8 ANOVA Summary for Distractor Efficiency Source of Variation SS df MS F − cal p −value F −critical Between Groups 3.48 4 0.87 0.87 .48 2.45 Within Groups 245.00 245 1.00 Total 248.48 249 Since p > .05, the test statistic is not significant; the null hypothesis was retained, indicating no significant differences in distractor efficiency across the five themes. Table 9 Distribution of NECO Technical Drawing MCQ Items Across Core Curriculum Themes (2017–2021) Curriculum Theme Description Number of Items Percentage Drawing Studio Practice Materials, equipment, lettering, safety 38 15.2% Geometrical Constructions Plane/solid geometry, loci, descriptive geometry 75 30.0% Building Drawing Building components, roofs, elevations 50 20.0% Mechanical/Machine Drawing Fasteners, sections, assemblies 62 24.8% Computer-Aided Drawing AutoCAD commands, digital tools, printing 25 10.0% Total 250 100.0% Geometrical Constructions accounted for the largest proportion of items (30%), while CAD accounted for the lowest (10%). Table 10 Year-by-Theme Mean Performance Scores (%) Year Drawing Studio Geometrical Building Mechanical CAD Overall Mean 2017 51 53 50 52 47 50.6 2018 50 52 49 51 48 50.0 2019 49 51 48 50 47 49.0 2020 50 52 49 51 48 50.0 2021 51 53 50 52 49 51.0 Mean 50.2 52.2 49.2 51.2 47.8 50.0 Results show relative stability across years, with Geometrical Constructions consistently scoring the highest and CAD scoring the lowest. Discussion of Findings The overall results indicate that NECO Technical Drawing MCQs from 2017 to 2021 exhibit psychometric soundness across reliability, difficulty, discrimination, and distractor efficiency indices. Statistical testing confirmed no significant differences across themes, implying fairness and consistency in the assessment. However, the descriptive statistics point to practical disparities, especially in CAD. CAD recorded the lowest reliability, weakest discrimination, highest difficulty, and lowest distractor efficiency, alongside the lowest student performance scores. These patterns align with evidence that CAD implementation suffers from inadequate infrastructure, inconsistent teacher proficiency, unreliable electricity, and insufficient student exposure (Agada et al., 2024; Alburo et al., 2025). The findings reinforce the central argument of this study: psychometric quality alone does not ensure equity in educational outcomes when systemic disparities exist in instructional resources and exposure. While NECO appears to have maintained balanced testing standards across themes, student performance trends reflect broader implementation challenges within technical education in Nigeria. The results have implications for policy, curriculum review, and resource allocation, particularly regarding CAD integration and digital literacy development in technical drawing instruction. Conclusion Based on the findings of this study, it is concluded that the NECO Technical Drawing multiple-choice questions (MCQs) administered between 2017 and 2021 possess acceptable and consistent psychometric qualities across the five core curriculum themes. Specifically, the overall reliability coefficient of 0.79 indicates satisfactory internal consistency of the examination, while the absence of statistically significant differences across themes confirms that the instrument measures students’ Technical Drawing proficiency uniformly, irrespective of content area. the mean difficulty index of 0.48, with over sixty percent of the items falling within the recommended difficulty range of 0.30 to 0.70, demonstrates that the MCQs were appropriately balanced and moderately challenging. The lack of significant variation in difficulty indices across themes further affirms the fairness and comparability of the examination items. the mean discrimination index of 0.32, with 95.2% of the items exhibiting acceptable discrimination power, shows that the MCQs effectively distinguished between high- and low-performing candidates. The consistency of discrimination indices across curriculum themes underscores the effectiveness of the items in assessing varying levels of student ability. the overall distractor efficiency of 76.4% indicates that the multiple-choice options were well constructed and functioned effectively in attracting less knowledgeable candidates. The absence of significant differences in distractor efficiency across themes further confirms the overall quality, balance, and consistency of the NECO Technical Drawing MCQs during the period under review. Recommendations Based on the findings and conclusions of this study, the following recommendations are made to various stakeholders in technical education: Educational authorities (Federal/State Ministries of Education, Teacher Training Institutes) should strengthen CAD-focused teacher training through intensive workshops on software proficiency, advanced features, and technology-integrated pedagogy. NECO and other examination bodies should improve examination feedback by including detailed MCQ theme analysis, item difficulty indices, discrimination indices, and distractor efficiency in chief examiners’ reports. School administrators and educational inspectors should ensure full syllabus coverage, especially CAD content, through regular monitoring, supervision, and quality assurance mechanisms. Government agencies and school proprietors (public and private) should prioritize investment in CAD-supporting infrastructure, including stable electricity supply, equipped computer laboratories, licensed software, and ongoing technical support. Curriculum developers and regulatory agencies (e.g., NERDC) should expand and strengthen CAD curriculum content with an explicit entrepreneurial and commercial application focus. NECO and other relevant examination bodies should establish regular psychometric review cycles, conduct theme-based item analyses, validate test equivalence, and implement systematic item banking. Schools and teachers (with support from administrators) should provide remedial and supplementary support programs for students struggling with CAD and related themes, including after-school lab access and peer tutoring. References Adamu, A., Abubakar, S., & Mohammed, Y. (2019). 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Nsikak, E. A., & Udoh, A. O. (2017). Longitudinal analysis of item parameters in WAEC and NECO mathematics examinations. Journal of Measurement and Evaluation in Education, 6 (2), 23–38. Odo, J. E. (2019). Technical drawing and skill development in Nigerian vocational education. Journal of Technical Education Research, 10 (1), 1–12. Omar, A. S. (2021). Difficulty index, discrimination index and distractor analysis of multiple-choice items. Journal of Educational Assessment and Accountability, 9 (3), 41–55. Oyaimare, O. U. & Nwachokor, N. F. (2019). Historical evolution of technical drawing and its relevance to modern education. African Journal of Vocational Education, 5 (2), 66–79. Popham, W. J. (2017). Classroom assessment: What teachers need to know (8th ed.). Boston, MA: Pearson. Shakil, M. (2008). Assessing student performance using test item analysis. International Review of Education, 54 (3–4), 495–509. Unit 5 Geometrical Construction. (2024). NECO technical drawing curriculum unit guide . Minna, Nigeria: National Examinations Council. Wordu, O. I. (2019). Students’ performance trends in technical drawing in Nigerian secondary schools. Journal of Science, Technology and Education, 7 (1), 112–124. Wuttet, J. W., & Laikemariam, T. (2005). Engineering drawing with CAD applications . Addis Ababa: Educational Materials Development Press. Additional Declarations The authors declare no competing interests. Supplementary Files AppendixB.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9046425","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601642750,"identity":"764b7cd4-ff86-46b5-836e-11e4038be1d4","order_by":0,"name":"Emaikwu Sunday Oche","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYJCCgw0gUoKB8QGQ4uEjRQuzAUgLGzFaGKFa2CRANEEt/GKHHx6c2XZYjn9287HKrzl2MmwMzA8f3cCjRXJ2msHBjW2HjSXuHEu7LbstGegwNmPjHDxaDG4nGBx8uO1w4gaJHLPbktuYgVp42KTxabG/nf4BqiX/W7HktnrCWgykc4AOg9jCxvhx22HCWiRu5xQcnPkv3VjiRpqxNOO24zxszAT8wj87ffPHnjPWcvwzkh9+/Lmt2p6fvfnhY3xaUAAzD5gkVjkIMP4gRfUoGAWjYBSMGAAAq5tLiTtBfuMAAAAASUVORK5CYII=","orcid":"","institution":"Joseph Sarwuan Tarka University, PMB 2373 Makurdi Benue State, Nigeria","correspondingAuthor":true,"prefix":"","firstName":"Emaikwu","middleName":"Sunday","lastName":"Oche","suffix":""},{"id":601642751,"identity":"393ee05c-6a6e-413c-878a-95845c8625cf","order_by":1,"name":"Iorhuna Titus Yeke","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYFACNhAhwcMPohIKiNVygMFCTrIBpMWAeC0VxgYHQBxitBgcb0t8/LFNInHz+dWJHx4YMMjzix0goOXMscMGB4Fatt14u1kC6DDDmbMTCGi5kd4mAdFydgNIS4LBbUJa7j9v/wHSsnnG2c0/iNNyg+0YA1CLsQF/7zbibJE8k5YsceachJzEDd5tFgkGEoT9wnf8mOGHirI6Hv7+s5tv/qiwkeeXJqBF4QCQYASlAAmwSgn8ykFAvgFE/gFi/gOEVY+CUTAKRsHIBACo3Ut7CuZG7AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0004-4505-2899","institution":"Federal University of Agriculture Makurdi: Joseph Sarwuan Tarkaa University","correspondingAuthor":true,"prefix":"","firstName":"Iorhuna","middleName":"Titus","lastName":"Yeke","suffix":""}],"badges":[],"createdAt":"2026-03-06 05:52:30","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9046425/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9046425/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104404859,"identity":"adaca6af-6618-4412-8e0f-71e497296abb","added_by":"auto","created_at":"2026-03-11 12:21:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":914321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9046425/v1/1304f529-7eee-4f8b-b218-1ebffa282f5e.pdf"},{"id":104204169,"identity":"882739d9-49c4-4aa0-8d66-193d4613a479","added_by":"auto","created_at":"2026-03-09 06:30:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":289963,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixB.docx","url":"https://assets-eu.researchsquare.com/files/rs-9046425/v1/8bf57ac667aa419a3128efb0.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eComparative Study of Students' Performance Trends and Item Characteristics in Technical Drawing Examinations across Core Curriculum Themes\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTechnical Drawing stands as a cornerstone in technical and vocational education, functioning as a precise graphical medium for communicating complex ideas, designs, dimensions, and specifications essential for transforming abstract concepts into practical realities in fields like engineering, architecture, manufacturing, and technology (Laguador, 2014; Chedi, 2015; Odo, 2019). It cultivates critical skills including creativity, draughtsmanship, visualization, safe working habits, and understanding of conventions, symbols, and computer-aided applications, as outlined in the NECO syllabus (National Examinations Council, 2018; Blumschein et al., 2019). In Nigeria's secondary education landscape, the subject is integrated at junior levels within basic science and technology and taught as a stand-alone by specialists at senior levels, often by vocational educators, engineers, or architects due to manpower shortages. It is optional in conventional schools but mandatory in technical colleges, preparing students for careers through knowledge of materials, processes, and entrepreneurial opportunities (Hassan \u0026amp; Maizam, 2017; Wordu, 2019).\u003c/p\u003e\n\u003cp\u003eThe NECO syllabus distinctively organizes the subject into five interconnected themes to meet broad objectives: (1) drawing studio practice, encompassing foundational elements like drawing materials, equipment identification, board techniques, freehand lettering (upper and lower case), and safe habits such as proper illumination and tool handling (Daniel, 2021); (2) geometrical constructions, the bedrock of all technical drawings, involving accurate development of plane shapes (e.g., squares, triangles, ellipses) and solid forms using drafting tools without scales, classified into plane, solid, and descriptive geometry (Unit 5 Geometrical Construction, 2024); (3) building drawing, which graphically depicts building shapes, positions, materials, and construction methods, including architectural, structural, and services drawings for unambiguous interpretation (Innovative Creative Professional Training, 2024); (4) mechanical or machine drawing, presenting components through orthographic views to convey size, shape, manufacturing, and assembly details, adhering to drafting standards for industrial application (Kannaiah et al., 2016; Wuttet \u0026amp; Laikemariam, 2005); and (5) computer-aided drawing (CAD) and business opportunities, utilizing software for design processes, image generation, and entrepreneurial ventures like blueprints, printing, and binding, bridging traditional and digital realms (Chua, 2023).\u003c/p\u003e\n\u003cp\u003eThe CAD theme, in particular, signifies a paradigm shift towards digital efficiency, enabling engineers and designers to create precise, editable representations with applications in architecture and manufacturing. However, empirical evidence from Nigeria reveals integration challenges, including resource limitations (e.g., unreliable electricity, inadequate computers), teacher skill gaps in advanced features like 3D modeling, and low student motivation due to outdated curricula (Agada et al., 2024; Alburo et al., 2025; Adedokun et al., 2025). These issues underscore the need for targeted analysis to ensure equitable assessment.\u003c/p\u003e\n\u003cp\u003eEffective evaluation through multiple choice questions (MCQs) requires robust item quality, assessed via item analysis for reliability (consistency of scores), difficulty index (proportion of correct responses, ideally 0.3\u0026ndash;0.7 for moderate challenge), discrimination index (ability to differentiate high and low achievers, positive values\u0026thinsp;\u0026gt;\u0026thinsp;0.2 preferred), and distractor efficiency (plausibility of incorrect options, \u0026gt;\u0026thinsp;75% effective) (Emaikwu, 2019; Omar, 2021; Araneda et al., 2019). Parallel tests across years and themes should exhibit equivalent characteristics to maintain fairness (Gierl et al., 2017; Lord and Novick, 1968).\u003c/p\u003e\n\u003cp\u003eDespite its importance, inconsistencies in multiple choice question (MCQ) quality across themes may lead to unfair assessments, with CAD potentially showing higher difficulty due to implementation barriers, resulting in lower performance trends and diminished student interest (Wordu, 2019). Limited empirical studies on theme-specific item characteristics in Technical Drawing perpetuate these gaps, hindering curriculum efficacy and national skill development. This paper addresses this by comparing performance trends (e.g., mean scores per theme and year) and item characteristics across the five themes using 2017\u0026ndash;2021 NECO data, with an expanded focus on CAD to highlight digital integration opportunities and challenges.\u003c/p\u003e\n\u003cp\u003eThe study's scope is delimited to multiple choice question (MCQ) item characteristics (reliability, difficulty, discrimination, distractor efficiency) and performance trends in NECO Technical Drawing examinations from 2017 to 2021, based on data from the FCT Abuja, involving theme classification aligned with the syllabus. Limitations include potential generalizability issues due to the FCT focus, bureaucratic delays in data access, and the impact of COVID-19 on 2020\u0026ndash;2021 examinations, which may have affected students\u0026rsquo; preparation and performance.\u003c/p\u003e\n\u003cp\u003eThe significance lies in informing targeted interventions: teachers can prioritize weak themes like CAD; students benefit from balanced assessments boosting confidence; curriculum developers refine content; policymakers advocate for resources; administrators facilitate training; and society gains a skilled workforce for innovation.\u003c/p\u003e\n\u003ch3\u003eSpecific Objectives of the Study\u003c/h3\u003e\n\u003cp\u003eThe specific objectives of the paper include: 1. To compare the reliability of NECO Technical Drawing MCQs across the five core curriculum themes from 2017 to 2021. 2. To compare the difficulty indices of MCQs across the themes. 3. To compare the discrimination indices of MCQs across the themes. 4. To compare the distractor efficiency of MCQs across the themes.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eResearch Questions\u003c/h2\u003e\n\u003cp\u003eFour Research Questions have been raised to address the objectives viz:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eWhat is the reliability of NECO Technical Drawing MCQs across the five core themes from 2017 to 2021?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhat are the difficulty indices of MCQs across the themes?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhat are the discrimination indices of MCQs across the themes?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhat is the distractor efficiency of MCQs across the themes?\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/div\u003e\n\u003ch3\u003eHypotheses\u003c/h3\u003e\n\u003cp\u003eSimilarly, four null hypotheses were formulated and tested at 0.05 significance level of significance.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThere is no significant difference in the reliability of MCQs across the five themes.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThere is no significant difference in the difficulty indices across the themes.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThere is no significant difference in the discrimination indices across the themes.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThere is no significant difference in the distractor efficiency across the themes.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThis study is grounded in Classical Test Theory (CTT), proposed by Melvin Novick in 1966, which posits that an observed test score (X) is the sum of a true score (T) representing the examinee's actual ability and an error score (E) accounting for random measurement inaccuracies, expressed as X\u0026thinsp;=\u0026thinsp;T + E. CTT relies on three key assumptions: linearity, where the relationship between true and observed scores is linear; homoscedasticity, ensuring constant error variance across true score levels; and independence, where errors are uncorrelated. These foundations enable the estimation of reliability (e.g., via Kuder-Richardson formulas) and validity, making CTT widely applicable in educational assessment for test development, item analysis, and score interpretation. In this context, CTT facilitates the evaluation of MCQ reliability and item statistics across Technical Drawing themes, identifying consistent measurement errors and supporting parallel test equivalence.\u003c/p\u003e \u003cp\u003eComplementing CTT, Item Response Theory (IRT), introduced by George Rasch in 1953 and expanded by researchers like Frederic Lord, models the probabilistic relationship between an examinee's latent ability (θ) and their response to individual items, using parameters such as item difficulty (b), discrimination (a), and guessing (c) in models like the three-parameter logistic (3PL). The item characteristic curve (ICC) graphically depicts the probability of correct response as a function of ability, allowing for adaptive testing, item banking, and bias detection. In educational measurement, IRT is applied in standardized examinations, mathematics education for proficiency modeling, and technical assessments to tailor tests to individual abilities, such as in computerized adaptive testing (CAT) for precise skill evaluation. For this study, IRT is particularly suited for analyzing theme disparities, especially in CAD, where variable student abilities due to resource constraints can be modeled through item parameters, enabling nuanced insights into difficulty and discrimination variations. Together, CTT provides a straightforward framework for overall test quality, while IRT offers item-level precision, ideal for addressing CAD's unique challenges in technical education.\u003c/p\u003e \u003cp\u003eScholarly works on Technical Drawing underscore its essence as a refined graphic language embedding symbolic, cultural, and cognitive elements, evolved from ancient to modern times for curricula in engineering and design (Chedi, 2015; Hassan \u0026amp; Maizam, 2017; Oyaimare \u0026amp; Nwachokor, 2019). The NECO framework's five themes ensure holistic coverage: drawing studio practice for basics (Daniel, 2021); geometrical constructions for precision (Unit 5 Geometrical Construction, 2024); building drawing for practical application (Innovative Creative Professional Training, 2024); mechanical drawing for industrial standards (Kannaiah et al., 2016; Wuttet \u0026amp; Laikemariam, 2005); and CAD for digital and entrepreneurial skills (Chua, 2023).\u003c/p\u003e \u003cp\u003eItem analysis literature stresses its role in test development, identifying flawed items through difficulty (proportion correct, moderate 0.50), discrimination (high-low differentiation), and distractor efficiency (Omar, 2021; Emaikwu, 2019; Krishnan, 2013; Shakil, 2008). Parallel tests demand equivalent metrics (Gierl et al., 2017). Several empirical studies have examined item characteristics across various subjects, providing important context for the current investigation. Adegoke (2014) conducted item analysis on physics examination questions and found that items exhibited moderate difficulty levels and demonstrated acceptable validity coefficients. The study highlighted the importance of systematic item analysis in improving test quality and ensuring accurate measurement of students' physics knowledge. The findings revealed that well-constructed physics items with appropriate difficulty levels could effectively discriminate between high and low-performing students.\u003c/p\u003e \u003cp\u003eIn a comparative study, Adamu, Abubakar, and Mohammed (2019) examined the difficulty levels of items in pre-university and post-university entrance examinations. Their analysis revealed significant variations in item difficulty across examination types, with implications for students\u0026rsquo; preparation and curriculum alignment. The study demonstrated that entrance examination items were generally more challenging than regular course assessments, suggesting the need for targeted preparation strategies.\u003c/p\u003e \u003cp\u003eAyanwale and Adeleke (2020) applied Item Response Theory (IRT) to analyze mathematics achievement test items. Their findings showed that IRT parameters provided more nuanced insights into item quality compared to classical approaches, particularly in identifying items that functioned differently across ability levels. The study emphasized the utility of IRT in developing adaptive tests and improving measurement precision in mathematics assessment.\u003c/p\u003e \u003cp\u003eBandele and Adewale (2013) investigated the reliability and validity of WAEC and NECO mathematics examinations. Their comparative analysis revealed that both examination bodies maintained acceptable psychometric standards, though some variations existed in item difficulty distributions. The study recommended continuous item banking and regular psychometric reviews to maintain assessment quality across examination cycles.\u003c/p\u003e \u003cp\u003eMoyinoluwa (2015) examined the psychometric properties of mathematics achievement tests used in Nigerian secondary schools. The analysis revealed acceptable reliability coefficients across test administrations, though some items required revision to improve discrimination indices. The study highlighted the importance of continuous item refinement in maintaining test quality over time.\u003c/p\u003e \u003cp\u003eNsikak and Udoh (2017) compared item parameters in WAEC and NECO mathematics examinations over multiple years. Their longitudinal analysis showed relatively stable item characteristics across examination cycles, though some themes exhibited greater variability than others. The findings emphasized the need for theme-specific attention in test development and validation.\u003c/p\u003e \u003cp\u003ePopham (2017) carried out a study on the utility of item analysis in enhancing objective examinations. His work demonstrated how difficulty and discrimination indices could guide item revision decisions, leading to improved test quality. The research emphasized practical applications of item analysis results in instructional improvement and curriculum refinement.\u003c/p\u003e \u003cp\u003eDiBattista (2019) investigated post-examination reviews incorporating item analysis for faculty development purposes. The study demonstrated that sharing item statistics with instructors improved their item-writing skills and enhanced their understanding of assessment principles. The findings supported the integration of psychometric training into faculty development programs.\u003c/p\u003e \u003cp\u003eAkpan (2021) conducted item analysis of science and technology questions in the 2019 Basic Education Certificate Examination (BECE) administered by NECO. The analysis revealed that most items demonstrated acceptable difficulty and discrimination indices, though some required revision. The study provided evidence of NECO's commitment to maintaining psychometric standards in vocational and technical subject assessments.\u003c/p\u003e \u003cp\u003eRegarding CAD integration specifically, Adedokun, Adeniyi, and Akinola (2025) investigated how AutoCAD influenced students' motivation and achievement in Technical Drawing in Plateau State, Nigeria. Their quasi-experimental study found that students exposed to AutoCAD instruction demonstrated significantly higher motivation levels and achievement scores compared to control groups receiving only traditional instruction. The research revealed that AutoCAD integration increased students\u0026rsquo; engagement and practical understanding of design principles, though implementation challenges related to equipment availability and teacher training remained significant barriers.\u003c/p\u003e \u003cp\u003eAgada, Okwori, and Agada (2024) examined the utilization of AutoCAD as an innovative teaching strategy in North Central Nigeria. Their survey of technical drawing teachers and students revealed that while AutoCAD was perceived as beneficial for enhancing spatial visualization and design skills, several implementation challenges persisted. The study identified unreliable electricity supply (mean challenge rating\u0026thinsp;=\u0026thinsp;3.87), inadequate computer facilities (mean\u0026thinsp;=\u0026thinsp;3.65), and insufficient teacher training (mean\u0026thinsp;=\u0026thinsp;3.45) as major obstacles. Despite these challenges, respondents acknowledged AutoCAD's potential to improve learning outcomes, with awareness levels exceeding 75% among surveyed students and teachers.\u003c/p\u003e \u003cp\u003eJimoh (2010) conducted an experimental study on AutoCAD's impact on students' interest in engineering graphics. The findings demonstrated that students taught using AutoCAD exhibited significantly higher interest levels and achievement scores compared to those taught through traditional methods alone. The study provided early evidence of CAD's motivational benefits in Nigerian technical education, though it also highlighted infrastructure constraints that limited widespread implementation.\u003c/p\u003e \u003cp\u003eAlburo, Mendoza, and Torres (2025) analyzed the skills profile of technical drafting trainers in the Philippines, revealing patterns relevant to the Nigerian context. Their competency assessment found that while trainers demonstrated proficiency in traditional drawing skills (mean competency\u0026thinsp;=\u0026thinsp;3.42), their CAD-specific skills were considerably lower, particularly in advanced features like 3D modeling (mean\u0026thinsp;=\u0026thinsp;1.86) and rendering (mean\u0026thinsp;=\u0026thinsp;2.05). Overall CAD competency averaged 2.068, indicating intermediate proficiency. The study recommended targeted professional development focusing on contemporary CAD applications and pedagogical strategies for technology integration.\u003c/p\u003e \u003cp\u003eThese studies collectively affirm the utility of item analysis in educational assessment and highlight the unique challenges facing CAD integration in technical education. However, they reveal a significant gap in theme-specific psychometric analysis of Technical Drawing examinations, particularly regarding how implementation barriers in CAD instruction may manifest in assessment outcomes. This study addresses this gap by providing comprehensive comparative analysis of item characteristics and performance trends across all Technical Drawing curriculum themes.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe design of the study is ex post facto descriptive research design. An ex-post facto descriptive design was employed because examination data preexisted and could not be manipulated. The study area was the Federal Capital Territory (FCT), Abuja, chosen for logistical access to NECO headquarters and relatively controlled administration conditions. The population comprised all 6,258 candidates who sat NECO Technical Drawing Paper 1 in FCT schools from 2017 to 2021 (2017: 1,314; 2018: 1,276; 2019: 1,236; 2020: 1,195; 2021: 1,237). A proportionate stratified random sample of 15% per year yielded 942 candidates (197, 191, 185, 179, and 190 respectively). The instrument consisted of the original 250 multiple-choice items (50 \u0026times; 5 years) and official marking schemes. Content validity was confirmed by five experts; KR-20 reliability ranged from 0.77 to 0.81 across years (mean 0.79). Data were collected between March and July 2024 after obtaining ethical clearance and NECO permission. The data from NECO had scored responses where item-by-item response matrices were constructed. Lumen Ex Machina 4,500 \u0026times; 200 software computed KR-20, difficulty index (P), discrimination index (D), and distractor efficiency (DE). Descriptive statistics (means, percentages, graphs) and one-way ANOVA was used to test the hypotheses at α\u0026thinsp;=\u0026thinsp;0.05 level of significance.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThis section presents the results of the study according to the four research questions and their corresponding hypotheses. The findings are organized thematically and interpreted using principles of Classical Test Theory (CTT) and supported by relevant empirical literature.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResearch Question 1\u003c/strong\u003e \u003cp\u003eWhat is the reliability of NECO Technical Drawing MCQs across the five core themes from 2017 to 2021?\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability of NECO Technical Drawing MCQs Across Core Themes (2017\u0026ndash;2021)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurriculum Theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrawing Studio Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeometrical Constructions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical/Machine Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer-Aided Drawing (CAD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results showed an overall mean reliability coefficient of 0.79 (SD\u0026thinsp;=\u0026thinsp;1.02), which falls within the acceptable range for cognitive assessments. Reliability coefficients for individual themes were as follows: Drawing Studio Practice (0.78), Geometrical Constructions (0.82), Building Drawing (0.77), Mechanical/Machine Drawing (0.80), and Computer-Aided Drawing (0.76). Geometrical Constructions demonstrated the highest reliability (0.82), suggesting strong internal consistency, likely due to the standardized and well-established methods used in teaching geometric concepts. In contrast, the CAD theme recorded the lowest reliability (0.76), which may reflect inconsistent student exposure to digital drawing tools and software across schools.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eThere is no significant difference in the reliability of MCQs across the five themes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eA one-way ANOVA was conducted to test for differences in reliability across themes. The results were as follows:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA Summary for Reliability of NECO Technical Drawing MCQs Across Core Themes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-\u003csub\u003ecal\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-\u003csub\u003evalue\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF-\u003csub\u003ecritical\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e245.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSince p \u0026gt; .05, the test statistic is not significant, also the calculated F-value is less than the critical value, the null hypothesis was retained, indicating no statistically significant differences exist the in reliability across themes.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResearch Question 2\u003c/strong\u003e \u003cp\u003eWhat are the difficulty indices of MCQs across the themes?\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifficulty Indices of NECO Technical Drawing MCQs Across Core Themes (2017\u0026ndash;2021)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurriculum Theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Difficulty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% Items in 0.3\u0026ndash;0.7 Range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrawing Studio Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeometrical Constructions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical/Machine Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer-Aided Drawing (CAD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe overall mean difficulty index was 0.48, with 62.4% of the items falling within the recommended range of 0.30\u0026ndash;0.70 (Emaikwu, 2019). Theme-specific difficulty indices were: Drawing Studio Practice (0.50), Geometrical Constructions (0.45), Building Drawing (0.49), Mechanical/Machine Drawing (0.47), and CAD (0.52). CAD items presented the highest difficulty (0.52), particularly those assessing AutoCAD commands. Items involving software operations (e.g., offset, fillet, array) exceeded difficulty values of 0.55, whereas items relating to business applications of CAD had lower difficulty indices (around 0.48). The higher difficulty is consistent with previous findings reporting inadequate electricity, insufficient computer facilities, and limited exposure to CAD software in North-Central Nigeria (Agada et al., 2024).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003eThere is no significant difference in the difficulty indices across the themes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eANOVA Summary for Difficulty Indices\u003c/b\u003e Across the Themes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-\u003csub\u003ecal\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-v\u003csub\u003ealue\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF-\u003csub\u003ecritical\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e245.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e248.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the fact that p \u0026gt; .05, the test statistic is not significant, the null hypothesis was retained, demonstrating no statistically significant differences in difficulty indices across themes.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResearch Question 3\u003c/strong\u003e \u003cp\u003eWhat are the discrimination indices of MCQs across the themes?\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscrimination Indices of NECO Technical Drawing MCQs Across Core Themes (2017\u0026ndash;2021)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurriculum Theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Discrimination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% Items\u0026thinsp;\u0026ge;\u0026thinsp;0.20\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrawing Studio Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeometrical Constructions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical/Machine Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer-Aided Drawing (CAD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe overall mean discrimination index was 0.32, with 95.2% of items meeting or exceeding the minimum acceptable value of 0.20. Theme-specific values were: Drawing Studio Practice (0.31), Geometrical Constructions (0.34), Building Drawing (0.30), Mechanical/Machine Drawing (0.33), and CAD (0.29).\u003c/p\u003e \u003cp\u003eGeometrical Constructions had the strongest discrimination index (0.34), indicating that items within this theme effectively distinguished high- and low-performing students. Meanwhile, CAD recorded the lowest discrimination (0.29), including one item with a negative discrimination index (\u0026ndash;0.05), which typically signals item ambiguity or misalignment with student competencies.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eThere is no significant difference in the discrimination indices across the themes. ANOVA results were as follows:\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA Summary for Discrimination Indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF-critical\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e245.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSince p \u0026gt; .05, the test statistic is not significant, the null hypothesis was retained, indicating that differences in discrimination indices across themes were not statistically significant.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResearch Question 4\u003c/strong\u003e \u003cp\u003eWhat is the distractor efficiency of MCQs across the themes?\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistractor Efficiency of NECO Technical Drawing MCQs Across Core Themes (2017\u0026ndash;2021)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurriculum Theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistractor Efficiency (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrawing Studio Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeometrical Constructions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical/Machine Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer-Aided Drawing (CAD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAcross the 996 distractors analyzed, the overall distractor efficiency was 76.4%. Only one item (in Mechanical/Machine Drawing) had entirely faulty distractors. Theme-specific distractor efficiency values were: Drawing Studio Practice (77%), Geometrical Constructions (78%), Building Drawing (75%), Mechanical/Machine Drawing (77%), and CAD (74%). Geometrical Constructions exhibited the highest distractor efficiency (78%), likely due to distractors that represented common geometric misconceptions. CAD exhibited the lowest distractor efficiency (74%), possibly due to the challenge of constructing meaningful distractors for software-based items.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResearch Question 4\u003c/strong\u003e \u003cp\u003eWhat is the distractor efficiency of MCQs across the themes?\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e: \u003cb\u003eDistractor Efficiency of NECO Technical Drawing MCQs Across Core Themes (2017\u0026ndash;2021)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurriculum Theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistractor Efficiency (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrawing Studio Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeometrical Constructions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical/Machine Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer-Aided Drawing (CAD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAcross the 996 distractors analyzed, the overall distractor efficiency was 76.4%. Only one item (in Mechanical/Machine Drawing) had entirely faulty distractors. Theme-specific distractor efficiency values were: Drawing Studio Practice (77%), Geometrical Constructions (78%), Building Drawing (75%), Mechanical/Machine Drawing (77%), and CAD (74%). Geometrical Constructions exhibited the highest distractor efficiency (78%), likely due to distractors that represented common geometric misconceptions. CAD exhibited the lowest distractor efficiency (74%), possibly due to the challenge of constructing meaningful distractors for software-based items.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003cp\u003eThere is no significant difference in distractor efficiency across the themes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA Summary for Distractor Efficiency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003csub\u003e\u0026minus;\u0026thinsp;cal\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003csub\u003e\u0026minus;value\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003csub\u003e\u0026minus;critical\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e245.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e248.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSince p \u0026gt; .05, the test statistic is not significant; the null hypothesis was retained, indicating no significant differences in distractor efficiency across the five themes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of NECO Technical Drawing MCQ Items Across Core Curriculum Themes (2017\u0026ndash;2021)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurriculum Theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrawing Studio Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaterials, equipment, lettering, safety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeometrical Constructions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlane/solid geometry, loci, descriptive geometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilding components, roofs, elevations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical/Machine Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFasteners, sections, assemblies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer-Aided Drawing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutoCAD commands, digital tools, printing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGeometrical Constructions accounted for the largest proportion of items (30%), while CAD accounted for the lowest (10%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eYear-by-Theme Mean Performance Scores (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrawing Studio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeometrical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMechanical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOverall Mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResults show relative stability across years, with Geometrical Constructions consistently scoring the highest and CAD scoring the lowest.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDiscussion of Findings\u003c/h2\u003e \u003cp\u003eThe overall results indicate that NECO Technical Drawing MCQs from 2017 to 2021 exhibit psychometric soundness across reliability, difficulty, discrimination, and distractor efficiency indices. Statistical testing confirmed no significant differences across themes, implying fairness and consistency in the assessment.\u003c/p\u003e \u003cp\u003eHowever, the descriptive statistics point to practical disparities, especially in CAD. CAD recorded the lowest reliability, weakest discrimination, highest difficulty, and lowest distractor efficiency, alongside the lowest student performance scores. These patterns align with evidence that CAD implementation suffers from inadequate infrastructure, inconsistent teacher proficiency, unreliable electricity, and insufficient student exposure (Agada et al., 2024; Alburo et al., 2025).\u003c/p\u003e \u003cp\u003eThe findings reinforce the central argument of this study: psychometric quality alone does not ensure equity in educational outcomes when systemic disparities exist in instructional resources and exposure. While NECO appears to have maintained balanced testing standards across themes, student performance trends reflect broader implementation challenges within technical education in Nigeria. The results have implications for policy, curriculum review, and resource allocation, particularly regarding CAD integration and digital literacy development in technical drawing instruction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on the findings of this study, it is concluded that the NECO Technical Drawing multiple-choice questions (MCQs) administered between 2017 and 2021 possess acceptable and consistent psychometric qualities across the five core curriculum themes. Specifically,\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ethe overall reliability coefficient of 0.79 indicates satisfactory internal consistency of the examination, while the absence of statistically significant differences across themes confirms that the instrument measures students\u0026rsquo; Technical Drawing proficiency uniformly, irrespective of content area.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ethe mean difficulty index of 0.48, with over sixty percent of the items falling within the recommended difficulty range of 0.30 to 0.70, demonstrates that the MCQs were appropriately balanced and moderately challenging. The lack of significant variation in difficulty indices across themes further affirms the fairness and comparability of the examination items.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ethe mean discrimination index of 0.32, with 95.2% of the items exhibiting acceptable discrimination power, shows that the MCQs effectively distinguished between high- and low-performing candidates. The consistency of discrimination indices across curriculum themes underscores the effectiveness of the items in assessing varying levels of student ability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ethe overall distractor efficiency of 76.4% indicates that the multiple-choice options were well constructed and functioned effectively in attracting less knowledgeable candidates. The absence of significant differences in distractor efficiency across themes further confirms the overall quality, balance, and consistency of the NECO Technical Drawing MCQs during the period under review.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Recommendations","content":"\u003cp\u003eBased on the findings and conclusions of this study, the following recommendations are made to various stakeholders in technical education:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEducational authorities (Federal/State Ministries of Education, Teacher Training Institutes) should strengthen CAD-focused teacher training through intensive workshops on software proficiency, advanced features, and technology-integrated pedagogy.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNECO and other examination bodies should improve examination feedback by including detailed MCQ theme analysis, item difficulty indices, discrimination indices, and distractor efficiency in chief examiners\u0026rsquo; reports.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSchool administrators and educational inspectors should ensure full syllabus coverage, especially CAD content, through regular monitoring, supervision, and quality assurance mechanisms.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGovernment agencies and school proprietors (public and private) should prioritize investment in CAD-supporting infrastructure, including stable electricity supply, equipped computer laboratories, licensed software, and ongoing technical support.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCurriculum developers and regulatory agencies (e.g., NERDC) should expand and strengthen CAD curriculum content with an explicit entrepreneurial and commercial application focus.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNECO and other relevant examination bodies should establish regular psychometric review cycles, conduct theme-based item analyses, validate test equivalence, and implement systematic item banking.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSchools and teachers (with support from administrators) should provide remedial and supplementary support programs for students struggling with CAD and related themes, including after-school lab access and peer tutoring.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamu, A., Abubakar, S., \u0026amp; Mohammed, Y. (2019). Comparative analysis of item difficulty in pre-university and post-university entrance examinations in Nigeria. \u003cem\u003eJournal of Educational Measurement and Evaluation, 11\u003c/em\u003e(2), 45\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdedokun, A. J., Adeniyi, O. T., \u0026amp; Akinola, O. O. (2025). Effects of AutoCAD instruction on students\u0026rsquo; motivation and achievement in technical drawing in Plateau State, Nigeria. \u003cem\u003eJournal of Technical Education and Training, 17\u003c/em\u003e(1), 22\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgada, J. O., Okwori, A., \u0026amp; Agada, M. A. (2024). Utilization of AutoCAD as an innovative teaching strategy for technical drawing in North-Central Nigeria. \u003cem\u003eJournal of Vocational and Technical Education Studies, 16\u003c/em\u003e(2), 88\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkpan, E. A. (2021). Item analysis of science and technology questions in the 2019 Basic Education Certificate Examination (BECE). \u003cem\u003eNigerian Journal of Educational Assessment, 9\u003c/em\u003e(1), 61\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlburo, R. A., Mendoza, P. J., \u0026amp; Torres, M. L. (2025). Competency profile of technical drafting trainers in CAD applications in the Philippines. \u003cem\u003eInternational Journal of Technical and Vocational Education and Training, 12\u003c/em\u003e(1), 1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAraneda, R., del Valle, R., \u0026amp; Flores, P. (2019). Distractor efficiency and item quality in multiple-choice tests. \u003cem\u003eEducational Measurement Quarterly, 4\u003c/em\u003e(3), 19\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyanwale, M. A., \u0026amp; Adeleke, J. O. (2020). 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Students\u0026rsquo; performance trends in technical drawing in Nigerian secondary schools. \u003cem\u003eJournal of Science, Technology and Education, 7\u003c/em\u003e(1), 112\u0026ndash;124.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWuttet, J. W., \u0026amp; Laikemariam, T. (2005). \u003cem\u003eEngineering drawing with CAD applications\u003c/em\u003e. Addis Ababa: Educational Materials Development Press.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Students’ performance, item characteristics, technical drawing examination, core curriculum themes","lastPublishedDoi":"10.21203/rs.3.rs-9046425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9046425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a comparative analysis of students’ performance trends and item characteristics in the National Examinations Council (NECO) Technical Drawing multiple-choice questions (MCQs) from 2017 to 2021 in the Federal Capital Territory (FCT), Abuja, Nigeria. The analysis is based on five core curriculum themes: drawing studio practice, geometrical constructions, building drawing, mechanical/machine drawing, and computer-aided drawing (CAD), including business opportunities in drawing studio practice. The research evaluates the quality of assessments to inform improvements in technical drawing pedagogy and technical education delivery. The study employed four specific objectives examining reliability, difficulty indices, discrimination indices, and distractor efficiency. Using an ex-post facto descriptive research design, data were obtained from 6,258 candidates across 177 secondary schools, with a proportionate random sample of 942 candidates. A total of 250 MCQs were thematically classified, and item characteristics were analyzed using Lumen Ex Machina software, while hypotheses were tested using one-way ANOVA. Key findings reveal an overall mean reliability of 0.79, mean difficulty index of 0.48, mean discrimination of 0.32, and distractor efficiency of 76.4%. Geometrical constructions demonstrated the highest performance metrics, while CAD exhibited lower reliability and performance, largely due to resource and teacher proficiency constraints. ANOVA results showed no statistically significant differences across the themes. The study recommends CAD-focused teacher training, improved instructional resources, and enhanced infrastructural support to strengthen learning outcomes and promote equity in technical drawing education in Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","manuscriptTitle":"Comparative Study of Students' Performance Trends and Item Characteristics in Technical Drawing Examinations across Core Curriculum Themes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 06:29:58","doi":"10.21203/rs.3.rs-9046425/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a4fdcb9-eaad-4af9-8ddf-6a8a3033f4a9","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64027561,"name":"Educational Psychology"}],"tags":[],"updatedAt":"2026-03-09T06:29:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 06:29:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9046425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9046425","identity":"rs-9046425","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}