Journal of Computer Adaptive Testing in Africa
The Journal of Computer Adaptive Testing in Africa (JoCATiA) is the official journal of the Association of Computer Adaptive Testing in Africa (ACATA), a peer-reviewed electronic journal designed to advance the science and practice of computerised adaptive testing (CAT) aimed at advancing the frontiers of computerised testing in Africa to the second generation of computer-based assessment and beyond. To achieve this aim, JoCATiA publishes three types of manuscripts: Applications and implementations of CAT (Projects). These articles include, but are not limited to, a detailed description of deployed solutions for CAT in the area of learning and assessment and patient options for authors. Below are some of the relevant focal areas: item banking for CAT; item selection algorithms for CAT; security algorithms for CAT; administrative technology of adaptive testing; multistage designs for CAT; examinee reactions to CAT; DIF in CAT; validity studies for CAT; and IRT-based psychometrics models for CAT Cognitive diagnostic models for CAT simulation studies for CAT integrative critical and systematic reviews for CAT technology management for CAT Other Educational Assessment Approaches. Articles in this section broaden the journal’s scope to include high-quality research on non-adaptive but equally significant assessment methodologies. This includes studies grounded in classical and modern measurement theories, large-scale assessment systems, formative and summative evaluation practices, and innovative assessment designs. Manuscripts may explore topics such as test development and validation, standard-setting procedures, cognitive diagnostic assessment, and performance-based assessment models. While these studies may not explicitly employ adaptive algorithms, they are expected to demonstrate methodological rigour and contribute meaningfully to the improvement of assessment practices. Reviewers should examine whether such studies are theoretically sound, methodologically robust, and aligned with best practices in measurement and evaluation. Particular attention should be given to issues of reliability, validity, fairness, and contextual applicability, especially in relation to educational systems across Africa. Allied Research Areas. This section reflects the interdisciplinary nature of contemporary assessment research and acknowledges the growing influence of emerging technologies and data-driven approaches. This section welcomes contributions that intersect with assessment but extend into areas such as artificial intelligence, machine learning, learning analytics, educational data mining, and digital learning environments. Studies may investigate predictive modelling of learner performance, intelligent tutoring systems, automated scoring, or the integration of assessment within adaptive learning platforms. Research that addresses ethical considerations, data governance, and equity in technology-driven assessment is also highly valued. Reviewers assessing submissions in this section should ensure that the research demonstrates both technical sophistication and relevance to assessment practice, with clear implications for teaching, learning, and policy. Importantly, interdisciplinary studies should maintain a strong connection to assessment theory and practice, rather than functioning as purely technical or computational contributions. Articles in this section also include, but are not limited to, research on classical test theory (CCT) and generalisability theory (GT), among others.
The Association of Computer Adaptive Testing in Africa
Fairness-Aware Machine Learning for Ethical Student Performance Prediction in Education
The integration of artificial intelligence (AI) and machine learning (ML) into education has transformed how student performance is predicted and monitored. Despite these advances, concerns regarding fairness, transparency, interpretability, and potential demographic bias remain significant challenges in educational prediction systems. This study developed ethically aligned and interpretable ML models for predicting student academic performance using only behavioural engagement and academic context variables. The open-access xAPI-Edu-Data dataset containing 480 student records was obtained from Kaggle. Four supervised algorithms, Multinomial Logistic Regression, Decision Tree, Random Forest, and XGBoost, were implemented in Python 3.11 using Scikit-learn, SHAP, and LIME frameworks. To minimise data leakage, all preprocessing operations, including standardisation and categorical encoding, were embedded within a Scikit-learn Pipeline fitted exclusively on the training folds. A stratified train/validation/test split (70%/15%/15%) with random_state = 42 was employed, while hyperparameters were optimised using five-fold cross-validation on the training and validation sets only. Model performance was evaluated using accuracy, macro F1-score, ROC-AUC, Brier Score, and Expected Calibration Error (ECE). Results showed that Random Forest achieved the best overall performance and calibration, while Logistic Regression provided superior interpretability. Across all models, visited_resources and raisedhands consistently emerged as the strongest predictors of academic achievement, emphasising the importance of student engagement behaviours. The study demonstrates that fairness-by-design prediction systems that exclude demographic variables can support equitable and actionable early-warning interventions. The novelty of the study lies in its ethically grounded and deployment-oriented framework for interpretable educational prediction in resource-constrained contexts.
Copyright (c) 2026 Musa Adekunle Ayanwale, Ijeoma Joy Chikezie (Author) https://creativecommons.org/licenses/by-nc/4.0
Chained Equipercentile Equating of Senior School Certificate Multiple-Choice Test Items in Chemistry
This study investigated chained equipercentile equating of Senior School Certificate Multiple Choice Test Items in Chemistry conducted by the West African Examinations Council (WAEC), the National Examinations Council (NECO) and National Business and Technical Examination Board (NABTEB). The study adopted the Non-Equivalent Groups Anchor Test design. A sample of 1,461 students was selected among Senior Secondary III students in the southwest geopolitical zone of Nigeria using a multi-stage sampling procedure. The 2017 WAEC, NECO and NABTEB SSCE multiple choice Chemistry papers were used as instruments for data collection. The WAEC, NECO and NABTEB papers yielded coefficients of content validity of 0.78, 0.75, and 0.76 and reliability of 0.81, 0.77, and 0.78 respectively. Data obtained were analysed using mean and percentile rank.
Copyright (c) 2022 Oluwatosin Adeosun (Author) https://creativecommons.org/licenses/by-nc/4.0
Enhancing test quality in Lesotho Basic Education through Classical Test and Item Response Theory Analyses
This study developed and psychometrically evaluated a Grade 6 mathematics test for Lesotho’s Cambridge curriculum using Classical Test Theory (CTT) and Item Response Theory (IRT). A quantitative psychometric design involving 200 learners from Cambridge International Schools was employed. Data were analysed using jMetrik through CTT indices and 1PL and 2PL IRT models. Results revealed substantial psychometric weaknesses, including extreme item difficulty variation, weak and negative discrimination indices, low reliability coefficients (α = 0.1975), and several misfitting items. The 2PL model demonstrated a superior fit over the 1PL model based on deviance, AIC, and BIC statistics. While CTT identified global weaknesses in reliability and item discrimination, IRT provided richer diagnostic evidence regarding item functioning and conditional measurement precision across ability levels. The study concludes that integrating CTT and IRT strengthens assessment validity, reliability, fairness, and evidence-based test development in Lesotho basic education. The study recommends rigorous item piloting, routine use of CTT and IRT analyses, and enhanced psychometric training for teachers and assessment practitioners in Lesotho.
Copyright (c) 2025 Lefa Clement Thamae (Author) https://creativecommons.org/licenses/by-nc/4.0
Ensuring Equity and Inclusivity in AI-powered Educational Assessment
Artificial intelligence (AI) and computer-based testing (CBT) are increasingly central to educational assessment in Nigeria, promising efficiency, transparency, and fairness. However, concerns about equity and inclusivity persist, especially for rural learners and students with disabilities. This study critically examines how Nigeria’s education policies and practices address fairness in AI-enabled assessment, with particular attention to alignment with international ethical standards. A qualitative desk review was conducted, and policy documents, annual reports, and empirical studies published between 2015-2024 were analysed. Ribble Digital Equity Framework (2012) and UNESCO AI and Education: Guidance for Policymakers (2021) provided the analysis, focusing on access, participation, empowerment, and ethical use. As results reveal, the Nigerian policy texts express general commitments to digital access but do not provide specific mechanisms for the equitable integration of artificial intelligence. Students in rural areas are disproportionately marginalised by infrastructure shortages, such as low internet access and computer illiteracy. Students with disabilities, especially the visually impaired, experience systemic marginalisation because of the uneven distribution of assistive technology, resulting in poorer admission performance. Compared with UNESCO standards, Nigeria’s regulatory frameworks lack algorithmic transparency, non-discrimination, and inclusive design. The paper finds that, although Nigeria's assessment system remains primarily CBT-based, existing disparities in infrastructure, digital literacy, accessibility, and disability accommodation present significant risks for future AI-enabled assessment adoption. The findings suggest that, unless accompanied by deliberate safeguards relating to inclusive design, algorithmic accountability, accessibility, and digital capacity building, future AI-enabled assessment systems may reproduce or amplify existing educational inequalities rather than mitigate them.
Copyright (c) 2025 Mary Ifeoluwa Otegbade, Deborah Anuoluwapo Demurin, Babatunde Joseph KOLASHI, (PhD) (Author) https://creativecommons.org/licenses/by-nc/4.0
Awareness of Digital Footprint among Undergraduates in a Nigerian University
Information and communication technologies (ICTs) have become ubiquitous in modern society, and their use has given rise to the concept of digital footprint. Digital footprint refers to the trail of data that individuals leave behind as they interact with the internet and digital devices. While ICTs offer numerous benefits, they also raise concerns about privacy and the potential for personal information to be misused. This study assessed the awareness of digital footprint among undergraduate students in a Nigerian university. Descriptive research design was employed using a survey questionnaire. A total of 381 undergraduate students participated in the study. The questionnaire assessed participants' awareness of digital footprint through a series of Likert-scale items. The findings revealed that the participants had a good understanding of digital footprint. They were aware of the potential risks associated with sharing personal information online and took steps to protect their privacy. However, there was a significant difference in the awareness of digital footprint between male and female students, with female students exhibiting a higher level of awareness. The study suggests that undergraduate students in the selected Nigerian university have a good understanding of digital footprint and are taking steps to protect their privacy online. We conclude that there is a need for continued efforts to raising the awareness on digital footprint, particularly among male students. Educators, policymakers, and parents should play a role in educating individuals about the importance of digital footprint management and the potential consequences of sharing personal information online.
Copyright (c) 2024 Olawale Sunday Koledafe (Author) https://creativecommons.org/licenses/by-nc/4.0
Exploring the Use of Blended Assessment in Evaluating Undergraduates’ Learning Performance
Assessment is one of the processes for data gathering from the students in respect their talents, knowledge, skills, and other relevant attributes that can be of use for decisions-making regarding their academic advancement. The study was on the use of blended assessment in managing undergraduate learning outcomes. The study's research design was repeated measure. The population was 45,407 undergraduates in 15 faculties as the intended audience was 10,908 in the educational faculty. The sample size was 2,740 undergraduates in 200 level. The blended assessment focused on face-to-face, Google Classroom, online assignment; PPT, and CBT. Multiple-choice items were used and psychometric properties such as content validity and internal consistency of reliability were affirmed. A one-way ANOVA was employed in hypothesis tested and the finding revealed that Google Classroom has preference over other assessment techniques. It was concluded that blended assessment is an effective assessment tool especially in large class. It was recommended among others that Nigerian universities need to implement blended assessment, which necessitates a reliable, affordable Wi-Fi network for effective internet access and digital interaction.
Copyright (c) 2024 Mohammed Jimoh Idris (Author) https://creativecommons.org/licenses/by-nc/4.0
Harnessing IRT and CAT for Next-Gen Educational Assessment in the Fifth Industrial Revolution
The Fifth Industrial Revolution (5IR) emphasises the harmonious collaboration between technology and human-centred approaches, revolutionising educational assessment. Traditional standardised tests often lack adaptability, leading to inefficiencies and biases. This study examines the effectiveness of Item Response Theory (IRT) and Computer Adaptive Testing (CAT) in optimising assessment processes by improving efficiency, precision, and reliability. Despite growing interest in adaptive testing, research on its large-scale applicability in education remains limited, highlighting a critical gap this study addresses. A simulation-based quantitative methodology was employed, utilising Monte Carlo techniques to generate 1,000 examinee responses modelled through a four-parameter logistic (4PL) IRT model. Two test conditions—fixed-length CAT and variable-length CAT—were implemented to compare their effectiveness. Item selection followed the Maximum Fisher Information (MFI) criterion, while Bayesian Maximum A Posteriori (MAP) was used for ability estimation. The results reveal that variable-length CAT significantly reduces test length by approximately 30% while maintaining high measurement precision. Adaptive testing demonstrated lower estimation errors and higher reliability than fixed-length assessments, confirming its effectiveness in modern educational evaluation. Additionally, item parameter analysis provided insights into test design optimisation. These results underscore the advantages of integrating CAT in large-scale assessments, particularly in enhancing fairness, personalisation, and engagement. The study concludes that CAT is a viable alternative to traditional testing methods, aligning with the 5IR’s emphasis on technological and human synergy in education. Future research should explore AI-driven CAT enhancements to further refine assessment accuracy and accessibility.
Copyright (c) 2023 Musa Adekunle Ayanwale (Author) https://creativecommons.org/licenses/by-nc/4.0
Comparative Psychometric Properties of Electronically-Tested and Conventionally-Tested Items of a Selected University Course in Nigeria
The study assessed the comparative psychometric properties of electronically and conventionally tested items of a selected university course in Nigeria using classical test theory and item response theory models. These were with a view to providing information on the overall quality of electronic mode of testing (E-Testing). The results showed that the electronic mode of testing was moderately reliable at KR20 value of 0.73 with an acceptable level of content validity. Also, the conventional mode of item testing was equally reliable with KR value of 0.70. This indicated that the two modes of testing were moderately reliable with a slight difference of 0.03. Item analysis of the testing modes showed that both theories produced similar results on the difficulty and discrimination strengths of the items. Comparing assessment modes, result revealed that they were significantly equivalent and homogenous at Z2 = 0.75. The SDs of 4.72 and 4.44 for the testing modes indicated that for C-testing, majority of the examinees scored between 15 and 25 marks while for E-testing some of the examinees scored between 14 and 23 marks. Item analysis showed that the models harmoniously supported the acceptability and otherwise of 17 and 18 items for the testing modes respectively in terms of the difficulty and discrimination strengths of the items. In conclusion, the psychometric properties of the assessment modes were comparable in terms of relationship, reliability, difficulty, and discrimination strengths of the items with E-Testing being more effective in terms of the reliability of test mode effects.
Copyright (c) 2023 Olumide Dorcas Olagundoye (Author) https://creativecommons.org/licenses/by-nc/4.0
Automated Essay Grading Software Sustainability in Assessment: A Critical Review for Quality Feedback and Stakeholders Involvement
This paper explores critical review of literatures on automated essay grading software and system development procedure through the nomenclature of technology in assessment. Various techniques and methodology used in essay grading software were identified as well as different software that are valid and reliable in scoring both short and extended essay test items which various stakeholders can leverage on for cost effectiveness, scoring consistency, objectivity, timely result delivery, and quick feedback. Software development stages that are required in the developing automated scoring system are discussed. The state of heart as regard the AES software that require training of manually marked essays and those that does not require training are embedded in this review with various advantages automated essay scoring exhibits over human scoring and its criticism. The evaluation matrices for validating automated essay grading system with human raters were also identified. This reviewed study conclude that with the development in artificial intelligence a reliable and valid assessment in scoring of short-answer and extended essays is viable and realisable with prompt feedback, reduced cost and time wastage and thereby promote objectivity and fairness in scoring to learners that human expert scoring may not achieve. Finally, it was recommended from this review that more automated essay grading software that does not require training with manually marked essay and able to marked different subjects need to be developed and explored.
Copyright (c) 2023 Damilola D. Olaoye (Author) https://creativecommons.org/licenses/by-nc/4.0
Item Calibration for Computer Adaptive Mental Well-Being Scale for a non-illness Intervention using Graded Rating Model
Mental health is conceptualized as a state of well-being in which the individual realizes his or her abilities, can cope with the normal stresses of life, can work productively and fruitfully, and can contribute to his or her community. The purpose of the research was to calibrate an optimal item bank for a Mental Wellbeing Computer Adaptive Test (MWB-CAT) for use by university undergraduates in Nigeria which informed the study sample. The instrument for the study would be a Likert-scaled questionnaire with items based on indices of mental well-being. The scale would be face and content validated by medical, sociology and educational psychological experts while a trial test was carried out to determine the reliability of the test items. The scale parameters were analysed using Graded Rating Scale Model (GRM) for polytomously scored items and deployed through the Xcalibre 4.2 programme. This study yielded a calibrated item bank of 173 items with an alpha value of 0.8469, mean, and standard deviation of 0.0344(0.8658). This study is germane to the attainment of SDG goal 3 of ensuring healthy lives and promoting well-being for all.
Copyright (c) 2023 Jumoke I. Oladele (Author) https://creativecommons.org/licenses/by-nc/4.0
Examining item compromise: an introduction to deterministic gated item response theory model (DGIRT)
The calibration of item parameters (difficulty, discrimination and guessing parameters) estimate may only consider the true and not the cheating abilities of examinees. In an effort to detect the occurrence of test cheating due to compromise in multiple-Choice items, the Deterministic Gated Item Response Theory Model was developed to provide information about cheating effectiveness of examinees, measure the extent of item fit for the compromised items, assess the sensitivity and ascertain the specificity to detect cheating due to item compromise. Hence, the model was meant to provide information on the extent of which the item response theory psychometric estimates are sensitive to item compromise when cheating occurs in large-scale examinations. This paper examine the concepts of cheating, item compromise and provides a brief overview of the Deterministic Gated Item Response Theory Model (DGIRT). It was recommended among others that Psychometricians should consider the validation of Deterministic Gated IRT model and other new IRT models that will account for the cheating ability of examinee unlike the “normal” IRT model that produces the probability of an item response for varying values of θ (ability).
deterministic gated item response theory model (DGIRT)
Copyright (c) 2023 Yusuf Olayinka Shogbesan (Author) https://creativecommons.org/licenses/by-nc/4.0
Application of Generalizability Theory in Estimating Variance Components in Senior Secondary School Certificate Examination Physics Essay Questions
This study applied Generalizability Theory to estimate variance components in Senior Secondary School Certificate Examination (SSSCE) Physics essay questions using a two-facet fully crossed G-study and D-study design. The population comprised 1,767 Senior Secondary Three (SS3) students in Obio/Akpor, Rivers State. A multistage sampling technique selected 10 out of 25 schools, and 150 students were sampled using the Taro Yamane formula. The 2019 WASSCE Physics essay questions, rated on a 10-point marking scheme, were administered. Data analysis using SPSS under the Variance Components MINQUE method revealed that students contributed the most to measurement error (∂²si = 1.030), followed by items (∂²i = 0.851) and raters (∂²r = 0.016). Interaction effects were highest for student-item (∂²si = 2.957), while student-item-rater (∂²sir = -2.868) was negligible. Reliability estimates indicated a universe score variance of 1.030, relative error variance of 2.981, absolute error variance of 4.878, a G-study coefficient of 0.26, and an index of dependability of 0.17, suggesting question difficulty. The findings highlight inconsistencies in student responses across items, which affect reliability. It is recommended that the West African Examinations Council (WAEC) establish a psychometric unit to validate essay questions, ensuring higher reliability and dependability before administration.
Copyright (c) 2022 Blessing Iheanyichukwu Nwuchegbu (Author) https://creativecommons.org/licenses/by-nc/4.0
Detection of Differential Item Functioning Magnitude in Psychological Measurement with Missing Data
The paper investigated the effectiveness of missing data methods in detecting differential item functioning magnitude in polytomous scored non-cognitive items with a view to determining different levels of magnitude existing in non-cognitive items and the difference in the ability of missing data methods to detect DIF magnitude. Using the sample of 1,500 senior secondary school students, drawn through multistage sampling technique from Osun State, data were collected with the Achievement Motivation Inventory (AMI). The result showed that with Full Information Maximum Likelihood (FIML) 81.3% possess small DIF magnitude, while 4 (12.5%) items possess moderate DIF magnitude, while high DIF magnitude occurs in 2(6.3%) items, and 24 (75.0%) were categorised as having small DIF magnitude, 2 (6.3%) with moderate DIF magnitude, while 6 (18.8%) was classified as having high magnitude of DIF using Multiple Imputation (MI). The result further revealed that item effect size (magnitude) across different methods did not distinctly differ from one another (X2 = 0.16, df = 1, p > 0.05). The study concluded that the two missing data methods were effective in detecting magnitudes of DIF present in polytomous scored non-cognitive items, and the differences that exist in the abilities of these missing data methods is not statistically significant.
Copyright (c) 2022 https://creativecommons.org/licenses/by-nc/4.0
Testwiseness, Digital Accessibility and Attitudinal Disposition to Computer-Assisted Testing among Undergraduate Students in North-West, Nigeria
The purpose this study was to determine whether undergraduate students’ testwiseness and access to digital devices are key factors critical to enhancing their attitude towards Computer-Assisted Testing (CAT) in some selected Universities in the North-west geo-political zone of Nigeria. It also examined if a significant difference existed in undergraduate students’ attitude to CAT in Universities in the North-west Nigeria. These were with a view to improving the CAT outcome directed towards certifying the quality in education by the Nigerian universities. The study adopted a descriptive survey research design. The sample consisted of an intact class of 5,880 Part I undergraduate students selected from federal and state-owned Universities in the North-west. A self-developed instrument tagged: “Undergraduate’s Testwiseness Digital Accessibility and Attitude Inventory (UTDAAI)”, was used to collect data. Data were analysed using Stepwise Multiple Regression and independent t-test statistics. The results revealed that testwiseness and digital accessibility are not significantly combined to influence undergraduate students’ attitude to CAT in Universities in the North-west Nigeria. However, there existed a positive significant relationship between testwiseness and digital accessibility. Further, there was no significant sex difference in undergraduate students’ attitudinal disposition to CAT in Universities in the North-west Nigeria. The study concluded that testwiseness was not a most potent predictor of students’ attitude to CAT irrespective of their access to digital devices in the University. It was recommended that Universities’ Management should invest considerable resources in developing rapid response digital intervention initiatives to support the learning, teaching and assessment transition for undergraduate students in the country
Copyright (c) 2022 Abdul-Wahab Ibrahim (Author) https://creativecommons.org/licenses/by-nc/4.0
Assessment of Public Primary School Teachers Computer Literacy and Usage in Teaching and Learning
Education has become more involved in information and computer technology (ICT) because of rapid and expanding technical breakthroughs worldwide. The demand for computer literate teachers has increased in the modern world, and this demand can be met by incorporating ICTs into education. This study was supported by the diffusion of innovations theory, intended to determine teachers' levels of computer literacy and the use of ICTs in teaching and learning. This study examines teachers’ level of computer literacy and use in teaching and learning at public primary schools in Lagos Mainland Local Education District, Lagos State, Nigeria. The study adopted a mixed-methods research approach (qualitative and quantitative methods), with a suitable sample of eighty (80) public primary school subjects’ teachers. Data was collected via an open-ended questionnaire. The quantitative data was statistically evaluated, while the qualitative data was content analysed. The study discovered that ICTs were not used in teaching and learning for a variety of reasons. The study concluded that, in modern-day life, some public primary schools were far from improving teaching and learning by integrating ICTs. The study recommended that the government endeavor to sponsor teachers to workshops and conferences, particularly computer workshops, and offer computer products to schools so that teachers can easily access computers when needed.
Copyright (c) 2022 Shakirat Bola Ogunbayo (Author) https://creativecommons.org/licenses/by-nc/4.0
A Transformer-Based Cognitive Classification Framework for Integrating Bloom’s Taxonomy into Adaptive Testing Systems
This study proposes a transformer-based machine learning framework for automated classification of test items according to Bloom’s taxonomy of cognitive processes, aiming to enhance cognitive alignment in adaptive testing systems. Manual classification of assessment items is often time-intensive and susceptible to subjectivity, posing challenges for large-scale and real-time adaptive testing environments. To address this limitation, the study employs Natural Language Processing techniques, specifically fine-tuning a BERT-based model, to classify Economics test items into six cognitive levels. A balanced dataset comprising 3,000 expert-annotated items (Cohen’s κ = 0.87) was used, with stratified training, validation, and testing procedures to ensure robustness. Model performance was evaluated using accuracy, precision, recall, and F1-score, alongside stratified 5-fold cross-validation and comparative analysis with Logistic Regression and Support Vector Machine baselines. The proposed model achieved an accuracy of 97.7% on the held-out test set and a cross-validated mean accuracy of 96.7% (SD = 0.5), substantially outperforming baseline models. While classification performance was consistently high across most categories, relatively lower recall for the “Understand” level suggests inherent semantic overlap between adjacent cognitive processes. The findings demonstrate the potential of transformer-based models to capture nuanced cognitive demand in assessment items. Importantly, the study conceptualises this classification framework as a complementary layer to Item Response Theory-based adaptive testing systems, enabling item selection that accounts for both psychometric properties and cognitive complexity. The study contributes a scalable approach to improving the consistency and pedagogical alignment of item classification, while highlighting the need for further validation using diverse and imbalanced datasets and for integration into operational adaptive testing environments.
Copyright (c) 2025 ISAAC IFINJU, Mayowa O OGUNJIMI, Timothy Obasuyi OGHOGHO (Author) https://creativecommons.org/licenses/by-nc/4.0