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:
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 for Computer Adaptive Testing in Africa aims at advancing educational assessment technology across the continent.
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The articles in this journal issue emanated from the 2025 Conference of the Association of Computer Adaptive Testing in Africa (ACATA) with the theme 'Transforming Educational Assessments with Adaptive Technological Innovations', held from October 20 to 23 at the University of Johannesburg, South Africa.
This was the second biennial meeting of scholars, practitioners, and policymakers who want to improve technology-driven assessment across the continent. Convened as a hybrid international forum, the conference brought together interdisciplinary perspectives to interrogate emerging trends in computer adaptive testing (CAT) and their implications for educational measurement, equity, and innovation in Africa.
Set against the backdrop of rapid digital transformation in education, the conference emphasised the growing role of artificial intelligence, machine learning, data analytics, and related technologies in reshaping assessment systems. Contributions addressed how these innovations enable more personalised, efficient, and valid measurement of learning outcomes while also raising critical questions about fairness, accessibility, and contextual relevance within African education systems.
This issue curates a selection of rigorously peer-reviewed papers presented at the conference, reflecting cutting-edge empirical, methodological, and theoretical advances in CAT and allied fields. The included studies span diverse themes,, including adaptive test design, psychometric modelling, automated item generation, learning analytics, and the integration of emerging technologies into large-scale and classroom-based assessment. Collectively, they demonstrate the growing body of research supporting adaptive assessment practices in Africa and beyond.
Importantly, the contributions also foreground the conference’s central commitment to leveraging innovation for inclusive and equitable education. Authors critically examine how adaptive testing can be deployed to address longstanding disparities in access, quality, and outcomes, ensuring that technological progress translates into meaningful educational development across diverse contexts.
By bringing together these works, this special issue not only documents the scholarly output of the 2025 ACATA Conference but also advances ongoing dialogue on the future of assessment in the Fourth Industrial Revolution. It serves as a valuable resource for researchers, educators, policymakers, and assessment professionals seeking to harness adaptive technologies to strengthen educational systems in Africa and globally.
An official journal of the Association of Computer Adaptive Testing in Africa (ACATA)
E-ISSN: 2790-4407; P-ISSN: 2790-4393
