About the Journal
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.
