Submissions

Author Guidelines

Authors are invited to make a submission to this journal. All submissions will be assessed by an editor to determine whether they meet the aims and scope of this journal. Those considered to be a good fit will be sent for peer review before determining whether they will be accepted or rejected.

Before making a submission, authors are responsible for obtaining permission to publish any material included with the submission, such as photos, documents and datasets. All authors identified on the submission must consent to be identified as an author. Where appropriate, research should be approved by an appropriate ethics committee in accordance with the legal requirements of the study's country.

Submissions must be made using the JoCATiA Template for Manuscript Preparation, which can be accessed HERE. Also, it is required that authors prepare and submit a cover letter using the template, which can be accessed HERE.

Before submitting, please ensure that the study design and research argument are structured and articulated properly. The title should be concise, and the abstract should be able to stand on its own. This will increase the likelihood of reviewers agreeing to review the paper. When you're satisfied that your submission meets this standard, please follow the checklist below to prepare your submission.

Any of the managing editors may desk reject a submission if the provided journal template is NOT used or if it does not meet minimum standards of quality.

Submission Preparation Checklist

As part of the submission process, authors are required to check off their submission's compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines.

The submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
The submission file is in OpenOffice, Microsoft Word, or RTF document file format.
Where available, URLs for the references have been provided.
The text is single-spaced; uses a 12-point font; employs italics, rather than underlining (except with URL addresses); and all illustrations, figures, and tables are placed within the text at the appropriate points, rather than at the end.
The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines.

Computer Adaptive Testing Research

Computer Adaptive Research

This section is dedicated to studies that advance the theory, design, implementation, and evaluation of computer adaptive testing systems. Manuscripts submitted under this category are expected to demonstrate strong grounding in modern psychometric frameworks such as Item Response Theory and the Rasch Model, which underpin adaptive test construction and item calibration. Research in this section typically addresses adaptive algorithm development, item selection strategies, exposure control mechanisms, and stopping rules, as well as the optimisation of test efficiency and measurement precision. Contributions may also include simulation studies, empirical validations, and comparative analyses between adaptive and fixed-form testing. Given the journal's African context, emphasis is placed on scalable, resource-sensitive CAT solutions that can be implemented across diverse educational environments, including low-resource settings. Reviewers evaluating manuscripts in this section are expected to critically assess the robustness of modelling approaches, the quality of item banks, and the validity of inferences drawn from adaptive testing systems.

Other Assessment Approaches

Other Assessment Approaches,

This section broadens 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

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.

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