Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Application of cluster analysis to identify different reader groups through their engagement with a digital reading supplement

Published in Computers & Education, 2024

This paper advances literacy field by introducing a novel approach to analyse, structure and classify information from raw log files that sourced from digital reading apps. The key aim of the digital app is to enhance young learners’ reading skills and provide individualised reccomendations. Our analyses provided an innovative analytical and Cluster Analysis framework to guide researchers in navigating this novel and complex dataset to achieve this aim.

Recommended citation: Ma, Y., Cain, K., & Ushakova, A. (2024). Application of cluster analysis to identify different reader groups through their engagement with a digital reading supplement. Computers & Education, 214, 105025. https://doi.org/10.1016/j.compedu.2024.105025
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Submitted & In Preparation

A statistical framework for dynamic cognitive diagnosis in digital learning environments

Journal of the Royal Statistical Society: Series A (under review), 2025

This study advances cognitive diagnostic assessments by proposing a novel statistical framework that integrates Cognitive Diagnosis Models with longitudinal data, including assessments of multiple skill sets. Our paper enhances the accuracy of attribute mastery evaluations and the assessment of covariate impacts on learning transitions. The applicability of the method is demonstrated through real-world application.

Recommended citation: Ma, Y., Cain, K., Ushakova, A., & Wallin, G. (2024). A statistical framework for dynamic cognitive diagnosis in digital learning environments. Manuscript under peer review. Preregistration: https://osf.io/nqkub
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Measuring learners’ proficiency: Insights into adaptive digital educational environments

In submission, 2025

This paper introduces a comprehensive analytical approach based on Item Reponse Theory to assessing performance of reading skills in adaptive digital learning environments, capturing diverse learning trajectories and generating reliable scores that reflect each learner’s unique, mastery-paced path through multiple attempts.

Recommended citation: Ma, Y., Cain, K., & Ushakova, A. (2024). Measuring learners’ proficiency: Insights into adaptive digital educational environments. Manuscript submitted for publication. Preregistration: https://osf.io/gu73j
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Yawen Ma