Yawen Ma

Yawen Ma

Final-year PhD candidate in statistis at Lancaster University. Actively seeking postdoc and research fellow opportunities.

Posts by Collection

portfolio

publications

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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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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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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talks

Postgraduate Symposium

Published:

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

Amplify Knowledge Exchange

Published:

This talk was part of Lancaster University’s research groups quarterly knowledge exchange series with Amplify, our U.S.-based educational technology partner.

Postgraduate Symposium

Published:

Measuring learners’ proficiency: Insights into adaptive digital educational environments.

teaching

Maths and Stats Tutor (2022-2025)

Workshop/1-1s, Lancaster University, Faculty of Sciences and Technology/Faculty of Health and Medicine, 2022

  • Leading of the workshop in multiple statistics topics for undergraduate students across the university (field ranging from social sciences to mathematics), enhancing student stats skills.
  • Provision of personalized mentoring in one-to-one sessions for undergraduate and postgraduate students, addressing students’ specific challenges in various mathematical and statistical areas.
  • Feedback from students (average score = 4.5 or 5/5)