Prediction of Student Performance in Abacus-Based Calculation Using Matrix Factorization

Keita Tokuda, David Kaschub, Takuma Ota, Yasunobu Hashimoto, Naoya Fujiwara, Akihito Sudo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We conducted modeling of student learning status and tasks in abacus-based calculation by utilizing matrix factorization on student-generated learning data. The matrix consisted of performance scores on student-task pairs. We decomposed the raw matrix into two matrices, yielding the distributed representations of each student and each task. Prediction of student performance using those decomposed matrices achieved better results than baseline models that use the student biases and task biases. This suggests matrix factorization successfully extracted the interaction of multiple latent features of each task and each student's learning status in abacus-based calculation.

Original languageEnglish
Title of host publicationUMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages114-118
Number of pages5
ISBN (Electronic)9781450367110
DOIs
Publication statusPublished - 2020 Jul 14
Event28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020 - Genoa, Italy
Duration: 2020 Jul 142020 Jul 17

Publication series

NameUMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020
CountryItaly
CityGenoa
Period20/7/1420/7/17

Keywords

  • abacus
  • data accumulation in education
  • evaluation methodologies
  • performance prediction in education
  • stem education

ASJC Scopus subject areas

  • Software

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