Prediction of Student Performance in Abacus-Based Calculation Using Matrix Factorization
UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization Genoa Italy July, 2020(2020)
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.
MoreTranslated text
Key words
student performance,calculation,prediction,abacus-based
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined