Predicting student performance based on Lecture Materials data using Neural Network Models

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


Student Performance Prediction is essential for learning analysis of the students' learning behavior to discovering at-risk students for the early invention to support students. This study transforms the students' reading behavior into a two-dimensional matrix input based on each lecture material's reading behavior. The matrix input will be updated by accumulating the value for each week for performance prediction week by week. The multilayer perceptron neural network is employed to receive the matrix input and give feedback as a student's criteria consist of at-risk or no-risk students. This study considers the accuracy of a model considering between on contents information and weekly information. We also investigate the switching of learning materials' order, the feature importance of the reading operation on an event stream, and the difference in reading behavior between at-risk and no-risk students. These can help the instructors for an early invention to support at-risk students.

Original languageEnglish
Pages (from-to)11-20
Number of pages10
JournalCEUR Workshop Proceedings
Publication statusPublished - 2022
Event4th Workshop on Predicting Performance Based on the Analysis of Reading Behavior, DC in LAK 2022 - Virtual, Online, United States
Duration: Mar 22 2022 → …

All Science Journal Classification (ASJC) codes

  • General Computer Science


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