Predictive Student Modelling in an Online Reading Platform

Abstract

Use of technology-enhanced education and online learning systems has become more popular, especially after COVID-19. These systems capture a rich array of data as students interact with them. Predicting student performance is an essential part of technology-enhanced education systems to enable the generation of hints and provide recommendations to students. Typically, this is done through use of data on student interactions with questions without utilizing important data on the temporal ordering of students’ other interaction behavior, (e.g., reading, video watching). In this paper, we hypothesize that to predict students’ question performance, it is necessary to (i) consider other learning activities beyond question-answering and (ii) understand how these activities are related to question-solving behavior. We collected middle school physical science students’ data within a K12 reading platform, Actively Learn. This platform provides reading-support to students and collects trace data on their use of the system. We propose a transformer-based model to predict students' question scores utilizing question interaction and reading-related behaviors. Our findings show that integrating question attempts and reading-related behaviors results in better predictive power compared to using only question attempt features. The interpretable visualization of the transformer’s attention can be helpful for teachers to make tailored interventions in students’ learning.