Analysis and Prediction of the Factors Influencing Students’ Grades Based on Their Learning Behaviours in MOOCs

EasyChair Preprint 8196

Abstract

The outbreak of COVID-19 brought new challenges to learning and teaching, and MOOCs (massive open online courses), as online distance learning platforms, provide new opportunities for teaching and learning activities. However, student learning efficiency is difficult to ensure in distance learning. Researchers have studied the relationship between students’ grades and behaviours such as forum participation and video viewing; however, less research has been performed on students’ submission behaviours. In this paper, we investigate the influence of learning attitudes reflected by students’ submission behaviour and the trend in attitude change on grades. First, by studying students’ submission behaviours, we identify new features that affect students’ grades, such as students’ resubmission behaviours. Second, we define positive attitudinal trends that students possess through student behaviour studies: more adequate code, more page viewing actions, and more aggressive submission details performance. Finally, we use the selected features to predict the students’ performance. In the experiment, we predict student performance with an accuracy of 86.48%. This study will help teachers understand students’ attitudes based on student behaviours and identify students who are struggling academically.

Keyphrases: Massive Open Online Course, learning behaviour, learning initiative, performance prediction

@booklet{EasyChair:8196,
  author    = {Ziyi Zhao and Fengxi Kang and Jing Wang and Binhui Chen and Mingxuan Yang and Shaojie Qu},
  title     = {Analysis and Prediction of the Factors Influencing Students’ Grades Based on Their Learning Behaviours in MOOCs},
  howpublished = {EasyChair Preprint 8196},
  year      = {EasyChair, 2022}}