# Predicting Performance on MOOC Assessments using Multi-Regression Models

arXiv: https://arxiv.org/abs/1605.02269 · HTML: https://mlsanity.com/paper/1605.02269/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2016.

## Built on by (most cited first, 2 of 2)

- [Tri-Branch Convolutional Neural Networks for Top-k Focused Academic Performance Prediction](https://mlsanity.com/paper/2107.10424/) (2021)
- [Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation Learning](https://mlsanity.com/paper/2310.12281/) (2023)

## Abstract (arXiv)

The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a stu- dent may achieve on a given grade-related assessment based on information, considered as prior performance or prior ac- tivity in the course. We develop a personalized linear mul- tiple regression (PLMR) model to predict the grade for a student, prior to attempting the assessment activity. The developed model is real-time and tracks the participation of a student within a MOOC (via click-stream server logs) and predicts the performance of a student on the next as- sessment within the course offering. We perform a com- prehensive set of experiments on data obtained from three openEdX MOOCs via a Stanford University initiative. Our experimental results show the promise of the proposed ap- proach in comparison to baseline approaches and also helps in identification of key features that are associated with the study habits and learning behaviors of students.