Behavior Pattern and Compiled Information Based Performance Prediction in MOOCs

TL;DR

This study investigates students' assignment behaviors and compiled data in MOOCs to predict performance, achieving around 70% accuracy, and highlights differences in behavior patterns between passing and failing students.

Contribution

It introduces a novel approach focusing on assignment accomplishment behaviors and compiled information for performance prediction in MOOCs.

Findings

Students who failed exhibited clear sequence patterns.

Passing students showed no obvious sequence patterns.

Prediction accuracy reached approximately 70%.

Abstract

With the development of MOOCs massive open online courses, increasingly more subjects can be studied online. Researchers currently show growing interest in the field of MOOCs, including dropout prediction, cheating detection and achievement prediction. Previous studies on achievement prediction mainly focused on students' video and forum behaviors, and few researchers have considered how well students perform their assignments. In this paper, we choose a C programming course as the experimental subject, which involved 1528 students. This paper mainly focuses on the students' accomplishment behaviors in programming assignments and compiled information from programming assignments. In this paper, feature sequences are extracted from the logs according to submission times, submission order and plagiarism. The experimental results show that the students who did not pass the exam had obvious sequence patterns but that the students who passed the test did not have an obvious sequence pattern. Then, we extract 23 features from the compiled information of students' programming assignments and select the most distinguishing features to predict the students' performances. The experimental results show that we can obtain an accuracy rate of 0.7049 for predicting students' performances.