Using learning analytics to develop early-warning system for at-risk students

Abstract

Abstract In the current study interaction data of students in an online learning setting was used to research whether the academic performance of students at the end of term could be predicted in the earlier weeks. The study was carried out with 76 second-year university students registered in a Computer Hardware course. The study aimed to answer two principle questions: which algorithms and features best predict the end of term academic performance of students by comparing different classification algorithms and pre-processing techniques and whether or not academic performance can be predicted in the earlier weeks using these features and the selected algorithm. The results of the study indicated that the kNN algorithm accurately predicted unsuccessful students at the end of term with a rate of 89%. When findings were examined regarding the analysis of data obtained in weeks 3, 6, 9, 12, and 14 to predict whether the end-of-term academic performance of students could be predicted in the earlier weeks, it was observed that students who were unsuccessful at the end of term could be predicted with a rate of 74% in as short as 3 weeks’ time. The findings obtained from this study are important for the determination of features for early warning systems that can be developed for online learning systems and as indicators of student success. At the same time, it will aid researchers in the selection of algorithms and pre-processing techniques in the analysis of educational data.

Keywords

• Academic performance prediction
• Early-warning systems
• At-risk students
• Online learning
• Educational data mining
• Learning analytics