Enhancing Academic Success: Performance Early Prediction Using Machine Learning Algorithms

EasyChair Preprint no. 13395

Abstract

Emerging technologies, particularly artificial intelligence (AI) and machine learning (ML) algorithms, present valuable opportunities to analyse learning management system data (LMS) and considered as the corner stone of Learning analytics (LA). The aim is to analyse student performance during a course or a whole academic year. In particular, it identifies at risk students and enables educators to timely support this student’s category and can pro-vide clear guidance to improve teaching and learning strategies. This is why implementing an Early-Warning System is very crucial in this context to alert at risk student with weak performance, during first course sessions, to mitigate potential failures. The primary objective of this paper is to develop and implement an early-warning system that assists educators in identifying these students requiring attention and prompts them to be aware of their ac-ademic progress, thereby facilitating timely interventions to reduce the risk of failure. This study target web technologies course for the third-year engi-neering level in Esprit school of engineering, Tunisia. The course includes 4 assignments with a final evaluation exam. The experiments involve testing and comparing the performance of various classifiers, with a focus on Lo-gistic Regression (LR), Random Forest (RF), Naive Bayes (NB) and K-Nearest Neighbours (KNN). The classification process considers factors such as students' engagement in different activities over time, the scores obtained in these activities, class attendance, and final results. The ultimate goal is to early predict student performance by categorizing them into two groups: those requiring additional support (convocation) and those who achieve well, the initial weeks of the course.

Keyphrases: academic success, Early Prediction, learning analytics, Learning Management System, machine learning

@Booklet{EasyChair:13395,
  author = {Abderrazek Hachani and Maha Mallek and Yosra Jmal},
  title = {Enhancing Academic Success: Performance Early Prediction Using Machine Learning Algorithms},
  howpublished = {EasyChair Preprint no. 13395},

  year = {EasyChair, 2024}}