Study regarding the influence of a student’s personality and an LMS usage profile on learning performance using machine learning techniques

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 54; no. 8; pp. 6175 - 6197
• Main Authors: Rico-Juan, Juan Ramón, Cachero, Cristina, Macià, Hermenegilda
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2024; Springer Nature B.V
• Subjects: Academic achievement; Artificial Intelligence; Colleges & universities; Computer Science; Education; Higher education; Machine learning; Machines; Manufacturing; Mechanical Engineering; Personality; Processes; Students; Teachers; Student dropout; Observational study; Machine Learning (ML); eXplainable Artificial Intelligence (XAI); Academic performance
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-024-05483-1

Academic performance (AP) is crucial for lifelong success. Unfortunately, many students fail to meet expected academic benchmarks, leading to altered career paths or university dropouts. This issue is particularly pronounced in the early stages of higher education, highlighting the need for the instructors of these foundational courses to have access to simple yet effective tools for the early identification of students at high risk of academic failure. In this study, we propose a streamlined conceptual model inspired by the Model of Human Behavior (MHB) to which we have incorporated two dimensions: capacity and willingness. These dimensions are assessed through the definition of three variables: Prior Academic Performance (PAP), Personality and Academic Engagement, whose measurements can easily be obtained by the instructors. Furthermore, we outline a Machine Learning (ML) process that higher education instructors can use to create their own tailored models in order to predict AP and identify risk groups with high levels of transparency and interpretability. The application of our approach to a sample of 322 Spanish undergraduates studying two mathematical subjects at a Spanish university demonstrates its potential to detect failure early in the semester with a precision that is comparable with that of more complex models found in literature. Our tailored model identified that capacity was the primary predictor of AP, with a gain-to-baseline improvement of 21%, and the willingness variables increasing this to 27%. This approach is consistent over time. Implications for instructors are discussed and an open prediction and analysis tool is developed.