An artificial neural network for exploring the relationship between learning activities and students’ performance

This paper identifies the most significant learning factors impacting undergraduate academic performance using artificial neural networks (ANNs) and controlled student data collection. As higher education becomes increasingly common and important, finding the best ways to help students optimize their studies is vital. Questionnaires gathered data on student behaviors and achievement from five classes within a semester, constraining variability to compare learning activities directly. The questionnaire captured engagement, psychological factors, effort, course load, time management, and performance data. Statistical and exploratory analysis investigated the dataset. A multilayer perceptron model was developed, using backpropagation and cross-validation to optimize predictive accuracy. The model identified class attendance, sleep quality, and questioning during lectures as most correlated with high grades. Additional patterns emerged around research participation, motivation, cramming, and theoretical studying. This research demonstrates new techniques for associating detailed study behaviors with academic achievement through strictly controlled student data collection and the application of artificial neural networks for predictive modeling. The constrained variability in the dataset allows for isolating the impact from specific learning activities. The controlled student data and machine learning-driven predictive modeling provide information on the optimal grouping of student effort across engagement, health, and studying factors.