Adaptive Study Planning Using Machine Learning for Personalised Learning
Sumit Singha Chowdhury
Department of MCA, Acharya Institute of Technology, Bengaluru, India.
Abhijan S Kashyap *
Department of MCA, Acharya Institute of Technology, Bengaluru, India.
*Author to whom correspondence should be addressed.
Abstract
Personalised learning, which refers to an educational paradigm that allows individual students to dictate content and schedule of their learning, has begun to assume dominance as a new level of education transformation, and has been shown to promote better retention (by about 20%) when plans reflect the learner's states. This study delineates the implementation of a machine learning-based approach toward personalised study plan generation and personalised scheduling that takes into account individual lifestyle factors and attention profiles. This experimental study was carried out in the Department of MCA, Acharya Institute of Technology, Bengaluru, India (January 2024 - August 2024) and applied Random Forest regression (RF) and KMeans cluster analysis (k-means) to a dataset of student-specific schedules based upon eight lifestyle variables: sleep/hours, screen/hours, stress level, mental health rating, frequency of exercise, motivation level, time management score and daily study hours. The experimental study underwent a preprocessing stage; Random Forest exhibited strong performance (MAE of 3.46 and R-squared of 0.97) and identified importance ranking predictors of attention; participants' sleep and stress have been identified as the highest ranked predictors of attention in Random Forest. K-Means managed to classify learners into Low, Medium, and High focus profiles (k=3 and a silhouette score of 0.53 Which is moderately acceptable), which led to scheduled Pomodoro sessions being allocated on study plans. The Pomodoro sessions were allocated during study hours as the Largest Remainder Method. The system development utilised Flask and SQLite and was able to create a study plan in less than two seconds; system adaptations positively impacted feedback, enabling learners to complete 15% more of their study plans. The results presented here indicate that combining variables related to lifestyle will permit more personalised and effective study plans versus traditional methods. The main objective of the system presented was to have a scalable, responsive, and privacy-respecting solution to increase student focus, decrease procrastination behaviours, and improve learning outcomes, while also providing institutions with systems that can assist in adaptive, data-informed educational practices in the future.
Keywords: Machine learning, personalised learning, study plan generation, adaptive learning, random forest, kmeans clustering