Development of Predictive Models for Students Learning Outcomes based on Spatial Learning Analytics

Abstract

Learning analytics is a data-driven methodology that provides instructors with important insights regarding student interactions with course materials, allowing them to make informed decisions about content delivery and structure. We investigated the use of spatial learning analytics in the context of online and remote education, with a focus on fulfilling the United Nations Sustainable Development Goal (SDG) 4 - guaranteeing inclusive and high-quality education for everyone. By combining data from Moodle logs and device usage and user activity metrics from Microsoft Teams, this study aimed to predict students quiz performance and enhance personalized learning interventions. The primary objectives were to create accurate predictive models for student learning outcomes and evaluate their effectiveness. Additionally, mapping tools were utilized such as Leaflet and ArcGIS to craft interactive maps, enriching the data-driven learning journey. Study employed supervised machine learning techniques, including Random Forest, Decision Trees, Support Vector Machine, LightGBM, K-Nearest Neighbors, Logistic Regression, and Neural Networks. These predictive models were trained and tested on the preprocessed dataset to predict quiz scores using binary classification method technique with threshold of 10. The predictive models' performance was evaluated using metrics such as precision, recall, F1 score, training time, and prediction time. Results indicate that the Support Vector Machine (SVM) model achieved the highest recall (100%) and F1 score (85.19%). Logistic Regression and Neural Networks also performed well, with Logistic Regression showing a recall of 95.57% and an F1 score of 83.84%, and Neural Networks exhibiting a recall of 94.38% and an F1 score of 83.56%. This study contributes to learning analytics by demonstrating the potential of spatial data in predicting and improving student outcomes, aligning with United Nations Sustainable Development Goal 4 for accessible and equitable education. Future research can refine these models by incorporating additional data sources and advanced machine learning techniques. Enhancing geographic insights and addressing ethical considerations in data usage will be crucial.