TCLPI: Machine Learning-Driven Framework for Hybrid Learning Mode Identification

Abstract

Since the COVID-19 pandemic, teachers and students have started using online and hybrid learning in education. There might be several obstacles to adopting hybrid learning in theory classes or lab practice sessions. Based on student opinions, deciding what is appropriate for theoretical class and lab practice is challenging. We employed machine learning approaches to forecast the hybrid learning mode for theory classes and lab practices. We introduce a framework that utilizes machine learning to automate the identification of hybrid learning for Theory Class and Lab practice (TCLPI). Four machine learning models form the foundation of this framework: Random Forest (RDT), Support Vector Machine (SVN), Logistic Regression (LGR), and Extreme Gradient Boosting (XBT). In the context of Theory Class Identification (TCI), the SVN achieves a maximum test accuracy of 0.93, whereas the LGR achieves a minimum accuracy of 0.90. On the other hand, the Lab Practice Identification (LPI), XBT, RDT, and SVN achieved a test accuracy of 0.80. The outcome of trained algorithms is assessed using the Shapley Additive Explanation (SHAP), an explainable Artificial intelligence (AI) approach. This research found that student-teacher interaction decreased during lab practice, which is crucial. Internet disconnections, a lack of support during technological malfunctions, and the likelihood of cheating in exams without monitoring are also issues. We also found that students were accepting of hybrid learning for theory classes. Each model’s intrinsic feature relevance and SHAP values helped prove this. Research shows that hybrid learning works more for theory classes; it is less needed for lab practice for students.

Keywords

• ATL
• classification
• hybrid learning
• LPI
• student
• SHAP