TY - JOUR
T1 - TCLPI
T2 - Machine Learning-Driven Framework for Hybrid Learning Mode Identification
AU - Verma, Chaman
AU - Illes, Zoltan
AU - Kumar, Deepak
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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.
AB - 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.
KW - ATL
KW - classification
KW - hybrid learning
KW - LPI
KW - Prediction
KW - SHAP
KW - student
KW - TCLPI
UR - http://www.scopus.com/inward/record.url?scp=85199107633&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3428332
DO - 10.1109/ACCESS.2024.3428332
M3 - Article
AN - SCOPUS:85199107633
SN - 2169-3536
VL - 12
SP - 98029
EP - 98045
JO - IEEE Access
JF - IEEE Access
ER -