Predicting Student Engagement Levels in Language-Based AI Curriculum: A Hybrid BERT-MLP Model Approach

• In Event: Artificial Intelligence (AI) Literacy and Computational Thinking Education

Abstract

This study introduces a novel approach for predicting student engagement levels in a language-based AI curriculum. The curriculum was integrated into English Language Arts classrooms, in which 106 students from five classes participated five web-based machine learning and text mining modules for 2 weeks. Sentiment and categorical analyses, performed by a hybrid model of Bidirectional Encoder Representations from Transformers (BERT) and Multilayer Perceptron (MLP), were employed to predict students’ engagement levels. The input textual data and categorical data were extracted from the learning modules, resulting in a testing accuracy of 78.5%. This innovative engagement level identification approach provides an objective method for student engagement auto-prediction and paves the way for targeted interventions to optimize AI learning experiences.

Authors

• Shiyi Liu, Lehigh University

• Juan Zheng, Lehigh University

• Tingting Wang, Renmin University of China

• Zeda Xu, Carnegie Mellon University

• Jie Chao, The Concord Consortium

• Shiyan Jiang, North Carolina State University