ATL-BP: A Student Engagement Dataset and Model for Affect Transfer Learning for Behavior Prediction

Abstract

We propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS) by analyzing their faces and gestures. The ability to predict such outcomes enables tutoring systems to adjust interventions and ultimately yield improved student learning. We collected and released a labeled dataset of 2,749 problem-solving interaction samples of 54 students working with an intelligent online math tutor. Our transfer-learning challenge was then to design a representation in the source domain of images obtained from the Internet for facial expression analysis, and transfer this learned representation for human behavior prediction in the domain of webcam videos of students in a classroom environment. We developed a novel facial affect representation and a user-personalized training scheme that unlocks the potential of this representation. We designed several variants of a recurrent neural network that models the temporal structure of video sequences. Our final model, named ATL-BP for Affect Transfer Learning for Behavior Prediction, achieves a relative increase in the mean F-score of 50% over the state-of-the-art method on this new dataset. We also propose an additional set of annotations to predict students' engagement while solving a specific problem, and present models that can predict such engagement.

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

“…Yang et al[56] used students' homework data to predict students' course grades in Moodle through students' procrastination behavior, but the data used for student grade prediction was not considered comprehensively, and more student activity data could be used, such as student text data, student learning resources, and network access records, etc. Meanwhile, the existing methods still have some deficiencies in introducing deep learning, integrating student behavior text information, and intelligently identifying some unknown abnormal behaviors in the network[57][58][59]. Many scholars have analyzed the abnormal behavior prediction and early warning of students.…”