A key to personalized online learning is presenting content at an appropriate difficulty level; content that is too difficult can cause frustration and content that is too easy may result in boredom. Appropriate content can improve students' engagement and learning outcome. In this research, we propose a computer vision enhanced problem selector (COVES), a deep learning model to select a personalized difficulty level for each student. A combination of visual information and traditional log data is used to predict student-problem interactions, which are then used to guide problem difficulty selection in real time. COVES was trained on a dataset of fifty-one sixth-grade students interacting with the online math tutor MathSpring. Once COVES was integrated into the tutor, its effectiveness was tested with twenty-two seventh-grade students in controlled experiments. Students who received problems at an appropriate difficulty level, based on real-time predictions of their performance, demonstrated improved engagement with the math tutor. Results indicate that COVES leads to higher mastery of math concepts, better timing, and higher scores, thus providing a positive learning experience for the participants.

In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS). By analyzing a student's face and gestures, our method predicts the outcome of a student answering a problem in an ITS from a video feed. Our work is motivated by the reasoning that the ability to predict such outcomes enables tutoring systems to adjust interventions, such as hints and encouragement, and to ultimately yield improved student learning. We collected a large labeled dataset of student interactions with an intelligent online math tutor consisting of 68 sessions, where 54 individual students solved 2,749 problems. We will release this dataset publicly upon publication of this paper. It will be available at https://www.cs.bu.edu/faculty/betke/research/learning/. Working with this dataset, our transfer-learning challenge was to design a representation in the source domain of pictures obtained “in the wild” for the task of facial expression analysis, and transferring this learned representation to the task of 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 of students solving math problems. Our final model, named ATL-BP for Affect Transfer Learning for Behavior Prediction, achieves a relative increase in mean F -score of 50 % over the state-of-the-art method on this new dataset.

This research seeks to improve learning by integrating intelligent coaching with peer-to-peer collaboration. We combine advanced technologies that support student freedom and exploration, allow for collaboration and peer tutoring, and provide task advice by modeling student work and behavior. We empirically evaluated both the effect of added collaborative features that enable peer-to-peer interaction and the potential to use this collaboration to improve the intelligent coaching features of the system. We found that collaboration enhances student work and that the coaching features provide opportunities to promote collaboration.