Towards Breaking the Learning System Wall Using Multimodal Tutoring Transcriptions
Danielle R. Thomas, Marie Cynthia Abijuru Kamikazi, Ashish Gurung, Ishan Miglani, Shivang Gupta, Zachary Levonian, Conrad Borchers, Kenneth R. Koedinger
Abstract
Past research using log data has faced the "learning system wall," whereby few methods exist for generalizing models of student learning across platforms. Increasingly, online learning is captured by richer forms of data, including dialog and video, with new affordances. An example of this is remote tutoring programs, where human tutors support students who use learning systems while video conferencing. Toward better platform-general modeling of learning, we introduce an AI-driven multimodal transcription system that processes screen-recording videos into unified screenplay-style transcripts containing audio dialogue and annotated learning log actions. We describe a planned method for temporally aligning AI-generated multimodal transcripts with MATHia learning logs and for identifying and classifying student learning processes to align with MATHia logs. Lastly, we highlight challenges and potential solutions in capturing learning processes in one system, offering initial steps towards generalizing log data across diverse systems.