Interactive Video Tutoring Module

The online video-based tutoring industry is growing fast but there is a lack of interaction between student and tutor. Some survey studies suggest that interactive video-based tutoring like giving bookmarks, hints, nudges, and quizzes in between video lectures helps the student in improving concentration and learning. We propose an interactive video tutoring module which given a student profile and past behavior (in different videos), predict the time points where a student would pause a video (or bookmark it), speed up or return to a video after exercise, or a combination of these. This can be formulated as a bookmark recommendation system and a standard approach to solve this problem is Collaborative Filtering. We will use a baseline based on matrix factorization, which is a class of collaborative filtering algorithm. Later, we aim to experiment with more advanced techniques that may use deep learning.

Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Korbit Technologies

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Université de Montréal

Program: