Sequencing Educational Content Using Diversity Aware Bandits

One important function of e-learning systems is to sequence learning material for students. E-learning systems use data, such as demographics, past performance, preferences, skillset, etc. to construct an accurate model of each student so that the sequencing of educational content can be personalized. Some of these student features are �shallow� traits which sel- dom change (e.g. age, race, gender) while others are deep traits that are more volatile (e.g. performance, goals, in- terests). In this work, we explore how reasoning about this diversity of student features can enhance the sequencing of educational content in an e-learning environment. By mod- eling the sequencing process as a Reinforcement Learning (RL) problem, we introduce Diversity Aware Bandit for Se- quencing Educational Content (DABSEC), a novel contex- tual multi-armed bandit algorithm that leverages the dy- namics within user features to cluster similar users together when making sequencing recommendations