Expert Data Augmentation in Imitation Learning (Student Abstract)

Imitation of Expert Judgement

Prediction of Student Learning Styles using Data Mining Techniques

This paper focuses on the prediction of student learning styles using data mining techniques within their institutions. This prediction was aimed at finding out how different learning styles are achieved within learning environments which are specifically influenced by already existing factors. These learning styles, have been affected by different factors that are mainly engraved and found wit...

Visual Data Augmentation through Learning

The rapid progress in machine learning methods has been empowered by i) huge datasets that have been collected and annotated, ii) improved engineering (e.g. data pre-processing/normalization). The existing datasets typically include several million samples, which constitutes their extension a colossal task. In addition, the state-ofthe-art data-driven methods demand a vast amount of data, hence...

Towards Understanding Expert Coding of Student Disengagement in Online Learning

Gaming the system, a behavior where students disengage from a learning environment and attempt to succeed by exploiting properties of the system, has been shown to be associated with lower learning. Machine learned and knowledge engineered models have been created to identify gaming behaviors, but few efforts have been made to precisely identify how experts code gaming behaviors. In this paper,...

Data-Driven Ghosting using Deep Imitation Learning

Current state-of-the-art sports statistics compare players and teams to league average performance. For example, metrics such as “Wins-above-Replacement” (WAR) in baseball [1], “Expected Point Value” (EPV) in basketball [2] and “Expected Goal Value” (EGV) in soccer [3] and hockey [4] are now commonplace in performance analysis. Such measures allow us to answer the question “how does this player...