Video viewing is an important component of online learning, yet little is known about what information about learning outcomes can be derived from students’ video control actions. We investigate the extent to which information on student learning is contained in their video-watching clickstreams (e.g. pausing, playing) immediately after watching a video. We analyzed data from 10,492 students who used an online learning platform for their Algebra 1 course. Our experiments encode students’ video-control clickstreams into sequences in several ways (e.g. aggregate actions, shuffle actions, and merge action types), and train Long Short-term Memory (LSTM) neural network models to predict after-video quiz scores (N = 32,482) from the sequences in a student-independent fashion. The results suggest that the action sequences contain a limited amount of information about student learning (r = 0.108 between model-predicted- and actual- quiz scores), with most of the information in simple counts of actions (r = 0.081) rather than the temporal ordering of actions. Combining information from video action sequences and traditional knowledge estimates from item-response theory (IRT) outperformed (r = 0.224) either approach independently. Implications for student modeling and adaptive learning support for viewing lecture videos are discussed.

In interactive e-learning environments such as Intelligent Tutoring Systems, there are pedagogical decisions to make at two main levels of granularity: whole problems and single steps. In recent years, there is growing interest in applying data-driven techniques for adaptive decision making that can dynamically tailor students' learning experiences. Most existing data-driven approaches, however, treat these pedagogical decisions equally, or independently, disregarding the long-term impact that tutor decisions may have across these two levels of granularity. In this paper, we propose and apply an offline Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework to induce a hierarchical pedagogical policy that makes decisions at both problem and step levels. An empirical classroom study shows that the HRL policy is significantly more effective than a Deep Q-Network (DQN) induced policy and a random yet reasonable baseline policy.

Motivated by the recent advances of reinforcement learning and the traditional grounded Self Determination Theory (SDT), we explored the impact of hierarchical reinforcement learning (HRL) induced pedagogical policies and data-driven explanations of the HRL-induced policies on student experience in an Intelligent Tutoring System (ITS). We explored their impacts first independently and then jointly. Overall our results showed that 1) the HRL induced policies could significantly improve students' learning performance, and 2) explaining the tutor's decisions to students through data-driven explanations could improve the student-system interaction in terms of students' engagement and autonomy.

Two factors characterize a good feature selection algorithm: its accuracy and stability. This paper aims at introducing a new approach to stable feature selection algorithms. The innovation of this paper centers on a class of stable feature selection algorithms called feature weighting as regularized energy-based learning (FREL). Stability properties of FREL using L1 or L2 regularization are investigated. In addition, as a commonly adopted implementation strategy for enhanced stability, an ensemble FREL is proposed. A stability bound for the ensemble FREL is also presented. Our experiments using open source real microarray data, which are challenging high dimensionality small sample size problems demonstrate that our proposed ensemble FREL is not only stable but also achieves better or comparable accuracy than some other popular stable feature weighting methods.