Lessons Learned from Development of a Software Tool to Support Academic Advising

We detail some lessons learned while designing and testing a decision-theoretic advising support tool for undergraduates at a large state university. Between 2009 and 2011 we conducted two surveys of over 500 students in multiple majors and colleges. These surveys asked students detailed questions about their preferences concerning course selection, advising, and career paths. We present data from this study which may be helpful for faculty and staff who advise undergraduate students. We find that advising support software tools can augment the student-advisor relationship, particularly in terms of course planning, but cannot and should not replace in-person advising.Comment: 5 Figures, revised version including more figures and cross-referencin

Learn to Interpret Atari Agents

Deep Reinforcement Learning (DeepRL) agents surpass human-level performances in a multitude of tasks. However, the direct mapping from states to actions makes it hard to interpret the rationale behind the decision making of agents. In contrast to previous a-posteriori methods of visualizing DeepRL policies, we propose an end-to-end trainable framework based on Rainbow, a representative Deep Q-Network (DQN) agent. Our method automatically learns important regions in the input domain, which enables characterizations of the decision making and interpretations for non-intuitive behaviors. Hence we name it Region Sensitive Rainbow (RS-Rainbow). RS-Rainbow utilizes a simple yet effective mechanism to incorporate visualization ability into the learning model, not only improving model interpretability, but leading to improved performance. Extensive experiments on the challenging platform of Atari 2600 demonstrate the superiority of RS-Rainbow. In particular, our agent achieves state of the art at just 25% of the training frames. Demonstrations and code are available at https://github.com/yz93/Learn-to-Interpret-Atari-Agents

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in