Feature engineering for clustering student solutions

Open-ended homework problems such as coding assignments give students a broad range of freedom for the design of solutions. We aim to use the diversity in correct solutions to enhance student learning by automatically suggesting alternate solutions. Our approach is to perform a two-level hierarchical clustering of student solutions to first partition them based on the choice of algorithm and then partition solutions implementing the same algorithm based on low-level implementation details. Our initial investigations in domains of introductory programming and computer architecture demonstrate that we need two different classes of features to perform effective clustering at the two levels, namely abstract features and concrete features

A Speaker Diarization System for Studying Peer-Led Team Learning Groups

Peer-led team learning (PLTL) is a model for teaching STEM courses where small student groups meet periodically to collaboratively discuss coursework. Automatic analysis of PLTL sessions would help education researchers to get insight into how learning outcomes are impacted by individual participation, group behavior, team dynamics, etc.. Towards this, speech and language technology can help, and speaker diarization technology will lay the foundation for analysis. In this study, a new corpus is established called CRSS-PLTL, that contains speech data from 5 PLTL teams over a semester (10 sessions per team with 5-to-8 participants in each team). In CRSS-PLTL, every participant wears a LENA device (portable audio recorder) that provides multiple audio recordings of the event. Our proposed solution is unsupervised and contains a new online speaker change detection algorithm, termed G 3 algorithm in conjunction with Hausdorff-distance based clustering to provide improved detection accuracy. Additionally, we also exploit cross channel information to refine our diarization hypothesis. The proposed system provides good improvements in diarization error rate (DER) over the baseline LIUM system. We also present higher level analysis such as the number of conversational turns taken in a session, and speaking-time duration (participation) for each speaker.Comment: 5 Pages, 2 Figures, 2 Tables, Proceedings of INTERSPEECH 2016, San Francisco, US

LEARNING HOW STUDENTS ARE LEARNING IN PROGRAMMING LAB SESSIONS

Department of Computer Science and EngineeringProgramming lab sessions help students learn to program in a practical way. Although these sessions are typically valuable to students, it is not uncommon for some participants to fall behind throughout the sessions and leave without fully grasping the concepts covered during the session. In my thesis, I will be presenting LabEX, a system for instructors to understand students' progress and learning experience during programming lab sessions. LabEX utilizes statistical techniques that help distinguishing struggling students and understand their degree of struggle. LabEX also helps instructors to provide in-situ feedback to students with its real-time code review. LabEX was evaluated in an entry-level programming course taken by more than two hundred students in UNIST, establishing that it increases the quality of programming lab sessions.ope