Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2019

Abstract

Online question pools like LeetCode provide hands-on exercises of skills and knowledge. However, due to the large volume of questions and the intent of hiding the tested knowledge behind them, many users find it hard to decide where to start or how to proceed based on their goals and performance. To overcome these limitations, we present PeerLens, an interactive visual analysis system that enables peer-inspired learning path planning. PeerLens can recommend a customized, adaptable sequence of practice questions to individual learners, based on the exercise history of other users in a similar learning scenario. We propose a new way to model the learning path by submission types and a novel visual design to facilitate the understanding and planning of the learning path. We conducted a within-subject experiment to assess the efficacy and usefulness of PeerLens in comparison with two baseline systems. Experiment results show that users are more confident in arranging their learning path via PeerLens and find it more informative and intuitive.

Keywords

Question Pool, Learning Path Planning, Visualization

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, Scotland, May 4-9

Identifier

10.1145/3290605.3300864

Publisher

ACM

City or Country

Glasgow, Scotland UK

Additional URL

https://doi.org/10.1145/3290605.3300864

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