This paper presents, 'Interact', an interactive backchannel system providing an array of features to both the students and instructors. It allows the students to post comments, ask questions, and rate their understanding of various concepts (prepopulated by the instructor), taught in a lecture session. Additionally, the system employs text analytics using Latent Dirichlet allocation (LDA), to extract topics from the comments, and aggregate sentiments for each topic using AFINN-165 wordlist. The instructors can view: (1) top-ranked 'm' topics discussed in 'n' lectures; (2) sentiment trends for 'm' topics, and 'n' lectures, including positive and negative words; (3) popular questions asked in 'n' lectures. A user-study was conducted with 40 students and 10 academics were interviewed to understand their perception towards the system. Additionally, a crowdsource study examined the effectiveness of aggregation techniques. We believe that Interact will help to improve teaching and develop effective learning, with the aid of automatic processing, summarization and visualization of student feedback obtained through comments, questions and rating concepts.
|Title of host publication||OZCHI '17 Proceedings of the 29th Australian Conference on Computer-Human Interaction|
|Number of pages||5|
|Publication status||Published - 28 Nov 2017|
|Event||29th Australian Computer-Human Interaction Conference, OzCHI 2017 - Brisbane, Australia|
Duration: 28 Nov 2017 → 1 Dec 2017
|Conference||29th Australian Computer-Human Interaction Conference, OzCHI 2017|
|Period||28/11/17 → 1/12/17|