Another ACM paper accepted!

Figure 1_v2
“Korero” paper conditionally accepted to CSCW 2018
August 22, 2017

Another ACM paper accepted!

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Our heartiest congratulations to Assistant Professor Joseph Williams and co-authors (Jakub Macina, Ivan Srba and Maria Bielikova). Their paper “Educational Question Routing in Online Student Communities” has been accepted for the 11th ACM Conference on Recommender Systems!

This paper focuses on student participation in Massive Open Online Courses (MOOCs) discussion forums and educational Community Question Answering (CQA) systems. It addresses the common issue of instructor overload and low student response rates by presenting an approach for recommendation of new questions to students who are likely to provide answers.

The proposed method was shown to outperform a baseline method (non-educational question routing enhanced with workload restriction) in recommendation accuracy, involving more community members, and average number of contributions.

 

Abstract:
Students’ performance in Massive Open Online Courses (MOOCs) is enhanced by high quality discussion forums or recently emerging educational Community Question Answering (CQA) systems. Nevertheless, only a small number of students answer questions asked by their peers. This results in instructor overload, and many unanswered questions. To increase students’ participation, we present an approach for recommendation of new questions to students who are likely to provide answers. Existing approaches to such question routing proposed for non-educational CQA systems tend to rely on a few experts, what is not applicable in educational domain where it is important to involve all kinds of students. In tackling this novel educational question routing problem, our method (1) goes beyond previous question-answering data as it incorporates additional non-QA data from the course (to improve prediction accuracy and to involve more of the student community) and (2) applies constraints on users’ workload (to prevent user overloading). We use an ensemble classifier for predicting students’ willingness to answer a question, as well as students’ expertise for answering. We conducted an online evaluation of the proposed method using an A/B experiment in our CQA system deployed in edX MOOC. The proposed method outperformed a baseline method (non-educational question routing enhanced with workload restriction) by improving recommendation accuracy, keeping more community members active, and increasing an average number of their contributions.

 

Citation: 
Jakub Macina, Ivan Srba, Joseph Jay Williams, and Maria Bielikova. 2017. Educational Question Routing in Online Student Communities. In Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17). ACM, New York, NY, USA, 47-55. DOI: https://doi.org/10.1145/3109859.3109886
Full paper: http://dl.acm.org/citation.cfm?id=3109886

 

For more information about the paper, you may contact Dr. Joseph Williams.

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