Abstract
Professionals increasingly turn to online learning communities (OLCs) such as Stack Overflow (SO) to get help with their questions. It is important that the help is appropriate to the learning needs of the professional and is received in a timely fashion. However, we observed in SO a rise in the proportion of questions either answered late or not answered at all, from 5% in 2009 to 23% in 2016. There is clearly a need to be able to quickly find appropriate answerers for the questions asked by users. Our research goal is thus to find techniques that allow us to predict from SO data (using only information available at the time the question was asked) the actual answerers who provided the best answers and the most timely answers to users’ questions. Such techniques could then be deployed proactively at the time a question is asked to recommend an appropriate answerer. We used a variety of tag-based, response-based, and hybrid approaches in making these predictions. Comparing the approaches, we achieved success rates that varied from a low of .88% to a high of 89.64%, with the hybrid approaches being the best. We also explored the effect of excluding from the pool of possible answerers those users, who had already answered a question “recently”, with “recent” varying from 15 min up to 12 h, so as to have well rested helpers. We still achieved reasonable success rates at least for smaller exclusion periods of up to an hour, although naturally not as good as the time exclusion grew longer. We believe our work shows promise for allowing us to predict prospective answerers for questions who are not overworked, hence reducing the number of questions that would otherwise be answered late or not answered at all.
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- 1.
Learning needs are defined as the gaps in the knowledge of a learner that might be perceived or unperceived by the learner. In this study, we focused on the perceived learning needs of learners which are evident by the questions they asked within the OLC.
- 2.
We will use the term “user” in this paper rather than “learner” when specifically discussing SO users since they are likely not explicitly learners in their own minds. However, in the future most professionals will be using such forums to meet their lifelong learning needs. Since our research is aimed at helping develop tools for such professional lifelong learners, especially tools that support personalization to each such learner, it is, we believe, deeply and broadly relevant to artificial intelligence in education.
- 3.
In SO, the score a question earns is computed by the aggregate of the number of up votes and down votes to the answers provided by other community members in response to the question.
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We would like to thank the Natural Sciences and Engineering Research Council of Canada and the University of Saskatchewan for financially supporting this research.
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(Ishola) Idowu, O.M., McCalla, G. (2018). Better Late Than Never but Never Late Is Better: Towards Reducing the Answer Response Time to Questions in an Online Learning Community. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10947. Springer, Cham. https://doi.org/10.1007/978-3-319-93843-1_14
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