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ERIC Number: ED571525
Record Type: Non-Journal
Publication Date: 2016-Jul-1
Pages: 10
Abstractor: As Provided
Reference Count: 24
Axis: Generating Explanations at Scale with Learnersourcing and Machine Learning
Williams, Joseph Jay; Kim, Juho; Rafferty, Anna; Heffernan, Neil; Maldonado, Samuel; Gajos, Krzysztof Z.; Lasecki, Walter S.; Heffernan, Neil
Online Submission, Paper presented at the ACM Conference on Learning @ Scale (3rd, Edinburgh, Scotland, Apr 25-26, 2016)
While explanations may help people learn by providing information about why an answer is correct, many problems on online platforms lack high-quality explanations. This paper presents AXIS (Adaptive eXplanation Improvement System), a system for obtaining explanations. AXIS asks learners to generate, revise, and evaluate explanations as they solve a problem, and then uses machine learning to dynamically determine which explanation to present to a future learner, based on previous learners' collective input. Results from a case study deployment and a randomized experiment demonstrate that AXIS elicits and identifies explanations that learners find helpful. Providing explanations from AXIS also objectively enhanced learning, when compared to the default practice where learners solved problems and received answers without explanations. The rated quality and learning benefit of AXIS explanations did not differ from explanations generated by an experienced instructor.
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A