AXIS: Generating explanations at scale with learnersourcing and machine learning

Cited 73 time in webofscience Cited 0 time in scopus
  • Hit : 405
  • Download : 0
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.
Publisher
Association for Computing Machinery, Inc
Issue Date
2016-04-25
Language
English
Citation

3rd Annual ACM Conference on Learning at Scale, L@S 2016, pp.379 - 388

DOI
10.1145/2876034.2876042
URI
http://hdl.handle.net/10203/222570
Appears in Collection
CS-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 73 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0