One Decision Tree is Enough to Make Customization
Authors: 
Hao Wan, Joseph E. BeckAuthors Info & Claims
Hao Wan, Joseph E. BeckAuthors Info & ClaimsPages 193 - 196
Abstract
The ability to customize instruction to individuals is a great potential for adaptive educational software. Unfortunately, beyond mastery learning and learner control, there has not been much work with adapting instruction to individuals. This paper provides an approach to determine what type of learner does best with a different intervention. We focused on constructing a decision tree that discriminated difference between tutoring interventions, and thus to make customization for each student. We evaluated our model on simulated and on real data. In the simulated data set, it outperformed other methods and the constructed models captured a pre-defined customization structure. With the real data, the customized learning approach achieved stronger learning gains than simply picking the best overall teaching option. Surprisingly, it was difficult to outperform a decision tree that simply used how quickly students tended to learn a skill. That is, more features and more complex models did not result in a more effective system.
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Anthony Botelho, Hao Wan, and Neil Heffernan. The prediction of student first response using prerequisite skills. in Proceedings of the Second (2015) ACM Conference on Learning@ Scale. 2015. ACM.
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Index Terms
- One Decision Tree is Enough to Make Customization
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Information & Contributors
Information
Published In
April 2017
352 pages
ISBN:9781450344500
DOI:10.1145/3051457
- General Chair:
- Claudia Urrea,
- Program Chairs:
- Justin Reich,
- Candace Thille
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Published: 12 April 2017
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L@S 2017
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L@S 2017: Fourth (2017) ACM Conference on Learning @ Scale
April 20 - 21, 2017
Massachusetts, Cambridge, USA
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L@S '17 Paper Acceptance Rate 14 of 105 submissions, 13%;
Overall Acceptance Rate 117 of 440 submissions, 27%
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