One Decision Tree is Enough to Make Customization
Autor: | Joseph E. Beck, Hao Wan |
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Rok vydání: | 2017 |
Předmět: |
Structure (mathematical logic)
Incremental decision tree Computer science business.industry 05 social sciences Control (management) Decision tree 02 engineering and technology Mastery learning computer.software_genre Machine learning 050105 experimental psychology Personalization ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business Set (psychology) computer Educational software |
Zdroj: | L@S |
Popis: | 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. |
Databáze: | OpenAIRE |
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