DiAd
Autor: | Dan Roth, Suma Bhat, Snigdha Chaturvedi, Ziheng Zeng |
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Rok vydání: | 2019 |
Předmět: |
Domain adaptation
business.industry Computer science 05 social sciences 050301 education Diad Machine learning computer.software_genre Online course 0502 economics and business ComputingMilieux_COMPUTERSANDEDUCATION medicine Labeled data 050211 marketing Artificial intelligence medicine.symptom business 0503 education Classifier (UML) computer Confusion Learning at scale |
Zdroj: | LAK |
DOI: | 10.1145/3303772.3303810 |
Popis: | Massive online courses occupy an important place in the educational landscape of today. We study an approach to scale predictive analytic models derived from online course discussion fora--specifically that of confusion detection--onto other courses. The primary challenge here is the lack of labeled examples in a new course and this calls for unsupervised domain adaptation (DA). As a first step in exploring DA in the education domain, we propose a simple algorithm, DiAd, which adapts a classifier trained on a course with labeled data by selectively choosing instances from a new course (with no labeled data) that are most dissimilar to the course with labeled data and on which the classifier is very confident of classification. Our algorithm is empirically validated on the confusion detection task across multiple online courses. We find that DiAd outperforms other methods on the target domain, while showing a comparable performance to a popular method that uses labeled data from the target domain. |
Databáze: | OpenAIRE |
Externí odkaz: |
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