Improving Predictive Modeling for At-Risk Student Identification: A Multistage Approach
Autor: | Brett E. Shelton, Jui-Long Hung, Juan Yang, Xu Du |
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Rok vydání: | 2019 |
Předmět: |
Higher education
Computer science business.industry 05 social sciences General Engineering Learning analytics Educational technology 050301 education 02 engineering and technology Machine learning computer.software_genre Computer Science Applications Education Identification (information) Learner engagement 0202 electrical engineering electronic engineering information engineering Performance prediction 020201 artificial intelligence & image processing Artificial intelligence business 0503 education computer At-risk students Predictive methods |
Zdroj: | IEEE Transactions on Learning Technologies. 12:148-157 |
ISSN: | 2372-0050 |
Popis: | Performance prediction is a leading topic in learning analytics research due to its potential to impact all tiers of education. This study proposes a novel predictive modeling method to address the research gaps in existing performance prediction research. The gaps addressed include: the lack of existing research focus on performance prediction rather than identifying key performance factors; the lack of common predictors identified for both K-12 and higher education environments; and the misplaced focus on absolute engagement levels rather than relative engagement levels. Two datasets, one from higher education and the other from a K-12 online school with 13 368 students in more than 300 courses, were applied using the predictive modeling technique. The results showed the newly suggested approach had higher overall accuracy and sensitivity rates than the traditional approach. In addition, two generalizable predictors were identified from instruction-intensive and discussion-intensive courses. |
Databáze: | OpenAIRE |
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