1. Recommendation for Effective Standardized Exam Preparation
- Author
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Chanyou Hwang, Hyunbin Loh, Youngduck Choi, Jineon Baek, Seewoo Lee, Yeongmin Cha, Dongmin Shin, Youngnam Lee, Juneyoung Park, and Soonwoo Kwon
- Subjects
Psychometrics ,business.industry ,Computer science ,Process (engineering) ,05 social sciences ,050301 education ,02 engineering and technology ,Personalized learning ,Recommender system ,Machine learning ,computer.software_genre ,Intelligent tutoring system ,Test (assessment) ,Item response theory ,0202 electrical engineering, electronic engineering, information engineering ,Learning theory ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,0503 education ,computer - Abstract
Finding an optimal learning trajectory is an important question in educational systems. Existing Artificial Intelligence in Education (AiEd) technologies mostly used indirect methods to make the learning process efficient such as recommending contents based on difficulty adjustment, weakness analysis, learning theory, psychometric analysis, or domain specific rules. In this study, we propose a recommender system that optimizes the learning trajectory of a student preparing for a standardized exam by recommending the learning content(question) which directly maximizes the expected score after the consumption of the content. In particular, the proposed RCES model computes the expected score of a user by effectively capturing educational effects. To validate the proposed model in an end-to-end system, we conduct an A/B test on 1713 real students by deploying 4 recommenders to a real mobile application. Result shows that RCES has better educational efficiencies than traditional methods such as expert designed models and item response theory based models.
- Published
- 2021