Learning Trend Analysis and Prediction Based on Knowledge Tracing and Regression Analysis
| Autor: | Yingwang Wang, Ke Niu, Yali Cai, Zhendong Niu |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
business.industry
Computer science Process (engineering) Regression analysis Specific knowledge Tracing Machine learning computer.software_genre Task (project management) Trend analysis Learning curve ComputingMilieux_COMPUTERSANDEDUCATION Bayesian Knowledge Tracing Artificial intelligence business computer |
| Zdroj: | Database Systems for Advanced Applications ISBN: 9783319223230 DASFAA Workshops |
| DOI: | 10.1007/978-3-319-22324-7_3 |
| Popis: | Estimating students’ knowledge is a fundamental and important task for student modeling in intelligent tutoring systems. Since the concept of knowledge tracing was proposed, there have been many studies focusing on estimating students’ mastery of specific knowledge components, yet few studies paid attention to the analysis and prediction on a student’s overall learning trend in the learning process. Therefore, we propose a method to analyze a student’s learning trend in the learning process and predict students’ performance in future learning. Firstly, we estimate the probability that the student has mastered the knowledge components with the model of Bayesian Knowledge Tracing, and then model students’ learning curves in the overall learning process and predict students’ future performance with Regression Analysis. Experimental results show that this method can be used to fit students’ learning trends well and can provide prediction with reference value for students’ performances in the future learning. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|