Course-Specific Model for Prediction of At-Risk Students Based on Case-Based Reasoning
| Autor: | Haris Supic, Dzenana Donko |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
Computer science business.industry 05 social sciences 050301 education Machine learning computer.software_genre Course (navigation) Early prediction ComputingMilieux_COMPUTERSANDEDUCATION Key (cryptography) Early warning system 0501 psychology and cognitive sciences Case-based reasoning Artificial intelligence Specific model business 0503 education computer At-risk students |
| Zdroj: | 2020 IEEE Integrated STEM Education Conference (ISEC). |
| DOI: | 10.1109/isec49744.2020.9280572 |
| Popis: | Identifying at-risk students is a crucial step in different learning settings. Predictive modeling technique can be used to create an early warning system which predicts students’ success in courses and informs both the teacher and the student of their performance. In this paper we describe a course-specific model for prediction of at-risk students. The proposed model uses the case-based reasoning (CBR) methodology to predict at-risk students at three specific points in time during the first half of the semester. In general, CBR is an approach of solving new problems based on solutions of similar previously experienced problem situation encoded in the form of cases. The proposed model classifies students as at-risk based on the most similar past cases retrieved from the casebase by using the k-NN algorithm. According to the experimental evaluation of the model accuracy, CBR model that is being developed for a specific course showed potential for an early prediction of at-risk students. Although the presented CBR model has been applied for one specific course, the key elements of predictive model can be easily reused by other courses. |
| Databáze: | OpenAIRE |
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