Predicting the E-Learners Learning Style by using Support Vector Regression Technique
| Autor: | M E Pooja, Suresh K, J Meghana |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Class (computer programming) Computer science media_common.quotation_subject education 02 engineering and technology Educational data mining Style (sociolinguistics) Support vector machine Variable (computer science) Presentation 020901 industrial engineering & automation Anticipation (artificial intelligence) 0202 electrical engineering electronic engineering information engineering Mathematics education 020201 artificial intelligence & image processing Learning Management media_common |
| Zdroj: | 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). |
| DOI: | 10.1109/icais50930.2021.9396018 |
| Popis: | Predicting student performance is a prominent key factor of Educational Data Mining, Support Vector Regression are exposed to be useful factor for assessing under studies presentation in an e-learning atmosphere. In E-learning platform, college scholars performance, Study and effectively take an interest in the learning management system. Support Vector regression have been undertaken to analysis, student id, gender, region, highest education, studied credits, Disability, final result. It is difficult to characterize the amount of important factors are in the Support Vector Regression, organizations gives the predicting of information factors. At enduring, various factors were exposed determined involvement in live class, involvement in undertaking regular assessments, and more involvement in the time contributed significantly to the anticipation profit variable. This paper aims to collect the larger data sets followed by the utilization of one of the machine learning concepts called support vector regression to predict the learner’s learning style. Then, the final results will help to predict the student’s performance. |
| Databáze: | OpenAIRE |
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