Student Performance Prediction and Classification Using Machine Learning Algorithms
| Autor: | Boran Sekeroglu, Kamil Dimililer, Kubra Tuncal |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Value (ethics)
Higher education business.industry Computer science media_common.quotation_subject Teaching method 05 social sciences 050301 education 02 engineering and technology Educational evaluation Machine learning computer.software_genre Excellence 0202 electrical engineering electronic engineering information engineering Performance prediction 020201 artificial intelligence & image processing Quality (business) Artificial intelligence business Raw data 0503 education Algorithm computer media_common |
| Zdroj: | Proceedings of the 2019 8th International Conference on Educational and Information Technology. |
| DOI: | 10.1145/3318396.3318419 |
| Popis: | For a productive and a good life, education is a necessity and it improves individuals' life with value and excellence. Also, education is considered a vital need for motivating self-assurance as well as providing the things are needed to partake in today's World. Throughout the years, education faced a number of challenges. Different methods of teaching and learning are suggested to increase the learning quality. In today's world, computers and portable devices are employed in every phase of daily life and many materials are available online anytime, anywhere. Technologies like Artificial Intelligence had a surprising evolution in many fields especially in educational teaching and learning processes. Higher education institutions have started to adopt the use of technology into their traditional teaching mechanisms for enhancing learning and teaching. In this paper, two datasets have been considered for the prediction and classification of student performance respectively using five machine learning algorithms. Eighteen experiments have been performed and preliminary results suggest that performances of students might be predictable and classification of these performances can be increased by applying pre-processing to the raw data before implementing machine learning algorithms. |
| Databáze: | OpenAIRE |
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