Multi-Class Assessment Based on Random Forests
| Autor: | Gaëtan Rey, Mehdi Berriri, Sofiane Djema, Christel Dartigues-Pallez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Public Administration
Higher education Computer science Process (engineering) Physical Therapy Sports Therapy and Rehabilitation 02 engineering and technology Machine learning computer.software_genre orientation Education 0202 electrical engineering electronic engineering information engineering Developmental and Educational Psychology Computer Science (miscellaneous) ComputingMilieux_COMPUTERSANDEDUCATION Use case Set (psychology) Random Forest selection feature business.industry Orientation (computer vision) 05 social sciences 050301 education Ranging Class (biology) Computer Science Applications Random forest machine learning 020201 artificial intelligence & image processing Artificial intelligence business lcsh:L 0503 education computer lcsh:Education |
| Zdroj: | Education Sciences Volume 11 Issue 3 Education Sciences, Vol 11, Iss 92, p 92 (2021) |
| ISSN: | 2227-7102 |
| DOI: | 10.3390/educsci11030092 |
| Popis: | Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes. |
| Databáze: | OpenAIRE |
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