Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students
It is essential to predict distance education students’ year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Athabasca University Press
2013-12-01
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| Series: | International Review of Research in Open and Distributed Learning |
| Online Access: | http://www.irrodl.org/index.php/irrodl/article/view/1595/2716 |
| _version_ | 1818904735125602304 |
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| author | Osman Yildiz Abdullah Bal Sevinc Gulsecen |
| author_facet | Osman Yildiz Abdullah Bal Sevinc Gulsecen |
| author_sort | Osman Yildiz |
| collection | DOAJ |
| description | It is essential to predict distance education students’ year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educational quality. The present study was on the development of a mathematical model intended to predict distance education students’ year-end academic performance using the first eight-week data on the learning management system. First, two fuzzy models were constructed, namely the classical fuzzy model and the expert fuzzy model, the latter being based on expert opinion. Afterwards, a gene-fuzzy model was developed optimizing membership functions through genetic algorithm. The data on distance education were collected through Moodle, an open source learning management system. The data were on a total of 218 students who enrolled in Basic Computer Sciences in 2012. The input data consisted of the following variables: When a student logged on to the system for the last time after the content of a lesson was uploaded, how often he/she logged on to the system, how long he/she stayed online in the last login, what score he/she got in the quiz taken in Week 4, and what score he/she got in the midterm exam taken in Week 8. A comparison was made among the predictions of the three models concerning the students’ year-end academic performance. |
| first_indexed | 2024-12-19T21:12:09Z |
| format | Article |
| id | doaj.art-11faab1f853d45b7a7b2e108907b3514 |
| institution | Directory Open Access Journal |
| issn | 1492-3831 |
| language | English |
| last_indexed | 2024-12-19T21:12:09Z |
| publishDate | 2013-12-01 |
| publisher | Athabasca University Press |
| record_format | Article |
| series | International Review of Research in Open and Distributed Learning |
| spelling | doaj.art-11faab1f853d45b7a7b2e108907b35142022-12-21T20:05:27ZengAthabasca University PressInternational Review of Research in Open and Distributed Learning1492-38312013-12-01145Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education StudentsOsman Yildiz0Abdullah Bal1Sevinc Gulsecen2Yildiz Technical University, TurkeyYildiz Technical University, TurkeyIstanbul University, TurkeyIt is essential to predict distance education students’ year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educational quality. The present study was on the development of a mathematical model intended to predict distance education students’ year-end academic performance using the first eight-week data on the learning management system. First, two fuzzy models were constructed, namely the classical fuzzy model and the expert fuzzy model, the latter being based on expert opinion. Afterwards, a gene-fuzzy model was developed optimizing membership functions through genetic algorithm. The data on distance education were collected through Moodle, an open source learning management system. The data were on a total of 218 students who enrolled in Basic Computer Sciences in 2012. The input data consisted of the following variables: When a student logged on to the system for the last time after the content of a lesson was uploaded, how often he/she logged on to the system, how long he/she stayed online in the last login, what score he/she got in the quiz taken in Week 4, and what score he/she got in the midterm exam taken in Week 8. A comparison was made among the predictions of the three models concerning the students’ year-end academic performance.http://www.irrodl.org/index.php/irrodl/article/view/1595/2716 |
| spellingShingle | Osman Yildiz Abdullah Bal Sevinc Gulsecen Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students International Review of Research in Open and Distributed Learning |
| title | Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students |
| title_full | Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students |
| title_fullStr | Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students |
| title_full_unstemmed | Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students |
| title_short | Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students |
| title_sort | improved fuzzy modelling to predict the academic performance of distance education students |
| url | http://www.irrodl.org/index.php/irrodl/article/view/1595/2716 |
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