Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern
While modelling students’ learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly pred...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/7463631 |
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| author | Danial Hooshyar Yeongwook Yang |
| author_facet | Danial Hooshyar Yeongwook Yang |
| author_sort | Danial Hooshyar |
| collection | DOAJ |
| description | While modelling students’ learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior model constitute a comprehensive students’ learning behavioral pattern which is later used for prediction of their course achievement. Lastly, using a multiple criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision Tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with a high accuracy through modelling their learning behavior during online courses. |
| format | Article |
| id | doaj-art-8360901476c84726a1226fb8669477d1 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8360901476c84726a1226fb8669477d12025-02-03T06:43:56ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/74636317463631Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral PatternDanial Hooshyar0Yeongwook Yang1Institute of Education, University of Tartu, Tartu, EstoniaDivision of Computer Engineering, Hanshin University, Osan, Republic of KoreaWhile modelling students’ learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior model constitute a comprehensive students’ learning behavioral pattern which is later used for prediction of their course achievement. Lastly, using a multiple criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision Tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with a high accuracy through modelling their learning behavior during online courses.http://dx.doi.org/10.1155/2021/7463631 |
| spellingShingle | Danial Hooshyar Yeongwook Yang Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern Complexity |
| title | Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern |
| title_full | Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern |
| title_fullStr | Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern |
| title_full_unstemmed | Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern |
| title_short | Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern |
| title_sort | predicting course grade through comprehensive modelling of students learning behavioral pattern |
| url | http://dx.doi.org/10.1155/2021/7463631 |
| work_keys_str_mv | AT danialhooshyar predictingcoursegradethroughcomprehensivemodellingofstudentslearningbehavioralpattern AT yeongwookyang predictingcoursegradethroughcomprehensivemodellingofstudentslearningbehavioralpattern |