Identifying Factors of Students’ Failure in Blended Courses by Analyzing Students’ Engagement Data
Our modern era has brought about radical changes in the way courses are delivered and various teaching methods are being introduced to answer the purpose of meeting the modern learning challenges. On that account, the conventional way of teaching is giving place to a teaching method which combines c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
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| Series: | Education Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7102/10/9/242 |
| _version_ | 1797554074110918656 |
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| author | Ioannis Georgakopoulos Miltiadis Chalikias Vassilis Zakopoulos Evangelia Kossieri |
| author_facet | Ioannis Georgakopoulos Miltiadis Chalikias Vassilis Zakopoulos Evangelia Kossieri |
| author_sort | Ioannis Georgakopoulos |
| collection | DOAJ |
| description | Our modern era has brought about radical changes in the way courses are delivered and various teaching methods are being introduced to answer the purpose of meeting the modern learning challenges. On that account, the conventional way of teaching is giving place to a teaching method which combines conventional instructional strategies with contemporary learning trends. Thereby, a new course type has emerged, the blended course in the context of which online teaching and conventional instruction are efficiently mixed. This paper demonstrates a way to identify factors affecting students’ critical performance in blended courses through a binary logistics regression analysis on students’ engagement data. The binary logistics regression analysis has led to a risk model which identifies and prioritizes these factors in proportion to their contribution to the risk occurrence. The risk model is demonstrated in the context of two specific blended courses sharing the same learning design. Additionally, the outcome of the study has proved that factors related to the e-learning part have critically affected the students’ performance in the respective blended courses. |
| first_indexed | 2024-03-10T16:25:38Z |
| format | Article |
| id | doaj.art-9b8a2b1dab7b4ac691fa900c79ff8556 |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-03-10T16:25:38Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-9b8a2b1dab7b4ac691fa900c79ff85562023-11-20T13:15:09ZengMDPI AGEducation Sciences2227-71022020-09-0110924210.3390/educsci10090242Identifying Factors of Students’ Failure in Blended Courses by Analyzing Students’ Engagement DataIoannis Georgakopoulos0Miltiadis Chalikias1Vassilis Zakopoulos2Evangelia Kossieri3Department of Accounting and Finance, University of West Attica, 12243 Egaleo, GreeceDepartment of Accounting and Finance, University of West Attica, 12243 Egaleo, GreeceDepartment of Accounting and Finance, University of West Attica, 12243 Egaleo, GreeceDepartment of Accounting and Finance, University of West Attica, 12243 Egaleo, GreeceOur modern era has brought about radical changes in the way courses are delivered and various teaching methods are being introduced to answer the purpose of meeting the modern learning challenges. On that account, the conventional way of teaching is giving place to a teaching method which combines conventional instructional strategies with contemporary learning trends. Thereby, a new course type has emerged, the blended course in the context of which online teaching and conventional instruction are efficiently mixed. This paper demonstrates a way to identify factors affecting students’ critical performance in blended courses through a binary logistics regression analysis on students’ engagement data. The binary logistics regression analysis has led to a risk model which identifies and prioritizes these factors in proportion to their contribution to the risk occurrence. The risk model is demonstrated in the context of two specific blended courses sharing the same learning design. Additionally, the outcome of the study has proved that factors related to the e-learning part have critically affected the students’ performance in the respective blended courses.https://www.mdpi.com/2227-7102/10/9/242risk modelrisk factorsstudents’ achievementengagementblended courses |
| spellingShingle | Ioannis Georgakopoulos Miltiadis Chalikias Vassilis Zakopoulos Evangelia Kossieri Identifying Factors of Students’ Failure in Blended Courses by Analyzing Students’ Engagement Data Education Sciences risk model risk factors students’ achievement engagement blended courses |
| title | Identifying Factors of Students’ Failure in Blended Courses by Analyzing Students’ Engagement Data |
| title_full | Identifying Factors of Students’ Failure in Blended Courses by Analyzing Students’ Engagement Data |
| title_fullStr | Identifying Factors of Students’ Failure in Blended Courses by Analyzing Students’ Engagement Data |
| title_full_unstemmed | Identifying Factors of Students’ Failure in Blended Courses by Analyzing Students’ Engagement Data |
| title_short | Identifying Factors of Students’ Failure in Blended Courses by Analyzing Students’ Engagement Data |
| title_sort | identifying factors of students failure in blended courses by analyzing students engagement data |
| topic | risk model risk factors students’ achievement engagement blended courses |
| url | https://www.mdpi.com/2227-7102/10/9/242 |
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