Student engagement and academic performance in pandemic-driven online teaching: An exploratory and machine learning approach
Fostering student engagement to acquire knowledge and achieve academic performance requires understanding how students engage in learning and its influence on academic achievement. This provides valuable insights that help improve learning experiences and outcomes. The paper relies on a mixed method...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2023-12-01
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| Series: | Management şi Marketing |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/mmcks-2023-0017 |
| _version_ | 1797375869730160640 |
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| author | Campeanu Emilia Mioara Boitan Iustina Alina Anghel Dan Gabriel |
| author_facet | Campeanu Emilia Mioara Boitan Iustina Alina Anghel Dan Gabriel |
| author_sort | Campeanu Emilia Mioara |
| collection | DOAJ |
| description | Fostering student engagement to acquire knowledge and achieve academic performance requires understanding how students engage in learning and its influence on academic achievement. This provides valuable insights that help improve learning experiences and outcomes. The paper relies on a mixed methods approach by expanding the traditional dimensions of student engagement and by employing a machine learning framework to identify which specific dimension of student engagement exhibits the main impact on student academic achievement. A questionnaire-based survey is conducted for the period 2020-2021 among a cohort of Romanian students. The outcomes of this preliminary exploratory analysis are further embedded into a machine learning framework by performing a LASSO regression. The findings reveal that the most relevant dimensions of student engagement, during remote education, that contribute the most to outcomes were represented by the behavioural, social, cognitive, and emotional engagement dimensions. Furthermore, the switch to online education appeared to have inverted the positive relationship between social and cognitive engagement and academic achievement. Despite the inherent challenges, the student’s interest in class participation and homework completion was stimulated, and they managed to adapt without difficulty to study independently. |
| first_indexed | 2024-03-08T19:30:29Z |
| format | Article |
| id | doaj.art-7ce49da28e6348429022042bc34873aa |
| institution | Directory Open Access Journal |
| issn | 2069-8887 |
| language | English |
| last_indexed | 2024-03-08T19:30:29Z |
| publishDate | 2023-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Management şi Marketing |
| spelling | doaj.art-7ce49da28e6348429022042bc34873aa2023-12-26T07:42:55ZengSciendoManagement şi Marketing2069-88872023-12-0118s131533910.2478/mmcks-2023-0017Student engagement and academic performance in pandemic-driven online teaching: An exploratory and machine learning approachCampeanu Emilia Mioara0Boitan Iustina Alina1Anghel Dan Gabriel21Bucharest University of Economic Studies, Bucharest, Romania2Bucharest University of Economic Studies, Bucharest, Romania3Bucharest University of Economic Studies, Institute for Economic Forecasting, Romanian Academy, Bucharest, RomaniaFostering student engagement to acquire knowledge and achieve academic performance requires understanding how students engage in learning and its influence on academic achievement. This provides valuable insights that help improve learning experiences and outcomes. The paper relies on a mixed methods approach by expanding the traditional dimensions of student engagement and by employing a machine learning framework to identify which specific dimension of student engagement exhibits the main impact on student academic achievement. A questionnaire-based survey is conducted for the period 2020-2021 among a cohort of Romanian students. The outcomes of this preliminary exploratory analysis are further embedded into a machine learning framework by performing a LASSO regression. The findings reveal that the most relevant dimensions of student engagement, during remote education, that contribute the most to outcomes were represented by the behavioural, social, cognitive, and emotional engagement dimensions. Furthermore, the switch to online education appeared to have inverted the positive relationship between social and cognitive engagement and academic achievement. Despite the inherent challenges, the student’s interest in class participation and homework completion was stimulated, and they managed to adapt without difficulty to study independently.https://doi.org/10.2478/mmcks-2023-0017student engagementacademic achievementremote educationlinear regressionmachine learning |
| spellingShingle | Campeanu Emilia Mioara Boitan Iustina Alina Anghel Dan Gabriel Student engagement and academic performance in pandemic-driven online teaching: An exploratory and machine learning approach Management şi Marketing student engagement academic achievement remote education linear regression machine learning |
| title | Student engagement and academic performance in pandemic-driven online teaching: An exploratory and machine learning approach |
| title_full | Student engagement and academic performance in pandemic-driven online teaching: An exploratory and machine learning approach |
| title_fullStr | Student engagement and academic performance in pandemic-driven online teaching: An exploratory and machine learning approach |
| title_full_unstemmed | Student engagement and academic performance in pandemic-driven online teaching: An exploratory and machine learning approach |
| title_short | Student engagement and academic performance in pandemic-driven online teaching: An exploratory and machine learning approach |
| title_sort | student engagement and academic performance in pandemic driven online teaching an exploratory and machine learning approach |
| topic | student engagement academic achievement remote education linear regression machine learning |
| url | https://doi.org/10.2478/mmcks-2023-0017 |
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