Prediction of Students’ Performance in e-Learning Environment using Data Mining/ Machine Learning Techniques Article Swipe
Brijesh Verma
,
Nidhi Srivastava
,
Hemant Kumar Singh
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.51201/jusst/21/05179
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.51201/jusst/21/05179
The COVID-19 pandemic has drastically changed the way od of learning. During this pandemic the learning has shifted from offline to online. student’s performance prediction based on their relevant information has emerged new area for educational institutions for improving teaching learning process, changes in course curriculum. Machine leaning technology can be helpful in predicting the performance of student and accordingly the institutions can make required changes in in their lecture delivery and curriculum. This paper utilized some machine learning methodologies to predict the students’ performance. Educational data of open University(OU) is analyzed Based on parameters that are demographic, engagement and performance. In the experimental analysis. In the experimental analysis, the k-NN approach performed best in some cases and ANN performed best in other cases among all compared algorithms on OU dataset.
Related Topics
Concepts
Machine learning
Computer science
Curriculum
Artificial intelligence
Process (computing)
Coronavirus disease 2019 (COVID-19)
Psychology
Pedagogy
Operating system
Infectious disease (medical specialty)
Pathology
Disease
Medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.51201/jusst/21/05179
- https://jusst.org/wp-content/uploads/2021/05/Prediction-of-Students-Performance-in-e-Learning-Environment-using-Data-Mining-Machine-Learning-Techniques.pdf
- OA Status
- diamond
- Cited By
- 10
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3164125683
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3164125683Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.51201/jusst/21/05179Digital Object Identifier
- Title
-
Prediction of Students’ Performance in e-Learning Environment using Data Mining/ Machine Learning TechniquesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-25Full publication date if available
- Authors
-
Brijesh Verma, Nidhi Srivastava, Hemant Kumar SinghList of authors in order
- Landing page
-
https://doi.org/10.51201/jusst/21/05179Publisher landing page
- PDF URL
-
https://jusst.org/wp-content/uploads/2021/05/Prediction-of-Students-Performance-in-e-Learning-Environment-using-Data-Mining-Machine-Learning-Techniques.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://jusst.org/wp-content/uploads/2021/05/Prediction-of-Students-Performance-in-e-Learning-Environment-using-Data-Mining-Machine-Learning-Techniques.pdfDirect OA link when available
- Concepts
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Machine learning, Computer science, Curriculum, Artificial intelligence, Process (computing), Coronavirus disease 2019 (COVID-19), Psychology, Pedagogy, Operating system, Infectious disease (medical specialty), Pathology, Disease, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2023: 5, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.information | 29 |
| abstract_inverted_index.performance | 23, 55 |
| abstract_inverted_index.students’ | 83 |
| abstract_inverted_index.student’s | 22 |
| abstract_inverted_index.demographic, | 97 |
| abstract_inverted_index.experimental | 103, 107 |
| abstract_inverted_index.institutions | 36, 61 |
| abstract_inverted_index.performance. | 84, 100 |
| abstract_inverted_index.methodologies | 79 |
| abstract_inverted_index.University(OU) | 89 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.87586908 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |