Students Performance Prediction by Mining Behavioural Pattern Article Swipe
Huanliang Sun
,
S. He
,
Olaoluwa Esho
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1881/4/042051
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1881/4/042051
Analysing and monitoring students’ progress and performance is an active research area in educational data mining. Some research work uses direct construct relating to academic achievements such as GPA, SAT in predicting performance while others make use of psychometric construct in measuring skill and knowledge, ability and educational achievement from data gotten from questionnaires. In this paper, we propose the use of psychometric construct as a method of extracting non-cognitive features from campus check-ins data in predicting academic performance of college students. A P-FRAME framework was developed and non-cognitive attributes extracted through psychological theories were used as the predicting variables. The extracted variable was applied to various penalized regression algorithm and the results were compared. The result of our experiments showed a high correlation between the actual score and the predicted score which is within 2:0 of reported score.
Related Topics
Concepts
Construct (python library)
Cognition
Frame (networking)
Computer science
Correlation
Academic achievement
Regression analysis
Variable (mathematics)
Educational data mining
Psychology
Regression
Artificial intelligence
Machine learning
Mathematics education
Applied psychology
Mathematics
Geometry
Telecommunications
Neuroscience
Programming language
Mathematical analysis
Psychoanalysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/1881/4/042051
- OA Status
- diamond
- Cited By
- 2
- References
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3216446020
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3216446020Canonical identifier for this work in OpenAlex
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https://doi.org/10.1088/1742-6596/1881/4/042051Digital Object Identifier
- Title
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Students Performance Prediction by Mining Behavioural PatternWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-04-01Full publication date if available
- Authors
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Huanliang Sun, S. He, Olaoluwa EshoList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/1881/4/042051Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1742-6596/1881/4/042051Direct OA link when available
- Concepts
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Construct (python library), Cognition, Frame (networking), Computer science, Correlation, Academic achievement, Regression analysis, Variable (mathematics), Educational data mining, Psychology, Regression, Artificial intelligence, Machine learning, Mathematics education, Applied psychology, Mathematics, Geometry, Telecommunications, Neuroscience, Programming language, Mathematical analysis, PsychoanalysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2022: 2Per-year citation counts (last 5 years)
- References (count)
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7Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.score. | 139 |
| abstract_inverted_index.showed | 121 |
| abstract_inverted_index.within | 135 |
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| abstract_inverted_index.check-ins | 74 |
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| corresponding_author_ids | https://openalex.org/A5101040661 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I83714178 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
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| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.72729704 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |