Students’ performance dataset for using machine learning technique in physics education research
Abstract There is a need to help advance research on using machine learning and data mining techniques in physics education research (PER), which might still be difficult due to the unavailable dataset for the specific purpose of PER. The SPHERE (Students’ Performance Dataset in Physics Education Re...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04913-0 |
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| author | Purwoko Haryadi Santoso Bayu Setiaji Yohanes Kurniawan Wahyudi Syamsul Bahri Fathurrahman Mobinta Kusuma Indah Urwatin Wusqo Nuri Dewi Muldayanti Arif Didik Kurniawan Johan Syahbrudin |
| author_facet | Purwoko Haryadi Santoso Bayu Setiaji Yohanes Kurniawan Wahyudi Syamsul Bahri Fathurrahman Mobinta Kusuma Indah Urwatin Wusqo Nuri Dewi Muldayanti Arif Didik Kurniawan Johan Syahbrudin |
| author_sort | Purwoko Haryadi Santoso |
| collection | DOAJ |
| description | Abstract There is a need to help advance research on using machine learning and data mining techniques in physics education research (PER), which might still be difficult due to the unavailable dataset for the specific purpose of PER. The SPHERE (Students’ Performance Dataset in Physics Education Research) is presented as an educational dataset of physics learning collected through research-based assessments (RBAs) established by the PER scholars. In this study, students’ performance in physics at four public high schools was probed in three learning domains. It encompassed students’ conceptual understanding, scientific ability, and learning attitude toward physics. The employed RBAs were identified based on the curriculum of physics contents taught to the eleventh-grade students in the ongoing academic year. In this paper, we provide an example that SPHERE could be insightful for training machine learning models to predict students’ performance at the end of the learning process. We also revealed that its predictive performance was superior to the former method of students’ performance prediction as labeled by the physics teachers. |
| format | Article |
| id | doaj-art-add9abc3e0d94ec49e4c85cdecfd267a |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-add9abc3e0d94ec49e4c85cdecfd267a2025-08-20T03:20:59ZengNature PortfolioScientific Data2052-44632025-06-0112111110.1038/s41597-025-04913-0Students’ performance dataset for using machine learning technique in physics education researchPurwoko Haryadi Santoso0Bayu Setiaji1Yohanes Kurniawan2Wahyudi3Syamsul Bahri4Fathurrahman5Mobinta Kusuma6Indah Urwatin Wusqo7Nuri Dewi Muldayanti8Arif Didik Kurniawan9Johan Syahbrudin10Department of Physics Education, Universitas Sulawesi BaratDepartment of Physics Education, Universitas Negeri YogyakartaDepartment of Educational Technology, Universitas Negeri MalangDepartment of Educational Research and Evaluation, Universitas Negeri YogyakartaDepartment of Educational Research and Evaluation, Universitas Negeri YogyakartaDepartment of Educational Research and Evaluation, Universitas Negeri YogyakartaDepartment of Educational Research and Evaluation, Universitas Negeri YogyakartaDepartment of Educational Research and Evaluation, Universitas Negeri YogyakartaDepartment of Educational Research and Evaluation, Universitas Negeri YogyakartaDepartment of Educational Research and Evaluation, Universitas Negeri YogyakartaDepartment of Educational Research and Evaluation, Universitas Negeri YogyakartaAbstract There is a need to help advance research on using machine learning and data mining techniques in physics education research (PER), which might still be difficult due to the unavailable dataset for the specific purpose of PER. The SPHERE (Students’ Performance Dataset in Physics Education Research) is presented as an educational dataset of physics learning collected through research-based assessments (RBAs) established by the PER scholars. In this study, students’ performance in physics at four public high schools was probed in three learning domains. It encompassed students’ conceptual understanding, scientific ability, and learning attitude toward physics. The employed RBAs were identified based on the curriculum of physics contents taught to the eleventh-grade students in the ongoing academic year. In this paper, we provide an example that SPHERE could be insightful for training machine learning models to predict students’ performance at the end of the learning process. We also revealed that its predictive performance was superior to the former method of students’ performance prediction as labeled by the physics teachers.https://doi.org/10.1038/s41597-025-04913-0 |
| spellingShingle | Purwoko Haryadi Santoso Bayu Setiaji Yohanes Kurniawan Wahyudi Syamsul Bahri Fathurrahman Mobinta Kusuma Indah Urwatin Wusqo Nuri Dewi Muldayanti Arif Didik Kurniawan Johan Syahbrudin Students’ performance dataset for using machine learning technique in physics education research Scientific Data |
| title | Students’ performance dataset for using machine learning technique in physics education research |
| title_full | Students’ performance dataset for using machine learning technique in physics education research |
| title_fullStr | Students’ performance dataset for using machine learning technique in physics education research |
| title_full_unstemmed | Students’ performance dataset for using machine learning technique in physics education research |
| title_short | Students’ performance dataset for using machine learning technique in physics education research |
| title_sort | students performance dataset for using machine learning technique in physics education research |
| url | https://doi.org/10.1038/s41597-025-04913-0 |
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