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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.22334 |
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| _version_ | 1866912608499531776 |
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| author | Kizito, Michael Kayongo, Ivan Nyende, Hawa Chongomweru, Halimu Muyama, Lillian Asiku, Roy Alia Mugisha, Alice |
| author_facet | Kizito, Michael Kayongo, Ivan Nyende, Hawa Chongomweru, Halimu Muyama, Lillian Asiku, Roy Alia Mugisha, Alice |
| contents | Understanding student behaviour in higher education is essential for improving academic performance, supporting mental well-being, and informing institutional policies. However, most existing behavioural datasets originate from Western institutions and overlook the unique socioeconomic and infrastructural contexts of African institutions, limiting the global applicability of resulting insights. This paper introduces MakOne, a novel multimodal dataset collected over six weeks from 72 students at Makerere University, Kampala, using iLog, a mobile sensing application. The dataset integrates passive smartphone sensor data-including location, physical activity, and screen usage-with ecological momentary assessments (EMAs) that capture students' moods and daily routines. Designed to reflect the lived experiences of students in an African setting, MakOne offers a foundation for research in behaviour modeling, inclusive context-aware system design, mental health analytics, and culturally grounded educational technologies. It contributes a critical African perspective to the growing body of data-driven studies on student behaviour. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MakOne: Behavioural Data of University Students' Smart Devices in Uganda Kizito, Michael Kayongo, Ivan Nyende, Hawa Chongomweru, Halimu Muyama, Lillian Asiku, Roy Alia Mugisha, Alice Computers and Society Understanding student behaviour in higher education is essential for improving academic performance, supporting mental well-being, and informing institutional policies. However, most existing behavioural datasets originate from Western institutions and overlook the unique socioeconomic and infrastructural contexts of African institutions, limiting the global applicability of resulting insights. This paper introduces MakOne, a novel multimodal dataset collected over six weeks from 72 students at Makerere University, Kampala, using iLog, a mobile sensing application. The dataset integrates passive smartphone sensor data-including location, physical activity, and screen usage-with ecological momentary assessments (EMAs) that capture students' moods and daily routines. Designed to reflect the lived experiences of students in an African setting, MakOne offers a foundation for research in behaviour modeling, inclusive context-aware system design, mental health analytics, and culturally grounded educational technologies. It contributes a critical African perspective to the growing body of data-driven studies on student behaviour. |
| title | MakOne: Behavioural Data of University Students' Smart Devices in Uganda |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2509.22334 |