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Academic Performance Patterns Detection Using Digital Phenotyping
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DOI：10.1007/978-3-032-10731-2_19.png)
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Abstract
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This article analyzes some factors that influence poor academic performance by studying their digital phenotype. The research uses passive and active data collected from 15 volunteer students over a two-week period. Data collection was performed using a free software tool, and the information was stored in a cloud-hosted database. The results show that students with greater social interaction and better sleep quality had better academic performance, while those who had greater mobility between their school and home tended to have lower performance. These findings highlight the usefulness of digital phenotyping as a tool to detect students at risk of poor academic performance.
Keywords:
Academic performance
Digital phenotype
data analysis
Pattern detection
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