Detecting Subtle Signs of School Attendance Issues Using Smartphone-Based Sensing
In recent years, school attendance issues among university students have been increasing, which can lead to repeating courses, dropping out of school, or even social withdrawal. Despite the existence of counseling services, students often delay help-seeking, which can cause symptoms to worsen and ma...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10816417/ |
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| author | Viktor Erdelyi Teruhiro Mizumoto Yuichiro Kitai Daiki Ishimaru Hiroyoshi Adachi Teruo Higashino Manabu Ikeda |
| author_facet | Viktor Erdelyi Teruhiro Mizumoto Yuichiro Kitai Daiki Ishimaru Hiroyoshi Adachi Teruo Higashino Manabu Ikeda |
| author_sort | Viktor Erdelyi |
| collection | DOAJ |
| description | In recent years, school attendance issues among university students have been increasing, which can lead to repeating courses, dropping out of school, or even social withdrawal. Despite the existence of counseling services, students often delay help-seeking, which can cause symptoms to worsen and make support more difficult. Thus, it is essential to identify at-risk students early and encourage them to seek help. A realistic approach must minimize the burden on students, rely only on devices they already own, and operate correctly even for students who are less engaged or prone to social withdrawal. While several techniques have been proposed to estimate individual indicators, they fail to address one of these requirements due to requiring additional devices or requiring user attention and interaction. In this paper, we propose an unobtrusive screening method for detecting subtle signs of school attendance issues in university students. We develop a smartphone app to collect sensor data and collect ground truth information using questionnaires for 1) sleep problems; and 2) decreased student engagement. We collect data from 58 university students for about 10 months, and build estimation models for the above indicators. Our evaluation shows that the estimation models are sufficiently accurate in flagging problematic cases. The indicators can then be used to notify at-risk students and medical practitioners, enabling timely intervention. This screening is not intended to replace traditional face-to-face medical examinations, but rather to selectively flag at-risk students and connect them with medical experts. |
| format | Article |
| id | doaj-art-d6b54cd676a44c35966d9d157b69c871 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d6b54cd676a44c35966d9d157b69c8712025-01-14T00:02:34ZengIEEEIEEE Access2169-35362025-01-01134652466910.1109/ACCESS.2024.352310810816417Detecting Subtle Signs of School Attendance Issues Using Smartphone-Based SensingViktor Erdelyi0https://orcid.org/0000-0002-6880-2902Teruhiro Mizumoto1https://orcid.org/0000-0003-0281-1205Yuichiro Kitai2Daiki Ishimaru3https://orcid.org/0000-0002-9213-8220Hiroyoshi Adachi4https://orcid.org/0000-0001-9259-3197Teruo Higashino5https://orcid.org/0000-0001-5685-0424Manabu Ikeda6Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, JapanGraduate School of Information Science and Technology, Osaka University, Suita, Osaka, JapanGraduate School of Information Science and Technology, Osaka University, Suita, Osaka, JapanDepartment of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Osaka, JapanHealth and Counseling Center, Osaka University, Toyonaka, Osaka, JapanDepartment of Information and Computer Science, Kyoto Tachibana University, Kyoto, Kyoto, JapanDepartment of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Osaka, JapanIn recent years, school attendance issues among university students have been increasing, which can lead to repeating courses, dropping out of school, or even social withdrawal. Despite the existence of counseling services, students often delay help-seeking, which can cause symptoms to worsen and make support more difficult. Thus, it is essential to identify at-risk students early and encourage them to seek help. A realistic approach must minimize the burden on students, rely only on devices they already own, and operate correctly even for students who are less engaged or prone to social withdrawal. While several techniques have been proposed to estimate individual indicators, they fail to address one of these requirements due to requiring additional devices or requiring user attention and interaction. In this paper, we propose an unobtrusive screening method for detecting subtle signs of school attendance issues in university students. We develop a smartphone app to collect sensor data and collect ground truth information using questionnaires for 1) sleep problems; and 2) decreased student engagement. We collect data from 58 university students for about 10 months, and build estimation models for the above indicators. Our evaluation shows that the estimation models are sufficiently accurate in flagging problematic cases. The indicators can then be used to notify at-risk students and medical practitioners, enabling timely intervention. This screening is not intended to replace traditional face-to-face medical examinations, but rather to selectively flag at-risk students and connect them with medical experts.https://ieeexplore.ieee.org/document/10816417/School attendance issuessleep state estimationsubjective sleep quality estimationstudy engagement estimationsmartphone sensing |
| spellingShingle | Viktor Erdelyi Teruhiro Mizumoto Yuichiro Kitai Daiki Ishimaru Hiroyoshi Adachi Teruo Higashino Manabu Ikeda Detecting Subtle Signs of School Attendance Issues Using Smartphone-Based Sensing IEEE Access School attendance issues sleep state estimation subjective sleep quality estimation study engagement estimation smartphone sensing |
| title | Detecting Subtle Signs of School Attendance Issues Using Smartphone-Based Sensing |
| title_full | Detecting Subtle Signs of School Attendance Issues Using Smartphone-Based Sensing |
| title_fullStr | Detecting Subtle Signs of School Attendance Issues Using Smartphone-Based Sensing |
| title_full_unstemmed | Detecting Subtle Signs of School Attendance Issues Using Smartphone-Based Sensing |
| title_short | Detecting Subtle Signs of School Attendance Issues Using Smartphone-Based Sensing |
| title_sort | detecting subtle signs of school attendance issues using smartphone based sensing |
| topic | School attendance issues sleep state estimation subjective sleep quality estimation study engagement estimation smartphone sensing |
| url | https://ieeexplore.ieee.org/document/10816417/ |
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