Detecting Subtle Signs of School Attendance Issues Using Smartphone-Based Sensing

Abstract

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.

Keywords

• School attendance issues
• sleep state estimation
• subjective sleep quality estimation
• study engagement estimation
• smartphone sensing