DEPRESS: Dataset on Emotions, Performance, Responses, Environment, and Satisfaction during COVID-19

TL;DR

This paper introduces a comprehensive dataset tracking the mental health and academic performance of college students during the COVID-19 pandemic.

Contribution

The paper presents the first multimodal dataset capturing mental health, online learning, and environmental factors during the pandemic.

Findings

The dataset includes longitudinal data from 184 students over a year.

It combines mental health metrics with environmental and academic performance data.

Multimodal data sources include surveys, sensors, and wearable devices.

Abstract

COVID-19 posed a significant threat to the mental health of the population in general and college students in particular, severely disrupting their daily routines due to protective measures and lockdown policies. The abrupt transition from in-person to online learning further introduced uncertainty regarding academic performance. To comprehensively assess the impacts of the pandemic on college students, this study collected longitudinal data from June 2020 to June 2021, involving 184 undergraduate students at Worcester Polytechnic Institute. The dataset includes demographic and socioeconomic status information of participants, measures of mental health outcomes, online student engagement, computer and Internet performance, daily activity diary, general indoor environment satisfaction, Fitbit data, sensor measured indoor environment quality, facial expression, and GPA. To our best knowledge, this dataset is also the first dataset that covers multimodal assessment of mental health outcomes, online learning, and potential influencing variables during COVID-19. Data was gathered through online surveys, video recordings, IoT indoor environmental sensors, and Fitbit wristbands.

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