StudentLife: Using Smartphones to Assess Mental Health and Academic Performance of College Students

Wang, Rui; Chen, Fanglin; Chen, Zhenyu; Li, Tianxing; Harari, Gabriella M.; Tignor, Stefanie M.; Zhou, Xia; Ben‐Zeev, Dror; Campbell, Andrew T.

doi:10.1007/978-3-319-51394-2_2

Cited by 173 publications

(307 citation statements)

References 34 publications

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“…During recruitment, our aim was to have a nightlife population as representative as possible. This population is significantly different than those reported in previous Ubicomp research, e.g., undergraduate and graduate students in Korea [5] or the US [48].…”

Section: Analysis Of Survey Data (Rq1)contrasting

confidence: 63%

“…As we explain later, the intentionality of our crowdsourcing task also allows to study issues related to the perception of social acceptability of video recording in everyday life. Finally, our study covers a much larger geographic area than [5,48,47], including two cities with linguistic and cultural differences, but also many areas around each city.…”

Section: Place Characterizationmentioning

confidence: 99%

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The night is young

Santani

Biel

Labhart

et al. 2016

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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We present a mobile crowdsourcing study to capture and examine the nightlife patterns of two youth populations in Switzerland. Our contributions are three fold. First, we developed a smartphone application to capture data on places, social context and nightlife activities, and to record mobile videos capturing the ambiance of places. Second, we conducted an "in-the-wild" study with more than 200 participants over a period of three months in two Swiss cities, resulting in a total of 1,394 unique place visits and 843 videos that spread across place categories (including personal homes and public parks), social and ambiance variables. Finally, we investigated the use of automatic ambiance features to estimate the loudness and brightness of places at scale, and found that while features are reliable with respect to video content, videos do not always reflect the place ambiance reported by people in-situ. We believe that the developed methodology provides an opportunity to understand the physical mobility, activities, and social context of youth as they experience different aspects of nightlife.

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Section: Analysis Of Survey Data (Rq1)contrasting

confidence: 63%

Section: Place Characterizationmentioning

confidence: 99%

The night is young

Santani

Biel

Labhart

et al. 2016

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

View full text Add to dashboard Cite

show abstract

“…Yet, there are exemplary studies that demonstrate how performance prediction studies should be conducted, how prediction parameters and results should be analysed, how comparisons with other approaches should be presented and that provide elaborate explanations of expected outcomes and peculiarities of observations. Examples of such research are discussed in various recent works ( [38], [39], [44], [41], [42], [49], [15]). …”

Section: Discussionmentioning

confidence: 99%

“…Other studies that follow less common approaches include those that use Markov Networks ( [39]), Collaborative Multi-Regression models ( [40]), smartphone data to investigation the correlation between students' social and study behaviour and academic performance ( [41]) and those that perform Sentiment Analysis of discussion form posts in MOOCs ( [42]). Yet, some studies discuss algorithms developed for the sole purpose of student performance prediction ( [43], [44]).…”

Section: Prediction Techniques and Algorithmsmentioning

confidence: 99%

A Comparative Analysis of Selected Studies in Student Performance Prediction

Ashenafi¹

2017

IJDKP

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“…III. RELATED WORK Rui Wang [1] carried out a SmartGPA study using students' direct reporting and passive sensing data collected from student smartphones, while endevouring to understand student behavioural patterns. He analysed the gps data, sensing data and audio data to understand the behaviour of students of academic different abilities.…”

Section: A Learning Analytics (La)mentioning

confidence: 99%

Identifying the impact of friends on their peers academic performance

Scanlon

2016

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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Abstract-Historically data collection in the research process involves either surveys, interviews or observation, or any combination of all three. Recent developments in the area of formative educational methods have enabled other data collection options. Data sources now available include university Virtual Learning Environments (VLEs), E-learning and many other knowledge management systems. Data-sets harvested from these sources are less susceptible to the inherent biases introduced through the intervention of human interpretation. Data is often structured, complete and traceable. The research in this paper aims to utilise one of these unique digital data-sets which represents the footprints created by student activities within a university environment and through Social Network Analysis to identify their influences within peer groups.

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StudentLife: Using Smartphones to Assess Mental Health and Academic Performance of College Students

Cited by 173 publications

References 34 publications

The night is young

The night is young

A Comparative Analysis of Selected Studies in Student Performance Prediction

Identifying the impact of friends on their peers academic performance

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