Merve Cerit
Ph.D. Student in Education, admitted Autumn 2020
Bio
My research aims to understand digital behaviors and their relationship with well-being through computational models and in-situ behavior change interventions. For my research, I have been working on a large-scale smartphone dataset, Screenome. I work closely with Nick Haber and Roy Pea from the School of Education and Nilam Ram and Byron Reeves from the Communication Department.
In my work, I mostly use machine learning and human-centered design principles.
Honors & Awards
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Stanford Interdisciplinary Graduate Fellowship (SIGF), Stanford University (2023-2026)
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Fulbright Scholar, Fulbright Commission (2019)
Education & Certifications
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MS, Stanford University, Learning, Design and Technology (LDT) (2020)
Research Interests
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Brain and Learning Sciences
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Data Sciences
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Social and Emotional Learning
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Technology and Education
Current Research and Scholarly Interests
My research aims to understand digital behaviors and their relationship with well-being through computational models and in-situ behavior change interventions. For my research, I have been working on a large-scale smartphone dataset, Screenome. I work closely with Nick Haber and Roy Pea from the School of Education and Nilam Ram and Byron Reeves from the Communication Department.
In my work, I mostly use machine learning and human-centered design principles.
Lab Affiliations
All Publications
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Person-Specific Analyses of Smartphone Use and Mental Health: An Intensive Longitudinal Study Over One Year.
JMIR formative research
2024
Abstract
Contrary to popular concerns about the harmful effects of media use on mental health, research on this relationship is ambiguous, stalling advances in theory, interventions, and policy. Scientific explorations of the relationship between media and mental health have mostly found null or small associations, with the results often blamed on the use of cross-sectional study designs or imprecise measures of media use and mental health.This exploratory empirical demonstration aimed to answer whether mental health effects are associated with media use experiences by (1) redirecting research investments to granular and intensive longitudinal recordings of digital experiences to build models of media use and mental health for single individuals over the course of one entire year, (2) using new metrics of fragmented media use to propose explanations of mental health effects that will advance person-specific theorizing in media psychology, and (3) identifying combinations of media behaviors and mental health symptoms that may be more useful for studying media effects than single measures of dosage and affect or assessments of clinical symptoms related to specific disorders.The activity on individuals' smartphone screens was recorded every 5 seconds when devices were in use over one year, resulting in a dataset of 6,744,013 screenshots and 123 fortnightly surveys from 5 adult participants. Each participant contributed between 0.8 and 2.7 million screens. 6 media use metrics were derived from smartphone metadata. Fortnightly surveys captured symptoms of depression, ADHD, state anxiety, and positive affect. Idiographic filter models (p-technique canonical correlation analyses) were applied to explore person-specific associations.Canonical correlations revealed substantial person-specific associations between media use and mental health, ranging from r = .82 (P = .008) to r = .92 (P = .031). The specific combinations of media use metrics and mental health dimensions were different for each person, reflecting significant individual variability. For instance, the media use canonical variate for one participant was characterized by higher loadings for app-switching, which, in combination with other behaviors, correlated strongly with a mental health variate emphasizing anxiety symptoms. For another, prolonged screen time, alongside other media use behaviors, contributed to a mental health variate weighted more heavily toward depression symptoms. These within-person correlations are among the strongest reported in this literature.Results suggest that the relationships between media use and mental health are highly individualized, with implications for the development of personalized models and precision smartphone-informed interventions in mental health. We discuss how our approach can be extended generally, while still emphasizing the importance of idiographic approaches. This study highlights the potential for granular, longitudinal data to reveal person-specific patterns that can inform theory development, personalized screening, diagnosis, and interventions in mental health.
View details for DOI 10.2196/59875
View details for PubMedID 39808832
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Loneliness and suicide mitigation for students using GPT3-enabled chatbots.
Npj mental health research
2024; 3 (1): 4
Abstract
Mental health is a crisis for learners globally, and digital support is increasingly seen as a critical resource. Concurrently, Intelligent Social Agents receive exponentially more engagement than other conversational systems, but their use in digital therapy provision is nascent. A survey of 1006 student users of the Intelligent Social Agent, Replika, investigated participants' loneliness, perceived social support, use patterns, and beliefs about Replika. We found participants were more lonely than typical student populations but still perceived high social support. Many used Replika in multiple, overlapping ways-as a friend, a therapist, and an intellectual mirror. Many also held overlapping and often conflicting beliefs about Replika-calling it a machine, an intelligence, and a human. Critically, 3% reported that Replika halted their suicidal ideation. A comparative analysis of this group with the wider participant population is provided.
View details for DOI 10.1038/s44184-023-00047-6
View details for PubMedID 38609517
View details for PubMedCentralID 6284019