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Record W2809091360 · doi:10.2196/10069

How an Environment of Stress and Social Risk Shapes Student Engagement With Social Media as Potential Digital Learning Platforms: Qualitative Study

2018· article· en· W2809091360 on OpenAlexvenueno aff
Becky Hartnup, Lin Dong, Andreas B. Eisingerich

Bibliographic record

VenueJMIR Medical Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsSocial mediaStress (linguistics)Qualitative researchPsychologyMathematics educationComputer scienceSociologyWorld Wide WebSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Social media has been increasingly used as a learning tool in medical education. Specifically, when joining university, students often go through a phase of adjustment, and they need to cope with various challenges such as leaving their families and friends and trying to fit into a new environment. Research has shown that social media helps students to connect with old friends and to establish new relationships. However, managing friendships on social media might intertwine with the new learning environment that shapes students' online behaviors. Especially, when students perceive high levels of social risks when using social media, they may struggle to take advantage of the benefits that social media can provide for learning. OBJECTIVE: This study aimed to develop a model that explores the drivers and inhibitors of student engagement with social media during their university adjustment phase. METHODS: We used a qualitative method by interviewing 78 undergraduate students studying medical courses at UK research-focused universities. In addition, we interviewed 6 digital technology experts to provide additional insights into students' learning behaviors on social media. RESULTS: Students' changing relationships and new academic environment in the university adjustment phase led to various factors that affected their social media engagement. The main drivers of social media engagement were maintaining existing relationships, building new relationships, and seeking academic support. Simultaneously, critical factors that inhibited the use of social media for learning emerged, namely, collapsed online identity, uncertain group norms, the desire to present an ideal self, and academic competition. These inhibitors led to student stress when managing their social media accounts, discouraged them from actively engaging on social media, and prevented the full exploitation of social media as an effective learning tool. CONCLUSIONS: This study identified important drivers and inhibitors for students to engage with social media platforms as learning tools. Although social media supported students to manage their relationships and support their learning, the interaction of critical factors, such as collapsed online identity, uncertain group norms, the desire to present an ideal self, and academic competition, caused psychological stress and impeded student engagement. Future research should explore how these inhibitors can be removed to reduce students' stress and to increase the use of social media for learning. More specifically, such insights will allow students to take full advantage of being connected, thus facilitating a richer learning experience during their university life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.385
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations42
Published2018
Admission routes1
Has abstractyes

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