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Record W3160335138 · doi:10.2196/26197

Evaluating a Middle-School Digital Citizenship Curriculum (Screenshots): Quasi-Experimental Study

2021· article· en· W3160335138 on OpenAlexvenueno aff
David S. Bickham, Summer Moukalled, Heather K Inyart, Rona Zlokower

Bibliographic record

VenueJMIR Mental Health · 2021
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumProsocial behaviorPsychologyMedical educationMental healthPoison controlApplied psychologyPedagogyMathematics educationSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Background Screenshots is an in-school curriculum that seeks to develop positive digital social skills in middle school students with the long-term goal of improving their health and well-being. The program imparts knowledge and teaches skills upon which young adolescents can build a set of beliefs and behaviors that foster respectful interactions, prosocial conflict resolutions, and safe and secure use of communication technology. Intervening in this way can improve young people’s mental health by limiting their exposure to cyberbullying and other forms of negative online interactions. This study reports on an evaluation of the Screenshots program conducted with seventh graders in a public school system in a midsized New England city. Objective This study aims to determine the effectiveness of the Screenshots program in increasing participants’ knowledge about key concepts of digital citizenship and in shifting beliefs and intended behaviors to align with prosocial and safe online interactions. In addition, the study examines whether the program has varying effects on males’ and females’ conflict and bullying resolution strategies. Methods This quasi-experimental evaluation was conducted in four middle schools in which one group of seventh graders received the Screenshots curriculum and another did not. Before and after the curriculum, all students completed a questionnaire that measured their knowledge of and beliefs about digital citizenship and related online behavioral concepts, their attitudes regarding strategies for stopping online bullying, and their intended online conflict resolution behaviors. Results The sample included 92 students who received the curriculum and 71 students who were included in the comparison group. Pre- to postinstruction retention rates ranged from 52% (33/63) to 84% (21/25), varying by school and condition. The results showed an increase in knowledge about key curricular concepts for some students (F1,32=9.97; P=.003). In response to some individual items, students decreased their belief supportive of a negative online behavior (F1,76=9.00; P=.004) and increased their belief consistent with an online safety behavior (F1,42=4.39; P=.04) compared with the comparison group. Gender moderated the results related to conflict resolution, with males from one school reducing their endorsement of an aggressive option (F2,40=5.77; P=.006) and males from another school increasing their reported tendency to pursue a nonaggressive option (F2,28=3.65; P=.04). On average, participants reported learning something new from the classes. Conclusions This study represents a rare evaluation of an in-school digital citizenship program and demonstrates the effectiveness of Screenshots. Students’ increased knowledge of key curricular concepts represents a foundation for developing future beliefs and healthy behaviors. Differences in how adolescent males and females experience and perpetrate online aggression likely explain the conflict resolution findings and emphasize the need to examine gender differences in response to these programs. Students’ high ratings of the relevance of Screenshots’ content reinforce the need for this type of intervention.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.075
GPT teacher head0.420
Teacher spread0.345 · 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.

Study designObservational
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

Citations38
Published2021
Admission routes1
Has abstractyes

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