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Record W4407068649 · doi:10.1007/s10643-025-01855-5

The Efficacy of the TEACH e-Learning Course at Improving Early Childhood Education Students’ Physical Activity and Sedentary Behaviour Related Self-Efficacy, Knowledge, Intentions, and Perceived Behavioural Control

2025· article· en· W4407068649 on OpenAlexafffundabout
Myranda Hawthorne, Matthew Bourke, Brianne A. Bruijns, Leigh M. Vanderloo, Jennifer D. Irwin, Rachel Heydon, Valerie Carson, Andrew M. Johnson, Kristi B. Adamo, Shauna M. Burke, Brian W. Timmons, Patricia Tucker

Bibliographic record

VenueEarly Childhood Education Journal · 2025
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsMcMaster UniversityChildren’s Health Research InstituteUniversity of OttawaUniversity of AlbertaWestern University
FundersSocial Sciences and Humanities Research Council
KeywordsSociology of EducationSelf-efficacyPsychologyEarly childhood educationSelf-controlCourse (navigation)Physical activityDevelopmental psychologyEarly childhoodControl (management)Social psychologyPedagogyMedicine

Abstract

fetched live from OpenAlex

Promoting high levels of physical activity and reducing sedentary behaviour in early childhood is essential for children’s health and wellbeing. However, despite existing recommendations, pre-service early childhood educators do not receive adequate training to promote physical activity and reduce sedentary behaviour among young children in childcare settings. This study aimed to evaluate the efficacy of the TEACH e-Learning course in enhancing physical activity and sedentary behaviour-related self-efficacy, knowledge, and behavioural intentions among a sample of pre-service early childhood educators (ECEs) across Canada. For this study, a quasi-experimental design was used, with participants (175 intervention, 117 comparison) recruited from 19 English-speaking Canadian colleges and universities offering pre-service ECE programs. The intervention group completed a 4-module e-Learning course on physical activity and sedentary behaviour, including subtopics on risky play and screen time. Data were collected at baseline, post-intervention, and 3-month follow-up using validated questionnaires. Changes in outcomes (e.g., self-efficacy, knowledge, behaviour intentions and control) were examined using linear mixed-effects models. Significant improvements were observed in the intervention group’s task self-efficacy (d = 0.54), knowledge of physical activity and sedentary behaviour guidelines (d = 0.66), and intentions to promote physical activity and reduce sedentary behaviour (0.38 ≤ d ≤ 0.44) post-intervention compared to the comparison group. However, these gains were not sustained at the 3-month follow-up. Perceived behavioural control showed limited improvement, and the intervention did not significantly impact intentions to avoid screen time. The TEACH e-Learning course improved short-term self-efficacy, knowledge, and behavioural intentions related to physical activity and sedentary behaviour among pre-service ECEs. However, the results of this study have shown that there are factors affecting desired outcomes that can not be adequately resolved through short-term training. Integrating such courses into pre-service training programs could enhance ECEs’ competencies in promoting physical activity, benefiting children’s long-term health. Further research should explore more long-term strategies to promote retention of increases in self-efficacy, knowledge and behavioural intentions related to physical activity and sedentary behaviour among ECEs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.283
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes3
Has abstractyes

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