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Self-efficacy

Info literacy skills predict teachers’ ICT self-efficacy

Peciuliauskiene P, Tamoliune G, Trepule E · International journal of educational technology in higher education · 2022

Open access · cc by · source: Europe PMC

For Lithuanian pre-service teachers, stronger perceived information evaluation and search literacy predicted higher ICT self-efficacy for teaching.

Study at a glance

Design
Cross-sectional — SEM survey linking information literacy to ICT teaching self-efficacy
N
N=310 · 310 pre-service teachers at two Lithuanian universities
Population
Lithuanian pre-service teachers
Outcome
ICT self-efficacy for teaching predicted by information evaluation and search literacy

Structured fields used in claim comparison tables when every cited study has a complete layer.

Key findings

Perceived information evaluation literacy positively predicted information search literacy and ICT self-efficacy; information search literacy also positively predicted ICT self-efficacy, including an indirect path from evaluation literacy through search literacy.

Methodology

Researchers surveyed pre-service teachers at Vytautas Magnus University and Vilnius University and tested a structural equation model relating information evaluation literacy, information search literacy, and ICT self-efficacy in teaching.

Limitations

Self-report SEM on a cross-sectional sample cannot prove that training literacy causes higher teaching ICT self-efficacy, and findings are limited to Lithuanian teacher-education contexts.

How this study connects

Role on claims

Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.

  • SupportsSelf-Efficacyconcept

    Digital learning experiences and training contexts are studied as correlates of ICT or teaching-related self-efficacy and related engagement outcomes.

    Evidence for the claim as stated.

  • SupportsFactor Analysismethod

    Among 310 Lithuanian pre-service teachers, a structural equation model used literacy and self-efficacy constructs: perceived information-evaluation literacy predicted information-search literacy (B = 0.993) and ICT self-efficacy (B = 0.369), with an additional path from search literacy to ICT self-efficacy. Cross-sectional self-report SEM cannot prove that training evaluation literacy would raise teaching ICT self-efficacy.

    Evidence for the claim as stated.

  • SupportsFactor Analysismethod

    Measurement models and change scores are different jobs. TIQS uses EFA/CFA then SEM paths, including an unexpected negative individualised-teaching path. The online-quality paper uses EFA to rank importance, not to predict engagement. The teacher-education paper uses SEM path coefficients among self-beliefs. The faculty paper reports pre/post attitude change for 695 trainees without a control. One software family (factors, SEM, scales) is not one empirical result.

    Evidence for the claim as stated.

  • Among Lithuanian pre-service teachers, perceived information-evaluation literacy predicted information-search literacy and ICT self-efficacy, with an indirect path through search literacy. Self-report SEM on a cross-sectional sample cannot prove that literacy training would raise teaching ICT self-efficacy.

    Evidence for the claim as stated.

  • What gets to be a mediator is a modelling choice, not a discovery of a unique true path. The TEL course paper puts LMS engagement and quizzes between attendance and grades; the TIQS paper puts quality dimensions and frequency on engagement and competencies, including an unexpected negative path; the teacher-education paper chains evaluation literacy → search literacy → ICT self-efficacy. Different diagrams on different variables are not replications of one mediation law.

    Evidence for the claim as stated.

Open questions

Tensions this paper is part of

From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.

  • Scope difference — different assays, populations, or outcomes

    Measurement models and change scores are different jobs. TIQS uses EFA/CFA then SEM paths, including an unexpected negative individualised-teaching path. The online-quality paper uses EFA to rank importance, not to predict engagement. The teacher-education paper uses SEM path coefficients among self-beliefs. The faculty paper reports pre/post attitude change for 695 trainees without a control. One software family (factors, SEM, scales) is not one empirical result.

  • Scope difference — different assays, populations, or outcomes

    What gets to be a mediator is a modelling choice, not a discovery of a unique true path. The TEL course paper puts LMS engagement and quizzes between attendance and grades; the TIQS paper puts quality dimensions and frequency on engagement and competencies, including an unexpected negative path; the teacher-education paper chains evaluation literacy → search literacy → ICT self-efficacy. Different diagrams on different variables are not replications of one mediation law.

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