Mediating Effect of Technological Self-Efficacy on Digital Competence and Learning Engagement Among Alternative Learning System Learners

This study explored the mediating effect of Technological Self-Efficacy (TSE) on the relationship between Digital Competence (DC) and Learning Engagement (LE) among Alternative Learning System (ALS) Junior High School learners. Using a quantitative-correlational design, data were gathered from ALS learners through standardized survey instruments. Descriptive analysis showed that learners had high levels of DC, TSE, and LE, with emotional engagement rated the highest. Pearson correlation results revealed strong positive and significant relationships among all variable pairs: DC and LE, DC and TSE, and TSE and LE. Regression analysis confirmed that DC significantly predicted both TSE (path a) and LE (path c), and that TSE also significantly predicted LE (path b). When both DC and TSE were included in the model, the effect of DC on LE was reduced, indicating partial mediation. This was statistically supported by the Sobel test. The findings suggest that while digital competence directly contributes to learning engagement, its influence is stronger when learners have high technological self-efficacy. The study offers recommendations for ALS stakeholders, including strategies for integrating digital tools, strengthening learners’ confidence in using technology, and enhancing curriculum and policy support for technology-based learning environments.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-25
DOI
https://doi.org/10.5281/zenodo.17153326
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mediating Effect of Technological Self-Efficacy on Digital Competence and Learning Engagement Among Alternative Learning System Learners

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Mediating Effect of Technological Self-Efficacy on Digital Competence and Learning Engagement Among Alternative Learning System Learners

article en

Abstract

This study explored the mediating effect of Technological Self-Efficacy (TSE) on the relationship between Digital Competence (DC) and Learning Engagement (LE) among Alternative Learning System (ALS) Junior High School learners. Using a quantitative-correlational design, data were gathered from ALS learners through standardized survey instruments. Descriptive analysis showed that learners had high levels of DC, TSE, and LE, with emotional engagement rated the highest. Pearson correlation results revealed strong positive and significant relationships among all variable pairs: DC and LE, DC and TSE, and TSE and LE. Regression analysis confirmed that DC significantly predicted both TSE (path a) and LE (path c), and that TSE also significantly predicted LE (path b). When both DC and TSE were included in the model, the effect of DC on LE was reduced, indicating partial mediation. This was statistically supported by the Sobel test. The findings suggest that while digital competence directly contributes to learning engagement, its influence is stronger when learners have high technological self-efficacy. The study offers recommendations for ALS stakeholders, including strategies for integrating digital tools, strengthening learners’ confidence in using technology, and enhancing curriculum and policy support for technology-based learning environments.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 35%
Geochemistry and Geologic Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.