Dissemin is shutting down on January 1st, 2025

Published in

Springer, Education and Information Technologies, 10(29), p. 12735-12757, 2023

DOI: 10.1007/s10639-023-12361-9

Links

Tools

Export citation

Search in Google Scholar

Modelling STEM learners’ academic performance in advanced level secondary schools with smart classrooms in Rwanda

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

AbstractSmart classroom setting is an effective technology-enhanced pedagogy when an instructor capitalises on the key drivers underlying its success. The objectives of the study are twofold. First, to investigate the factors that impact learners’ academic performance in the smart classroom setting. Second, to determine whether learners’ performance varies based on their gender and prior experience in the smart classroom. Three schools were selected and sampled from a pool of public advanced level (A’ Level) STEM secondary schools with smart classrooms in Rwanda because they were among the first to set up smart classrooms. Data were randomly obtained from 389 learners, majoring in six different combinations. Exploratory factor analysis (EFA) was used to assess the validity and reliability of the measurement scale. A valid and thought-provoking learners’ academic performance model, to enhance their learning outcomes, is presented. The model was validated using Structural equation modelling (SEM). The results indicated that technology self-efficacy, perceived ease of use, and learning satisfaction were all predictors of learners’ performance. Learners’ perceived ease of use and satisfaction were direct predictors of their performance. Besides, learners’ perceived ease of use and usefulness were directly affected by technology self-efficacy. On the other hand, technology self-efficacy indirectly impacts learners’ performance. The invariance analysis across gender groups revealed that male learners perceive smart classroom technology to be easier to use than females. Meanwhile, no evidence of metric invariance estimation of experience was found. We argue that effective pedagogical approaches can mitigate gender disparities in perceived usability of smart classroom technologies.