Investigating Effects of Perceived Technology-enhanced Environment on Self-regulated Learning: Beyond P-values

TL;DR

This study investigates how technology-enhanced environments influence eighth-grade students' self-regulated learning, highlighting the roles of self-efficacy, perceptions of ICT, and self-assessment tools, using advanced statistical modeling.

Contribution

It introduces a comprehensive analysis of technology's impact on self-regulation, emphasizing monitoring strategies and Bayesian SEM for better insights.

Findings

Self-efficacy indirectly affects self-regulation through perceived technology use.

Self-assessment tools significantly predict self-regulation outcomes.

Bayesian SEM provides more accurate uncertainty estimates.

Abstract

This study examined the effects of a technology-enhanced intervention on the self-regulation of 262 eighth-grade students, employing information and communication technology (ICT) and web-based self-assessment tools set against science learning. The data were analyzed using both maximum likelihood and Bayesian structural equation modeling to unravel the intricate relationships between self-regulation, self-efficacy, perceptions of ICT, and self-assessment tools. Our research findings underscored the direct and indirect impacts of self-efficacy, perceived ease of use, and perceived use of technology on self-regulation. The results revealed the predictive power of self-assessment tools in determining self-regulation outcomes, underlining the potential of technology-enhanced self-regulated learning environments. The study posited the necessity to transcend mere technology incorporation and to emphasize the inclusion of monitoring strategies explicitly designed to augment self-regulation. Interestingly, self-efficacy appeared to indirectly influence self-regulation outcomes through perceived the use of technology rather than direct influence. Analytically, this research indicated that Bayesian estimation could offer a more comprehensive insight into structural equation modeling by more accurately assessing our estimates' uncertainty. This research substantially contributes to comprehending the influence of technology-enhanced environments on students' self-regulated learning, stressing the importance of constructing practical tools explicitly designed to cultivate self-regulation.