2021 EDSIG Proceedings: Abstract Presentation

Bridging Cognitive Load and Self-regulated Learning: A meta-UTAUT Perspective

Chengqi （John） Guo
James Madison University

Ping Wang
James Madison University

Abstract
Abstract The magnitude of students' reliance on Information & Communications Technologies (ICT) to learn rises to an unprecedented level nowadays, as many use ICT to support daily activities and learn ICT content. The parallel process of wielding various ICT manifestations to support learning activities and digesting instructional materials afforded by ICT courses can pose intriguing challenges to students, demanding studies to renew our understanding of individuals' perceptions or attitudes of ICT in learning, especially when these perceptions can exert impact on academic performance. Furthermore, educational researchers have been tirelessly asking for more integrative theoretical frameworks and approaches to examine how students optimally learn under new and evolving conditions (e.g., the COVID-19 pandemic), given the limitations of human processing ability. Some argued that promising developments could emerge in the opportunities to bridge cognitive load and self-regulated learning (SRL) theories, whose lines of research showed little overlap in the past even though they often stem from the same or similar research questions. Hence, the main goals of this paper are to exhibit and elucidate a nomological network in which individuals' cognitive beliefs about ICT, as a theoretical connection, integrates (aspects of) cognitive load theory and self-regulated learning theory. Consequently, we leverage the constructs of meta-UTAUT, a seminal technology acceptance framework, to shed light on the neglected yet essential role of individual-level cognitive beliefs about ICT as an alternative bridge to self-management, self-evaluation, and other theoretical anchors that are proposed by earlier studies. More specifically, we argue that (1) students' perceptions of ICT affect both extraneous and intrinsic cognitive load, which can help a teacher optimize the design of instructional materials to achieve enhanced learning outcomes. (2) Behavioral intention (the exogenous variable in meta-UTAUT) correlates with meta-cognitive strategy from SRL in a way that a strong meta-cognitive strategy tends to engender students' heightened behavioral intention to use ICT instructional content in learning. Combining (1) and (2), we consider cognitive load and SRL to correspond with students' cognitive beliefs about ICT; such beliefs, in turn, constitute one but not the only critical predictor of actual academic performance in an ICT-centered learning context (e.g., ICT courses). Our pilot study used a sample of over 200 college students who completed the survey online. Instrument items with poor measurement characteristics were removed. The second round of data collection and analysis presented measurement and structural models with competent acceptable fit indices. With slightly revised meta-UTAUT, cognitive load, and SRL items, our early-stage empirical findings show solid support to our propositions that (1) meta-cognitive strategy correlates with behavioral intention to learn ICT content and, together, affect the actual academic performance measured by primary assessment instruments in an ICT-focused course. (2) Unlike conventional UTAUT, students' attitudes toward learning ICT content emerge as a significant predictor of subsequent behavioral intention. (3) Students' cognitive load is responsive to variances within their perceptions of task efficacy and self-efficacy, whose effects are less well-known to educational scholars. Couching against the empirical findings, our study highlights how synthesizing two vast bodies of research (cognitive load and SRL) can provide the foundation for investigations of contemporary and future issues in educational science. The findings help us link the conceptualization of cognitive load, SRL, and meta-UTAUT to their strategy use in learning that is pervasively facilitated by modern ICT.