The Interrelationships between Metacognition and Modeling Competency: The Moderating Role of the Academic Year

Abstract

<p style="text-align: justify;">Several concerted movements toward mathematical modeling have been seen in the last decade, reflecting the growing global relationship between the role of mathematics in the context of modern science, technology and real life. The literature has mainly covered the theoretical basis of research questions in mathematical modeling and the use of effective research methods in the studies. Driven by the Realistic Mathematics Education (RME) theory and empirical evidence on metacognition and modeling competency, this research aimed at exploring the interrelationships between metacognition and mathematical modeling and academic year level as a moderator via the SEM approach. This study involved 538 students as participants. From this sample, 133 students (24.7%) were from the first academic year, 223 (41.4%) were from the second and 182 (33.8%) were from the third. A correlational research design was employed to answer the research question. Cluster random sampling was used to gather the sample. We employed structural equation modeling (SEM) to test the hypothesized moderation employing IBM SPSS Amos version 18. Our findings confirmed the direct correlation between metacognition and mathematical modeling was statistically significant. Academic year level as a partial moderator significantly moderates the interrelationships between the metacognitive strategies and mathematical modeling competency. The effect of metacognition on mathematical modeling competency was more pronounced in the year two group compared to the year one and three groups.</p>

Analysis of independence and creative thinking skills of students through assignment

“…The performance capacity of the brain can be maintained and optimized as long as it is used to learn and think continuously. Science process skills include basic and integrated skills, including formulating problems, collecting data, and proposing problem-solving solutions [20]. Learning encourages students to become active subjects who produce knowledge and not just passive objects which become knowledge consumers [21].…”

Examining the Optimal Choice of SEM Statistical Software Packages for Sustainable Mathematics Education: A Systematic Review

“…A study from [92] demonstrates that while task and self-approach had a significant and positive impact on mathematical modeling competency, task avoidance goals had a significant and detrimental impact. Furthermore, the findings depicted that metacognition and mathematical modeling had a direct cor-relation that was statistically significant [93]. Additionally, the authors argued that the interrelationships between mathematical modeling competency and metacognitive strategies are considerably moderated by academic year level, which acts as a partial moderator.…”

“…CB-SEM Statistical Applications [84,90,99,111] Lisrel (N = 4 studies) [80][81][82][83][91][92][93]97,109,117,119] Amos (N = 11 studies) [94,98,100,[106][107][108]112] Mplus (N = 7 studies)…”

“…Likewise, refs. [92,93] handled AMOS (v.18). A study from [92] demonstrates that while task and self-approach had a significant and positive impact on mathematical modeling competency, task avoidance goals had a significant and detrimental impact.…”

A systematic literature review of measurement of mathematical modeling in mathematics education context

“…Moreover, the researchers' reasoning for utilizing the atomistic approach in mathematics teaching is that it promotes mathematical learning (Frejd & Bergsten, 2018). To date, recent research has sought to use the atomistic approach to measure students' mathematical modeling (Fu & Xie, 2013;Hidayat et al, 2018Hidayat et al, , 2021b and combine between atomistic and holistic criteria (Durandt et al, 2021;Zöttl et al, 2011).…”