Understanding Detectors for SMART Model Cognitive Operation in Mathematical Problem-Solving Process: An Epistemic Network Analysis

Abstract

Understanding indicators in self-regulated learning (SRL) that affect mathematical success using quantitative techniques such as epistemic networks hold potential for providing effective scaffolds that draw directly from the learner’s perspective. Tied to learning success, SRL provides a range of frameworks for identifying students' affective, cognitive, and metacognitive performance in a computer-based learning environment. This research can investigate how ENA can contribute as a visualization device to understanding of the metacognitive aspect of math learning. With the aim, we collected text responses from an online math problem-solving environment that encouraged reflections on self-regulated learning patterns that differ by the rate of correctness and familiarity with the educational tool. Student responses consisted of their explanations of strategies and solutions after the scaffolding instructions. Our team deductively designed detectors reflecting on assembling and translating operations (Winne’s SMART model) to examine differences in the learner’s self-regulated learning behaviors. We then leveraged Epistemic Network Analysis (ENA) using these detected indicators as codes to compare the results within two categories: performance on correctness and familiarity developed over time. Models show stronger co-occurrence between numerical representation and contextual representation and highlight the critical impact of outcome orientation on learner success. When the final answer is correct, or learners are more familiar with the educational tool, there is a strong outcome orientation connected to contextual representation within SRL operations.