Case study in a grounded theory perspective: Students' reasoning abilities in Lithner's framework across self-regulated

Abstract

Students' performance in understanding and developing algorithms from numerical methods is crucial because these concepts require them to engage in reasoning. DL-CA facilitates learning for fifth-semester bachelor students, specifically those in mathematics education, in comprehending numerical methods, algorithm concepts, and program development. Computer-assisted learning is appropriate for internalizing discovery learning and supporting autonomous study. The condition that differentiates individual learners is the level of self-regulated learning (SRL), which significantly enhances reasoning abilities by enabling goal setting, progress monitoring, and strategy adaptation, resulting in improved critical analysis and problem-solving skills. This grounded theory research investigates the reasoning outcomes of students in learning algorithm concepts for non-linear equation problems using computer-assisted discovery learning (DL-CA) through a web platform. It examines levels of self-regulated learning (SRL) and explores students' reasoning perspectives to describe differences in each level of SRL. Data analysis involved open coding through to categorization as essential steps in grounded theory, supported by method triangulation to enhance the validity and reliability of the findings. It was conducted using HyperRESEARCH output, a tool that aids in following comprehensive qualitative research procedures. The output suggests the following conjecture: The reasoning abilities of students in the high self-regulated learning (SRL) group encompass four categories: memorization, algorithm, plausibility, and mathematical foundation. In contrast, students in the medium and low SRL groups only demonstrate imitative reasoning abilities. Novel reasoning abilities are not sufficiently explained by these students, potentially due to limitations in the instruments or misunderstandings of the problems.