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All Journal Jurnal Infinity Theorems (The Original Research of Mathematics) Beta: Jurnal Tadris Matematika Martabe : Jurnal Pengabdian Kepada Masyarakat Journal of Didactic Mathematics Jurnal Informatika dan Teknologi Komputer ( J-ICOM) JITU (Jurnal Ilmiah Teknik Unida) Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA) Jurnal Infinity Musyawarah: Jurnal Pengabdian Masyarakat
Ginda M.A.Siregar
Universitas Samudra
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Transportation Other

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Case study in a grounded theory perspective: Students' reasoning abilities in Lithner's framework across self-regulated Siregar, Ginda Maruli Andi; Wahyudin, Wahyudin; Herman, Tatang
Jurnal Infinity Vol 14 No 1 (2025): VOLUME 14, NUMBER 1, INFINITY
Publisher : IKIP Siliwangi and I-MES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22460/infinity.v14i1.p259-282

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.
Computational thinking obstacles in students’ responses to AKM level 5 problems Siregar, Ginda Maruli Andi; Hidayat, Hidayat; Sukmawarti, Sukmawarti; Maulida, Aya Shofia
Journal of Didactic Mathematics Vol 6, No 3 (2025): December
Publisher : Mahesa Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/jdm.v6i3.2937

Abstract

The objective of this research is to elucidate the impediments encountered by students when attempting to solve Level 5 Minimum Competency Assessment (AKM) problems within the realm of Computational Thinking (CT). Employing a descriptive qualitative approach with a case study design, the research involved eleventh-grade students from a senior high school. Data were gathered through Level 5 AKM tests, in-depth interviews, and the analysis of students’ written responses. These data were subsequently analyzed using open coding, selective coding, and axial coding. The findings reveal that students encounter CT obstacles in several critical domains. Specifically, in the decomposition indicator, students demonstrated difficulties in breaking down graphical information, selectively extracting data without comprehending the interrelationships among values. In pattern recognition, students failed to discern upward-downward trends in harvest data or proportional relationships in probability tasks, thereby hindering their ability to draw comprehensive conclusions. Abstraction challenges emerged when students were unable to discern pertinent information, such as conflating actual frequencies with theoretical probabilities. In algorithmic thinking, students were unable to construct systematic steps in calculations or engage in logical reasoning. Furthermore, logical reasoning and evaluation were deficient, as evidenced by their inability to assess the plausibility of results or validate their answers.
Co-Authors Afrahun Naziah Fahril, M. Ari Fitriani Fitriani Hidayat Hidayat Irena Puji Luritawaty Jarnawi Afgani Dahlan Jofrishal Jofrishal M. Ari Fahril Maulida, Aya Shofia Musfirah, Rizka Naziah, Afrahun Novianda Novianda Nurul Fadillah Putra, Rachmad Almi Rahmatul Fajri Rika Mulyati Mustika Sari Sufyani Prabawanto, Sufyani Tatang Herman, Tatang Tisna Harmawan Wahyudin Wahyudin Wahyudin