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All Journal Journal of Mathematics Instruction, Social Research and Opinion Polyhedron International Journal in Mathematics Education
Lidaini Lidaini
Universitas Nahdlatul Wathan Mataram
Humanities Education Languange, Linguistic, Communication & Media Mathematics Social Sciences

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Optimizing Transdisciplinary Epistemic Knowledge Through STEAM+X-Integrated Epistemic Learning Patterns in Mathematical Comparison Muhamad Galang Isnawan; R. Didi Kuswara; Salman Salman; Lidaini Lidaini; Naif Mastoor Alsulami
Journal of Mathematics Instruction, Social Research and Opinion Vol. 5 No. 3 (2026): September
Publisher : MASI Mandiri Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58421/misro.v5i3.1318

Abstract

Low transdisciplinary epistemic knowledge is one of the problems in mathematical comparison learning. Therefore, this study aims to optimize this competency through the development of a teaching module based on epistemic learning patterns integrated with STEAM+X. This study used a design-based research design. The instruments used were a practical problem questionnaire, a transdisciplinary epistemic knowledge test, and a teaching module. The participants were 36 people, consisting of 15 students (aged 13–15 years) and 21 educational stakeholders (teaching experience 0–25 years). Qualitative data were analyzed using thematic analysis, while quantitative data were analyzed using the Wilcoxon Signed Rank Test. The results revealed that identity and orientation crises, lack of spatial experience, pedagogical mismatches, and ecosystem limitations were the contributing factors to low student competency. The learning principles expected to be optimized were student-centered learning, technology integration, and learning ecosystem support. ELP-STEAM+X-AI was then implemented because it aligned with the previous principles. The study concluded that the learning was able to optimize transdisciplinary epistemic knowledge (Z = -2,96; p = 0,003; and r = 0,76 with large impact) because students were facilitated in constructing mathematical comparison concepts through various STEAM+X-based epistemic activities. This finding recommends that other subjects adopt ELP-STEAM+X-AI to optimize student competency.
The effect of learning intensity on students’ statistics course scores: A multiple regression approach with Ridge regularization Muh. Rusmayadi; Samsuriadi; Lidaini Lidaini; Muhamad Galang Isnawan; Naif Mastoor Alsulami; Aditya Purnama
Polyhedron International Journal in Mathematics Education Vol. 4 No. 1 (2026): pijme
Publisher : Nashir Al-Kutub Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59965/pijme.v4i1.294

Abstract

Low achievement in statistics courses is a persistent concern in higher education, yet the contributions of study time and attendance remain underexplored, particularly in Indonesian undergraduate contexts and without correcting for predictor multicollinearity. This study addresses this gap by applying Ridge regularization to estimate the independent effects of daily study hours and attendance rate on statistics course scores among 20 Indonesian undergraduates. Classical OLS assumptions were tested; Ridge regression with Leave-One-Out Cross-Validation was applied when violations occurred. Study hours and attendance jointly explained 97.54% of score variance (R² = 0.975; F = 337.70; p < 0.001); however, severe multicollinearity (VIF = 11.77) destabilized OLS estimates. Ridge regression (λ = 1.0) produced stable coefficients (b₁ = 2.87; b₂ = 0.80) with negligible accuracy loss (R² = 0.974). The near-perfect collinearity of the predictors indicates that study hours and attendance are part of the same underlying construct academic engagement rather than acting through independent pathways. These findings challenge the conventional additive model of learning time and imply that interventions targeting self-regulated learning may be more effective than policies addressing attendance or study time in isolation.
Co-Authors Aditya Purnama Isnawan, Muhamad Galang Kuswara, R. Didi Naif Mastoor Alsulami Rusmayadi, Muh Salman Salman Samsuriadi