Design Thinking and Meta-Analysis for a DECABOT-Assisted Instructional Design in Critical Thinking

Dimas Nugroho; Ida Dwijayanti; Aryo Andri Nugroho

doi:10.30998/formatif.v16i2.4273

Authors

Dimas Nugroho Universitas PGRI Semarang Author
Ida Dwijayanti Universitas PGRI Semarang Author
Aryo Andri Nugroho Universitas PGRI Semarang Author

DOI:

https://doi.org/10.30998/formatif.v16i2.4273

Keywords:

Design thinking, critical thinking, meta-analysis, empathic-PBL, innovative learning

Abstract

Indonesian students face persistent challenges in developing critical thinking skills in mathematics, as reflected in low international assessment performance and national reports linking weak reasoning to classrooms dominated by one-way, conventional teaching. Accordingly, this study sought to identify the underlying learning problems and propose an evidence-based instructional design. Employing a mixed-method, design-oriented approach that combined a qualitative Design Thinking needs analysis with quantitative meta-analytic synthesis, the researchers gathered data from 85 Grade XI students and 6 mathematics teachers across three senior high schools in Central Java through questionnaires, interviews, and classroom observation. Empathize-phase data showed over 85% of students reporting mathematics anxiety, 70% fearing mistakes, and calculus (functions, limits, derivatives, integrals) as the hardest topic; empathy maps for students and teachers converged on a five-part problem profile: critical thinking held back by mathematics anxiety, monotonous teaching media, weak contextual grounding, slow feedback, and unaddressed socio-emotional needs. These findings point toward the Empathic-PBL model, supported by DECABOT (Desmos Classroom Activities and Chatbot AI), as the resulting design solution. To ground this proposal empirically without directly validating DECABOT, a random-effects meta-analysis of 13 analogous-component studies (not direct trials of DECABOT) covering PBL, SEL integration, interactive digital media, and AI chatbot assistance produced a large pooled effect size (Hedges' g = 0.859, 95% CI: 0.825–0.892, I² = 11.3%, p < .001) via a random-effects model. DECABOT thus stands as a theoretically grounded, conceptual instructional design candidate for senior high school calculus, supported by evidence from analogous interventions rather than a tested product, and still awaiting experimental confirmation.

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