Diagnosing Junior High School Students’ Misconceptions and Confidence on Force and Motion Using the Certainty of Response Index (CRI) and Written Reasoning Analysis - Tia Sarawati; Ismail Fikri Natadiwijaya; Insih Wilujeng; Laifa Rahmawati (AL-ISHLAH: Jurnal Pendidikan, 2025)
Misconceptions in force and motion are persistent in junior high school and often remain hidden when assessment focuses only on correctness. Integrating students’ confidence with their answers may improve diagnosis and inform instruction while also revealing critical thinking quality in written justifications. This descriptive study involved 50 eighth-grade students (SMP Evans Indonesia). Students completed 10 reasoned multiple-choice diagnostic items on force and motion (e.g., velocity–time graphs, force–acceleration relation, Newton’s First Law), each accompanied by a Certainty of Response Index (CRI, 0–5). A cutoff of CRI ≥ 2.5 distinguished high vs low confidence. Written justifications were analyzed using Facione’s critical thinking indicators (interpretation, inference, explanation, evaluation). Five students representing different conceptual profiles were selected for in-depth reasoning analysis. Misconceptions were most frequent in velocity–time graph interpretation and other abstract representations (about four in ten students showed confident, incorrect answers), while more concrete ideas (e.g., balanced forces) showed relatively higher understanding. Students classified as having misconceptions reported a higher mean CRI (~3.15) than students who answered correctly but were uncertain (~2.85), indicating a strong tendency towards false confidence. In the qualitative subsample, justification analysis revealed that interpretation (80%) and explanation (60%) were more common than inference (40%), while evaluation (0%) was absent. CRI-based diagnostics reveal deeply held misconceptions and limited higher-order reasoning. Instruction should explicitly target conceptual change and scaffold inference and evaluative thinking through simulations, graph-focused tasks, and structured argumentation.