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All Journal International Journal of Pedagogy and Teacher Education JKPK (Jurnal Kimia dan Pendidikan Kimia)
Dimas Gilang Ramadhani
Chemistry Education, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Semarang
Chemical Engineering, Chemistry & Bioengineering Chemistry Education Social Sciences

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Evaluating Chemistry Teacher’s Questioning Skills in Microteaching Based on Artificial Intelligence (AI) Using an Assessment Rubric Nala Izzul Muna; Sri Susilogati Sumarti; Harjono Harjono; Woro Sumarni; Dimas Gilang Ramadhani
International Journal of Pedagogy and Teacher Education Vol 9, No 2 (2025): International Journal of Pedagogy and Teacher Education - October
Publisher : The Faculty of Teacher Training and Education (FKIP), Universitas Sebelas Maret, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijpte.v9i2.106168

Abstract

This study evaluates the questioning skills of chemistry teachers during microteaching using an AI-assisted assessment rubric. A total of 200 publicly available YouTube videos (2019–2024) were selected using defined criteria: chemistry instruction, teacher–student questioning, Indonesian language, minimum audio clarity of 45 dB, and at least 5 minutes in duration. All videos featured pre-service teachers. Transcripts were generated using Otter.ai and segmented into discrete questioning episodes. Evaluation was performed using Gemini Flash 2.0 (build: 2025.03, temperature: 0.0), a large language model configured via prompt design and anchored exemplars to assess six pedagogical indicators: question type, content relevance, question complexity, wait time, teacher’s response, and student interaction. Each indicator was rated on a 4-point scale. Reliability checks against human-coded samples (n = 40) yielded strong agreement (Cohen’s κ = 0.78). Results showed that 25% of sessions were classified as high-performing, with open-ended and cognitively demanding questions, extended wait time, and rich student engagement. In contrast, 42% were low-performing, marked by factual recall, short pauses, and minimal interaction. Clustering analysis (Gower k-medoids) identified three distinct performance profiles (average silhouette = 0.41). This AI-based framework enables reliable, scalable, and interpretable evaluation of questioning practices. A prototype feedback tool was developed, providing per-indicator scores, question examples, and suggested improvements supporting formative teacher development. Ethical compliance was ensured through the exclusive use of public, anonymized content.
Profiling Multicomponent Chemical Reasoning: A Learning Analytics Approach to Applied and Socio-Chemical Dimensions Nur Hidayat; Woro Sumarni; Endah Fitriani Rahayu; Sri Kadarwati; Kasmui Kasmui; Dimas Gilang Ramadhani
JKPK (Jurnal Kimia dan Pendidikan Kimia) Vol 10, No 2 (2025): JKPK (Jurnal Kimia dan Pendidikan Kimia)
Publisher : Program Studi Pendidikan Kimia FKIP Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jkpk.v10i2.104248

Abstract

Scientific reasoning in chemistry involves the ability to apply conceptual knowledge in problem-solving, as well as to evaluate issues within broader social, ethical, and environmental contexts. However, conventional assessments often fail to capture this multidimensionality by reducing performance to a single final score. This study uses an integrated learning analytics approach to analyze students’ reasoning performance across two core domains of chemistry learning—applied reasoning and socio-chemical reasoning. A quantitative descriptive design was employed, involving 56 pre-service chemistry teachers who completed four open-ended essay questions, two in each reasoning domain. Student responses were scored using an analytical rubric assessing conceptual accuracy, logical coherence, and justification relevance. Data were analyzed using single-domain and multicomponent strategies, including quadrant profiling, trajectory mapping, clustering, and distribution analysis. Visual tools such as radar charts, spaghetti plots, contour density plots, and alluvial diagrams were used to depict students’ reasoning profiles. Results revealed that most students demonstrated moderate reasoning abilities, although notable inconsistencies were observed between the domains. Individual trajectories exhibited non-linear variations, highlighting diverse cognitive patterns. Clustering and heatmaps indicated distinct learner segments, while alluvial diagrams illustrated transitions between reasoning levels across domains. These findings suggest that students’ reasoning abilities are varied and dynamic. It is concluded that chemistry reasoning is multidimensional and should be assessed through integrated, data-driven methods. The study recommends the adoption of formative, analytics-supported assessments to inform differentiated instruction and promote deeper conceptual and ethical engagement in chemistry education.
Unveiling Students' Understanding of Ammonia as a Weak Base through Scaffolding-Based Chemical Reasoning Assessment Yuda Faturohman; Endang Susilaningsih; Harjono Harjono; Murbangun Nuswowati; Ella Kusumastuti; Dimas Gilang Ramadhani
JKPK (Jurnal Kimia dan Pendidikan Kimia) Vol 10, No 1 (2025): JKPK (Jurnal Kimia dan Pendidikan Kimia)
Publisher : Program Studi Pendidikan Kimia FKIP Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jkpk.v10i1.100779

Abstract

Reasoning is a basic cognitive ability in science learning, especially in chemistry, in which students must connect macroscopic, symbolic, and microscopic levels. However, most students seem to have difficulty learning chemical reasoning, especially in the ionization of weak bases (examples: NH₃). This study uses a scaffold-based assessment to evaluate students' explanations for ammonia as a base. A paper-and-pencil test was applied to 91 first-year preservice chemistry students to test them on phenomenological, mechanical, and structural types of reasoning. Two raters rated responses, and scoring reliability was assessed using Cohen’s Kappa (0.925). The data analysis consisted of descriptive statistics, correlation analysis, clustering (K-Means and t-SNE), and regression prediction with XGBoost. The results demonstrate that structural reasoning exhibits the highest level, but phenomenological reasoning has the most variation. There appears to be a high correlation between phenomenological empirical generalization and structural reasoning (r = 0.35+). Clustering outputs show three categories of students: high (R3), moderate (R2), and low (R1) reasoning, and most of the students are categorized at the moderate reasoning level, indicating some misconceptions. The XGBoost model performs well in predicting high-reasoning students but not in the moderate-reasoning classification. This paper indicates the power of scaffolding-embedded assessment for deducing reasoning patterns and misconceptions in ammonia ionization. The results can guide adaptive learning approaches for improving students' chemical reasoning.
Co-Authors Ella Kusumastuti Endah Fitriani Rahayu Endang Susilaningsih Harjono Harjono Kasmui Kasmui Murbangun Nuswowati Nala Izzul Muna Nur Hidayat Sri Kadarwati Sri Susilogati Sumarti Woro Sumarni Woro Sumarni Yuda Faturohman