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All Journal INKUIRI : Jurnal Pendidikan IPA International Journal of Pedagogy and Teacher Education JKPK (Jurnal Kimia dan Pendidikan Kimia) International Journal of Active Learning
Dimas Gilang Ramadhani, Dimas Gilang
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Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Energy Environmental Science Materials Science & Nanotechnology Physics Social Sciences Other

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Development of Virtual Reality-Based Ethno-SDGs Learning Media on Colloid Material to Improve Students' Chemical Literacy Ladjidji, Delfris Ariya Pratama; Sudarmin, Sudarmin; Kurniawan, Cepi; Ramadhani, Dimas Gilang; Yulianto, Agung Ade; Massora, Kris; Widastra, Holifiled
International Journal of Active Learning Vol. 9 No. 2 (2024): October 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research aims to develop Ethno-SDGs-based learning media integrated with Virtual Reality (VR) technology on the colloid material to improve students' chemical literacy. This media integrates the local wisdom of Palu, such as Kaledo, Kapurung, Palumara, and Utadada, with the chemical concept of colloids. The background of this research is the low chemical literacy of students due to the lack of connection between learning and local contexts and technological innovations. The focus of this research is the development of contextually relevant and technology-based learning media. The research employs a Research and Development (R&D) method with the 4D model (Define, Design, Develop, Disseminate). Data were collected through expert validation of media and content, peer reviews, small-scale trial questionnaires, and pretest and posttest assessments. Expert validation indicated the media's feasibility, with scores of 80% for content aspects and 79% for media aspects, both within the acceptable range. Peer reviews resulted in an approval score of 82% for both content and media aspects. The student responses from the small-scale trial showed a satisfaction rate of 92%, categorized as very feasible. Students also indicated that the media was engaging and helped increase their motivation to learn. The N-Gain test results showed an improvement in chemical literacy skills after using this media. The research concludes that the Ethno-SDGs-based learning media integrated with VR is effective in enhancing students' chemical literacy.
Enhancing Students’ Green Chemistry Awareness through Small Scale Chemistry: Evidence from Acid-Base Natural Indicator Experiments Ramadhani, Dimas Gilang; Harjono, Harjono; Priatmoko, Sigit; Sulistyani, Martin; Subhan, Subhan
INKUIRI: Jurnal Pendidikan IPA Vol 15, No 1 (2026): INKUIRI: Jurnal Pendidikan IPA
Publisher : Magister Pendidikan Sains Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/inkuiri.v15i1.108710

Abstract

Integrasi Small-Scale Chemistry (SSC) dalam pembelajaran kimia merupakan pendekatan inovatif untuk mendukung prinsip Green Chemistry dengan cara meminimalkan penggunaan bahan kimia, meningkatkan keselamatan laboratorium, dan mendorong penggunaan indikator alami yang lebih ramah lingkungan. Penelitian ini bertujuan untuk menganalisis bagaimana pelatihan SSC terstruktur memengaruhi persepsi siswa terkait keselamatan, desain ramah lingkungan, kepercayaan diri, dan keberlanjutan. Sebanyak 30 siswa kelas XI SMA Negeri 6 Semarang berpartisipasi dalam kegiatan ini yang dirancang secara kolaboratif oleh Departemen Kimia FMIPA Universitas Negeri Semarang bersama Sogang University dan program LUPIC. Instrumen penelitian berupa kuesioner dengan 8 butir pertanyaan berbasis empat indikator kompetensi. Analisis data dilakukan menggunakan statistik deskriptif, uji reliabilitas, analisis korelasi, Principal Component Analysis (PCA), serta association rule mining dengan dukungan visualisasi. Hasil menunjukkan bahwa dimensi keselamatan dan efisiensi (C1) memperoleh skor tertinggi, sedangkan desain ramah lingkungan (C2) terendah. PCA mengidentifikasi tiga profil siswa: berfokus pada keselamatan, indikator alami, dan keberlanjutan. Analisis lanjutan menegaskan bahwa keberlanjutan (C4) merupakan prediktor terkuat persepsi keseluruhan, serta adanya keterkaitan erat antara desain ramah lingkungan dan keberlanjutan. Temuan ini menegaskan bahwa SSC tidak hanya meningkatkan keterampilan teknis tetapi juga menumbuhkan kesadaran keberlanjutan, sekaligus mendukung SDG 4, SDG 12, dan SDG 13.The integration of Small-Scale Chemistry (SSC) into chemistry education represents an innovative approach to supporting Green Chemistry principles by minimizing reagent use, enhancing laboratory safety, and promoting the use of natural indicators as environmentally friendly alternatives. This study aimed to evaluate how structured SSC training influenced students’ perceptions across four dimensions: safety, eco-friendly design, confidence, and sustainability. A total of 30 Grade XI students from SMA Negeri 6 Semarang participated in this training, collaboratively designed by the Department of Chemistry, FMIPA Universitas Negeri Semarang, in partnership with Sogang University and the LUPIC program. The research instrument was an 8-item questionnaire covering four competency indicators. Data analysis included descriptive statistics, reliability testing, correlation analysis, Principal Component Analysis (PCA), and association rule mining supported by visualization. The results indicated that safety and efficiency (C1) scored the highest, while eco-friendly design (C2) was the lowest. PCA revealed three student profiles: safety-focused, natural indicator–focused, and pro-sustainability. Advanced analysis showed that sustainability (C4) was the strongest predictor of overall perception, with strong associations between eco-design and sustainability as well as between natural indicators and confidence. These findings confirm that SSC enhances not only technical competence but also sustainability awareness, contributing directly to SDG 4, SDG 12, and SDG 13.
Misconception Propagation, Clustering, and Score Consistency in Acid-Base Submicroscopic Representations: A Bayesian Network and Machine Learning Approach Wicaksono, Ibrahim; Harjono, Harjono; Ramadhani, Dimas Gilang; Susatyo, Eko Budi; Priatmoko, Sigit
International Journal of Pedagogy and Teacher Education Vol 9, No 1 (2025): International Journal of Pedagogy and Teacher Education - April
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.v9i1.100807

Abstract

Misconceptions in chemistry continue to challenge students' conceptual understanding, particularly in submicroscopic representations such as particle structure, solution properties, ionic interaction, and chemical reactions. This study aims to investigate the propagation of misconceptions, cluster students based on their misconception profiles, and analyze the consistency of scores about these patterns. The participants were 52 second-semester pre-service chemistry teachers who completed a diagnostic test consisting of four particle-level diagrams with open-ended questions. Bayesian Network analysis and Granger Causality testing examined probabilistic and causal relationships between misconceptions. Clustering analysis using K-Means and visualization through t-SNE identified three distinct student groups with varying misconception levels. Score consistency analysis using correlation, ANOVA, and regression revealed that misconceptions in particle structure strongly influenced errors in other concepts and were significantly correlated with lower scores (r = -0.26). Sankey diagrams demonstrated how misconceptions in early questions propagated to subsequent concepts, indicating error flow. The findings suggest that early identification and correction of key misconceptions are crucial, and clustering analysis can inform adaptive teaching strategies. This research highlights the importance of integrating causal analysis and machine learning in chemistry education research to understand better and address student misunderstanding patterns
Evaluating Chemistry Teacher’s Questioning Skills in Microteaching Based on Artificial Intelligence (AI) Using an Assessment Rubric Muna, Nala Izzul; Sumarti, Sri Susilogati; Harjono, Harjono; Sumarni, Woro; Ramadhani, Dimas Gilang
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.
Preservice Chemistry Teachers' Views on the Use of Artificial Intelligence in the Classroom Ramadhani, Dimas Gilang; Sumarti, Sri Susilogati; Harjono, Harjono; Kusumastuti, Ella
International Journal of Pedagogy and Teacher Education Vol 9, No 1 (2025): International Journal of Pedagogy and Teacher Education - April
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.v9i1.89534

Abstract

As Artificial Intelligence (AI) becomes increasingly integrated into education, understanding how future educators perceive its use is essential. This study explores the perceptions of 150 preservice chemistry teachers in Indonesia regarding the integration of AI in chemistry education. Participants completed a validated 12-item Likert-scale survey covering four dimensions: Pedagogical Benefit, Technical Benefit, Risk to Student, and Risk to Teacher. The data were analyzed using descriptive statistics, correlation, regression, clustering, and Principal Component Analysis (PCA). Results indicate that participants perceived AI as highly beneficial, particularly for simplifying material preparation and supporting understanding of abstract concepts. However, concerns also emerged, especially around potential declines in student motivation, critical thinking, and the teachers’ readiness to use AI effectively. Correlation analysis revealed that benefit and risk perceptions were evaluated independently. Regression models identified “real-life connection” and “AI knowledge gap” as significant benefit and risk perception predictors. Cluster analysis grouped respondents into three profiles: Cautious Adopters, Enthusiastic Supporters, and Selective Optimists, each reflecting different levels of acceptance and concern. These findings underscore the need for differentiated teacher training programs that address technical competence and pedagogical reflection. Limitations include the reliance on self-report data and a single-country sample. The study emphasizes the importance of preparing educators to critically and effectively integrate AI into science instruction.
Unveiling Students' Understanding of Ammonia as a Weak Base through Scaffolding-Based Chemical Reasoning Assessment Faturohman, Yuda; Susilaningsih, Endang; Harjono, Harjono; Nuswowati, Murbangun; Kusumastuti, Ella; Ramadhani, Dimas Gilang
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.
In Silico Pharmacokinetic and Microbiota-Integrated Profiling of Resveratrol Analogs Wibowo, Dimas Aryo; Ramadhani, Dimas Gilang; Kasmui, Kasmui
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.v10i1.100848

Abstract

Resveratrol, a polyphenolic compound, possesses extensive biological activities; however, its use in clinical applications is restricted due to its poor bioavailability and rapid metabolism. In the present work, resveratrol and 14 of its structural analogs were screened by a combined in silico methodology. The methodology integrated density functional theory (DFT) calculations, quantitative structure–activity relationship (QSAR) modeling, physiologically based pharmacokinetic (PBPK) simulations, and microbiota-associated interaction considerations. Molecular descriptors were generated from optimized geometries at the DFT level of theory to predict permeability and metabolic characteristics. PBPK modelling was used to simulate the distribution of compounds in different physiological states. In contrast, bioinformatics analysis was used to support the gene expression modulation and the response of the microbial community to the analog structure. Several analogs predicted permeability and metabolic stability significantly better than native resveratrol. Furthermore, some compounds exhibited good associations with gut microbiota and metabolic pathways that may have regulatory functions. The results indicate that certain resveratrol analogs are potential drug candidates for further in vitro and in vivo studies. Furthermore, we report a full computational framework to aid the discovery of rational bioavailable polyphenol-related drugs.
Profiling Multicomponent Chemical Reasoning: A Learning Analytics Approach to Applied and Socio-Chemical Dimensions Hidayat, Nur; Sumarni, Woro; Rahayu, Endah Fitriani; Kadarwati, Sri; Kasmui, Kasmui; Ramadhani, Dimas Gilang
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.
Co-Authors Cepi Kurniawan Eko Budi Susatyo Ella Kusumastuti, Ella Endang Susilaningsih Faturohman, Yuda Harjono Harjono Kasmui -, Kasmui Ladjidji, Delfris Ariya Pratama Massora, Kris Muna, Nala Izzul Murbangun Nuswowati Nur Hidayat Rahayu, Endah Fitriani Sigit Priatmoko Sri Kadarwati Sri Susilogati Sumarti Subhan Subhan Sudarmin Sudarmin Sulistyani, Martin Wibowo, Dimas Aryo Wicaksono, Ibrahim Widastra, Holifiled Woro Sumarni Yulianto, Agung Ade