The Effect of the 9E Learning Cycle Model on Science Learning Outcomes: A Quasi-Experimental Study in Fourth-Grade Elementary School

  • Riri Marfilinda Universitas Adzkia, Padang, Indonesia
  • Oca Aprilia Universitas Adzkia, Padang, Indonesia
  • Alfroki Martha Universitas Adzkia, Padang, Indonesia
  • Sisri Wahyuni Universitas Adzkia, Padang, Indonesia
  • Romi Kurniawan Universitas Pattimura Ambon, Maluku, Indonesia
Keywords: 9E Learning Cycle, Learning Outcomes, Science, Elementary School

Abstract

This study addressed the issue of low science learning outcomes among fourth-grade students at SDN 04 Kinali. The purpose of this research was to examine the impact of the 9E Learning Cycle model on students' science learning outcomes. The study employed a quantitative approach with a quasi-experimental design, specifically the posttest-only control group design. The population included all fourth-grade students at SDN 04 Kinali. The sample was divided into two groups: class IVA as the experimental group (27 students) and class IVB as the control group (28 students). Data collection was carried out using a 15-item multiple-choice test that had undergone validation for validity, reliability, discriminating power, and difficulty level. Data analysis involved normality testing, homogeneity testing, and hypothesis testing (t-test) with the assistance of SPSS 27. The findings revealed that the experimental group achieved an average score of 81, whereas the control group obtained 72. The hypothesis test produced a significance value of 0.013 < 0.05, indicating that H0 was rejected and H1 was accepted. This means that the 9E Learning Cycle model had a notable effect on the science learning outcomes of fourth-grade students at SDN 04 Kinali. The model proved effective as it encouraged students to actively build their understanding through exploration, elaboration, and evaluation stages. Teachers are advised to consider the 9E Learning Cycle model as an alternative instructional strategy, and future researchers may explore affective and psychomotor domains or incorporate other variables such as learning motivation.

References

Abakah, E. (2023). Teacher learning from continuing professional development (CPD) participation: A sociocultural perspective. International Journal of Educational Research Open, 4, Article 100242. https://doi.org/10.1016/j.ijedro.2023.100242

Abrigo, M. R. M., Francisco, K. A., & Santos, K. C. P. (2026). School closures, shelter-use and learning outcomes in the Philippines: Evidence from 2019 TIMSS. Economics of Education Review, 110, Article 102738. https://doi.org/10.1016/j.econedurev.2025.102738

Ahmed, I. I. (2026). Enhancing philosophical concept acquisition through the 5E learning cycle model. Jurnal Educative: Journal of Educational Studies, 11(1), 1–15. https://doi.org/10.30983/educative.v11i1.11334

Alfian, M. A., Parno, P., Wisodo, H., & Khamis, N. (2024). The influence of the 9E learning cycle with a STEM approach on students’ science process skills in static fluid topics. Jurnal Penelitian Pendidikan IPA, 10(11), 9012–9019. https://doi.org/10.29303/jppipa.v10i11.8916

Askari Zaman, M., Nasiriani, K., Keshmiri, F., & Antikchi, M. (2026). The effect of a magnetic puzzle-based educational game on ECG interpretation skills in nursing students: A randomized controlled trial. Teaching and Learning in Nursing, Article 100948. https://doi.org/10.1016/j.teln.2026.07.009

Azim, H. M., Lee, R. Y., Sprague, A. V., & Near, C. P. (2026). A primer on critical constructivism and its relevance to medical education. Discover Education, 5(1), Article 873. https://doi.org/10.1007/s44217-026-02141-1

Bodas, A., & Kelemen, D. A. (2026). Conceptualizing science as explanatory predicts elementary students’ life science learning: The relative influence of science conceptions, science identity and mindset. Journal of Applied Developmental Psychology, 104, Article 101948. https://doi.org/10.1016/j.appdev.2026.101948

Borg, A., Schiött, J., Ivegren, W., Gentline, C., Huss, V., Hugelius, A. M., Jobs, B., Ruiz, M., Edelbring, S., Georg, C., Skantze, G., & Parodis, I. (2026). AI-generated feedback following social robotic virtual patient interactions and medical student performance: Nonrandomized quasi-experimental study. JMIR Medical Education, 12(1), Article e90368. https://doi.org/10.2196/90368

Chiu, M. C., & Hwang, G. J. (2024). Enhancing students’ critical thinking and creative thinking: An integrated mind mapping and robot-based learning approach. Education and Information Technologies, 29(17), 22779–22812. https://doi.org/10.1007/s10639-024-12752-6

De Witte, B., Reynaert, V., Hutain, J., Kieken, D., Jabbour, J., & Possik, J. (2024). Immersive learning of factual knowledge while assessing the influence of cognitive load and spatial abilities. Computers and Education: X Reality, 5, Article 100085. https://doi.org/10.1016/j.cexr.2024.100085

Dey, R., & Kumar, S. (2026). Detecting special Toeplitz error structure and testing for homogeneity of effects in one-way ANOVA models. Computational Statistics and Data Analysis, 222, Article 108397. https://doi.org/10.1016/j.csda.2026.108397

Dias, J. A., Chen, T., Xing, H., Wang, X., Rodriguez, A. A., Madduri, R. K., Kraft, P., & Zhang, H. (2025). Evaluating multi-ancestry genome-wide association methods: Statistical power, population structure, and practical implications. American Journal of Human Genetics, 112(10), 2493–2508. https://doi.org/10.1016/j.ajhg.2025.08.006

Dogaru, M., Negreanu, M. C., Pisicǎ, O., & Pîrvu, A. F. (2025). STEM career: Essential factors for students to achieve success in STEM (supportive individuals, skills/abilities, motivational factors). Frontiers in Education, 10, Article 1611178. https://doi.org/10.3389/feduc.2025.1611178

Elsa, H. A., & Olivia, V. (2026). The 7E learning cycle model for improving mathematical problem-solving skills: A systematic literature review. Journal on Mathematics Education Research (J-MER), 7(1), 103–118. https://doi.org/10.17509/j-mer.v7i1.887

Falloon, G. (2023). An exploration of online technoliteracy capability teaching and learning in early years classrooms. Education and Information Technologies, 29(1), 625–654. https://doi.org/10.1007/s10639-023-12239-w

Ferguson, P., Leeming, P., & Derrah, R. H. (2025). Measuring elementary school teacher self-efficacy across different academic disciplines: Is the subject matter important? Teaching and Teacher Education, 168, Article 105258. https://doi.org/10.1016/j.tate.2025.105258

Fernández-Lorente, L., Rodríguez-Almagro, J., López-Reina-Roldan, J. M., Martinez-Rodriguez, S., & Hernández-Martínez, A. (2026). Development and validation of the Mentor-Nurse scale: An instrument to assess mentoring competencies in clinical nursing. Nurse Education in Practice, 91, Article 104690. https://doi.org/10.1016/j.nepr.2025.104690

Fey, C. F., Hu, T., & Delios, A. (2023). The measurement and communication of effect sizes in management research. Management and Organization Review, 19(1), 176–197. https://doi.org/10.1017/mor.2022.2

Géant, C. B., Gustave, M. N., & Schmitz, S. (2025). Developing an indicator for assessing wetland degradation based on soil quality, water drainage, and human-related landscape factors. Ecological Indicators, 179, Article 113987. https://doi.org/10.1016/j.ecolind.2025.113987

Geng, F., Yu, S., & Yuan, R. (2026). You just appreciate every emotional moment: Investigating L2 writing teachers’ emotional experiences in the classroom. Teaching and Teacher Education, 177, Article 105548. https://doi.org/10.1016/j.tate.2026.105548

Giuliani, M. F., Llobenes, L., & Miernes, G. (2026). Fostering presence in education: A pilot study of a social-emotional learning intervention for public school educators in Argentina. Social and Emotional Learning: Research, Practice, and Policy, 7, Article 100191. https://doi.org/10.1016/j.sel.2026.100191

Gonzalez Jauregui, R., Kong, L., Wengryn, D., Beltran, G., Sevick, C., Pyrzanowski, J., Wesemann, L., McCoo, M., Chidsey, B., Vemulakonda, V., Hecht, S., & Buchanan, C. L. (2026). Effectiveness of an educational video for caregivers of children with neurogenic bladder: A randomized controlled trial. Journal of Pediatric Urology, 22(5), Article 106064. https://doi.org/10.1016/j.jpurol.2026.106064

Greco, A., Spingola, C., & Vento, M. (2026). STEP-FACE: Sequential TExtual–Visual Prompting for multi-task face analysis. Knowledge-Based Systems, 351, Article 116679. https://doi.org/10.1016/j.knosys.2026.116679

Grosch, K., Haeckl, S., & Kocher, M. G. (2026). Closing the gender STEM interest gap: A randomized-controlled trial in elementary schools. European Economic Review, 185, Article 105300. https://doi.org/10.1016/j.euroecorev.2026.105300

Hacıeminoğlu, E., Yıldız, N. G., & Şeker, R. (2022). Factors related to cognitive reasoning of pre-service teachers’ science process skills: Role of experiments at home on meaningful learning. Sustainability, 14(13), Article 7703. https://doi.org/10.3390/su14137703

Hamed, R., & Rebello, N. S. (2026). Cognitive load and situational interest in physics laboratories: A comparative study across three instructional modalities [Preprint]. arXiv. https://arxiv.org/abs/2602.06143

Harjanti, F. D., Suhartono, Ardiansyah, R., & Suryandari, S. (2026). Implementing the Pancasila Student Profile through the Merdeka Curriculum: A case study from Surabaya in advancing SDG 4. Social Sciences and Humanities Open, 13, Article 102728. https://doi.org/10.1016/j.ssaho.2026.102728

He, J., & Huang, Q. (2026). Effects of digital context-shift learning tasks grounded in quantum context dependence on EFL students’ goal orientation motivation: A mixed-methods inquiry. Learning and Motivation, 94, Article 102267. https://doi.org/10.1016/j.lmot.2026.102267

Heckman, M. G., Davis, J. M., & Crowson, C. S. (2022). Post hoc power calculations: An inappropriate method for interpreting the findings of a research study. The Journal of Rheumatology, 49(8), 867–870. https://doi.org/10.3899/jrheum.211115

Hodgkiss, A., Thomas, M. S. C., Tolmie, A. K., & Farran, E. K. (2025). Associations between spatial skills and physics knowledge in primary school: Spatial skills are more important for conceptual scientific knowledge than for factual scientific knowledge. Journal of Experimental Child Psychology, 252, Article 106135. https://doi.org/10.1016/j.jecp.2024.106135

Hsieh, F. P., Chen, Y. A., Wu, H. J., & Tsai, C. Y. (2023). Promoting first graders’ scientific thinking through picture books with the 5E model. The Journal of Educational Research, 116(3), 147–158. https://doi.org/10.1080/00220671.2023.2219635

Hsu, T. C., & Hsu, T. P. (2026). Effects of game-based learning integrated with different thinking-guided methods on computational thinking of elementary school students. Thinking Skills and Creativity, 60, Article 102056. https://doi.org/10.1016/j.tsc.2025.102056

Huang, H. M., Tai, W. S., Huang, T. C., & Lo, C. Y. (2024). Optimizing inquiry-based science education: Verifying the learning effectiveness of augmented reality and concept mapping in elementary school. Universal Access in the Information Society, 24(1), 681–694. https://doi.org/10.1007/s10209-024-01098-y

Ibourk, A., & El Aynaoui, K. (2026). Educational effectiveness in rural areas: What SDIs teach us about multigrade classes. International Journal of Educational Development, 120, Article 103482. https://doi.org/10.1016/j.ijedudev.2025.103482

Imjai, N., Meesook, K., Homlaor, T., Usman, B., & Aujirapongpan, S. (2025). Do data science literacy and analytical thinking skill matter for developing sustainable agile leadership among Gen Z accounting students? International Journal of Management Education, 23(3), Article 101275. https://doi.org/10.1016/j.ijme.2025.101275

Iqbal, M. Z., Khan, T., Kamran, M., Qureshi, S. K., & Alkhoraif, A. A. (2026). Clear the fog: Academic confidence, critical thinking, and career decision ambiguity tolerance among business students. International Journal of Management Education, 24(3), Article 101441. https://doi.org/10.1016/j.ijme.2026.101441

Jack, G. U., & Ogunleye, D. O. (2025). Effect of 7E learning cycle model on students’ attitude towards chemistry at secondary school level. African Journal of Educational Management, Teaching and Entrepreneurship Studies, 14(1). https://ajemates.org/index.php/ajemates/article/view/615

Jibril, B. A. (2024). Use of 5E learning-cycle and concept-mapping strategies for improving student’s performance in genetics in secondary schools in Kwara State, Nigeria. Journal of Science and Science Education, 5(1), 1–9. https://doi.org/10.29303/jossed.v5i1.6956

Johannesson, P. (2022). Development of professional learning communities through action research: Understanding professional learning in practice. Educational Action Research, 30(3), 411–426. https://doi.org/10.1080/09650792.2020.1854100

Karkar-Esperat, T. M., Nash, B. L., Ivanyuk, L., Shimek, C., & Pierce, K. M. (2026). Collaborative structures for longitudinal learning across levels in teacher education: Professors and preservice teachers learning in tandem about new literacies. Teaching and Teacher Education, 175, Article 105432. https://doi.org/10.1016/j.tate.2026.105432

Kaya, Z., Kaya, O. N., Aydemir, S., & Ebenezer, J. (2021). Knowledge of student learning difficulties as a plausible conceptual change pathway between content knowledge and pedagogical content knowledge. Research in Science Education, 52(2), 691–723. https://doi.org/10.1007/s11165-020-09971-5

Konopatov, A. V., Shidlovsky, Y. V., Shtil, A. A., & Bylino, O. V. (2024). Kolmogorov-Smirnov and Shapiro-Wilk single distribution analysis methods in examining sample structure, strength of genetic background, and effects of interventions in lifespan and aging studies. bioRxiv. https://doi.org/10.1101/2024.12.12.628153

Kozaklı Ülger, T., Kıryak, Z., & Ülger, B. B. (2026). Teachers’ noticing and social interactions in the classroom: Insights from a skill-based science curriculum. Thinking Skills and Creativity, 61, Article 102225. https://doi.org/10.1016/j.tsc.2026.102225

Labrousse, A., Reboursière, E., Remilly, M., Roulland, R., Mergenthaler, G., Hulet, C., & Drigny, J. (2026). Development and validation of a scale to assess risk-taking intentions during return to sport following anterior cruciate ligament reconstruction. Journal of ISAKOS, 17, Article 101052. https://doi.org/10.1016/j.jisako.2025.101052

Lasaiba, M. A., & Lasaiba, D. (2024). Enhancing academic achievement through the application of the 5E learning cycle model. INSECTA: Integrative Science Education and Teaching Activity Journal, 5(1), 71–86. https://doi.org/10.21154/insecta.v5i1.8518

Lee, J. S. (2024). Nursing process performance measurement tool based on Bloom’s revised taxonomy: A validity and reliability analysis. Nurse Education in Practice, 81, Article 104165. https://doi.org/10.1016/j.nepr.2024.104165

Martins, P. C., Tinoca, L., & Alves, M. G. (2024). On the effects of Erasmus+ KA 1 mobilities for continuing professional development in teachers’ biographies: A qualitative research approach with teachers in Portugal. International Journal of Educational Research Open, 7, Article 100368. https://doi.org/10.1016/j.ijedro.2024.100368

Mekonnen, Z. B., Yehualaw, D. D., Mengistie, S. M., & Mersha, B. S. (2024). The effect of 7E learning cycle enriched with computer animations on students’ conceptual understanding and overcoming misconceptions. Journal of Pedagogical Research, 8(2), 325–356. https://doi.org/10.33902/jpr.202425017

Meldayani, S., Farida, F., Azizah, N., Saputra, M. I., & Kuswanto, R. T. (2025). Student learning activeness: An experimental study on the effectiveness of the 9E learning cycle model. Journal of Educational Sciences, 9(2), 1008–1020. https://doi.org/10.31258/jes.9.2.p.1008-1020

Misal, S., & Hakke, V. S. (2026). Impact of a structured flipped classroom model on conceptual understanding and controller design skills in process control engineering. Education for Chemical Engineers, 57, Article 100534. https://doi.org/10.1016/j.ece.2026.100534

Mohamed, A. H., Alsaqri, S. H., Shaban, M., Razek, R. A. A., Grande, R. A. N., Berdida, D. J. E., & Sayed, Y. M. (2026). Effectiveness of a nurse-led health belief model–based educational intervention for reducing smartphone addiction in adolescents: A randomized controlled trial. Journal of Pediatric Nursing, 89, 18–28. https://doi.org/10.1016/j.pedn.2026.03.049

Musfiroh, M., Suwarma, I. R., & Efendi, R. (2024). Enhancing physics learning through the 7E learning cycle model: A systematic literature review. Indonesian Journal of Science and Mathematics Education, 7(3), 531–544. https://doi.org/10.24042/ijsme.v7i3.24125

Ngajie, B. N., Su, C. Y., Li, Y., & Tiruneh, D. T. (2025). Impact of a systematically designed computer-supported argument visualization tutorial on the skill of argument analysis. Thinking Skills and Creativity, 56, Article 101786. https://doi.org/10.1016/j.tsc.2025.101786

Nkrumah, R. B., Sinha, V., Hanley, J., & Denov, M. (2026). Beyond macro-indicators: Exploring micro-level educational experiences (MLEs) reinforcing learning inequality in rural northern Ghana. International Journal of Educational Development, 120, Article 103471. https://doi.org/10.1016/j.ijedudev.2025.103471

Noreen, Z., Iqbal, M., & Hayat, K. (2024). Effect of the 7E learning cycle model on students’ achievement in the subject of science at the elementary school level. Qlantic Journal of Social Sciences, 5(2), 34–45. https://doi.org/10.55737/qjss.315497375

Oberdiek, S., Gottschling, T., Miehe, R., & Sauer, A. (2026). Transfer learning for Bayesian networks in manufacturing: A conceptual framework towards industrial deployment. Knowledge-Based Systems, 351, Article 116842. https://doi.org/10.1016/j.knosys.2026.116842

Ole, F. C. B., & Gallos, M. R. (2023). Impact of formative assessment based on feedback loop model on high school students’ conceptual understanding and engagement with physics. Journal of Turkish Science Education, 20(2), 333–355. https://doi.org/10.36681/tused.2023.019

Park, S., Mao, X., & Choi, S. (2025). Understanding the relationship between teacher collaboration and instructional clarity via teacher self-efficacy: A moderated mediation model of organizational commitment. Teaching and Teacher Education, 156, Article 104922. https://doi.org/10.1016/j.tate.2024.104922

Perez-Ferreiro, P. M., Alonso-Moral, J. M., Catala, A., & Bugarín-Diz, A. (2026). Fusion of fuzzy rule-based factual and counterfactual explanations with large language models. International Journal of Approximate Reasoning, 199, Article 109828. https://doi.org/10.1016/j.ijar.2026.109828

Pitsia, V., & Karakolidis, A. (2026). Educational inequalities in primary and secondary education across the European Union: Evidence from PISA, PIRLS, and TIMSS. Studies in Educational Evaluation, 91, Article 101664. https://doi.org/10.1016/j.stueduc.2026.101664

Romero-Cano, L. A. (2025). Modular simulation as a teaching tool: Integrating MATLAB-Simulink into heat transfer courses to promote active learning and conceptual understanding. Education for Chemical Engineers, 53, 171–177. https://doi.org/10.1016/j.ece.2025.07.004

Rua-Gomez, C., Urrea, G., Cruz, M., & Arias-Gaviria, J. (2026). More graduated but less promoted: A dynamic perspective of the leaky pipeline of women in academic science, technology, engineering, and mathematics. European Journal of Operational Research, 335(1), 253–267. https://doi.org/10.1016/j.ejor.2026.03.007

Sahni, J., Darda, P., & Gaur, S. S. (2025). Integration of principles for responsible management education principles (PRME) in Saudi Arabian universities: A thematic analysis of claims on university websites. Society and Business Review, 20(3), 673–696. https://doi.org/10.1108/sbr-11-2024-0374

Shi, M., Hu, X., & Zhu, X. (2026). Ethical considerations of undergraduate nursing students using artificial intelligence for learning: A qualitative study. Applied Nursing Research, 91, Article 152140. https://doi.org/10.1016/j.apnr.2026.152140

Singh, M., Yang, Y., & Sun, D. (2026). What drives learning in the educational metaverse? Unpacking the interplay of affective, cognitive, and interactive factors in mathematics education. Computers and Education: X Reality, 9, Article 100163. https://doi.org/10.1016/j.cexr.2026.100163

Sintia, A., Nursilawati, N., Deani, P. R., Warastri, N. D., Apita, D. H., Azizah, N., & Susilawati, B. (2025). Learning cycle 9E learning model: How does it affect students’ problem solving skills? Action Research Journal Indonesia (ARJI), 7(3), 1723–1737. https://doi.org/10.61227/arji.v7i3.418

Song, C., Shin, S. Y., & Shin, K. S. (2023). Optimizing foreign language learning in virtual reality: A comprehensive theoretical framework based on constructivism and cognitive load theory (VR-CCL). Applied Sciences, 13(23), Article 12557. https://doi.org/10.3390/app132312557

Soto, J. A. G., & Vergara, F. J. G. (2024). Developing students’ scientific competencies using the school’s environmental context in rural and semi-rural settings. Journal of Scientific Metrics and Evaluation, 2(1), 89–115. https://doi.org/10.69821/josme.v2ii.13

Sriwilna, D., Ahmal, A., & Fiqri, Y. A. (2026). The effect of the project based learning model using infographics on students’ creative thinking abilities in history learning in grade XI of SMK Darel Hikmah Pekanbaru. Journal of Educational Sciences, 10(8), 1091–1105. https://doi.org/10.31258/jes.10.8.p.1091-1105

Stein, J. P., Breves, P. L., & Anders, N. (2024). Parasocial interactions with real and virtual influencers: The role of perceived similarity and human-likeness. New Media and Society, 26(6), 3433–3453. https://doi.org/10.1177/14614448221102900

Sultana, S., & Fang, F. (2024). English as the medium of instruction and mother-tongue-based translanguaging: Challenges and prospects for tertiary education in Bangladesh and China. International Journal of Educational Development, 104, Article 102951. https://doi.org/10.1016/j.ijedudev.2023.102951

Tesfamicael, F., Belachew, W., & Demissie, T. (2026). The effectiveness of an integrated 9E learning cycle and tetrahedral representation model on students’ motivation and attitude toward hydrocarbons [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-10670604/v1

Tian, W., Fu, C., Qin, S., & Yan, H. (2026). Teacher efficacy, professional commitment, and career decision-making difficulties in pre-service teachers: Exploring dynamics via cross-lagged panel and random intercept cross-lagged panel model. Teaching and Teacher Education, 181, Article 105759. https://doi.org/10.1016/j.tate.2026.105759

Uddin, M. J., & Agarwal, P. C. (2026). Enhancing physics problem-solving through brainstorming: A metacognitive approach for socioeconomically disadvantaged learners. Learning and Motivation, 95, Article 102303. https://doi.org/10.1016/j.lmot.2026.102303

Ulyashin, A. G., Hadjadj, A., & Butt, M. A. (2022). Thin-film coating methods: A successful marriage of high-quality and cost-effectiveness a brief exploration. Coatings, 12(8), Article 1115. https://doi.org/10.3390/coatings12081115

Vahedian Movahed, S., & Martin, F. G. (2025). Ask me anything: Exploring children’s attitudes toward an age-tailored AI-powered chatbot. International Journal of Artificial Intelligence in Education, 35(6), 3979–4001. https://doi.org/10.1007/s40593-025-00523-4

Verhelst, N., & Yıldırım, H. H. (2026). Exploring measurement invariance in TIMSS: A longitudinal perspective with an emphasis on gender effects. Studies in Educational Evaluation, 89, Article 101584. https://doi.org/10.1016/j.stueduc.2026.101584

Wang, L. J. (2026). Question-only AI Socratic dialogue as dialogic feedback in L2 argumentative writing: A quasi-experimental study. Journal of Second Language Writing, 73, Article 101381. https://doi.org/10.1016/j.jslw.2026.101331

Wongwanich, T., Pathumcharoenwattana, W., & Ratana-Ubol, A. (2026). Design principles for developing the module to enhance lifelong learning mindset, learning habits, and self-directed learning of elementary school teachers. Social Sciences and Humanities Open, 13, Article 102651. https://doi.org/10.1016/j.ssaho.2026.102651

Yuan, J., Brigandi, C. B., Rambo-Hernandez, K. E., & Manley, C. L. (2025). Innovative ongoing support within a multifaceted computational thinking professional learning program improves teachers’ self-efficacy and classroom practices. Computers and Education, 225, Article 105174. https://doi.org/10.1016/j.compedu.2024.105174

Zhang, T. C. (2026). AI-mediated learning as a quantum measurement process: Effects of adaptive AI feedback on motivation stability, cognitive load, and learning efficiency. Learning and Motivation, 95, Article 102322. https://doi.org/10.1016/j.lmot.2026.102322

 

Published
2026-10-31
How to Cite
Riri Marfilinda, Oca Aprilia, Alfroki Martha, Sisri Wahyuni, & Romi Kurniawan. (2026). The Effect of the 9E Learning Cycle Model on Science Learning Outcomes: A Quasi-Experimental Study in Fourth-Grade Elementary School. Berkala Ilmiah Pendidikan, 6(3), 281-299. https://doi.org/10.51214/bip.v6i3.2386