Deep Learning Applications in STEM Education: A Systematic Review of Pedagogical Innovations in Numeracy and Science Learning
DOI:
10.29303/jppipa.v12i3.14846Published:
2026-04-14Downloads
Abstract
This research examines the application of Deep Learning (DL) technologies in mathematics and science education, focusing on technological approaches, pedagogical integration, and educational impacts. Following PRISMA 2020 guidelines, 225 studies published between January 2020 and February 2026 were analyzed from Scopus, Web of Science, PubMed, and SINTA databases. Results indicate that Convolutional Neural Networks (34.2%) and Recurrent Neural Networks/Long Short-Term Memory models (28.9%) dominate STEM applications, primarily implemented through Intelligent Tutoring Systems and adaptive learning platforms. Pedagogically, DL tools align predominantly with adaptive learning (38.7%) and inquiry-based approaches (34.2%). Evidence suggests positive impacts on learning outcomes (82.7% of studies reported significant improvements) and Higher-Order Thinking Skills, particularly critical thinking and problem-solving. However, implementation challenges persist, including technical infrastructure limitations (41.3%), data privacy concerns (36.9%), and insufficient teacher readiness (29.8%). This review concludes that while Deep Learning offers transformative potential for personalized STEM education, successful integration requires addressing ethical considerations, developing explainable AI systems, and enhancing educator preparation. Future research should prioritize longitudinal studies and equitable access to ensure DL technologies genuinely enhance rather than hinder mathematics and science learning experiences.
Keywords:
Deep Learning Numeracy and Science Learning STEM EducationReferences
Ahmed, T., Kowalkowski, C., & Sklyar, A. (2026). Platform evolution for data-driven servitization: an affordance perspective. Journal of Business & Industrial Marketing, 41(13), 28–45. https://doi.org/10.1108/JBIM-12-2024-0932. DOI: https://doi.org/10.1108/JBIM-12-2024-0932
Alfalah, A. A. (2025). Redefining work dynamics: How technology affordance and remote flexibility drive organizational excellence? Acta Psychologica, 260. https://doi.org/10.1016/j.actpsy.2025.105702 DOI: https://doi.org/10.1016/j.actpsy.2025.105702
Alhashem, F., Agha, N., & Mohammad, A. (2021). Required competencies for e-learning among science and mathematics supervisors: post-pandemic features of education. International Journal of Information and Learning Technology, 39(3), 240–255. https://doi.org/10.1108/IJILT-07-2021-0108 DOI: https://doi.org/10.1108/IJILT-07-2021-0108
Alhassan, A. M., & Altmami, N. I. (2026). Intrusion detection using advanced salvation optimizer-based multi-head attention random multimodal deep learning model. Ain Shams Engineering Journal, 17(5). https://doi.org/10.1016/j.asej.2026.104110 DOI: https://doi.org/10.1016/j.asej.2026.104110
Ametefe, D. S., John, D., Aliu, A. A., Ametefe, G. D., Hamid, A., & Darboe, T. (2025). Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound images. Results in Engineering, 26. https://doi.org/10.1016/j.rineng.2025.105047 DOI: https://doi.org/10.1016/j.rineng.2025.105047
Anchunda, H. Y., Prachanban, P., Sawangmek, T., & Anchunda, S. (2025). Development of a culturally responsive, technology-assisted peer coaching collaborative program to enhance foreign teachers’ instructional skills and learner empowerment in Thailand. Social Sciences and Humanities Open, 12. https://doi.org/10.1016/j.ssaho.2025.102035 DOI: https://doi.org/10.1016/j.ssaho.2025.102035
Ashraf, S., Hameed, M. S., Iqbal, W., Simic, V., Aydin, S., Pamucar, D., & Bacanin, N. (2025). Harmonizing sustainability and affordability in desalination: A disc spherical fuzzy weighted aggregated sum product assessment approach. Engineering Applications of Artificial Intelligence, 159. https://doi.org/10.1016/j.engappai.2025.111626 DOI: https://doi.org/10.1016/j.engappai.2025.111626
Avci, H., Lunn, S. J., & Hazari, Z. (2025). Exploring STEM educators’ perspectives on the integration of AI-enabled technologies in teaching and learning. Computers and Education Open, 9, 100304. https://doi.org/10.1016/j.caeo.2025.100304 DOI: https://doi.org/10.1016/j.caeo.2025.100304
Ayanwale, M. A., & Omeh, C. B. (2026). AI-supported problem-based learning for enhancing computational thinking skills in STEM education. Computers in Human Behavior: Artificial Humans, 7, 100263. https://doi.org/10.1016/j.chbah.2026.100263 DOI: https://doi.org/10.1016/j.chbah.2026.100263
Boche, H., Fono, A., & Kutyniok, G. (2025). Mathematical algorithm design for deep learning under societal and judicial constraints: The algorithmic transparency requirement. Applied and Computational Harmonic Analysis, 77. https://doi.org/10.1016/j.acha.2025.101763 DOI: https://doi.org/10.1016/j.acha.2025.101763
Børte, K., & Lillejord, S. (2024). Learning to teach: Aligning pedagogy and technology in a learning design tool. Teaching and Teacher Education, 148. https://doi.org/10.1016/j.tate.2024.104693 DOI: https://doi.org/10.1016/j.tate.2024.104693
Brezovec, E., Zelić, M., & Zagode, A. M. (2025). Stabilizing truth in educational sciences: a systematic review of generative AI in education. Kybernetes, 55(13), 1–17. https://doi.org/10.1108/K-09-2025-2339 DOI: https://doi.org/10.1108/K-09-2025-2339
Burgess, K. E., Bradley, E., Dray, K., Powell, S., & Runswick, O. (2025). The state of research in teaching and learning in sport and exercise science: A scoping review. Journal of Hospitality, Leisure, Sport and Tourism Education, 37. https://doi.org/10.1016/j.jhlste.2025.100573 DOI: https://doi.org/10.1016/j.jhlste.2025.100573
Cameron, T., Brown, V. A., Katz-Buonincontro, J., Anderson, R. C., Edmunds, A., Land, J., & Livie, M. (2024). ‘Mirrors and windows:’ a case study of educators’ culturally responsive teaching aspirations and syllabi transformation in the arts. Teaching and Teacher Education, 148. https://doi.org/10.1016/j.tate.2024.104714. DOI: https://doi.org/10.1016/j.tate.2024.104714
Chen, R. Q., Lee, Y., & Li, J. (2025). The Science Behind Machine Learning, Deep Learning, and Active Learning. In Dental Clinics of North America (Preprint). https://doi.org/10.1016/j.cden.2025.11.006 DOI: https://doi.org/10.1016/j.cden.2025.11.006
Chen, X., Yang, Y., Wang, Y., Shen, L., Wang, Z., Chu, S., & Zhuang, J. (2026). Effects of a serious game on undergraduate nursing students’ learning motivation, engagement and outcomes in community nursing home visit education: A cluster quasi-experimental study. Nurse Education in Practice, 93. https://doi.org/10.1016/j.nepr.2026.104785 DOI: https://doi.org/10.1016/j.nepr.2026.104785
Cheng, Q. (2026). Effects of multicultural professional development and English learner percentage on STEM teachers’ instructional quality: A path analysis of the mediating role of teacher self-efficacy in multicultural classrooms. Teaching and Teacher Education, 175. https://doi.org/10.1016/j.tate.2026.105438. DOI: https://doi.org/10.1016/j.tate.2026.105438
Citarella, A. A., Battistoni, P., Coscarelli, C., De Marco, F., Di Biasi, L., & Wang, M. (2025). EmbryoVision AI: An explainable deep learning framework for enhanced blastocyst selection in assisted reproductive technologies. Image and Vision Computing, 165. https://doi.org/10.1016/j.imavis.2025.105795 DOI: https://doi.org/10.1016/j.imavis.2025.105795
Cortes, S. T. (2025). Recent trends and future directions in artificial intelligence (AI) applications for coastal ecosystems Conservation: Insights from a bibliometric analysis. Watershed Ecology and the Environment, 8, 88–99. https://doi.org/10.1016/j.wsee.2025.11.004 DOI: https://doi.org/10.1016/j.wsee.2025.11.004
Dargude, S., Shinde, S., Jagdale, S., Polshettiwar, S., & Rajput, A. (2025). Exploring the evolution of 5D and 6D printing: Current progress, challenges, technological innovations, and transformative biomedical applications. Hybrid Advances, 10. https://doi.org/10.1016/j.hybadv.2025.100470 DOI: https://doi.org/10.1016/j.hybadv.2025.100470
Deehan, J., Redshaw, S., Danaia, L., Postlethwaite, F., Donnelly, A., & Morris, C. (2025). Understanding STEM beyond the cities: A comprehensive review of non-metropolitan STEM education research. International Journal of Educational Research Open, 9, 100496. https://doi.org/10.1016/j.ijedro.2025.100496 DOI: https://doi.org/10.1016/j.ijedro.2025.100496
Deng, X. (2026). AI-driven emotional intelligence in piano education: Deep learning models for expressive performance coaching. Acta Psychologica, 263, 106264. https://doi.org/10.1016/j.actpsy.2026.106264 DOI: https://doi.org/10.1016/j.actpsy.2026.106264
Do, T. M., & Phan, C. T. (2026). Towards accurate and efficient waste image classification: A hybrid deep learning and machine learning approach. Ain Shams Engineering Journal, 17(4). https://doi.org/10.1016/j.asej.2026.104062 DOI: https://doi.org/10.1016/j.asej.2026.104062
Feng, W., Lai, X., Zhang, X., Fan, X., & Du, Y. (2025). Research on the construction and application of intelligent tutoring system for english teaching based on generative pre-training model. Systems and Soft Computing, 7. https://doi.org/10.1016/j.sasc.2025.200232 DOI: https://doi.org/10.1016/j.sasc.2025.200232
Feulner, S., Guggenberger, T., Lautenschlager, J., Urbach, N., & Völter, F. (2025). Self-sovereign identity in the public sector: Affordances, experimentation, and actualization. Government Information Quarterly, 42(3). https://doi.org/10.1016/j.giq.2025.102052 DOI: https://doi.org/10.1016/j.giq.2025.102052
Gamlem, S. M., Johnston, S. K., Moltudal, S., McGrane, J., & Hopfenbeck, T. N. (2026). Generative AI in initial teacher education: Exploring the alignment of perceptions and experiences of pre-service teachers and their teacher educators. Teaching and Teacher Education, 172, 105382. https://doi.org/10.1016/j.tate.2026.105382 DOI: https://doi.org/10.1016/j.tate.2026.105382
Ghimire, N. (2026). Beyond Boolean: A dual-model framework for organizing English learner research literature. Social Sciences and Humanities Open, 13. https://doi.org/10.1016/j.ssaho.2026.102518 DOI: https://doi.org/10.1016/j.ssaho.2026.102518
Go, E., & Kim, T. (2025). Mapping user gratifications in the age of LLM-based chatbots: An affordance perspective. Computers in Human Behavior: Artificial Humans, 7, 100240. https://doi.org/10.1016/j.chbah.2025.100240 DOI: https://doi.org/10.1016/j.chbah.2025.100240
Herculano-Houzel, S. (2025). Brain evolution through novel affordances: A new story of the rise of behavioral flexibility, that isintelligence, in animals. In Reference Module in Neuroscience and Biobehavioral Psychology (Preprint). https://doi.org/10.1016/b978-0-443-27380-3.00078-6 DOI: https://doi.org/10.1016/B978-0-443-27380-3.00078-6
Hong, Q. N., Pluye, P., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., & Vedel, I. (2019). Improving the content validity of the mixed methods appraisal tool: a modified e-Delphi study. Journal of Clinical Epidemiology, 111, 49–59 1. https://doi.org/10.1016/j.jclinepi.2019.03.008 DOI: https://doi.org/10.1016/j.jclinepi.2019.03.008
Jiang, X., Wang, J., & Deng, N. (2025). Creating existential authenticity experience by combining technology capability with gamification in the metaverse: An affordance perspective. Technology in Society, 83. https://doi.org/10.1016/j.techsoc.2025.103019 DOI: https://doi.org/10.1016/j.techsoc.2025.103019
Kärkkäinen, K., Jääskelä, P., & Tynjälä, P. (2023). How does university teachers’ pedagogical training meet topical challenges raised by educational research? A case study from Finland. Teaching and Teacher Education, 128. https://doi.org/10.1016/j.tate.2023.104088 DOI: https://doi.org/10.1016/j.tate.2023.104088
Kohout-Diaz, M. (2025). Making sense of AI in teacher education: A qualitative study of perceptions, practices and pedagogical tensions. Teaching and Teacher Education, 171. https://doi.org/10.1016/j.tate.2025.105342. DOI: https://doi.org/10.1016/j.tate.2025.105342
Leahy, K., Calderón, A., O’Meara, N., Macphail, A., & O’Flaherty, J. (2025). Navigating times of change through communities of practice: A focus on teacher educators’ realities and professional learning. Teaching and Teacher Education, 156. https://doi.org/10.1016/j.tate.2025.104925 DOI: https://doi.org/10.1016/j.tate.2025.104925
Li, Y., & Ding, F. (2025). Affordances and learner agency: Chinese EFL learners’ English vlogging practices on Douyin. System, 138. https://doi.org/10.1016/j.system.2025.103963 DOI: https://doi.org/10.1016/j.system.2025.103963
Liang, Z., Wang, L., Su, J., Sun, B., Wang, D., & Yang, J. (2025). Unraveling the neural dynamics of mathematical interference in english reading: A novel approach with deep learning and fNIRS data. Brain Research Bulletin, 227. https://doi.org/10.1016/j.brainresbull.2025.111398 DOI: https://doi.org/10.1016/j.brainresbull.2025.111398
Liberali, P., & Schier, A. F. (2024). The evolution of developmental biology through conceptual and technological revolutions. Cell, 187(14), 3461–3495. https://doi.org/10.1016/j.cell.2024.05.053 DOI: https://doi.org/10.1016/j.cell.2024.05.053
Liu, L., & Shen, H. (2026). Fostering Critical Thinking Through STEM Undergraduate Research: Mechanisms and Challenges in a Chinese Research University. Thinking Skills and Creativity, 61. https://doi.org/10.1016/j.tsc.2026.102173 DOI: https://doi.org/10.1016/j.tsc.2026.102173
Moayyed, H., Moradzadeh, A., Abdeltawab, H., Mohammadi-Ivatloo, B., Faria, P., Muyeen, S. M., & Vale, Z. (2025). Innovative defense strategies: Fusion deep learning approach to counter false data injection attacks in power systems. Reliability Engineering and System Safety, 268. https://doi.org/10.1016/j.ress.2025.112003. DOI: https://doi.org/10.1016/j.ress.2025.112003
Park, W., Kim, D., & Kang, D. Y. (2021). Research trends in science and mathematics education in South Korea 2014–2018: A cross-disciplinary analysis of publications in selected local journals. Asia-Pacific Science Education, 5(1), 1–29. https://doi.org/10.1163/23641177-bja10029 DOI: https://doi.org/10.1163/23641177-bja10029
Perez, J. E., & Ong, E. C. (2025). Prompting for Engagement: Using the ICAP Framework to Guide Prompt Design in LLM-Powered Dialogue-Based Tutoring System for Novice Programmers. Procedia Computer Science, 272, 113–121. https://doi.org/10.1016/j.procs.2025.10.186 DOI: https://doi.org/10.1016/j.procs.2025.10.186
Qin, X., Shen, J., Qiao, Y., & Hui, E. C. M. (2025). Intercity talent flow and the evolution of urban technological diversification: Evidence from China. Cities, 168. https://doi.org/10.1016/j.cities.2025.106453 DOI: https://doi.org/10.1016/j.cities.2025.106453
Rampa, R., & Parmentier, G. (2023). The affordances of technology and strategic roadmapping: An exploration of its instrumental, symbolic, and political functions. Journal of Engineering and Technology Management. https://doi.org/10.1016/j.jengtecman.2023.101778 DOI: https://doi.org/10.1016/j.jengtecman.2023.101778
Riofrío-Luzcando, D., Ramírez, J., & Berrocal-Lobo, M. (2026). Comparing Automaton-based approach with Machine Learning models for Predicting Student Errors in Procedural Training to support Intelligent Tutoring Systems. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2026.131158 DOI: https://doi.org/10.2139/ssrn.5387196
Risgaard, R. D., Doll, H. M., & Sousa, A. M. M. (2025). A historical and technological perspective on the evolution of the human brain transcriptome. In Reference Module in Neuroscience and Biobehavioral Psychology (Preprint). https://doi.org/10.1016/b978-0-443-27380-3.00053-1 DOI: https://doi.org/10.1016/B978-0-443-27380-3.00053-1
Rücker, C. R., & Becker-Genschow, S. (2025). Enhancing enthusiasm for STEM education with AI: Domain-specific chatbot as personalized learning assistant. Computers and Education Open, 9. https://doi.org/10.1016/j.caeo.2025.100315 DOI: https://doi.org/10.1016/j.caeo.2025.100315
Santos, L. R., Behr, A., & Duarte, G. R. (2025). Recognizing accounting as a STEM discipline through professional skills in accounting information systems☆. Journal of Accounting Education, 72. https://doi.org/10.1016/j.jaccedu.2025.100970 DOI: https://doi.org/10.1016/j.jaccedu.2025.100970
Shah, W. H., Fatima, S. R., Jaimes-Reátegui, R., Arévalo-Simental, D. E., Villalobos-Gutiérrez, P. T., & Pisarchik, A. N. (2026). A systematic review of machine and deep learning techniques for acute lymphoblastic leukemia diagnosis. In Artificial Intelligence in Medicine (p. 176). https://doi.org/10.1016/j.artmed.2026.103393 DOI: https://doi.org/10.1016/j.artmed.2026.103393
Shen, J. (2025). Deep learning and explainable Artificial Intelligence for large-scale photovoltaic suitability analysis in Australian cities. Renewable Energy, 259. https://doi.org/10.1016/j.renene.2025.125025 DOI: https://doi.org/10.1016/j.renene.2025.125025
Shi, Y. R., Sin, K. F. K., & Wang, Y. Q. (2025). Teacher professional development of digital pedagogy for inclusive education in post-pandemic era: Effects on teacher competence, self-efficacy, and work well-being. Teaching and Teacher Education, 168. https://doi.org/10.1016/j.tate.2025.105230 DOI: https://doi.org/10.1016/j.tate.2025.105230
Sultan, S. M., Tso, C. P., Abdullah, M. Z., & Sopian, K. (2025). A new performance assessment method for photovoltaic module enhancing techniques based on power and cost effectiveness factor. Results in Engineering, 27. https://doi.org/10.1016/j.rineng.2025.106168 DOI: https://doi.org/10.1016/j.rineng.2025.106168
Wang, C. K. J., Reeve, J., Liu, W. C., Kee, Y. H., Ng, B., Chua, L. L., & Kong, L. C. (2025). An autonomy-supportive intervention program for STEM teachers to enhance engagement among students. Heliyon, 11(3). https://doi.org/10.1016/j.heliyon.2025.e42150 DOI: https://doi.org/10.1016/j.heliyon.2025.e42150
Weihs, B. J., Tang, Z., Roy, S., Tian, Z., Heuschele, D. J., Zhang, Z., & Xu, Z. (2025). No more laborious stem counting: AI-powered computer vision enables identification and quantification of solid and hollow alfalfa stems at the pixel level. Smart Agricultural Technology, 12. https://doi.org/10.1016/j.atech.2025.101278 DOI: https://doi.org/10.1016/j.atech.2025.101278
Wester, E. R., Walsh, L. L., Arango-Caro, S., Bray Speth, E., & Callis-Duehl, K. (2025). Beyond emergency remote teaching: student engagement rebounds in planned online STEM laboratory courses in fall 2020. Journal of Microbiology & Biology Education, 26(3). https://doi.org/10.1128/jmbe.00098-25 DOI: https://doi.org/10.1128/jmbe.00098-25
Wieselmann, J. R., Menon, D., Price, B. C., Johnson, A., Asim, S., Haines, S., & Morison, G. (2025). What is STEM? Preservice elementary teachers’ conceptions of integrated STEM education. Teaching and Teacher Education, 165. https://doi.org/10.1016/j.tate.2025.105108 DOI: https://doi.org/10.1016/j.tate.2025.105108
Woods, P. J., & Copur-Gencturk, Y. (2023). Examining the role of student-centered versus teacher-centered pedagogical approaches to self-directed learning through teaching. Teaching and Teacher Education, 138. https://doi.org/10.1016/j.tate.2023.104415 DOI: https://doi.org/10.1016/j.tate.2023.104415
Xu, D. M., Xu, Z., Wang, W. C., Zhao, Y. W., & Zang, H. F. (2025). Revolutionizing flood forecasting by integrating rainfall-runoff correlation analysis with advanced deep learning techniques. Results in Engineering, 28. https://doi.org/10.1016/j.rineng.2025.107804 DOI: https://doi.org/10.1016/j.rineng.2025.107804
Yao, X., & He, B. (2026). Microstructure evolution and phase transition of interface zone in Cu/Ti dissimilar metals fabricated by laser-directed energy deposition. Journal of Alloys and Compounds Communications. https://doi.org/10.1016/j.jacomc.2026.100160 DOI: https://doi.org/10.1016/j.jacomc.2026.100160
Young, K., Küçük, Z. D., Delahunty, T., Dempsey, M., & Maglaperidze, N. (2026). Evaluating the impact of a digital competency-based placement model on STEM pre-service teachers’ digital competence and teaching experiences. Teaching and Teacher Education, 175, 105466. https://doi.org/10.1016/j.tate.2026.105466 DOI: https://doi.org/10.1016/j.tate.2026.105466
Yu, Z., Lin, Y., Gao, Y., Huo, J., Liu, Y., & Wang, J. (2025). Quantitative microstructure analysis of nano-enhanced cement after high temperatures by deep learning technology. Construction and Building Materials, 501. https://doi.org/10.1016/j.conbuildmat.2025.144302 DOI: https://doi.org/10.1016/j.conbuildmat.2025.144302
Żammit, J., & Farrugia Caruana, L. (2026). Heartfelt pedagogy across borders: Emotional experiences of language teachers in multilingual educational contexts with insights from Malta. Teaching and Teacher Education, 176, 105481. https://doi.org/10.1016/j.tate.2026.105481 DOI: https://doi.org/10.1016/j.tate.2026.105481
Zhang, J., Yang, M., Wang, G., Dang, J., Hao, X., Kong, D., & Ouyang, M. (2025). Advancing the proton exchange membrane water electrolysis: Perspective on the affordable hydrogen production cost. ETransportation, 26. https://doi.org/10.1016/j.etran.2025.100481 DOI: https://doi.org/10.1016/j.etran.2025.100481
Zhao, H., Han, Z., Yang, N., & Zhen, Z. (2025). From limitation to possibility: affordances and tactical evolution in user–GenAI interaction. Journal of Documentation, 1586–1607. https://doi.org/10.1108/JD-07-2025-0183 DOI: https://doi.org/10.1108/JD-07-2025-0183
Zhao, H., Han, Z., Yin, S., & Hansen, P. (2025). From interface to inference: mapping the impact of generative artificial intelligence affordances on user risk perception. Telematics and Informatics, 101. https://doi.org/10.1016/j.tele.2025.102299 DOI: https://doi.org/10.1016/j.tele.2025.102299
Zhao, Y., Fan, J., Lu, X., Zhang, Y., Wen, W., Huang, G., & Chen, L. (2025). From leaf to canopy: Inversion of lettuce pigment distribution using hyperspectral imaging technology combined with deep learning algorithms. Plant Phenomics, 7(4). https://doi.org/10.1016/j.plaphe.2025.100104 DOI: https://doi.org/10.1016/j.plaphe.2025.100104
Zhu, W., Ouyang, P., & Kong, M. (2024). Research on the evolution mechanism of intelligent manufacturing transformation of Chinese pharmaceutical manufacturing enterprises based on system dynamics. Heliyon, 10(13). https://doi.org/10.1016/j.heliyon.2024.e33959 DOI: https://doi.org/10.1016/j.heliyon.2024.e33959
License
Copyright (c) 2026 Mira Sudiarti, Yulyanti Harisman
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
