Transforming Mechanics Education through Innovative Digital Problem-Based Learning: A Bibliometric Analysis
DOI:
https://doi.org/10.33394/j-ps.v14i2.19648Keywords:
Digital, Learning, Mechanics, Problem-based learningAbstract
The digital transformation in education has driven increased attention to the application of problem-based learning (PBL) in technology-based mechanics learning. However, thematic developments, knowledge structures, and research directions in this field have not been systematically mapped. This study aims to analyze publication trends, knowledge network structures, and the development of research topics related to PBL in digital mechanics learning using a bibliometric approach. The research data consists of 189 documents indexed in the Scopus database for the period 2015–2025. Analysis of publication trends, geographic distribution, and relationships between keywords was conducted using VOSviewer software. The analysis results show an increase in the number of publications over the past decade, with contributions concentrated in several countries. Keyword mapping shows the relationship between PBL and topics such as e-learning, game-based learning, and terms related to computing technology such as machine learning and deep learning. Furthermore, temporal visualizations indicate changes in the trend of term usage over time. However, these findings are descriptive and reflect patterns in the analyzed literature, and therefore do not directly indicate a causal relationship or the level of conceptual integration between PBL and digital technology. This research provides a contribution in the form of bibliometric mapping which can be the basis for further research to examine the implementation and integration of PBL in digital-based mechanics learning in more depth.
References
Al‑Emran, M., Mezhuyev, V., & Kamaludin, A. (2022). Technology acceptance and adoption of learning management systems in higher education: A systematic review. Education and Information Technologies, 27(3), 3517–3544. https://doi.org/10.1007/s10639‑021‑10785‑6
Anjugam, M., & Chellamani, K. P. (2024). Trends and challenges in problem-based learning research: A systematic review. Education and Information Technologies, 29(3), 4121–4143. https://doi.org/10.1007/s10639-023-11987-6i
Aria, M. (2022). Bibliometric analysis: Methods, indicators, and applications. Journal of Informetrics, 16(4), 101295. https://doi.org/10.1016/j.joi.2022.101295
Aria, M., & Cuccurullo, C. (2022). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 16(1), 101207. https://doi.org/10.1016/j.joi.2021.101207
Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2021). Mapping research in digital education: A bibliometric analysis. Computers & Education, 159, 104009. https://doi.org/10.1016/j.compedu.2020.104009
Chen, X., & Zhang, Y. (2021). Deep learning method based on physics-informed neural networks for flow modeling. Water (Switzerland), 13(9), 1245. https://doi.org/10.3390/w13091245
Clark, R. M., & Ernst, J. V. (2021). PBL in engineering mechanics education: Effects on conceptual understanding. European Journal of Engineering Education, 46(5), 734–749. https://doi.org/10.1080/03043797.2020.1835825
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2021). Science mapping analysis software tools: Review and comparative analysis. Journal of the American Society for Information Science and Technology, 72(6), 764–783. https://doi.org/10.1002/asi.24436
Diaz-Barrera, M. E., Alfaro-Aucca, C., & Pacheco-Mendoza, J., et al. (2025). Bibliometric analysis of prominent topics in global scientific production on sustainable development goals in Scopus (2013–2022). Discover Sustainability, 6, 74. https://doi.org/10.1007/s43621-024-00700-w
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Dwikoranto, D., Surasmi, W. A., Suparti, S., & Setiani, R. (2024). Assessing the validity and effectiveness of student worksheets and creativity tests in enhancing the creativity of Open University students. Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram, 12(2), 376–389. https://doi.org/10.33394/j-ps.v12i2.9979
Dwikoranto, (co-author) Lintangesukmanjaya, R. T., Iswardani, A., Prahani, B. K., et al. (2025). Improving critical thinking skills of high school students in physics learning with smartphone-simulation assisted inquiry model. Journal of Digitalization in Physics Education, 1(2). https://doi.org/10.26740/jdpe.v1i2.42129
Fransiska, V. O., Dwikoranto, & Fajriyah Citra, N. (2025). Literature review: STEAM approach to improve high school students’ problem-solving ability in physics learning. Journal of Digitalization in Physics Education, 1(2), Article 39011. https://doi.org/10.26740/jdpe.v1i2.39011
Hmelo-Silver, C. E., Bridges, S. M., McKeown, M. G., & Walker, A. E. (2020). Designing for knowledge integration in PBL. Educational Psychologist, 55(4), 204–220. https://doi.org/10.1080/00461520.2020.1782056
Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2022). The difference between emergency remote teaching and online learning. Educause Review, 27(1), 1–12.
Idris, N., Zakaria, E., & Md Yusoff, N. (2022). Systematic review of PBL in STEM education. International Journal of Instruction, 15(2), 415–432. https://doi.org/10.29333/iji.2022.15223a
Jamaludin, J., Batlolona, J. R., & Dulhasyim, A. B. P. (2025). Building new understanding through experiments: Students’ conceptual shifts on projectile motion. Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram, 13(4), 999–1021. https://doi.org/10.33394/j-ps.v13i4.15627
Lintangesukmanjaya, R. T., Prahani, B. K., Dwikoranto, Alhusni, H. Z., & Kurtuluş, M. A. (2025). Technology integration for SDGs-oriented social science education: A bibliometric perspective. E3S Web of Conferences, 640, 2017. https://doi.org/10.1051/e3sconf/202564002017
Lintangesukmanjaya, R. T., et al., & Dwikoranto. (2025). Improving critical thinking skills of high school students in physics learning with smartphone-simulation assisted inquiry model. Journal of Digitalization in Physics Education, 1(2). https://doi.org/10.26740/jdpe.v1i2.42129
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2022). Intelligence unleashed: An argument for AI in education. Pearson Education.
Mahdalysa Dayu, Rahmatta Thoriq Lintangesukmanjaya & Lindsay N. Bergsma. (2025). The effectiveness of guided inquiry learning model with digital simulations to enhance students’ critical thinking skills in physics. Journal of Digitalization in Physics Education, 1(3). https://doi.org/10.26740/jdpe.v1i3.43522
Muhali, M., Hulyadi, H., Khaeruman, K., Gargazi, G., & Azmi, I. (2025). Identifying analytical thinking skills in forestry students: Understanding climate change awareness in the 21st century context. Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram, 13(2), 283–293. https://doi.org/10.33394/j-ps.v13i2.13644
OECD. (2023). Innovating education and educating for innovation. OECD Publishing. https://doi.org/10.1787/9789264304274-en
Page, M. J., McKenzie, J. E., Bossuyt, P. M., et al. (2021). The PRISMA 2020 statement. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Peng, Y., Wu, J., & Li, X. (2023). Digital simulations and AI-supported learning in mechanics education. Computers & Education, 188, 104567. https://doi.org/10.1016/j.compedu.2022.104567
Razilu, Z., Sulfasyah, S., & Nappu, S. (2025). Mapping global research trends in interactive learning media: A bibliometric analysis of Scopus publications (2021–2025). PPSDP International Journal of Education, 4(2), 313–328. https://doi.org/10.59175/pijed.v4i2.743
Saky, S. A. T. M., Inayati, N. L., & Islam, M. N. (2025). Research patterns in formative assessment: A bibliometric review of primary and secondary school studies. Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram, 13(1), 107–122. https://doi.org/10.33394/j-ps.v13i1.14219
Santos, J., Pereira, F., & Silva, A. (2022). Problem-based learning supported by digital technologies in engineering education. Education Sciences, 12(6), 401. https://doi.org/10.3390/educsci12060401
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in education: A bibliometric analysis. Technological Forecasting and Social Change, 166, 120640. https://doi.org/10.1016/j.techfore.2021.120640
Sufiawati, I., Insyafiana, A., Rahman, R., et al. (2025). A bibliometric analysis reveals a dynamic growth in the use of artificial intelligence in oral cancer research over three decades. Discover Oncology, 16, 1432. https://doi.org/10.1007/s12672-025-03293-6
Tripura, T., & Chakraborty, S. (2023). Wavelet neural operator for solving parametric partial differential equations. Computer Methods in Applied Mechanics and Engineering, 404, 115805. https://doi.org/10.1016/j.cma.2022.115805
Van Eck, N. J., & Waltman, L. (2020). VOSviewer manual. Leiden University.
Vadyala, S. R., Betgeri, S. N., & Krishnan, S. (2022). A review of physics-based machine learning in engineering. Results in Engineering, 13, 100360. https://doi.org/10.1016/j.rineng.2022.100360
Wardani, F. A., Sumartiningsih, S., & Avrilianda, D. (2025). Systematic literature review: Pemanfaatan teknologi AR dalam pembelajaran berbasis kearifan lokal. Pendas: Jurnal Ilmiah Pendidikan Dasar.
Wahyuni, S., & Pramudya, I. (2025). Digital scaffolding in e-learning worksheets. Jurnal Inovasi Pendidikan Fisika, 14(1), 44–53.
Widodo, E., & Hartanti, D. (2023). Physics misconceptions analysis using diagnostic tests. Jurnal Pendidikan IPA Indonesia, 12(2), 210–219.
Wulandari, R., & Prasetyo, Z. K. (2023). Reflective learning in problem-based physics instruction. Journal of Baltic Science Education, 22(3), 420–432.
Zawacki-Richter, O., Bond, M., Marin, V. I., & Gouverneur, F. (2022). Systematic review of AI applications in higher education. International Journal of Educational Technology in Higher Education, 19(1), 1–27. https://doi.org/10.1186/s41239-022-00300-4
Zuo, C., Chen, Q., Gu, G., et al. (2022). Deep learning in optical metrology: A review. Light: Science & Applications, 11, 39. https://doi.org/10.1038/s41377-022-00700-1
Zupic, I., & Čater, T. (2021). Bibliometric methods in management and organization. Organizational Research Methods, 24(3), 447–478. https://doi.org/10.1177/1094428120911941
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Copyright (c) 2026 Safina Ridka Pratiwi, Dwi Koranto, Rahmatta Thoriq Lintangesukmanjaya, Sukarni Sukarni, Indri Hapsari Khansa, Lindsay Natalia Bergsma

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