Bibliometric Analysis: Collaboration Networks In Discovery Learning Research

Authors

Andi Suparjo , Edwin Musdi , Yerizon , I Made Arnawa

DOI:

10.29303/jppipa.v10i2.7002

Published:

2024-02-28

Issue:

Vol. 10 No. 2 (2024): February

Keywords:

Bibliometrics, Discovery learning, Scopus database

Review

Downloads

How to Cite

Suparjo, A., Musdi, E. ., Yerizon, Y., & Arnawa, I. M. . (2024). Bibliometric Analysis: Collaboration Networks In Discovery Learning Research. Jurnal Penelitian Pendidikan IPA, 10(2), 45–53. https://doi.org/10.29303/jppipa.v10i2.7002
Crossref
Scopus
Google Scholar
Europe PMC

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Abstract

This research identifies publications related to Discovery learning. Discovery learning is a learning model that encourages active learning in students through self-discovery and research so that the results achieved are long-lasting and difficult to forget. This research aims to identify and analyze articles researching Discovery Learning that have been published in several reputable international journals published in the 2014-2023 period, which was carried out using bibliometric studies. Bibliometric analysis produced four findings: publications about Discovery Learning in Scopus-indexed journals have been in a fluid and balanced pattern every year for the last ten years; 328 of the ten journals producing the most articles have been published. The top-ranked journal published 15 articles, and the tenth-ranked journal published five articles; The most citations occurred in articles published in 2020 with a total of 1101 citations. The most cited article was written by B.R. Goldsmith with 241 citations; The author's keywords that are most frequently used in the top three are discovery, machine, and machine learning

References

Ariftian, I., Madjdi, A. H., & Murtono, M. (2021). Science-Based Quantum Learning Models in Elementary School. Journal of Physics: Conference Series, 1823(1), 012085. https://doi.org/10.1088/

-6596/1823/1/012085

Ayuningsih, S., & Muna, L. N. (2023). Influence of the Discovery Learning Learning Model on Critical Thinking Abilities and Student Learning Outcomes in Buffer Solution Material. Jurnal Penelitian Pendidikan IPA, 9(11), 9438–9446. https://doi.org/10.29303/jppipa.v9i11.4469

BariÄević, M., & Luić, L. (2023). From Active Learning to Innovative Thinking: The Influence of Learning the Design Thinking Process among Students. Education Sciences, 13(5), 455. https://doi.org/

3390/educsci13050455

Brennan, R. W., Nelson, N., & Paul, R. (2021). Estimating the Effect of Timetabling Decisions on the Spread of SARS-CoV-2 in Medium-to-Large Engineering Schools in Canada: An Agent-Based Modeling Study. CMAJ Open, 9(4), E1252–E1259. https://doi.org/10.9778/cmajo.20200280

Buckley, P., & Lee, P. (2021). The Impact of Extra-Curricular Activity on the Student Experience. Active Learning in Higher Education, 22(1), 37–48. https://doi.org/10.1177/1469787418808988

Cai, C., Wang, S., Xu, Y., Zhang, W., Tang, K., Ouyang, Q., Lai, L., & Pei, J. (2020). Transfer Learning for Drug Discovery. Journal of Medicinal Chemistry, 63(16), 8683–8694. https://doi.org/10.1021/

acs.jmedchem.9b02147

Chusni, M. M., Saputro, S., Suranto, S., & Rahardjo, S. B. (2020). The Potential of Discovery Learning Models to Empower Students’ Critical Thinking Skills. Journal of Physics: Conference Series, 1464(1), 012036. https://doi.org/10.1088/1742-6596/1464/

/012036

Collins, C. S., & Stockton, C. M. (2018). The Central Role of Theory in Qualitative Research. International Journal of Qualitative Methods, 17(1), 160940691879747. https://doi.org/10.1177/160940

Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for Educational Practice of the Science of Learning and Development. Applied Developmental Science, 24(2), 97–140. https://doi.org/10.1080/10888691.

1537791

Deiana, A. M., Tran, N., Agar, J., Blott, M., Di Guglielmo, G., Duarte, J., Harris, P., Hauck, S., Liu, M., Neubauer, M. S., Ngadiuba, J., Ogrenci-Memik, S., Pierini, M., Aarrestad, T., Bähr, S., Becker, J., Berthold, A.-S., Bonventre, R. J., Müller Bravo, T. E., & Warburton, T. K. (2022). Applications and Techniques for Fast Machine Learning in Science. Frontiers in Big Data, 5, 787421. https://doi.org/10.3389/fdata.2022.787421

Getie, A. S. (2020). Factors Affecting the Attitudes of Students towards Learning English as a Foreign Language. Cogent Education, 7(1), 1738184. https://doi.org/10.1080/2331186X.2020.1738184

Goldsmith, B. R., Esterhuizen, J., Liu, J. X., Bartel, C. J., & Sutton, C. (2018). Machine Learning for Heterogeneous Catalyst Design and Discovery. AIChE Journal, 64(7), 2311–2323. https://doi.org/

1002/aic.16198

Guo, Y., He, X., Su, Y., Dai, Y., Xie, M., Yang, S., Chen, J., Wang, K., Zhou, D., & Wang, C. (2021). Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction. Journal of the American Chemical Society, 143(15), 5755–5762. https://doi.org/10.1021/jacs.1c00339

Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the Role of Digital Technologies in Education: A Review. Sustainable Operations and Computers, 3, 275–285. https://doi.org/10.1016/j.susoc.2022.05.004

Hariram, N. P., Mekha, K. B., Suganthan, V., & Sudhakar, K. (2023). Sustainalism: An Integrated Socio-Economic-Environmental Model to Address Sustainable Development and Sustainability. Sustainability, 15(13), 10682. https://doi.org/

3390/su151310682

Janet, J. P., Chan, L., & Kulik, H. J. (2018). Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network. Journal of Physical Chemistry Letters, 9(5), 1064–1071. https://doi.org/10.1021/acs.jpclett.8b00170

Jayanto, I. F., Noer, S. H., & Caswita, C. (2019). Development of Guided Discovery Learning to Improve Reflective Thinking. International Journal of Trends in Mathematics Education Research, 2(2), 106–111. https://doi.org/10.33122/ijtmer.v2i2.116

José De Oliveira, O., Francisco Da Silva, F., Juliani, F., César Ferreira Motta Barbosa, L., & Vieira Nunhes, T. (2019). Bibliometric Method for Mapping the State-of-the-Art and Identifying Research Gaps and Trends in Literature: An Essential Instrument to Support the Development of Scientific Projects. In S. Kunosic & E. Zerem (Eds.), Scientometrics Recent Advances. IntechOpen. https://doi.org/

5772/intechopen.85856

Kennedy, J., Baxter, P., & Belpaeme, T. (2015). Comparing Robot Embodiments in a Guided Discovery Learning Interaction with Children. International Journal of Social Robotics, 7(2), 293–308. https://doi.org/10.1007/s12369-014-0277-4

Kim, S., Raza, M., & Seidman, E. (2019). Improving 21st-Century Teaching Skills: The Key to Effective 21st-Century Learners. Research in Comparative and International Education, 14(1), 99–117. https://doi.org/10.1177/1745499919829214

Legrain, F., Carrete, J., Van Roekeghem, A., Madsen, G. K. H., & Mingo, N. (2018). Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus Ab Initio Methods. Journal of Physical Chemistry B, 122(2), 625–632. https://doi.org/10.1021/acs.jpcb.7b05296

Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017). Materials Discovery and Design Using Machine Learning. Journal of Materiomics, 3(3), 159–177. https://

doi.org/10.1016/j.jmat.2017.08.002

Lodge, J. M., Kennedy, G., Lockyer, L., Arguel, A., & Pachman, M. (2018). Understanding Difficulties and Resulting Confusion in Learning: An Integrative Review. Frontiers in Education, 3, 49. https://doi.org/10.3389/feduc.2018.00049

López, F., Contreras, M., Nussbaum, M., Paredes, R., Gelerstein, D., Alvares, D., & Chiuminatto, P. (2023). Developing Critical Thinking in Technical and Vocational Education and Training. Education Sciences, 13(6), 590. https://doi.org/10.3390/

educsci13060590

Mahmood, A., Irfan, A., & Wang, J. L. (2022). Machine Learning and Molecular Dynamics Simulation-Assisted Evolutionary Design and Discovery Pipeline to Screen Efficient Small Molecule Acceptors for PTB7-Th-Based Organic Solar Cells with Over 15% Efficiency. Journal of Materials Chemistry A, 10(8), 4170–4180. https://doi.org/

1039/d1ta09762h

Mensah, J. (2019). Sustainable Development: Meaning, History, Principles, Pillars, and Implications for Human Action: Literature Review. Cogent Social Sciences, 5(1), 1653531. https://doi.org/10.1080/

2019.1653531

Muhayati, E. I., Trisnawaty, W., & Subaidah, S. (2023). Implementation of Discovery Learning Models to Improve Student's Mathematic Learning Outcomes. Jurnal Penelitian Pendidikan IPA, 9(5), 3975–3980. https://doi.org/10.29303/jppipa.v9i5.

Munawar, H. S., Qayyum, S., Ullah, F., & Sepasgozar, S. (2020). Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis. Big Data and Cognitive Computing, 4(2), 4. https://doi.org/10.3390/

bdcc4020004

Payu, C. S. (2023). Effect of Experiment-Based Discovery Learning Model on Psychomotor Learning Outcomes in Static Fluid Materials. Jurnal Penelitian Pendidikan IPA, 9(5), 2647–2652. https://doi.org/10.29303/jppipa.v9i5.3573

Pedaste, M., Mäeots, M., Siiman, L. A., De Jong, T., Van Riesen, S. A. N., Kamp, E. T., Manoli, C. C., Zacharia, Z. C., & Tsourlidaki, E. (2015). Phases of Inquiry-Based Learning: Definitions and the Inquiry Cycle. Educational Research Review, 14, 47–61. https://doi.org/10.1016/j.edurev.2015.02.003

Raslan, G. (2023). Critical Thinking Skills Profile of High School Students in AP Chemistry Learning. In K. Al Marri, F. Mir, S. David, & A. Aljuboori (Eds.), BUiD Doctoral Research Conference 2022 (Vol. 320, pp. 79–96). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-27462-6_8

Rejeb, A., Rejeb, K., Appolloni, A., Kayikci, Y., & Iranmanesh, M. (2023). The Landscape of Public Procurement Research: A Bibliometric Analysis and Topic Modeling Based on Scopus. Journal of Public Procurement, 23(2), 145–178. https://doi.org/10.1108/JOPP-06-2022-0031

Serdyukov, P. (2017). Innovation in Education: What Works, What Doesn’t, and What To Do About It? Journal of Research in Innovative Teaching & Learning, 10(1), 4–33. https://doi.org/10.1108/JRIT-10-2016-0007

Sewagegn, A., & Diale, B. M. (2019). Empowering Learners Using Active Learning in Higher Education Institutions. In S. Manuel Brito (Ed.), Active Learning—Beyond the Future. IntechOpen. https://doi.org/10.5772/intechopen.80838

Shmilovich, K., Mansbach, R. A., Sidky, H., Dunne, O. E., Panda, S. S., Tovar, J. D., & Ferguson, A. L. (2020). Discovery of Self-Assembling π‑Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation Published.

Sinaga, B., Sitorus, J., & Situmeang, T. (2023). The Influence of Students’ Problem-Solving Understanding and Results of Students’ Mathematics Learning. Frontiers in Education, 8, 1088556. https://doi.org/10.3389/feduc.2023.

Snyder, H. (2019). Literature Review as a Research Methodology: An Overview and Guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Spellings, M., & Glotzer, S. C. (2018). Machine Learning for Crystal Identification and Discovery. AIChE Journal, 64(6), 2198–2206. https://doi.org/10.1002/

aic.16157

Tufail, S., Riggs, H., Tariq, M., & Sarwat, A. I. (2023). Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms. Electronics, 12(8), 1789. https://doi.org/10.3390/

electronics12081789

Ulissi, Z. W., Singh, A. R., Tsai, C., & Nørskov, J. K. (2016). Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning. Journal of Physical Chemistry Letters, 7(19), 3931–3935. https://doi.org/10.1021/acs.jpclett.6b01254

Ullah, R., Asghar, I., & Griffiths, M. G. (2022). An Integrated Methodology for Bibliometric Analysis: A Case Study of Internet of Things in Healthcare Applications. Sensors, 23(1), 67. https://doi.org/

3390/s23010067

Wu, Y.-Y., & Chou, W.-H. (2023). A Bibliometric Analysis to Identify Research Trends in Intervention Programs for Smartphone Addiction. International Journal of Environmental Research and Public Health, 20(5), 3840. https://doi.org/

3390/ijerph20053840

Author Biographies

Andi Suparjo, Universitas Negeri Padang

Program Studi Magister Pendidikan Matematika

Edwin Musdi, Universitas Negeri Padang

Program Studi Magister Pendidikan Matematika

Yerizon, Universitas Negeri Padang

Program Studi Magister Pendidikan Matematika

I Made Arnawa, Universitas Negeri Padang

Program Studi Magister Pendidikan Matematika

License

Copyright (c) 2024 Andi Suparjo, Edwin Musdi, Yerizon, I Made Arnawa

Creative Commons License

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:

  1. 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.
  2. 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.
  3. 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).