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A Broad View of the Problem-Based Learning Field Based on Machine Learning: A Large-Scale Study Based on Topic Modeling

Year 2023, Volume: 7 Issue: 15, 608 - 626, 21.10.2023
https://doi.org/10.31458/iejes.1320491

Abstract

The aim of the study is to examine Problem Based Learning (PBL) studies in terms of descriptive and semantic content analysis by using topic modeling. For this purpose, descriptive and topic modeling analyzes were used together in the research. In order to include the highest number of articles on Scopus, the term "problem based learning" was searched in the title, abstract and keywords and only journal articles (research and review) were selected. Thus, 7289 articles in 1987-2021 were included in the study. Firstly, the subject area, author and country distributions are listed. In addition, it showed that the most studied topics were education curriculum (39.15%), teaching strategies (14.90%), critical thinking skill (12.29%) and patient simulation (8.88%). When examined in seven five-year periods between 1987 and 2021, it was determined that the most voluminous topic was education curriculum, and the most accelerated topic was clinical education. Considering the number of publications in five-year periods, it was determined that the topics of critical thinking skills and teaching strategies accelerated more in the percentages calculated according to the topics. It is expected that the results obtained will be important reference points for the studies to be carried out in the field of PBL

Ethical Statement

Due to the scope and method of the study, ethics committee permission was not required.

References

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  • Barrows, H. S., & Tamblyn, R. M. (1980). Problem-based learning: An approach to medical education (Vol. 1). Springer Publishing Company.
  • Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.
  • Blei, D. M., & Lafferty, J. D. (2007). Correction: A correlated topic model of Science. The Annals of Applied Statistics, 1(2). https://doi.org/10.1214/07-aoas136
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
  • Blumenfeld, P. C., Marx, R. W., Soloway, E., & Krajcik, J. (1996). Learning with peers: From small group cooperation to collaborative communities. Educational Researcher, 25(8), 37-39.
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  • Ghani, A. S. A., Rahim, A. F. A., Yusoff, M. S. B., & Hadie, S. N. H. (2021). Effective learning behavior in problem-based learning: a scoping review. Medical Science Educator, 31(3), 1199-1211.
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  • Gürcan, F., & Özyurt, Ö. (2020). Emerging trends and knowledge domains in e-learning researches: Topic modeling analysis with the articles published between 2008. Journal of Computer and Education Research, 8(16), 738-756. https://doi.org/10.18009/jcer.769349
  • Gurcan, F., & Cagiltay, N. E. (2020). Research trends on distance learning: a text mining-based literature review from 2008 to 2018. In Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1815795
  • Gurcan, F., & Cagiltay, N. E. (2022). Exploratory analysis of topic interests and their evolution in bioinformatics research using semantic text mining and probabilistic topic modeling. IEEE Access, 10, 31480-31493.
  • Gurcan, F., Cagiltay, N. E., & Cagiltay, K. (2021). Mapping human–computer interaction research themes and trends from its existence to today: a topic modeling-based review of past 60 years. International Journal of Human-Computer Interaction, 37(3), 267–280. https://doi.org/10.1080/10447318.2020.1819668
  • Gurcan, F., Ozyurt, O., & Cagiltay, N. E. (2021). Investigation of emerging trends in the e-learning field using latent dirichlet allocation. International Review of Research in Open and Distance Learning, 22(2), 1–18. https://doi.org/10.19173/irrodl.v22i2.5358
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  • Li, Y., Wang, X., Zhu, X. R., Zhu, Y. X., & Sun, J. (2019). Effectiveness of problem-based learning on the professional communication competencies of nursing students and nurses: A systematic review. Nurse Education in Practice, 37, 45-55.
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  • Manalo, F., & Chua, E. (2020). Collaborative inquiry approaches and level of thinking and reasoning skills: basis for sustainable science education. IOER International Multidisciplinary Research Journal, 2(2), 91–98.
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A Broad View of the Problem-Based Learning Field Based on Machine Learning: A Large-Scale Study Based on Topic Modeling

Year 2023, Volume: 7 Issue: 15, 608 - 626, 21.10.2023
https://doi.org/10.31458/iejes.1320491

Abstract

The aim of the study is to examine Problem Based Learning (PBL) studies in terms of descriptive and semantic content analysis by using topic modeling. For this purpose, descriptive and topic modeling analyzes were used together in the research. In order to include the highest number of articles on Scopus, the term "problem based learning" was searched in the title, abstract and keywords and only journal articles (research and review) were selected. Thus, 7289 articles in 1987-2021 were included in the study. Firstly, the subject area, author and country distributions are listed. In addition, it showed that the most studied topics were education curriculum (39.15%), teaching strategies (14.90%), critical thinking skill (12.29%) and patient simulation (8.88%). When examined in seven five-year periods between 1987 and 2021, it was determined that the most voluminous topic was education curriculum, and the most accelerated topic was clinical education. Considering the number of publications in five-year periods, it was determined that the topics of critical thinking skills and teaching strategies accelerated more in the percentages calculated according to the topics. It is expected that the results obtained will be important reference points for the studies to be carried out in the field of PBL

References

  • Aggarwal, C. C., & Zhai, C. X. (2013). Mining text data. In Mining Text Data (Vol. 9781461432). https://doi.org/10.1007/978-1-4614-3223-4
  • Aksoy, B. (2011). Sosyal bilgiler programındaki coğrafya konularının öğretiminde probleme dayalı öğrenme yaklaşımı [Problem-based learning approach in teaching geography topics in social studies program]. Turan, R. Sünbül, A. M. Akdağ, H. (Ed) Sosyal Bilgiler Öğretiminde Yeni Yaklaşımlar-II, 232-249. Ankara: Pegem Yayıncılık.
  • Al-Azri, H., & Ratnapalan, S. (2014). Problem-based learning in continuing medical education: Review of randomized controlled trials. Canadian Family Physician, 60(2), 157-165.
  • Albanese, M. A., & Mitchell, S. (1993). Problem-based learning: A review of literature on its outcomes and implementation issues. Academic Medicine-Philadelphia, 68, 52-81.
  • Alfonseca, E., Carro, R. M., Martín, E., Ortigosa, A., & Paredes, P. (2006). The impact of learning styles on student grouping for collaborative learning: A case study. User Modeling and User-Adapted Interaction, 16(3-4), 377-401.
  • Azer, S. A., & Azer, D. (2015). Group interaction in problem‐based learning tutorials: A systematic review. European Journal of Dental Education, 19(4), 194-208.
  • Barrows, H. S., & Kelson, A. M. (1993). Problem-based learning: A total approach to education. Monograph. Southern Illinois University School of Medicine, Springfield, Illinois.
  • Barrows, H. S., & Tamblyn, R. M. (1980). Problem-based learning: An approach to medical education (Vol. 1). Springer Publishing Company.
  • Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.
  • Blei, D. M., & Lafferty, J. D. (2007). Correction: A correlated topic model of Science. The Annals of Applied Statistics, 1(2). https://doi.org/10.1214/07-aoas136
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
  • Blumenfeld, P. C., Marx, R. W., Soloway, E., & Krajcik, J. (1996). Learning with peers: From small group cooperation to collaborative communities. Educational Researcher, 25(8), 37-39.
  • Bransford, J. D., & McCarrell, N. S. (1977). A sketch of a cognitive approach to comprehension: Some thoughts about understanding what it means to comprehend. In Johnson-Laird, P. N., and Wason, P. C. (eds.), Thinking: Readings in Cognitive Science, Cambridge University Press, Cambridge, UK, pp. 377-399.
  • Brown, G. (2022). Proposing problem-based learning for teaching future forensic speech scientists. Science & Justice.
  • Carey, L., & Whittaker, K. A. (2002). Experiences of problem-based learning: issues for community specialist practitioner students. Nurse Education Today, 22(8), 661-668.
  • Cash, C. B., Letargo, J., Graether, S. P., & Jacobs, S. R. (2017). An analysis of the perceptions and resources of large university classes. CBE-Life Sciences Education, 16(2), 1-12.
  • Chan, T., Chen, C. M., Wu, Y. L., Jong, B. S., Hsia, Y. T., & Lin, T. W. (2010). Applying the genetic encoded conceptual graph to grouping learning. Expert Systems with Applications, 37(6), 4103-4118.
  • Chao, C. T., Tsai, C. L., Lin, M. W., Yang, C. W., Ho, C. C., Chen, H. L., ... & Sheu, B. C. (2021). Fully digital problem-based learning for undergraduate medical students during the COVID-19 period: Practical considerations. Journal of the Formosan Medical Association, 121(10), 2130-2134.
  • Cohen, L., Lawrence, M., & Morrison, K. (2017). Research methods in education. 6th ed. In Research Methods in Education. Routledge Taylor & Francis Group.
  • Dolmans, D., & Gijbels, D. (2013). Research on problem‐based learning: Future challenges. Medical Education, 47(2), 214-218.
  • Evia, C., Sharp, M. R., & Perez-Quinones, M. A. (2015). Teaching structured authoring and dita through rhetorical and computational thinking. IEEE Transactions on Professional Communication, 58(3), 328–343.
  • Galvao, T. F., Silva, M. T., Neiva, C. S., Ribeiro, L. M., & Pereira, M. G. (2014). Problem-based learning in pharmaceutical education: A systematic review and meta-analysis. The Scientific World Journal.
  • Ghani, A. S. A., Rahim, A. F. A., Yusoff, M. S. B., & Hadie, S. N. H. (2021). Effective learning behavior in problem-based learning: a scoping review. Medical Science Educator, 31(3), 1199-1211.
  • Goodnough, K., & Cashion, M. (2006). Exploring problem‐based learning in the context of high school science: Design and implementation issues. School Science and Mathematics, 106(7), 280-295.
  • Gurcan, F., & Cagiltay, N. E. (2019). Big data software engineering: analysis of knowledge domains and skill sets using lda-based topic modeling. IEEE Access, 7, 82541–82552. https://doi.org/10.1109/ACCESS.2019.2924075
  • Gürcan, F., & Özyurt, Ö. (2020). Emerging trends and knowledge domains in e-learning researches: Topic modeling analysis with the articles published between 2008. Journal of Computer and Education Research, 8(16), 738-756. https://doi.org/10.18009/jcer.769349
  • Gurcan, F., & Cagiltay, N. E. (2020). Research trends on distance learning: a text mining-based literature review from 2008 to 2018. In Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1815795
  • Gurcan, F., & Cagiltay, N. E. (2022). Exploratory analysis of topic interests and their evolution in bioinformatics research using semantic text mining and probabilistic topic modeling. IEEE Access, 10, 31480-31493.
  • Gurcan, F., Cagiltay, N. E., & Cagiltay, K. (2021). Mapping human–computer interaction research themes and trends from its existence to today: a topic modeling-based review of past 60 years. International Journal of Human-Computer Interaction, 37(3), 267–280. https://doi.org/10.1080/10447318.2020.1819668
  • Gurcan, F., Ozyurt, O., & Cagiltay, N. E. (2021). Investigation of emerging trends in the e-learning field using latent dirichlet allocation. International Review of Research in Open and Distance Learning, 22(2), 1–18. https://doi.org/10.19173/irrodl.v22i2.5358
  • Hallinger, P. (2021). Tracking the Evolution of the Knowledge Base on Problem-based Learning: A Bibliometric Review, 1972-2019. Interdisciplinary Journal of Problem-Based Learning, 15(1).
  • Hallinger, P., & Lu, J. (2011). Implementing problem-based learning in higher education in Asia: challenges, strategies and effect. Journal of Higher Education Policy and Management, 33(3), 267-285.
  • Haymana, B., & Dağhan, G. (2020). Kitlesel açık çevrimiçi derslerle ilgili yapılan araştırmaların incelenmesi: Tematik içerik analizi çalışması [Investigation of research about massive open online courses (MOOCs): A thematic content analysis study]. Journal of Computer and Education Research, 8(16), 787-820. https://doi.org/10.18009/jcer.772010
  • Hilton, S., & Phillips, F. (2010). Instructor-assigned and student-selected groups: A view from inside. Issues in Accounting Education, 25(1), 15-33.
  • Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review,16(3), 235-266.
  • Hoffman, B. (1998). Integrating the disciplines in the elementary grades with problem‐based learning. The Delta Kappa Gamma Bulletin, 64(3), 9‐14.
  • Hu, Y., Boyd-Graber, J., Satinoff, B., & Smith, A. (2014). Interactive topic modeling. Machine Learning, 95(3), 423-469. https://doi.org/10.1007/s10994-013-5413-0
  • Huxland, M., & Land, R. (2000). Assigning students in group work projects: Can we do better than random? Innovations in Education and Training International, 37(1), 17-22.
  • Jin, J., & Bridges, S. M. (2014). Educational technologies in problem-based learning in health sciences education: a systematic review. Journal of Medical Internet Research,16(12), e3240.
  • Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63-85.
  • Karami, M., Karami, Z., & Attaran, M. (2013). Integrating problem-based learning with ICT for developing student-teachers’ content knowledge and teaching skill. International Journal of Education and Development using ICT, 9(1), 36-49.
  • Koh, G. C. H., Khoo, H. E., Wong, M. L., & Koh, D. (2008). The effects of problem-based learning during medical school on physician competency: a systematic review. Cmaj, 178(1), 34-41.
  • Kong, L. N., Qin, B., Zhou, Y. Q., Mou, S. Y., & Gao, H. M. (2014). The effectiveness of problem-based learning on development of nursing students’ critical thinking: A systematic review and meta-analysis. International Journal of Nursing Studies, 51(3), 458-469.
  • Lei, S. A., Kuestermeyer, B. N., & Westmeyer, K. A. (2010). Group composition affecting student interaction and achievement: instructors' perspectives. Journal of Instructional Psychology, 37(4), 317.
  • Lewis, A., & Smith, D. (1993). Defining higher order thinking. Theory into Practice, 32(3), 131-137.
  • Li, Y., Wang, X., Zhu, X. R., Zhu, Y. X., & Sun, J. (2019). Effectiveness of problem-based learning on the professional communication competencies of nursing students and nurses: A systematic review. Nurse Education in Practice, 37, 45-55.
  • Majeski, R., & Stover, M. (2005). Interdisciplinary problem-based learning in gerontology: A plan of action. Educational Gerontology, 31(10), 733–43.
  • Manalo, F., & Chua, E. (2020). Collaborative inquiry approaches and level of thinking and reasoning skills: basis for sustainable science education. IOER International Multidisciplinary Research Journal, 2(2), 91–98.
  • Merisier, S., Larue, C., & Boyer, L. (2018). How does questioning influence nursing students' clinical reasoning in problem-based learning? A scoping review. Nurse education today, 65, 108-115.
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Details

Primary Language English
Subjects Other Fields of Education (Other)
Journal Section Research Article
Authors

Özcan Özyurt 0000-0002-0047-6813

Alper Aslan 0000-0003-2970-6114

Early Pub Date October 7, 2023
Publication Date October 21, 2023
Submission Date June 27, 2023
Published in Issue Year 2023 Volume: 7 Issue: 15

Cite

APA Özyurt, Ö., & Aslan, A. (2023). A Broad View of the Problem-Based Learning Field Based on Machine Learning: A Large-Scale Study Based on Topic Modeling. International E-Journal of Educational Studies, 7(15), 608-626. https://doi.org/10.31458/iejes.1320491

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