Skip to main content

Advertisement

Log in

Adaptive teaching of flipped classroom combined with concept map learning diagnosis- an example of programming design course

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

In recent years, flipped classroom has become a popular teaching method. Compared with the traditional teaching method, the flipped classroom gives learners and teachers more opportunities to discuss. However, the flipped classroom has also encountered some difficulties. If we do not consider the different learning conditions of each learner when conducting group discussions in the classroom, the learning effectiveness will not be as expected. Therefore, this study uses the Apriori algorithm in association rule analysis to diagnose learning and implement adaptive teaching, hoping to improve the deficiencies in the flipped classroom. This study developed a multimedia learning system applied in the experiment. In pre-class stage, learners were provided with teaching videos, conducted unit tests online, and then used Apriori association rules to analyze the test results for learning diagnose, derive association rules between concepts, and perform adaptive grouping according to learners' test results. Learners will carry out classroom tasks in the class stage, and then implement post-test and post-questionnaire to analyze whether there are significant differences among learners. Finally, we found that using adaptive teaching of flipped classroom combined with concept map learning diagnosis, there were significant differences in research issues such as learning effectiveness, learning motivation, self-efficacy, cognitive load and programming learning attitude. It is hoped that through the results of this study, meaningful contributions can be made in the research field of flipped classroom and adaptive teaching, and it is also hoped that there can be a theoretical basis for scholars who study these fields in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The data that support the findings of this study are available in the Zenodo repository with the identifier https://doi.org/10.5281/zenodo.6956502.

References

  • Acharya, A., & Sinha, D. (2017). An educational data mining approach to concept map construction for web based learning. Informatica Economica, 21(4), 41-58.

  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large databases, VLDB, 1215, 487–499. https://www.vldb.org/conf/1994/P487.PDF

  • Agyemang, A.-O., & Laitinen-Väänänen, S. (2018). Some experiences of implementing the concept of flipped classroom in the field of engineering. Evolving Pedagogy. http://urn.fi/urn:nbn:fi:jamk-issn-2489-2386-7

  • Alazab, A., Bevinakoppa, S., & Khraisat, A. (2018). Maximising competitive advantage on E-business websites: A data mining approach. In 2018 IEEE conference on big data and analytics (ICBDA) (pp. 111–116). IEEE. https://doi.org/10.1109/ICBDAA.2018.8629649

  • Amanah, S. S., Wibowo, F. C., & Astra, I. M. (2021). Trends of flipped classroom studies for physics learning: A systematic review. In Journal of Physics: Conference Series, 2019(1), 012044. IOP Publishing. https://doi.org/10.1088/1742-6596/2019/1/012044

  • Angeli, C., & Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105, 106185.

    Article  Google Scholar 

  • Ausubel, D. P. (1963). The psychology of meaningful verbal learning. New York, NY: Grune and Stratton.

  • Bergmann, J., & Sams, A. (2012). Flip your classroom: Reach every student in every class every day. International society for technology in education.

  • Burov, O. Y., Pinchuk, O. P., Pertsev, M., & Vasylchenko, Y. (2018). Using the learners’ state indices for design of adaptive learning systems. Informaiton Technologies and Learning Tools, 6(68), 20–32.

    Article  Google Scholar 

  • Cheah, C. S. (2020). Factors contributing to the difficulties in teaching and learning of computer programming: A literature review. Contemporary Educational Technology12(2), ep272.

  • Chen, X., Chen, S., Wang, X., & Huang, Y. (2021a). “ I was afraid, but now I enjoy being a streamer!” Understanding the Challenges and Prospects of Using Live Streaming for Online Education. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW3), 1–32.

    Google Scholar 

  • Chen, Y. C., Fan, K. K., & Fang, K. T. (2021b). Effect of flipped teaching on cognitive load level with mobile devices: The case of a graphic design course. Sustainability, 13(13), 7092.

    Article  Google Scholar 

  • Costa, R. S., Tan, Q., Pivot, F., Zhang, X., & Wang, H. (2022). Personalized and adaptive learning: educational practice and technological impact. Texto Livre, 14.

  • Da-Hong, L., Hong-Yan, L., Wei, L., Guo, J. J., & En-Zhong, L. (2020). Application of flipped classroom based on the Rain Classroom in the teaching of computer-aided landscape design. Computer Applications in Engineering Education, 28(2), 357–366.

    Article  Google Scholar 

  • Espinal, A., Vieira, C., & Guerrero-Bequis, V. (2022). Student ability and difficulties with transfer from a block-based programming language into other programming languages: a case study in Colombia. Computer Science Education, 1–33. https://doi.org/10.1080/08993408.2022.2079867

  • Farrokhnia, M., Pijeira-Díaz, H. J., Noroozi, O., & Hatami, J. (2019). Computer-supported collaborative concept mapping: The effects of different instructional designs on conceptual understanding and knowledge co-construction. Computers & Education, 142, 103640.

    Article  Google Scholar 

  • Fletcher, K. A., Hicks, V. L., Johnson, R. H., Laverentz, D. M., Phillips, C. J., Pierce, L. N., ... & Gay, J. E. (2019). A concept analysis of conceptual learning: A guide for educators. Journal of Nursing Education58(1), 7-15.

  • Gelles, L. A., Lord, S. M., Hoople, G. D., Chen, D. A., & Mejia, J. A. (2020). Compassionate flexibility and self-discipline: Student adaptation to emergency remote teaching in an integrated engineering energy course during COVID-19. Education Sciences, 10(11), 304.

    Article  Google Scholar 

  • Hamzah, M. L., Rukun, K., Rizal, F., & Purwati, A. A. (2019). A review of increasing teaching and learning database subjects in computer science. Revista ESPACIOS, 40(26). http://www.revistaespacios.com/a19v40n26/a19v40n26p06.pdf

  • Hsu, T. C., Chang, S. C., & Hung, Y. T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296–310.

    Article  Google Scholar 

  • Korkmaz, Ö., & Altun, H. (2014). A Validity and Reliability Study of the Attitude Scale of Computer Programming Learning (ASCOPL). Online Submission, 4(1), 30–43.

    Google Scholar 

  • Li, Y., Shao, Z., Wang, X., Zhao, X., & Guo, Y. (2018). A concept map-based learning paths automatic generation algorithm for adaptive learning systems. IEEE Access, 7, 245–255.

    Article  Google Scholar 

  • Lin, Y.-N., Hsia, L.-H., Sung, M.-Y., & Hwang, G.-H. (2019). Effects of integrating mobile technology-assisted peer assessment into flipped learning on students’ dance skills and self-efficacy. Interactive Learning Environments, 27(8), 995–1010.

    Article  Google Scholar 

  • Lin, Y.-T. (2019). Impacts of a flipped classroom with a smart learning diagnosis system on students’ learning performance, perception, and problem solving ability in a software engineering course. Computers in Human Behavior, 95, 187–196.

    Article  Google Scholar 

  • Machado, C. T., & Carvalho, A. A. (2020). Concept mapping: Benefits and challenges in higher education. The Journal of Continuing Higher Education, 68(1), 38–53.

    Article  Google Scholar 

  • Munir, M. T., Baroutian, S., Young, B. R., & Carter, S. (2018). Flipped classroom with cooperative learning as a cornerstone. Education for Chemical Engineers, 23, 25–33.

    Article  Google Scholar 

  • Muñoz, J. L. R., Ojeda, F. M., Jurado, D. L. A., Peña, P. F. P., Carranza, C. P. M., Berríos, H. Q., & Vasquez-Pauca, M. J. (2022). Systematic Review of Adaptive Learning Technology for Learning in Higher Education. Eurasian Journal of Educational Research, 98(98), 221–233.

    Google Scholar 

  • Pinandito, A., Prasetya, D. D., Hayashi, Y., & Hirashima, T. (2021). Design and development of semi-automatic concept map authoring support tool. Research and Practice in Technology Enhanced Learning, 16(1), 1–19.

    Article  Google Scholar 

  • Putranta, H., & Jumadi, J. (2019). Physics teacher efforts of Islamic high school in Yogyakarta to minimize students’ anxiety when facing the assessment of physics learning outcomes. Journal for the Education of Gifted Young Scientists, 7(2), 119–136.

    Article  Google Scholar 

  • Sianturi, F. A. (2018). Penerapan Algoritma Apriori Untuk Penentuan Tingkat Pesanan. Jurnal Mantik Penusa, 2(1), 50–57.

  • Sun, J. C. Y., & Lin, H. S. (2022). Effects of integrating an interactive response system into flipped classroom instruction on students’ anti-phishing self-efficacy, collective efficacy, and sequential behavioral patterns. Computers & Education, 180, 104430.

    Article  Google Scholar 

  • U.S. Department of Education. (2010). Transforming American education: Learning powered by technology. Office of Educational Technology.

    Google Scholar 

  • Wang, C., Fang, T., & Miao, R. (2018). Learning performance and cognitive load in mobile learning: Impact of interaction complexity. Journal of Computer Assisted Learning, 34(6), 917–927.

    Article  Google Scholar 

  • Wong, R. M., Sundararajan, N., Adesope, O. O., & Nishida, K. R. (2021). Static and interactive concept maps for chemistry learning. Educational Psychology, 41(2), 206–223.

    Article  Google Scholar 

  • Yang, J., Li, Y., Liu, Q., Li, L., Feng, A., Wang, T., & Lyu, J. (2020). Brief introduction of medical database and data mining technology in big data era. Journal of Evidence-Based Medicine, 13(1), 57–69.

  • Zacharis, N. Z. (2018). Classification and regression trees (CART) for predictive modeling in blended learning. IJ Intelligent Systems and Applications, 3, 1–9.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yu-Chen Kuo.

Ethics declarations

Conflict of interest

This study is supported in part by the National Science and Technology Council of the Republic of China under contract number MOST 111–2410-H-031–024.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuo, YC., Chang, YC. Adaptive teaching of flipped classroom combined with concept map learning diagnosis- an example of programming design course. Educ Inf Technol 28, 8665–8689 (2023). https://doi.org/10.1007/s10639-022-11540-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-022-11540-4

Keywords

Navigation