The Impact of Blended Learning on Students' Grades in Introductory Programming Courses
Abstract
Background and Aim: Blended learning has gradually become an important part of educational reform. This study selects first-year students majoring in Computer Science and Technology from Zhanjiang University of Science and Technology in China as the research subjects. It compares the application effects of blended learning and traditional teaching in introductory programming courses through a quasi-experimental design. The aim is to explore the impact of blended learning on students' programming course grades, verify its advantages over traditional teaching, and provide empirical references for computer programming education.
Materials and Methods: This study employed a quasi-experimental design, selecting 110 first-year Computer Science and Technology students. Participants were divided into experimental and control groups, each with 55 students. The experimental group used the Rain Classroom platform for blended learning, while the control group received traditional classroom instruction. Data on programming skills and course grades were collected for comparison.
Results: The findings revealed that there was no significant difference between the two groups in their understanding of programming concepts (p = 0.058, d = 0.366). Nevertheless, the experimental group demonstrated significantly higher performance than the control group in problem-solving skills (p < .001, d = 1.636), debugging and troubleshooting (p < .001, d = 1.974), and algorithmic thinking (p < .001, d = 0.974). Independent samples t-tests confirmed that blended learning significantly enhanced students' abilities in these practical skills and higher-order cognitive domains.
Conclusion: Compared with traditional teaching, blended learning has a significant effect on enhancing students' problem-solving skills, debugging and troubleshooting abilities, as well as algorithmic thinking. However, its direct impact on improving understanding of programming concepts may not be immediately evident. These findings provide strong evidence to support the application of blended learning in introductory programming courses and offer valuable references for future teaching practices and research.
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References
Abesadze, S., & Nozadze, D. (2020). Make 21st century education: The importance of teaching programming in schools. International Journal of Learning and Teaching, 6(3), 158-163.
Al-Fedaghi, S., & Alrashed, A. (2014). Visualization of Execution of Programming Statements. 2014 11th International Conference on Information Technology: New Generations, 363-370.
Ananga, P., & Biney, I. (2021). Comparing Face-To-Face And Online Teaching And Learning In Higher Education. MIER Journal of Educational Studies, Trends and Practices, 7, 165-179.
Bhatti, S., Dewani, A., Maqbool, S., & Memon, M. A. (2019). A Web-based Approach for Teaching and Learning Programming Concepts at Middle School Level. International Journal of Modern Education & Computer Science, 11(4), 46-53. DOI: 10.5815/ijmecs.2019.04.06
Byrka, M., Sushchenko, A., Svatiev, A., Mazin, V., & Veritov, O. (2021). A New Dimension of Learning in Higher Education: Algorithmic Thinking. Propósitos y Representaciones, 9 (2), 990. https://doi.org/10.20511/pyr2021.v9nSPE2.990
Dalenius, T. (1950). The Problem of Optimum Stratification. Scandinavian Actuarial Journal, 1950, 203-213.
Dawkins, H., Gillis, D., & McCuaig, J. (2020). Validation of an expert problem-solving behavior scale for computer science education. In ICERI2020 Proceedings (pp. 6755-6764). IATED.
Demaidi, M., Qamhieh, M., & Afeefi, A. (2019). Applying Blended Learning in Programming Courses. IEEE Access, 7, 156824-156833.
Dugard, P., & Todman, J. (1995). Analysis of Pre‐test‐Post‐test Control Group Designs in Educational Research. Educational Psychology, 15, 181-198.
Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior research methods, 41(4), 1149-1160.
Figueiredo, J., & García-Peñalvo, F. (2022). Strategies to increase success in learning programming. 2022 International Symposium on Computers in Education (SIIE), 1-6. https://doi.org/10.1007/978-981-97-1814-6_15
Futschek, G. (2006, November). Algorithmic thinking: the key to understanding computer science. In International conference on informatics in secondary schools-evolution and perspectives (pp. 159-168). Berlin, Heidelberg: Springer Berlin Heidelberg.
Gonda, D., Ďuriš, V., Tirpáková, A., & Pavlovičová, G. (2022). Teaching algorithms to develop the algorithmic thinking of informatics students. Mathematics, 10(20), 3857.
Gromova, S. F., & Latanskaya, I. V. (2021, July). Basic algorithms as a means of developing algorithmic thinking in students when learning a specialized computer science course. In 9th International Scientific & Practical Conference “Culture, Science, Education: Problems and Perspectives, 1 (1), 477-485.
Gul, S., Asif, M., Ahmad, W., & Ahmad, U. (2017). Teaching programming: A mind map-based methodology to improve learning outcomes. 2017 International Conference on Information and Communication Technologies (ICICT), 209-213.
Horvitz, D., & Thompson, D. (1952). A Generalization of Sampling Without Replacement from a Finite Universe. Journal of the American Statistical Association, 47, 663-685.
Hrastinski, S. (2019). What do we mean by blended learning? TechTrends, 63(5), 564-569.
Hromkovic, J., & Staub, J. (2021). The problem with debugging in current block-based programming environments is. Bulletin of EATCS, 135(3),1-12.
Hung, Y. (2008). The Effect of Problem-Solving Instruction on Computer Engineering Majors' Performance in Verilog Programming. IEEE Transactions on Education, 51, 131-137.
Hussaini, M. H. A. (2023). Effect of Information Technology on Education. Graduate Journal of Pakistan Review (GJPR), 3(1).https://www.pakistanreview.com/index.php/GJPR/article/view/134
Kaneko, Y. (2022). Introduction of Research Framework. SpringerBriefs in Economics, 1–19.
Kanselaar G. 2002. Constructivism and socio-constructivism. Article published on July 16, 2002.
Koop, P. (2003). Finding and evaluating potential research instruments. Canadian oncology nursing journal = Revue canadienne de nursing oncologique, 13(4), 207-208 .
Kristensen, B., & Østerbye, K. (1996). A conceptual perspective on the comparison of object-oriented programming languages. ACM SIGPLAN Notices, 31, 42-54.
Lamagna, E. (2015). Algorithmic thinking unplugged. Journal of Computing Sciences in Colleges, 30, 45-52.
Li, C., Chan, E., Denny, P., Luxton-Reilly, A., & Tempero, E. (2019). Towards a Framework for Teaching Debugging. 2019, 79-86.
Lin, X., Y., W., Liu, Y., & Tang, W. (2021). Using peer code review to improve computational thinking in a blended learning environment: A randomized control trial. Computer Applications in Engineering Education, 29, 1825 - 1835.
Malik, S., Mathew, R., Al-Nuaimi, R., Al-Sideiri, A., & Coldwell-Neilson, J. (2019). Learning problem-solving skills: Comparison of E-learning and M-learning in an introductory programming course. Education and Information Technologies, 1-18. DOI:10.1007/s10639-019-09896-1
Michaeli, T., & Romeike, R. (2021). Developing a Real World Escape Room for Assessing Preexisting Debugging Experience of K12 Students. 2021 IEEE Global Engineering Education Conference (EDUCON), 521-529.
Moraiti, I., Fotoglou, A., & Drigas, A. (2022). Coding with Block Programming Languages in Educational Robotics and Mobiles, Improved Problem Solving, Creativity & Critical Thinking Skills. Int. J. Interact. Mob. Technol., 16, 59-78.
Nida, N. F., Fauzie, M. M., & Istiqomah, S. H. (2021). Instrumentasi Pemeriksaan Sanitasi Pada Pembuatan Jamu Skala Industri Rumah Tangga. Sanitasi: Jurnal Kesehatan Lingkungan, 14(2), 92-99.
Nita, S. L., Mihailescu, M., Nita, S. L., & Mihailescu, M. (2017). Interactive Debugger for Development and Portability Applications Based on Big Data. Practical Concurrent Haskell: With Big Data Applications, 221-230.
Politis, J., & Politis, D. (2016). The Relationship Between an Online Synchronous Learning Environment and Knowledge Acquisition Skills and Traits: The Blackboard Collaborate Experience. Electronic Journal of e-Learning, 14, 196-203.
Reynolds, R., & Sverdlik, W. (1995). An Evolution-Based Approach to Program Understanding Using Cultural Algorithms. Int. J. Softw. Eng. Knowl. Eng., 5, 211-226.
Sambe, G., Drame, K., & Basse, A. (2021). Towards a Framework to Scaffold Problem-solving Skills in Learning Computer Programming. In CSEDU (1) (pp. 323-330).
Stevens, C. A., & Finlay, P. N. (1996). A Research Framework for Group Support Systems. Springer EBooks, 221–243.
Tritrakan, K., Kidrakarn, P., & Asanok, M. (2016). The Use of Engineering Design Concept for Computer Programming Course: A Model of Blended Learning Environment. Educational Research Review, 11, 1757-1765.
Tu, J., & Johnson, J. (1990). Can computer programming improve problem-solving ability? Acm Sigcse Bull., 22, 30-33.
Vinayakumar, R., Soman, K., & Menon, P. (2018). Alg-Design: Facilitates Learn Algorithmic Thinking for Beginners. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1-6.
Wade, M. J., & McCauley, D. E. (1980). Group selection: The phenotypic and genotypic differentiation of small populations. Evolution, 34(4), 799-812.https://doi.org/10.1111/j.1558-5646.1980.tb04019.x
Wang, F. L., & Wong, T. L. (2010). Hybrid Teaching and Learning of Computer Programming Language. In Handbook of Research on Hybrid Learning Models: Advanced Tools, Technologies, and Applications (pp. 487-502). IGI Global.