Resolving Misconception Challenges in the Teaching and Learning of Computer Science Amongst First-Year Undergraduate Students

Authors

DOI:

https://doi.org/10.57125/FED.2023.09.25.08

Keywords:

Computer science, Learning, Misconceptions, Practical activities, Teaching

Abstract

Misconceptions in the field of computer science can lead to confusion and loss of confidence for students. These misconceptions are based on students' concepts that are not scientifically accurate interpretations. One of the major reasons why students find certain computing and scientific concepts so tough to acquire is because they suffer an inequality or gap between their early classification of a concept and the new learning framework. Resolving misconception challenges in the teaching and learning of sciences is not new because according to the past studies, it is believed to manifest throughout the life of individuals where students are greatly involved when it comes to teaching and learning sciences. The study aims to resolve and correct students’ misconceptions using a practical approach and its impact on students’ gender differences. To address these misconceptions, an experimental design study was conducted in order to examine the effects of practical approach correcting misconceptions among students in computing. The study compared students' scores on Pre-Test and Post-Test, comparing their perceptions of computing and the effectiveness of practical methods to address them. The population of the study consisted of all undergraduate students in higher education institutions in Lagos State. Through the use of random sampling method, 75 undergraduate students from Lagos State University of Education were selected.  The instrument was subjected to reliability index after it was validated by experts in computing. An index of .887 results showed significant highly reliable instrument. The results of findings established significant differences between the pre-and post-test scores, but only a slight difference between male and female scores observed. The study recommends that students should be given the opportunity to investigate and test their own findings of computer science components and software functions in order to build a deeper understanding without preconceptions from their prior experiences.

 

References

Adigun, J., Onihunwa, J., Irunokhai, E., Sada, Y., & Adesina, O. (2015). Effect of gender on students’ academic performance in computer studies in secondary schools in New Bussa, Borgu Local Government of Niger State. Journal of Education and Practice, 6(33), 1–7. https://files.eric.ed.gov/fulltext/EJ1083613.pdf

Ajai, J. T., & Imoko, I. I. (2015). Gender differences in mathematics achievement and retention scores: A case of problem-based learning method. International Journal of Research in Education and Science, 1(1), 45–50. https://ijres.net/index.php/ijres/article/view/16

Ajayi, V. (2017). Misconceptions. Benue State University. https://www.researchgate.net/publication/320172303_Misconceptions

Ausubel, D. P., Novak, J. D., & Hanesian, H. (1968). Educational psychology: A cognitive view. Holt, Rinehart and Winston.

Bakanauskas, A., Kondrotienė, E., & Puksas, A. (2020). The theoretical aspects of attitude formation factors and their impact on health behaviour. Management of Organizations: Systematic Research, 83(1), 15–36. https://doi.org/10.1515/mosr-2020-0002

Brun, G., Herfeld, C., & Reuter, K. (2023). Introduction to the topical collection: Concept formation in the natural and social sciences: empirical and normative aspects. Synthese, 201(3), Article 89. https://doi.org/10.1007/s11229-023-04094-6

Cakir, M. (2008). Constructivist approaches to learning in science and their implications for science pedagogy: A literature review. International Journal of Environmental & Science Education, 3(4), 193–206. https://files.eric.ed.gov/fulltext/EJ894860.pdf

Chand, S. (2023). Exploring the contributions of Piaget, Vygotsky, and Bruner. International Journal of Science and Research, 12(7), 274–278. https://doi.org/10.21275/SR23630021800

Chavan, R., & Khandagale, V. (2022). Intricacies in identification of biological misconceptions. Scholarly Research Journal for Interdisciplinary Studies, 9(70), 16810–16819. https://files.eric.ed.gov/fulltext/ED619540.pdf

Firat, M. (2017). Growing misconception of technology: Investigation of elementary students’ recognition of and reasoning about technological artifacts. International Journal of Technology and Design Education, 27(2), 183–199. https://doi.org/10.1007/s10798-015-9351-y

Guzmán, V. F., González-Palta, I., & Larrain, A. S. (2022). Concept formation. In V. P. Glăveanu (Ed.), The Palgrave encyclopedia of the possible (pp. 224–231). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-90913-0_197

Hunt, E. B. (n.d.). Concept formation. In Encyclopedia Britannica. Retrieved August 17, 2023, from https://www.britannica.com/topic/concept-formation

Inuwa, M. I., & Varo, A. (2019, November 6–7). The intensity of misconception in software engineering [Conference paper]. 2019 1st international informatics and software engineering conference (UBMYK), Ankara, Turkey. https://doi.org/10.1109/UBMYK48245.2019.8965596

Kuhn, T. (2012). The structure of scientific revolutions. University of Chicago Press.

Larsson, Å., & Halldén, O. (2010). A structural view on the emergence of a conception: Conceptual change as the radical reconstruction of contexts. Science Education, 94(4), 640–664. https://doi.org/10.1002/sce.20377

Mandaar, P, S. & Vijayakumar, B. (2020). Theoretical foundations of design thinking – A constructivism learning approach to design thinking. Thinking Skills and Creativity, 36, Article 100637. https://doi.org/10.1016/j.tsc.2020.100637

Marsh, E., & Eliseev, E. (2019). Correcting student errors and misconceptions. In J. Dunlosky & K. Rawson (Eds.), The Cambridge handbook of cognition and education (pp. 437–459). Cambridge University Press. https://doi.org/10.1017/9781108235631.018

Mcleod, S. (2023, June 15). Constructivism learning theory & philosophy of education. SimplyPsychology. https://www.simplypsychology.org/constructivism.html

Miller, J. T. M. (2022). Hyperintensionality and ontological categories. Erkenntnis. https://doi.org/10.1007/s10670-022-00646-3

Munck, G. L. (2023). Democratic theory and concept formation: Ideals and realistic standards. SSRN. http://dx.doi.org/10.2139/ssrn.4365181

Okorocha, K. A., Nwokonkwo, O. C., Kalu, O., & Iroegbu, A. N. (2010). Myths and misconceptions in the use of the terms – ICT and IT. Proceedings of annual conference of IRDI research and development network, 5(6).

Oribhabor, C. (2020). The influence of gender on mathematics achievement of secondary school students in Bayelsa State. African Journal of Studies in Education, 14(2), 196–206.

Parker, W. (2023). Concept formation. Teachinghistory.org. https://teachinghistory.org/teaching-materials/teaching-guides/25184

Patil, S. J., Chavan, R. L., & Khandagale, V. S. (2019). Identification of misconceptions in science: Tools, techniques & skills for teachers. Aarhat Multidisciplinary International Education Research Journal (AMIERJ), 8(2), 466–472.

Qian, Y., & James, L. (2017). Students misconceptions and other difficulties in introductory programming: A literature review. ACM Transactions on Computing Education, 18(1), Article 1. https://doi.org/10.1145/3077618

Qian, Y., & Lehman, J. D. (2019). Using targeted feedback to address common student misconceptions in introductory programming: A data-driven approach. SAGE Open, 9(4). https://doi.org/10.1177/2158244019885136

Qian, Y., Hambrusch, S., Yadav, A., Gretter, S., & Li, Y. (2019). Teachers’ perceptions of student misconceptions in introductory programming. Journal of Educational Computing Research 58(2), 364–397. https://doi.org/10.1177/0735633119845413

Rose, S., Kazuyo, M., Constancia, V. M., & Aoife, M. D. (2023). Misconceptions, misinformation, and misperceptions: A case for removing the “mis-” when discussing contraceptive beliefs. Studies in Family Planning, 54(1), 309–321. https://doi.org/10.1111/sifp.12232

Sarbah, B. K. (2020). Constructivism learning approaches. https://doi.org/10.13140/RG.2.2.28138.34241

Schmidt, T., Cloete, A., Davids, A., Makola, L., Zondi, N., & Jantjies, M. (2020). Myths, misconceptions, othering and stigmatizing responses to Covid-19 in South Africa: A rapid qualitative assessment. PLOS ONE, 15(12), Article e0244420. https://doi.org/10.1371/journal.pone.0244420

Suhendi, A., & Purwarno, P. (2018). Constructivist learning theory: The contribution to foreign language learning and teaching. KnE Social Sciences, 3(4), 87–95. https://doi.org/10.18502/kss.v3i4.1921

Thomson, M., & Zakaria, Z., & Radut-Taciu, R. (2019). Perceptions of scientists and stereotypes through the eyes of young school children. Education Research International, 2019, Article 6324704. https://doi.org/10.1155/2019/6324704

Vosniadou, S., & Verschaffel, L. (2004). The conceptual change approach to mathematics learning and teaching. Learning and Instruction, 14(5), 445–548. https://www.sciencedirect.com/journal/learning-and-instruction/vol/14/issue/5

Downloads

Published

2023-09-25

How to Cite

Muraina, I. O. (2023). Resolving Misconception Challenges in the Teaching and Learning of Computer Science Amongst First-Year Undergraduate Students. Futurity Education, 3(3), 155–167. https://doi.org/10.57125/FED.2023.09.25.08