Email this article 
Students’ Cognitive Load on Computer Programming Instructional Process Using Example-Problem-Based Learning and Problem-Based Learning Instructional Model at Vocational High School
Muhammad Rifqy Ramadana, Universitas Brawijaya, Indonesia
Satrio Hadi Wijoyo, Universitas Brawijaya, Indonesia
Muneeroh Phadung, Yala Rajabhat University, Thailand
Abstract
This paper fills an essential gap in applying cognitive load theory in teaching computer programming within vocational settings. It is an important area to consider for improving students' learning processes who intend to enter the rapidly changing technology sector. This study assessed the distinct impacts of the instructional paradigms, specifically Example-Problem-Based Learning (EPBL) and Problem-Based Learning (PBL), on students’ cognitive loads upon framing an iterative structure lesson on computer programming. Vocational programming education is chosen for this purpose because vocational education faces unique challenges in integrating practical skills development with theoretical understanding, and programming tasks involve high cognitive demands. In a quasi-experimental design, 68 vocational high school students were assigned to an EPBL (n = 34) and a PBL (n = 34) group. The measurement of ICL was operationalized by RPI, the ECL by ME, and the GCL by LO. The relationship among the various components of the cognitive load was tested using the Spearman correlation test. There are significant differences in the profile of cognitive load between the two groups: the EPBL group was always associated with the lower ECL and higher GCL. In other words, the present study is original because it systematically compares EPBL with PBL in the context of vocational programming education and provides empirical evidence based on instructional design decisions. These findings suggest a further refinement of the CLT within domain-specific contexts and practical guidelines for optimizing instructional strategies in computer programming education in vocational schools.
Keywords
Full Text:
PDFReferences
P. A. Kirschner, J. Sweller, F. Kirschner, and J. R. Zambrano, “From Cognitive Load Theory to Collaborative Cognitive Load Theory,” Int J Comput Support Collab Learn, vol. 13, no. 2, pp. 213–233, 2018, doi: 10.1007/s11412-018-9277-y.
J. Sweller, J. J. G. van Merriënboer, and F. Paas, “Cognitive Architecture and Instructional Design: 20 Years Later,” Educ Psychol Rev, vol. 31, no. 2, pp. 261–292, 2019, doi: 10.1007/s10648-019-09465-5.
R. Duran, A. Zavgorodniaia, and J. Sorva, “Cognitive Load Theory in Computing Education Research: A Review,” ACM Transactions on Computing Education, vol. 22, no. 4, 2022, doi: 10.1145/3483843.
M. Klepsch, F. Schmitz, and T. Seufert, “Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load,” Front Psychol, vol. 8, no. NOV, 2017, doi: 10.3389/fpsyg.2017.01997.
A. Abbad-Andaloussi, “On the relationship between source-code metrics and cognitive load: A systematic tertiary review,” Journal of Systems and Software, vol. 198, 2023, doi: 10.1016/j.jss.2023.111619.
S. Sepp, S. J. Howard, S. Tindall-Ford, S. Agostinho, and F. Paas, “Cognitive Load Theory and Human Movement: Towards an Integrated Model of Working Memory,” Educ Psychol Rev, vol. 31, no. 2, pp. 293–317, 2019, doi: 10.1007/s10648-019-09461-9.
Y. Shin and D. Song, “The Effects of Self-Regulated Learning Support on Learners’ Task Performance and Cognitive Load in Computer Programing,” Journal of Educational Computing Research, vol. 60, no. 6, pp. 1490–1513, 2022, doi: 10.1177/07356331211052632.
S. C. Chang and C. Wongwatkit, “Effects of a peer assessment-based scrum project learning system on computer programming’s learning motivation, collaboration, communication, critical thinking, and cognitive load,” Educ Inf Technol (Dordr), vol. 29, no. 6, pp. 7105–7128, 2024, doi: 10.1007/s10639-023-12084-x.
A. Renkl, “Learning from worked-examples in mathematics: students relate procedures to principles,” ZDM - Mathematics Education, vol. 49, no. 4, pp. 571–584, 2017, doi: 10.1007/s11858-017-0859-3.
E. Lovellette, D. J. Bouvier, and J. Matta, “Contextualization, Authenticity, and the Problem Description Effect,” ACM Transactions on Computing Education, vol. 24, no. 2, 2024, doi: 10.1145/3643864.
T. Chamidy, I. N. S. Degeng, and S. Ulfa, “The effect of problem-based learning and tacit knowledge on problem-solving skills of students in computer network practice course,” Journal for the Education of Gifted Young Scientists, vol. 8, no. 2, pp. 691–700, 2020, doi: 10.17478/JEGYS.650400.
B. Wallace, D. Knudson, and N. Gheidi, “Incorporating problem-based learning with direct instruction improves student learning in undergraduate biomechanics,” J Hosp Leis Sport Tour Educ, vol. 27, 2020, doi: 10.1016/j.jhlste.2020.100258.
T. van Gog, N. Rummel, and A. Renkl, “Learning how to solve problems by studying examples.,” in The Cambridge handbook of cognition and education., New York, NY, US: Cambridge University Press, 2019, pp. 183–208. doi: 10.1017/9781108235631.
A. D. Herlambang, I. Ismaiwati, and S. H. Wijoyo, “The Effect of Problem-Based Instruction Learning Methods on Improving Cognitive and Psychomotor Learning Outcomes in Computer Programming Subjects on Branching Control Structure Material,” JICTE (Journal of Information and Computer Technology Education), vol. 4, no. 2, pp. 1–6–1–6, 2020, doi: 10.21070/jicte.v4i2.920.
O. Chen, J. C. Castro-Alonso, F. Paas, and J. Sweller, “Extending Cognitive Load Theory to Incorporate Working Memory Resource Depletion: Evidence from the Spacing Effect,” Educ Psychol Rev, vol. 30, no. 2, pp. 483–501, 2018, doi: 10.1007/s10648-017-9426-2.
X. Gao, L. Wang, J. Deng, C. Wan, and D. Mu, “The effect of the problem based learning teaching model combined with mind mapping on nursing teaching: A meta-analysis,” Nurse Educ Today, vol. 111, 2022, doi: 10.1016/j.nedt.2022.105306.
R. Ramadhani, R. Umam, A. Abdurrahman, and M. Syazali, “The effect of flipped-problem based learning model integrated with LMS-google classroom for senior high school students,” Journal for the Education of Gifted Young Scientists, vol. 7, no. 2, pp. 137–158, 2019, doi: 10.17478/jegys.548350.
A. Majid, A. D. Herlambang, and F. Amalia, “Pengaruh Metode Pembelajaran Problem Based Learning yang Diperkaya dengan ARCS Motivational Model terhadap Kualitas Manajemen Kelas dan Motivasi …,” … Informasi dan Ilmu Komputer, vol. 9, no. 1, pp. 129–136–129–136, 2022, [Online]. Available: http://jtiik.ub.ac.id/index.php/jtiik/article/view/5500
M. Robherta, A. Dwi Herlambang, and S. Hadi Wijoyo, “The Differences of Student’s Learning Outcomes and Instructional Interactions between Project Based Learning and Problem Based Learning Methods by Using Web Based Learning Technique in the Course of Videography at SMKN 1 Purwosari,” Journal of Information Technology and Computer Science, vol. 6, no. 3, pp. 225–235, 2021, doi: 10.25126/jitecs.202163305.
V. L. Barth, V. Piwowar, I. R. Kumschick, D. Ophardt, and F. Thiel, “The impact of direct instruction in a problem-based learning setting. Effects of a video-based training program to foster preservice teachers’ professional vision of critical incidents in the classroom,” Int J Educ Res, vol. 95, pp. 1–12, 2019, doi: 10.1016/j.ijer.2019.03.002.
F. Paas and J. J. G. van Merriënboer, “Cognitive-Load Theory: Methods to Manage Working Memory Load in the Learning of Complex Tasks,” Curr Dir Psychol Sci, vol. 29, no. 4, pp. 394–398, Aug. 2020, doi: 10.1177/0963721420922183.
J. Leppink, “Cognitive load theory: Practical implications and an important challenge,” J Taibah Univ Med Sci, vol. 12, no. 5, pp. 385–391, 2017, doi: 10.1016/j.jtumed.2017.05.003.
P. Toukiloglou and S. Xinogalos, “Ingame Worked Examples Support as an Alternative to Textual Instructions in Serious Games About Programming,” Journal of Educational Computing Research, vol. 60, no. 7, pp. 1615–1636, 2022, doi: 10.1177/07356331211073655.
Y. Shin, J. Jung, J. Zumbach, and E. Yi, “The Effects of Worked-Out Example and Metacognitive Scaffolding on Problem-Solving Programming,” Journal of Educational Computing Research, vol. 61, no. 6, pp. 1312–1331, 2023, doi: 10.1177/07356331231174454.
S. Ghanbari, F. Haghani, M. Barekatain, and A. Jamali, “A systematized review of cognitive load theory in health sciences education and a perspective from cognitive neuroscience.,” J Educ Health Promot, vol. 9, p. 176, 2020, doi: 10.4103/jehp.jehp_643_19.
D. Pérez-Marín, R. Hijón-Neira, A. Romero, and S. Cruz, “Is the use of makey makey helpful to teach programming concepts to primary education students?,” International Journal of Online Pedagogy and Course Design, vol. 9, no. 2, pp. 63–77, 2019, doi: 10.4018/IJOPCD.2019040105.
Ü. Çakiroğlu and Ş. Bilgi, “Exploring intrinsic cognitive load in the programming process: a two dimensional approach based on element interactivity,” Interactive Learning Environments, 2022, doi: 10.1080/10494820.2022.2137527.
I. Lavy, “Leveraging the Pied Piper Effect – The Case of Teaching Programming to Sixth-grade Students Via Music,” Informatics in Education, vol. 22, no. 1, pp. 45–69, 2023, doi: 10.15388/infedu.2023.06.
J. M. Schorn and B. J. Knowlton, “Interleaved practice benefits implicit sequence learning and transfer.,” Mem Cognit, vol. 49, no. 7, pp. 1436–1452, Oct. 2021, doi: 10.3758/s13421-021-01168-z.
A. Armougum, A. Gaston-Bellegarde, C. J.-L. Marle, and P. Piolino, “Expertise reversal effect: Cost of generating new schemas,” Comput Human Behav, vol. 111, p. 106406, Oct. 2020, doi: 10.1016/j.chb.2020.106406.
A. B. H. de Bruin, F. Biwer, L. Hui, E. Onan, L. David, and W. Wiradhany, “Worth the Effort: the Start and Stick to Desirable Difficulties (S2D2) Framework,” Educ Psychol Rev, vol. 35, no. 2, p. 41, Jun. 2023, doi: 10.1007/s10648-023-09766-w.
K. Ouwehand, A. van der Kroef, J. Wong, and F. Paas, “Measuring Cognitive Load: Are There More Valid Alternatives to Likert Rating Scales?,” Front Educ (Lausanne), vol. 6, Sep. 2021, doi: 10.3389/feduc.2021.702616.
F. Kamalov, D. Santandreu Calonge, and I. Gurrib, “New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution,” Sustainability, vol. 15, no. 16, p. 12451, Aug. 2023, doi: 10.3390/su151612451.
R. M. Ryan and E. L. Deci, “Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions,” Contemp Educ Psychol, vol. 61, p. 101860, 2020, doi: 10.1016/j.cedpsych.2020.101860.
A. Al-Sakkaf, M. Omar, and M. Ahmad, “Social worked-examples technique to enhance student engagement in program visualization,” Baghdad Science Journal, vol. 16, no. 2, pp. 453–461, 2019, doi: 10.21123/bsj.2019.16.2(SI)0453.
X. Zhang, Y. Chen, D. Li, L. Hu, G. J. Hwang, and Y. F. Tu, “Engaging Young Students in Effective Robotics Education: An Embodied Learning-Based Computer Programming Approach,” Journal of Educational Computing Research, vol. 62, no. 2, pp. 532–558, 2024, doi: 10.1177/07356331231213548.
J. Castelhano et al., “Software Bug Detection Causes a Shift From Bottom-Up to Top-Down Effective Connectivity Involving the Insula Within the Error-Monitoring Network,” Front Hum Neurosci, vol. 16, 2022, doi: 10.3389/fnhum.2022.788272.
A. Qayum, S. U. R. Khan, Inayat-Ur-Rehman, and A. Akhunzada, “FineCodeAnalyzer: Multi-Perspective Source Code Analysis Support for Software Developer Through Fine-Granular Level Interactive Code Visualization,” IEEE Access, vol. 10, pp. 20496–20513, 2022, doi: 10.1109/ACCESS.2022.3151395.
DOI: https://doi.org/10.21831/elinvo.v9i2.57882
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Elinvo (Electronics, Informatics, and Vocational Education)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Our Journal indexed by:
ISSN 2477-2399 (online) || ISSN 2580-6424 (print)
