Examining The Effects of Task Complexity and Task Difficulty on Students’ Knowledge Retention: A Cognitive Load Theory Perspective
DOI:
https://doi.org/10.33394/jk.v11i4.18326Keywords:
Task complexity, Task Difficulty, Cognitive Load, Knowledge RetentionAbstract
This study aims to analyze the effect of task complexity and task difficulty on knowledge retention through cognitive load. Previous studies have been largely outcome-oriented, focusing on student performance without examining long-term knowledge, thus leaving a gap for research that specifically elaborates on knowledge retention. A quantitative approach with a cross-sectional research design was employed. Using the Slovin formula with a 5% margin of error, the study involved 246 undergraduate students majoring in financial management from public universities located in Semarang, Surakarta, and Purwokerto, selected through purposive sampling. Data were analyzed using the variance-based structural equation modeling (VB-SEM) approach. The results reveal that cognitive load has a negative and significant effect on knowledge retention (β = -0.492, t = 4.167, p = 0.000). Task complexity shows a positive and significant effect on cognitive load (β = 0.300, t = 4.326, p = 0.000), but its direct effect on knowledge retention is not significant. Similarly, task difficulty has a positive and significant effect on cognitive load (β = 0.341, t = 4.628, p = 0.000), yet it does not directly affect knowledge retention. The findings indicate that both task complexity and task difficulty have negative and significant indirect effects on knowledge retention through cognitive load. These results demonstrate the importance of proportional instructional design, particularly the segmentation of complex content into smaller units to manage cognitive load. The use of educational technology is recommended to design tasks and instructional materials that align with students’ cognitive capacities, thereby enhancing knowledge retention.
References
Alhamad, B. R., & Agha, S. (2023). Comparing Knowledge Acquisition and Retention Between Mobile Learning and Traditional Learning in Teaching Respiratory Therapy Students: A Randomized Control Trial. Advances in Medical Education and Practice, 14. https://doi.org/10.2147/AMEP.S390794
Alzayed, M. A., & Alzamel, D. (2023). Assertion-Evidence versus Traditional PowerPoint: An Investigation of the Impact of Slide Structure on Engineering Students’ Cognitive load, Motivation, and Performance. Journal of Engineering Research (Kuwait), 11(1). https://doi.org/10.36909/jer.16963
Chen, O., Castro-Alonso, J. C., Paas, F., & Sweller, J. (2018). Extending Cognitive Load Theory to Incorporate Working Memory Resource Depletion: Evidence from the Spacing Effect. Educational Psychology Review, 30(2). https://doi.org/10.1007/s10648-017-9426-2
Chen, O., Paas, F., & Sweller, J. (2023). A Cognitive Load Theory Approach to Defining and Measuring Task Complexity Through Element Interactivity. In Educational Psychology Review (Vol. 35, Issue 2). https://doi.org/10.1007/s10648-023-09782-w
Duran, R., Zavgorodniaia, A., & Sorva, J. (2022). Cognitive Load Theory in Computing Education Research: A Review. ACM Transactions on Computing Education, 22(4). https://doi.org/10.1145/3483843
Ellah, B. O., Achor, E. E., & Enemarie, V. (2019). Problem-solving skills as correlates of attention span and working memory of low ability level students in senior secondary schools. Journal of Education and E-Learning Research, 6(3). https://doi.org/10.20448/journal.509.2019.63.135.141
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate Data Analysis Seventh Edition. In Pearson New International. https://doi.org/10.1007/978-3-319-01517-0_3
Hanham, J., Castro-Alonso, J. C., & Chen, O. (2023). Integrating cognitive load theory with other theories, within and beyond educational psychology. British Journal of Educational Psychology, 93(S2). https://doi.org/10.1111/bjep.12612
Korsgaard, M. T. (2020). Exemplarity and education: Retuning educational research. British Educational Research Journal, 46(6). https://doi.org/10.1002/berj.3636
Krawitz, J., Hartmann, L., & Schukajlow, S. (2024). Do task variables of self-generated problems influence interest? Authenticity, openness, complexity, and students’ interest in solving self-generated modelling problems. Journal of Mathematical Behavior, 73. https://doi.org/10.1016/j.jmathb.2024.101129
Leppink, J., Paas, F., Van der Vleuten, C. P. M., Van Gog, T., & Van Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4). https://doi.org/10.3758/s13428-013-0334-1
Li, J., Xue, E., Li, C., & He, Y. (2023). Investigating Latent Interactions between Students’ Affective Cognition and Learning Performance: Meta-Analysis of Affective and Cognitive Factors. Behavioral Sciences, 13(7). https://doi.org/10.3390/bs13070555
Lin, L., Lin, X., Zhang, X., & Ginns, P. (2024). The Personalized Learning by Interest Effect on Interest, Cognitive Load, Retention, and Transfer: A Meta-Analysis. Educational Psychology Review 2024 36:3, 36(3), 88-. https://doi.org/10.1007/S10648-024-09933-7
Mundy, C. E., Potgieter, M., & Seery, M. K. (2023). A design-based research approach to improving pedagogy in the teaching laboratory. Chemistry Education Research and Practice, 25(1). https://doi.org/10.1039/d3rp00134b
Nihalani, P. K., & Robinson, D. H. (2022). Balancing Collaboration and Cognitive Load to Optimize Individual and Group Desirable Difficulties. Journal of Educational Computing Research, 60(2). https://doi.org/10.1177/07356331211035188
Osabutey, E. L. C., Senyo, P. K., & Bempong, B. F. (2024). Evaluating the potential impact of online assessment on students’ academic performance. Information Technology and People, 37(1). https://doi.org/10.1108/ITP-05-2021-0377
Ouwehand, K., Kroef, A. van der, Wong, J., & Paas, F. (2021). Measuring Cognitive Load: Are There More Valid Alternatives to Likert Rating Scales? Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.702616
Palacios, C. A., Reyes-Suárez, J. A., Bearzotti, L. A., Leiva, V., & Marchant, C. (2021). Knowledge discovery for higher education student retention based on data mining: Machine learning algorithms and case study in chile. Entropy, 23(4). https://doi.org/10.3390/e23040485
Pavlov, A., Duhon, G., & Dawes, J. (2023). Examining the Impact of Task Difficulty on Student Engagement and Learning Rates. Journal of Behavioral Education, 32(3). https://doi.org/10.1007/s10864-021-09465-y
Robinson, P. (2001). Task complexity, task difficulty, and task production: Exploring interactions in a componential framework. Applied Linguistics, 22(1). https://doi.org/10.1093/applin/22.1.27
Saleh, Z. I. (2011). A framework to evaluate the likelihood of knowledge retention when college students obtain information from the internet. International Journal of Education Economics and Development, 2(4). https://doi.org/10.1504/IJEED.2011.043832
Schamberger, T. (2023). Conducting Monte Carlo simulations with PLS-PM and other variance-based estimators for structural equation models: a tutorial using the R package cSEM. Industrial Management and Data Systems, 123(6). https://doi.org/10.1108/IMDS-07-2022-0418
Seeber, M. (2020). Framework and operationalisation challenges for quantitative comparative research in higher education. Higher Education Quarterly, 74(2). https://doi.org/10.1111/hequ.12245
Seyderhelm, A. J. A., & Blackmore, K. L. (2023). How Hard Is It Really? Assessing Game-Task Difficulty Through Real-Time Measures of Performance and Cognitive Load. Simulation and Gaming, 54(3). https://doi.org/10.1177/10468781231169910
Siregar, N. R. (2023). Explicit Instruction and Executive Functioning Capacity: A New Direction in Cognitive Load Theory. Journal of Education, 203(2). https://doi.org/10.1177/00220574211033256
Spiegel, T., & Nivette, A. (2023). The relative impact of in-class closed-book versus take-home open-book examination type on academic performance, student knowledge retention and wellbeing. Assessment and Evaluation in Higher Education, 48(1). https://doi.org/10.1080/02602938.2021.2016607
Sun, T., & Kim, J. E. (2023). The Effects of Online Learning and Task Complexity on Students’ Procrastination and Academic Performance. International Journal of Human-Computer Interaction, 39(13). https://doi.org/10.1080/10447318.2022.2083462
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2). https://doi.org/10.1016/0364-0213(88)90023-7
Sweller, J. (2022). The Role of Evolutionary Psychology in Our Understanding of Human Cognition: Consequences for Cognitive Load Theory and Instructional Procedures. Educational Psychology Review, 34(4). https://doi.org/10.1007/s10648-021-09647-0
Sweller, J., Van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3). https://doi.org/10.1023/A:1022193728205
Udu, D. A., Nmadu, J., Uwaleke, C. C., Anudu, A. P., Okechineke, B. C., Attamah, P. C., Chukwuemeka, C. O., Nwalo, C. N., & Ogonna, O. C. (2022). Innovative Pedagogy and Improvement of Students’ Knowledge Retention in Science Education: Learning Activity Package Instructional Approach. Pertanika Journal of Social Sciences and Humanities, 30(3). https://doi.org/10.47836/pjssh.30.3.25
Xie, H., Wang, F., Hao, Y., Chen, J., An, J., Wang, Y., & Liu, H. (2017). The more total cognitive load is reduced by cues, the better retention and transfer of multimedia learning: A meta-analysis and two meta-regression analyses. PLoS ONE, 12(8). https://doi.org/10.1371/journal.pone.0183884
Youssef, A. Ben, Dahmani, M., & Ragni, L. (2022). ICT Use, Digital Skills and Students’ Academic Performance: Exploring the Digital Divide. Information (Switzerland), 13(3). https://doi.org/10.3390/info13030129
Zheng, X., & Li, C. (2024). Predicting Students’ Academic Performance Through Machine Learning Classifiers: A Study Employing the Naive Bayes Classifier (NBC). International Journal of Advanced Computer Science and Applications, 15(1). https://doi.org/10.14569/IJACSA.2024.0150199
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