Effect of instructional design based on cognitive load theory on students’ performances and the indicators of element interactivity
Keywords:Cognitive Load Theory, The Indicators of Element Interactivity, Complexity, Effective Learning, Thermodynamics
Thermodynamics is one of the most complex topics in chemistry. Cognitive Load Theory claims that the complexity of a subject is mainly due to element interactivity - how many elements an individual must organise simultaneously in her/his working memory to master a topic. The simultaneous processing of various chemistry and mathematics concepts to learn thermodynamics puts a strain on the working memory capacity of the learner. Accordingly, what kind of change occurs in a learner’s cognitive processes according to the level of element interactivity is an issue that needs to be investigated. The aim of this study is to reveal the basic indicators of element interactivity and investigate the effects of instructional design on understanding subjects with different element interactivity levels. With this objective in mind, educational software comprising eight distinct sessions for instructional design was developed in accordance with the Cognitive Load Theory. The sample consisted of 37 freshmen who were taking classes in the Chemistry Department of a public university in Turkey. The instructional design was implemented with the experimental group while the control group followed the lecturer's instructional design. The results indicate that, in terms of the cognitive load in the learning process, the study time and the learning at the retention and transfer level are among the basic indicators of the element interactivity. This study also determined that the instructional design that is developed according to Cognitive Load Theory can provide effective learning at the retention and transfer levels in subjects with high element interactivity.
Bekdemir, M., Okur, M., & Gelen, S. (2010). The effects of 2005 elementary mathematics education curriculum on the elementary seventh grade students' conceptual and procedural knowledge and skills, Erzincan University Journal of Education Faculty, 12(2), 131-147.
Berends, I. E., & Van Lieshout, E. C. (2009). The effect of illustrations in arithmetic problem-solving: Effects of increased cognitive load. Learning and Instruction, 19(4), 345-353. https://doi.org/10.1016/j.learninstruc.2008.06.012
Birgin, O., & Gürbüz, R. (2009). Investigation procedural and conceptual knowledge of Primary second Level Students about the rational numbers (İlköğretim II. Kademe Öğrencilerinin Rasyonel Sayılar Konusundaki İşlemsel ve Kavramsal Bilgi Düzeylerinin İncelenmesi). Journal of Uludağ University Faculty of Education, 22 (2), 529-550.
Blayney, P. Kalyuga, S., & Sweller, J. (2016). The impact of complexity on the expertise reversal effect: experimental evidence from testing accounting students, Educational Psychology, 36 (10), 1868–1885. https://doi.org/10.1080/01443410.2015.1051949
Bloom, B. S., Engelhart, M.D., Furst, E.J. Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educatıonal objectıves: The classification of educational goals. Longman.
Buchin, Z. L. (2021). Retrieval-based learning and element interactivity: The role of prior knowledge. [Unpublished Doctoral dissertation]. The University of North Carolina at Chapel Hill.
Carlson, R., Chandler, P., & Sweller, J. (2003). Learning and understanding science instructional material. Journal of Educational Psychology, 95, 629-640. https://doi.org/10.1037/0022-06184.108.40.2069
Carson, E. M., & Watson, J. R. (2002). Undergraduate students' understandings of entropy and gibbs free energy. University Chemical Education, 6, 4–12.
Chandler, P., & Sweller, J. (1991). Cognitive Load Theory and the format of instruction. Cognition and Instruction. 8 (4), 293–332. https://ro.uow.edu.au/edupapers/128
Chang, C.C., & Yang F.Y. (2010). Exploring the cognitive loads of high-school students as they learn concepts in web-based environments. Computers and Education, 55, 673-680. https://doi.org/10.1016/j.compedu.2010.03.001
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, 483-501, DOI 10.1007/s10648-017-9426-2
Chen, O., Kalyuga, S., & Sweller, J. (2017). The expertise reversal effect is a variant of the more general element interactivity effect. Educational Psychology Review, 29(2), 393-405. https://doi.org/10.1007/s10648-016-9359-1
Chen, O., Paas, F., & Sweller, J. (2021). Spacing and interleaving effects require distinct theoretical bases: A systematic review testing the cognitive load and discriminative-contrast hypotheses. Educational Psychology Review, 33(4), 1499-1522. https://doi.org/10.1007/s10648-021-09613-w
Chen, O., Woolcott, G., & Kalyuga, S. (2021). Comparing alternative sequences of examples and problem-solving tasks: the case of conceptual knowledge. The Educational and Developmental Psychologist, 38(1), 158-170. DOI: 10.1080/20590776.2021.1915098
Cierniak, G., Scheiter, K., & Gerjets, P. (2009). Explaining the split-attention effect: Is the reduction of extraneous cognitive load accompanied by an increase in germane cognitive load?. Computers in Human Behavior, 25, 315–324. https://doi.org/10.1016/j.chb.2008.12.020
Clark, R.C., Nguyen, F., & Sweller, J. (2006). Efficiency in learning: Evidence-Based guidelines to manage cognitive load. Published by Pfeiffer.
Cochran, M. (2005). Student understanding of the second law of thermodynamics and the underlying concepts of heat, temperature, and thermal equilibrium. [Unpublished Doctoral Dissertation]. The University of Washington.
Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education, Sixth edition, Taylor and Francis or Routledge.
Darejeh, A., Marcus, N., & Sweller, J. (2021). The effect of narrative-based E-learning systems on novice users' cognitive load while learning software applications. Educational Technology Research and Development, 69(5), 2451-2473. https://doi.org/10.1007/s11423-021-10024-5
Deng, A., Zhang, T., & Chen, A. (2021). Challenges in learning aerobic and anaerobic concepts: an interpretative understanding from the cognitive load theory perspective, Physical Education and Sport Pedagogy, 26(6), 633-648, DOI: 10.1080/17408989.2020.1849595
Ebel, R.L. (1967). The relation of item discrimination to test reliability. Journal of Educational Measurement, 4(3), 125-128.
Galili, L., & Lehavi, Y. (2006). Definitions of physical concepts: A study of physic teachers' knowledge and views. International Journal of Science Education, 28(5), 521-541. https://doi.org/10.1080/09500690500338847
Große, C.S., & Renkll, A. (2007). Finding and fixing errors in worked examples: Can this foster learning outcomes?. Learning and Instruction, 17, 612-634. https://doi.org/10.1016/j.learninstruc.2007.09.008
Kala, N., & Okal, A. S., (2016, September 28-30). The effect of the material developed according to split attention effect of Cognitive Load Theory on student achievement and loading in the subject of simple machines. [Paper presentation]. 12th National Science and Mathematics Education Congress, Trabzon.
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38 (1), 23–31. https://ro.uow.edu.au/edupapers/136
Kaya, D., & Keşan, C. (2012). Conceptual and operational implementations for numeric section students who are a candidate for university. Journal of Research in Education and Teaching, 1(3), 339-346.
Kılıç, E., & Karadeniz, Ş. (2004). Specifying students' cognitive load and disorientation level in hypermedia. Educational Administration: Theory and Practice, 40, 562–579.
Kırtak, V.N. (2010). Prospective physics, chemistry and biology teachers' levels of associating thermodynamics laws with daily life and environmental problems. [Unpublished Master Dissertation). Balıkesir University.
Kline, R.B. (2009). Becoming a behavioral science researcher: A guide to producing research that matters, The Guilford Press.
Klingner, J., Tversky, B., & Hanrahan, P. (2011). Effects of visual and verbal presentation on cognitive load in vigilance, memory, and arithmetic tasks. Psychophysiology, 48(3), 323-332. DOI: 10.1111/j.1469-8986.2010.01069.x
Kruger, C., Palacio, D., & Summers, M. (1992). Surveys of English primary teachers' conceptions of force, energy, and materials. Science Education, 76 (4), 339–351.
Leahy, W., Chandler, P., & Sweller, J. (2003). When audiotary presentations should and should not be a component of multimedia instruction. Applied Cognitive Psychology, 17, 401-418. DOI: 10.1002/acp.877
Leahy, W., Hanham, J., & Sweller, J. (2015). High element interactivity information during problem solving may lead to failure to obtain the testing effect. Educational Psychology Review, 27(2), 291-304. https://doi.org/10.1007/s10648-015-9296-4
Leahy, W., & Sweller, J. (2016), Cognitive Load Theory and the effects of transient information on the modality effect. Instructional Science, 44, 107–123. DOI 10.1007/s11251-015-9362-9
Miller, G.A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological review, 63(2), 81.
Moreno, R., & Mayer, R. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91, 358–368.
Mousavi, S.Y., Low, R., & Sweller, J. (1995). Reducing cognitive load by mixing auditory and visual presentation modes. Journal of Educational Pschology, 87, 319-334.
Mulop, N., Yusof, K. M., & Tasir, Z. (2012). A review on enhancing the teaching and learning of thermodynamics. Procedia-Social and Behavioral Sciences, 56, 703–712. https://doi.org/10.1016/j. sbspro.2012.09.706.
Ngu, B.H., & Phan, H.P. (2016). Unpacking the complexity of linear equations from a cognitive load theory perspective. Educational Psychology Review, 28, 95–118. DOI 10.1007/s10648-015-9298-2
O'Connell, J.P. (2019). Challenges to learning and teaching thermodynamics. Chemical Engineering Education, 53(1), 2-9.
Paas, F.G.W.C., Renkll, A. & Sweller, J. (2004). Cognitive Load Theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32, 1-8. https://www.jstor.org/stable/41953634
Paas, F.G.W.C., & van Merrienboer, J. J. G. (1993). The efficiency of instructional conditions: An approach to combine mental effort and performance measures. Human Factors, 35(4), 737-743.
Paas, F.G.W.C., & van Merrienboer, J. J. G. (1994). Instructional control of cognitive load in the training of complex cognitive tasks. Educational Psychology Review, 6, 351-372.
Paas, F.G.W.C., van Merriënboer, J. J., & Adam, J. J. (1994). Measurement of cognitive load in instructional research. Perceptual and motor skills, 79(1), 419-430.
Pinto', R., Couso, D., & Gutierrez, R. (2005). Using research on teachers' transformations of innovations to inform teacher education; The case of energy degradation. Science Education, 89 (1), 38–55
Sezgin, M.E. (2009). The effects of multimedia courseware designed based on cognitive theory of multimedia learning on cognitive load, performance levels and retention. [Unpublished Doctoral Dissertation]. Çukurova University.
Smith, P.L., & Ragan, T.J. (1999). Instructional design. 2nd Ed. Toronto: John Wiley and Sons.
Sözbilir, M., & Bennett, J. M. (2007). A Study of Turkish chemistry undergraduates' understandings of entropy. Journal of Chemical Education, 84(7), 1204-1208.
Sözbilir, M. (2002). Turkish chemistry undergraduate students. misunderstandings of Gibbs free energy. University Chemical Education, 6, 73-83.
Sreenivasulu, B., & Subramaniam, R. (2013). University students' understanding of chemical thermodynamics. International Journal of Science Education, 35(4), 601–635. https://doi.org/10.1080/09500693.2012.683460
Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68(1), 1–16. https://doi.org/10.1007/s11423-019-09701-3
Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychological Review, 22, 123–138. DOI 10.1007/s10648-010-9128-5
Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and Instruction, 12(3), 185–233. DOI: 10.1207/s1532690xci1203_1
Sweller, J., van Merriënboer, J.J.G., & Paas, F.G.W.C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.
Sweller, J., van Merriënboer, J.J.G., & Paas, F.G.W.C. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31, 261–292. https://doi.org/10.1007/s10648-019-09465-5
Tindall-Ford, S., Chandler, P., & Sweller, J. (1997). When two sensory modes are beter than one. Journal of Experimental Psychology: Applied, 3, 257-287. https://doi.org/10.1037/1076-898X.3.4.257
van Merrienboer, J.J.G., & Ayres, P. (2005). Research on Cognitive Load Theory and its design implications for e-learning. Educational Technology Research and Development, 53(3), 5-13.
Weng, C., Otanga, S., Weng, A., & Cox, J. (2018). Effects of interactivity in E-textbooks on 7th graders science learning and cognitive load. Computers and Education, 120, 172–184. https://doi.org/10.1016/j.compedu.2018.02.008
Copyright (c) 2023 Journal of Turkish Science Education
This work is licensed under a Creative Commons Attribution 4.0 International License.