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The effect of narrative-based E-learning systems on novice users’ cognitive load while learning software applications

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Abstract

When novice users try to learn to use a software application that includes a variety of high element interactivity tools, the complex structure of the software can increase cognitive load and render the tools incomprehensible. Accordingly, there is a need for an efficient teaching approach that can provide practical knowledge to users while decreasing their cognitive load. In this study, the use and choice of narrative were selected as procedures that can provide practical knowledge to software learners in addition to impacting cognitive load through providing a familiar theme to worked-examples. We compared the effects of familiar and unfamiliar narratives versus a no-narrative condition on cognitive load of users while learning software applications with both low and high interactivity tools through e-learning platforms. The results showed that an e-learning system with a familiar narrative could decrease cognitive load in comparison to the no-narrative and unfamiliar narrative systems for both low and high interactivity materials. It was concluded that people can learn new software applications more easily when familiar context worked-examples are used to integrate novel material with their existing knowledge.

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References

  • Alexander, M., & Kusleika, R. (2015). Access 2016 Bible. Wiley.

  • Antonenko, P., Paas, F., Grabner, R., & Van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425–438.

    Article  Google Scholar 

  • Ayres, P. (2006). Using subjective measures to detect variations of intrinsic cognitive load within problems. Learning and Instruction, 16(5), 389–400. https://doi.org/10.1016/j.learninstruc.2006.09.001

    Article  Google Scholar 

  • Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.

    Article  Google Scholar 

  • Bell, J., Sheth, S., & Kaiser, G. (2011, September). Secret ninja testing with HALO software engineering. In Proceedings of the 4th international workshop on Social software engineering (pp. 43–47). ACM.

  • Booth, J. L., McGinn, K. M., Young, L. K., & Barbieri, C. (2015). Simple practice doesn’t always make perfect: Evidence from the worked example effect. Policy Insights from the Behavioral and Brain Sciences, 2(1), 24–32.

    Article  Google Scholar 

  • Bransford, J. D., & Johnson, M. K. (1972). Contextual prerequisites for understanding: Some investigations of comprehension and recall. Journal of Verbal Learning and Verbal Behavior, 11(6), 717–726.

    Article  Google Scholar 

  • Brunken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38(1), 53–61.

    Article  Google Scholar 

  • Calderón, A., Ruiz, M., & O’Connor, R. V. (2017). ProDecAdmin: a game scenario design tool for software project management training. European Conference on software process improvement (pp. 241–248). Springer.

  • Caulfield, C. W., Veal, D., & Maj, S. P. (2011). Teaching Software Engineering Project Management–A Novel Approach for Software Engineering Programs. Modern Applied Science., 5(5), 28–43.

    Article  Google Scholar 

  • Chaves, R.O. et al. (2010). DESIGMPS: A game to support the teaching models of quality of software processes. Proceedings of the 17th Workshop on Informatics for Education, Aracaju, Brazil.

  • Chen, O., Kalyuga, S., & Sweller, J. (2015). The worked example effect, the generation effect, and element interactivity. Journal of Educational Psychology, 107(3), 689–704.

    Article  Google Scholar 

  • Chen, O., Retnowati, E., & Kalyuga, S. (2020). Element interactivity as a factor influencing the effectiveness of worked example–problem solving and problem solving–worked example sequences. British Journal of Educational Psychology, 90, 210–223.

    Article  Google Scholar 

  • Cranford, K. N., Tiettmeyer, J. M., Chuprinko, B. C., Jordan, S., & Grove, N. P. (2014). Measuring load on working memory: The use of heart rate as a means of measuring chemistry students’ cognitive load. Journal of Chemical Education, 91(5), 641–647.

    Article  Google Scholar 

  • Darejeh, A. (2011). Reference guide to access 2010. Saaher Engineering Press.

  • Darejeh, A., & Salim, S. S. (2016). Gamification solutions to enhance software user engagement: A systematic review. International Journal of Human-Computer Interaction, 32(8), 613–642.

    Article  Google Scholar 

  • DeLeeuw, K. E., & Mayer, R. E. (2008). A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. Journal of Educational Psychology, 100(1), 223–234.

    Article  Google Scholar 

  • Denning, T., Kohno, T., & Shostack, A. (2013, March). Control-Alt-Hack™: a card game for computer security outreach and education. In Proceeding of the 44th ACM technical symposium on Computer science education (pp. 729–729). ACM

  • Dickey, M. D. (2020). Narrative in Game-Based Learning. In J. L. Plass, R. E. Mayer, & B. D. Homer (Eds.), Handbook of Game-Based Learning (pp. 283–304). MIT Press.

  • Eagle, M., & Barnes, T. (2009). Experimental evaluation of an educational game for improved learning in introductory computing. ACM SIGCSE Bulletin, 41(1), 321–325.

    Article  Google Scholar 

  • Geary, D. C. (2008). An evolutionarily informed education science. Educational Psychologist, 43(4), 179–195.

    Article  Google Scholar 

  • Geary, D. (2012). Evolutionary educational psychology. In K. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook (Vol. 1, pp. 597–621). American Psychological Association.

  • Geary, D., & Berch, D. (2016). Evolution and children’s cognitive and academic development. In D. Geary & D. Berch (Eds.), Evolutionary perspectives on child development and education (pp. 217–249). Springer.

  • Gerjets, P. W., Hesse, F. W. H., Scheiter, K., Eysink, T. H. S., & Opfermann, M. (2009). Learning with hypermedia: The influence of representational formats and different levels of learner control on performance and learning behavior. Computers in Human Behavior, 25(2), 360–370. https://doi.org/10.1016/j.chb.2008.12.015

    Article  Google Scholar 

  • Granholm, E., Asarnow, R. F., Sarkin, A. J., & Dykes, K. L. (1996). Pupillary responses index cognitive resource limitations. Psychophysiology, 33(4), 457–461. https://doi.org/10.1111/j.1469-8986.1996.tb01071.x.PMID8753946

    Article  Google Scholar 

  • Grimes, M. and Valacich, J. (2015). Mind Over Mouse: The Effect of Cognitive Load on Mouse Movement Behavior. In Proceedings of the Thirty Sixth International Conference on Information Systems, Fort Worth, TX.

  • Gupta, U., & Zheng, R. Z. (2020). Cognitive load in solving mathematics problems: validating the role of motivation and the interaction among prior knowledge, worked examples, and task difficulty. European Journal of STEM Education, 5(1), 5.

    Article  Google Scholar 

  • Hainey, T., Connolly, T. M., Stansfield, M., & Boyle, E. A. (2011). Evaluation of a game to teach requirements collection and analysis in software engineering at tertiary education level. Computers & Education, 56(1), 21–35.

    Article  Google Scholar 

  • Jain, A., & Boehm, B. (2006, April). SimVBSE: Developing a game for value-based software engineering. In 19th Conference on Software Engineering Education & Training (CSEET'06) (pp. 103–114). IEEE.

  • Jordan, C., Knapp, M., Mitchell, D., Claypool, M., & Fisler, K. (2011, October). CounterMeasures: a game for teaching computer security. In 2011 10th Annual Workshop on Network and Systems Support for Games (pp. 1–6). IEEE.

  • Joseph, S. (2013). Measuring cognitive load: a comparison of self-report and physiological methods. Unpublished Doctoral Dissertation, Arizona State University.

  • Khawaji, A., Chen, F., Zhou, J., & Marcus, N. (2014, December). Trust and cognitive load in the text-chat environment: the role of mouse movement. In Proceedings of the 26th Australian Computer-Human Interaction Conference on Designing Futures: The Future of Design (pp. 324–327). ACM.

  • Knorr, E. M. (2020, February). Worked examples, cognitive load, and exam assessments in a senior database course. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 612–618).

  • Korbach, A., Brünken, R., & Park, B. (2017). Measurement of cognitive load in multimedia learning: A comparison of different objective measures. Instructional Science, 45(4), 515–536.

    Article  Google Scholar 

  • Kortum, P., & Acemyan, C. Z. (2016, September). The Relationship between user mouse-based performance and subjective usability assessments. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 60, No. 1, pp. 1174–1178). Sage Publications.

  • Kyun, S., Kalyuga, S., & Sweller, J. (2013). The effect of worked examples when learning to write essays in English literature. The Journal of Experimental Education, 81(3), 385–408.

    Article  Google Scholar 

  • Li, W., Grossman, T., & Fitzmaurice, G. (2012, October). GamiCAD: a gamified tutorial system for first time AutoCAD users. In Proceedings of the 25th Annual ACM symposium on User interface software and technology (pp. 103–112). ACM.

  • Luchins, A. (1942). Mechanisation in problem solving: The effect of Einstellung. Psychological Monographs. https://doi.org/10.1037/h0093502

    Article  Google Scholar 

  • Marcus, N., Cooper, M., & Sweller, J. (1996). Understand instructions. Journal of Educational Psychology, 88, 49–63.

    Article  Google Scholar 

  • Mayer, R. E. (2001). Multimedia learning. Cambridge University Press.

  • Nievelstein, F., Van Gog, T., Van Dijck, G., & Boshuizen, H. P. (2013). The worked example and expertise reversal effect in less structured tasks: Learning to reason about legal cases. Contemporary Educational Psychology, 38(2), 118–125.

    Article  Google Scholar 

  • Paas, F., Ayres, P., & Pachman, M. (2008). Assessment of cognitive load in multimedia learning: Theory, methods and applications. In D. H. Robinson & G. Schraw (Eds.), Recent innovations in educational technology that facilitate student learning (pp. 11–35). Information Age Publishing.

  • Paas, F., & van Gog, T. (2006). Optimising worked example instruction: Different ways to increase germane cognitive load. Learning and Instruction, 16(2), 87–91. https://doi.org/10.1016/j.learninstruc.2006

    Article  Google Scholar 

  • Pagulayan, R. J., Keeker, K., Wixon, D., Romero, R. L., & Fuller, T. (2002). User-centered design in games. The human-computer interaction handbook (pp. 915–938). CRC Press.

  • Palomino, P. T., Toda, A. M., Oliveira, W., Cristea, A. I., & Isotani, S. (2019). Narrative for gamification in education: Why should you care? In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT) (pp. 97–99). IEEE. https://doi.org/10.1109/ICALT.2019.00035

  • Pichert, J. W., & Anderson, R. C. (1977). Taking different perspectives on a story. Journal of Educational Psychology, 69(4), 309–315.

    Article  Google Scholar 

  • Plass, J. L., & Kalyuga, S. (2019). Four ways of considering emotion in cognitive load theory. Educational Psychology Review, 31(2), 339–359.

    Article  Google Scholar 

  • Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12(1), 61–86.

    Article  Google Scholar 

  • Renkl, A. (2014a). The worked-out examples principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 391–412). Cambridge University Press.

  • Renkl, A. (2014b). Toward an instructionally oriented theory of example-based learning. Cognitive Science, 38(1), 1–37.

    Article  Google Scholar 

  • Retnowati, E., Ayres, P., & Sweller, J. (2010). Worked example effects in individual and group work settings. Educational Psychology, 30(3), 349–367.

    Article  Google Scholar 

  • Rheem, H., Verma, V., & Becker, D. V. (2018, September). Use of mouse-tracking method to measure cognitive load. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 62, No. 1, pp. 1982–1986). Sage CA: Los Angeles

  • Robinson, P. (2001). Task complexity, cognitive resources, and syllabus design. In P. Robinson (Ed.), Cognition and second language instruction (pp. 287–318). Cambridge University Press.

  • Robinson, P. (2005). Cognitive complexity and task sequencing: Studies in a componential framework for second language task design. IRAL-International Review of Applied Linguistics in Language Teaching, 43(1), 1–32.

    Article  Google Scholar 

  • Robinson, P. (2007). Criteria for classifying and sequencing pedagogic tasks. In D. Singleton (Ed.), Investigating tasks in formal language learning (pp. 7–26). Multilingual Matters.

  • Saw, K. G. (2017). Cognitive load theory and the use of worked examples as an instructional strategy in physics for distance learners: A preliminary study. Turkish Online Journal of Distance Education, 18(4), 142–159.

    Article  Google Scholar 

  • Schwonke, R., Renkl, A., Krieg, C., Wittwer, J., Aleven, V., & Salden, R. (2009). The worked-example effect: Not an artefact of lousy control conditions. Computers in Human Behavior, 25(2), 258–266. https://doi.org/10.1016/j.chb.2008.12.011

    Article  Google Scholar 

  • Sentz, J., Stefaniak, J., Baaki, J., & Eckhoff, A. (2019). How do instructional designers manage learners’ cognitive load? An examination of awareness and application of strategies. Educational Technology Research and Development, 67(1), 199–245.

    Article  Google Scholar 

  • Shane, S. (2013). Clippy Returns for ribbon Hero 2 in MS Office’s Learning Tutorial. Gamification.co. Retrieved November 12, 2019, from http://www.gamification.co/2013/08/15/clippy-returns-for-ribbon-hero-2-in-ms-offices-learning-tutorial/

  • Singh, H., & Dyer, J.L. (2001). The computer background of Soldiers in Infantry courses: FY01 (Research Report 1784). Alexandria: U.S. Army Research Institute for the Behavioral and Social Sciences.

  • Spieler, B., Pfaff, N., & Slany, W. (2020, June). Reducing Cognitive Load through the Worked Example Effect within a Serious Game Environment. In 2020 6th International Conference of the Immersive Learning Research Network (iLRN) (pp. 1–8). IEEE.

  • Srinivasan, J., & Lundqvist, K. (2007, May). A constructivist approach to teaching software processes. In 29th International Conference on Software Engineering (ICSE'07) (pp. 664–672). IEEE.

  • Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.

    Article  Google Scholar 

  • Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312.

    Article  Google Scholar 

  • Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138.

    Article  Google Scholar 

  • Sweller, J. (2015). In academe, what is learned and how is it learned?. Current Directions in Psychological Science, 24, 190–194.

    Article  Google Scholar 

  • Sweller, J. (2016a). Cognitive load theory, evolutionary educational psychology, and instructional design. In D. Geary & D. Berch (Eds.), Evolutionary perspectives on child development and education (pp. 291–306). Springer.

  • Sweller, J. (2016b). Working memory, long-term memory and instructional design. Journal of Applied Research in Memory and Cognition, 5, 360–367.

    Article  Google Scholar 

  • Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68(1), 1–16.

    Article  Google Scholar 

  • Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer.

  • Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59–89.

    Article  Google Scholar 

  • Sweller, J., & Sweller, S. (2006). Natural information processing systems. Evolutionary Psychology, 4, 434–458.

    Article  Google Scholar 

  • Sweller, J., van Merriënboer, J., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31, 261–292.

    Article  Google Scholar 

  • Szilas, . (2015). Towards narrative-based knowledge representation in cognitive systems. In M. Finlayson, B. Miller, A. Lieto, & R. Ronfard (Eds.), Proceedings of the 6th workshop on computational models of narrative (pp. 133–141). Dagstuhl Publishing.

  • Thach, T. H., Blissett, S., & Sibbald, M. (2020). Worked examples for teaching ECG interpretation: Salient features or discriminating features? Medical Education. https://doi.org/10.1111/medu.14066

    Article  Google Scholar 

  • Tricot, A., & Sweller, J. (2014). Domain-specific knowledge and why teaching generic skills does not work. Educational Psychology Review, 26, 265–283. https://doi.org/10.1007/s10648-013-9243-1

    Article  Google Scholar 

  • Van Gerven, P., Paas, F., Van Merrienboer, J., & Schmidt, H. (2002). Cognitive load theory and aging: Effects of worked examples on training efficiency. Learning and Instruction, 12(1), 87–105. https://doi.org/10.1016/S0959-4752(01)00017-2

    Article  Google Scholar 

  • Van Gog, T., Kester, L., & Paas, F. (2011). Effects of worked examples, example-problem, and problem-example pairs on novices’ learning. Contemporary Educational Psychology, 36(3), 212–218.

    Article  Google Scholar 

  • Van Loon-Hillen, N., Van Gog, T., & Brand-Gruwel, S. (2012). Effects of worked examples in a primary school mathematics curriculum. Interactive Learning Environments, 20(1), 89–99.

    Article  Google Scholar 

  • Van Mierlo, C. M., Jarodzka, H., Kirschner, F., & Kirschner, P. A. (2012). Cognitive load theory in elearning. In Z. Yan (Ed.), Encyclopedia of cyberbehavior (pp. 1178–1211). IGI Global.

  • Wong, R. M., Adesope, O. O., & Carbonneau, K. J. (2020). Process-and product-oriented worked examples and self-explanations to improve learning performance. Journal of STEM Education: Innovations and Research, 20(2).

  • Yeo, L. M., & Tzeng, Y. T. (2020). Cognitive effect of tracing gesture in the learning from mathematics worked examples. International Journal of Science and Mathematics Education, 18(4), 733–751.

    Article  Google Scholar 

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  1. School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia

    Ali Darejeh & Nadine Marcus

  2. School of Education, University of New South Wales, Sydney, NSW, 2052, Australia

    John Sweller

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  1. Ali Darejeh
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  2. Nadine Marcus
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Darejeh, A., Marcus, N. & Sweller, J. The effect of narrative-based E-learning systems on novice users’ cognitive load while learning software applications. Education Tech Research Dev 69, 2451–2473 (2021). https://doi.org/10.1007/s11423-021-10024-5

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