Nothing Special   »   [go: up one dir, main page]

Skip to main content

Advertisement

Log in

Effects of self-explanation on applying decision rules in an online learning environment

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

This study aims to investigate the effect of employing self-explanation strategy with worked examples on university students’ skills in applying decision rules, on the retention of these skills, and on the cognitive load in an online learning environment. The study was designed as a quasi-experimental study with pre-/post-test and control group. A total of 56 juniors from the department of Computer Education and Instructional Technologies in the faculty of education at a state university during the 2015–2016 academic year participated in the study. Two online learning environments to teach decision rules were designed based on worked example method. The participants were assigned to an experimental group (n = 28) with self-explanation strategy and a control group (n = 28) without self-explanation. The data for this study were collected using the "Personal and Academic Information Form", the "Applying Basic Mathematics Literacy Skills Test for Adults", the "Cognitive Load Rating Scale" and the "Test of Skills of Applying Decision Rules". The results of the study showed that the online learning process based on the worked examples with self-explanation caused a significant change in the learners’ skills of applying decision rules. It was further determined that the experimental group, which made self-explanation, had higher cognitive load scores on the decision rules application questions. All the students exerted higher cognitive efforts on the decision rules, which were labeled as either complex or hard. In summary, it can be concluded that while the online learning environment based on worked examples with self-explanation improved the learners’ skills of applying decision rules and increased their cognitive load, it did not have an effect on their retention performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ainsworth, S., & Loizou, A. T. (2003). The effects of selfexplaining when learning with text or diagrams. Cognitive Science, 27, 669–681.

    Article  Google Scholar 

  • AlbayrakAtaklı, P. (2011). Factors related to basic numeracy skills of adults in Turkey. YayımlanmamışYüksekLisansTezi.

    Google Scholar 

  • Aleven, V., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26, 147–179.

    Article  Google Scholar 

  • Alhassan. (2017). The effect of employing self-explanation strategy with worked examples on acquiring computer programing skills. Journal of Education and Practice, 8, 6.

    Google Scholar 

  • Alpar, R. (2010). UygulamalıİstatistikveGeçerlik-Güvenirlik. DetayYayıncılık.

    Google Scholar 

  • Astleitner, H. (2008). Die lernrelevante Ordnung von Aufgaben nach der Aufgabenschwierigkeit. In Aufgaben als Katalysatoren von Lernprozessen, 65–80.

  • Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of Educational Research, 70, 181–214.

    Article  Google Scholar 

  • Berthold, K., Eysink, T. H. S., & Renkl, A. (2009). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 37(4), 345–363.

    Article  Google Scholar 

  • Bettman, J. R., Johnson, E. R., & Payne, J. P. (1991). Consumer Decision Making. In T. S. Robertson & H. H. Kassarjian (Eds.), Handbook of Consumer Behavior (pp. 50–84). Prentice-Hall.

    Google Scholar 

  • Bielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and Instruction, 13, 221–252.

    Article  Google Scholar 

  • Bisra, K., Liu, Q., Nesbit, J., Salimi, F., & Winne, P. (2018). Inducing self-explanation: A meta-analysis. Educational Psychology Review, 30, 703–725.

    Article  Google Scholar 

  • Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2007). Individual differences in adult decision making competence. Journal of Personality and Social Psychology, 92, 938–956.

    Article  Google Scholar 

  • Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2020). Decision-making competence: more than intelligence? Current Directions in Psychological Science, 29(2), 186–192.

    Article  Google Scholar 

  • Busch, C., Renkl, A., & Schworm, S. (2008). Towards a generic self-explanation training intervention for example-based learning. In G. Kanselaar, V. Jonker, P. A. Kirschner, & F. J. Prins (Eds.), Proceedings of the 8th International Conference of the Learning Sciences. Utrecht, NL: ICLS.

  • Demiraslan Çevik, Y., Dağhan, G., Somyurek, S., & Mumcu, F. (2020). Reflections on the implementation of an online learning environment designed to improve students' decision making skills. In 14th International Technology, Education and Development Conference (INTED) (pp. 5313–5322). Valencia.

  • Chamberland, M., St-Onge, C., Setrakian, J., Lanthier, L., Bergeron, L., Bourget, A., Mamede, S., & Rikers, R. (2011). The influence of medical students’ self-explanations on diagnostic performance. Medical Education, 45(7), 688–695.

    Article  Google Scholar 

  • Chang, J., Lee, M., Su, C., & Wang, T. (2017). Effects of Using Self-Explanation on a Web-Based Chinese Sentence-Learning System. Computer Assisted Language Learning, 30, 44–63.

    Article  Google Scholar 

  • Chankong, V., & Haimes, Y. Y. (1983). Multiobjective decision making: Theory and Methodology, North-Holland, New York, . Personality and Social Psychology, 92, 938–956.

    MATH  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 

  • Chi, T. Y., Olfman, L. & Berger, D. E. (2017). Computer Skill Acquisition : The Effects of Computer–aided Self Explanation on Knowledge Retention and Transfer. Proceedings of the 50th Hawaii International Conference on System Sciences.

  • Clark, R. C., Nguyen, F., & Sweller, J. (2006). Efficiency in learning: Evidence-based guidelines to manage cognitive load. Jossey-Bass.

    Google Scholar 

  • Cohen, J. (1988). Statistical power and Analysis for the behavioral sciences (2nd ed.). Lawrance Erlbaum Associates.

    MATH  Google Scholar 

  • Conati, C., & VanLehn, K. (2000). Toward computer-based support of meta-cognitive skills: A computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education, 11, 389–415.

    Google Scholar 

  • Crippen, K. J., & Earl, B. L. (2007). The impact of web-based worked examples and self-explanation on performance, problem solving, and self-efficacy. Computers & Education, 49, 809–821.

    Article  Google Scholar 

  • Del Missier, F., Mantyla, T., & Bruine de Bruin, W. (2012). Decision-making competence, executive functioning, and general cognitive abilities. Journal of Behavioral Decision Making, 25, 331–351.

    Article  Google Scholar 

  • Eberhardt, W., Bruine de Bruin, W., & Strough, J. (2019). Age differences in financial decision making: The benefits of more experience and less negative emotions. Journal of Behavioral Decision Making, 32, 79–93.

    Article  Google Scholar 

  • Fabic, G. V., Mitrovic, A., & Neshatian, K. (2019). Evaluation of parsons problems with menu-based self-explanation prompts in a mobile python tutor. International Journal of Artificial Intelligence in Education, 29, 507–535.

    Article  Google Scholar 

  • Field, A. (2009). Discovering statistics using SPSS. Sage Publications Limited.

    MATH  Google Scholar 

  • Fischhoff, B., & Broomell, S. B. (2020). Judgment and decision making. Annual Review of Psychology, 71, 331–355.

    Article  Google Scholar 

  • Fishburn, P. C. (1974). Lexicographic orders, utilities and decision rules: A survey. Management Science, 20(11), 1442–1471.

    Article  MathSciNet  MATH  Google Scholar 

  • Gadgil, S., Nokes-Malach, T. J., & Chi, M. T. H. (2012). Effectiveness of holistic mental model confrontation in driving conceptual change. Learning and Instruction, 22(1), 47–61.

    Article  Google Scholar 

  • Gal, I. (2000). Adult numeracy development: Theory, research, practice. Hampton Press.

    MATH  Google Scholar 

  • George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference, 17.0 update. Boston: Allyn & Bacon.

  • Girden, E. R. (1992). ANOVA Repeated Measures. Sage University Paper Series on Quantitative Applications in the Social Sciences, 07–084, 1–76 Newbury Park Sage.

  • Gresch, H., Hasselhorn, M., & Bögeholz, S. (2011). Training in decision-making strategies-An approach to enhance students’ competence to deal with socioscientific issues. International Journal of Science Education.

  • Grobe, C. S., & Renkl, A. (2007). Finding and fixing errors in worked examples: Can this foster learning outcomes? Learning and Instruction, 17, 612–634.

    Article  Google Scholar 

  • Hilbert, T. S., & Renkl, A. (2009). Learning how to use a computer-based concept-mapping tool: Self explaining examples helps. Computers in Human Behavior, 25, 267–274.

    Article  Google Scholar 

  • Hilbert, T. S., Schworm, S., & Renkl, A. (2004). Learning from worked-out examples: The transition from instructional explanations to self-explanation prompts. In P. Gerjets, J. Elen, R. Joiner, & P. Kirschner (Eds.), Instructional Design for Effective and Enjoyable Computer-Supported Learning (pp. 184–192). Knowledge Media Research Center.

    Google Scholar 

  • Hollingworth, R. W., & McLoughlin, C. (2001). Developing science students’ metacognitive problem solving skills online. Australasian Journal of Educational Technology, 17(1), 50–63.

    Article  Google Scholar 

  • Johnson, C. I., & Mayer, R. E. (2010). Applying the selfexplanation principle to multimedia learning in a computerbased game-like environment. Computers in Human Behavior, 26, 1246–1252.

    Article  Google Scholar 

  • Karadenizve, Ş, & Kılıç, E. (2004). HiperOrtamlardaKaybolmaÖlçeğininUyarlamaÇalışması. KuramveUygulamadaEğitimYönetimiDergisi, 39, 420–429.

    Google Scholar 

  • Kirschner, P. A. (2002). Cognitive load theory: Implications of cognitive load theory on the design of learning. Learning and Instruction, 12(1), 1–10.

    Article  Google Scholar 

  • Kwon, K., Kumalasari, C. D., & Howland, J. L. (2011). Self-explanation prompts on problem-solving performance in an interactive learning environment. Journal of Interactive Online Learning, 10(2), 96–112.

    Google Scholar 

  • Larsen, D. P., Butler, A. C., & Roediger, H. L. (2013). Comparative effects of test-enhanced learning and self-explanation on long-term retention. Medical Education, 47, 674–682.

    Article  Google Scholar 

  • Leppink, J., Paas, F., van Gog, T., van der Vleuten, C. P. M., & van Merrienboer, J. J. G. (2014). Effects of pairs of problems and examples on task performance and different types of cognitive load. Learning and Instruction, 30, 32–42.

    Article  Google Scholar 

  • Montgomery, H. (1981). Decision rules and the search for a dominance structure: Towards a process model of decision making. Dept. of Psychology, University of Göteborg.

    Google Scholar 

  • Moreno, R., & Park, B. (2010). Cognitive load theory: Historical development and relation to other theories. In J. L. Plass, R. Moreno, & R. Brunken (Eds.), Cognitive load theory (pp. 9–28). Cambridge University Press.

    Chapter  Google Scholar 

  • Neubrand, C., & Harms, U. (2017). Tackling the difficulties in learning evolution: Effects of adaptive self-explanation prompts. Journal of Biological Education, 51(4), 336–348.

    Article  Google Scholar 

  • Ngu, B. H., & Yeung, A. S. (2013). Algebra word problem solving approaches in a chemistry context: Equation worked examples versus text editing. Journal of Mathematical Behavior, 32(2), 197–208.

    Article  Google Scholar 

  • Nunnally, J. (1978). Psychometric methods. McGraw-Hill.

    Google Scholar 

  • Paas, F. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84, 429–434.

    Article  Google Scholar 

  • Paas, F. G. W. C., Renkl, A., & Sweller, J. (2004). Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32, 1–8.

    Article  Google Scholar 

  • Parker, A. M., & Fischhoff, B. (2005). Decision-making competence: External validation through an individual-differences approach. Journal of Behavioral Decision Making, 18, 1–27.

    Article  Google Scholar 

  • Payne, J. W., & Bettman, J. R. (2004). Walking with the scarecrow: The information processing approach to decision research. In D. J. Koehler & N. Harvey (Eds.), Blackwell handbook of judgment and decision making (pp. 110–132). Blackwell Publishing.

    Chapter  Google Scholar 

  • Payne, J. W., & Bettman, J. R. (2008). Walking with the Scarecrow: The Information Processing Approach to Decision Research, 110–132.

  • Payne, J. W., Bettman, J. R., Coupey, E., & Johnson, E. J. (1992). A constructive process view of decision making: Multiple strategies in judgment and choice. ActaPsychologica, 80, 107–141.

    Google Scholar 

  • Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker.

  • Peixoto, J. M., Mamede, S., Faria, R. M. D., Moura, A. S., Santos, S. M. E., & Schmidt, H. G. (2017). The effect of self-explanation of pathophysiological mechanisms of diseases on medical students’ diagnostic performance. Advances in Health Sciences Education: Theory and Practice, 22(5), 1183–1197.

    Article  Google Scholar 

  • Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21, 1–29.

    Article  Google Scholar 

  • Renkl, A. (2005). The worked-out examples principle in multimedia learning. In Mayer, R.E. (Eds.), The Cambridge Handbook of Multimedia Learning. Cambridge: Cambridge University Press. View in a new window.

  • Riedl, R., Brandstatter, E., & Roithmayr, F. (2008). Identifying decision strategies: A process-and outcome-based classification method. Behavior Research Methods, 20(3), 795–807.

    Article  Google Scholar 

  • Rovai, A. P., Baker, J. D., & Ponton, M. K. (2014). Social Sci. Research Design and Statistics: A Practitioner’s Guide to Research Methods and IBM SPSS Analysis. Watertree Press LLC.

    Google Scholar 

  • Rourke, A., & Sweller, J. (2009). The worked-example effect using ill-defined problems: Learning to recognise designers’ styles. Learning and Instruction, 19, 185–199.

    Article  Google Scholar 

  • Roy, M., & Chi, M. T. H. (2005). The self-explanation principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 271–286). Cambridge University Press.

    Chapter  Google Scholar 

  • Santosa, C. A. H. F., Suryadi, D., Prabawanto, S., & Syamsuri, S. (2018). The role of worked-example in enhancing students’ self-explanation and cognitive efficiency in calculus instruction. JurnalRisetPendidikanMatematika, 5(2), 168–180.

    Google Scholar 

  • Schworm, S., & Renkl, A. (2006). Computer-supported example-based learning: When instructional explanations reduce self-explanations. Computers & Education, 46(4), 426–445.

    Article  Google Scholar 

  • Schworm, S., & Renkl, A. (2007). Learning argumentation skills through the use of prompts for self-explaining examples. Journal of Educational Psychology, 99(2), 285–296.

    Article  Google Scholar 

  • Sendag, S., & Odabasi, H. F. (2009). Effects of an online problem based learning course on content knowledge acquisition and critical thinking skills. Computers & Education, 53(1), 132–141.

    Article  Google Scholar 

  • Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69, 99–118.

    Article  Google Scholar 

  • Sullivan, G. M., & Feinn, R. (2012). Using effect size - or why the p value is not enough. Journal of Graduate Medical Education, 4(3), 279–282.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sweller, J. (2011). Cognitive load and e-learning. Artificial Intelligence in Education, Lecture Notes in Computer Science, 6738, 5–6.

    Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Pearson/Allyn & Bacon.

    Google Scholar 

  • Tawfik, A. A., Sánchez, L., & Saparova, D. (2014). The effects of case libraries in supporting collaborative problem-solving in an online learning environment. Technology, Knowledge and Learning, 19(3), 1–22.

    Article  Google Scholar 

  • Tepgeç, M., & Demiraslan Çevik, Y. (2018). Comparison of three instructional strategies in teaching programming: restudying material, testing and worked example. Journal of Learning and Teaching in Digital Age, 3(2), 42–50.

    Google Scholar 

  • Tversky, A. (1969). Intransitivity of preferences. Psychological Review, 76, 31–48.

    Article  Google Scholar 

  • Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological Review, 79(4), 281–299.

    Article  Google Scholar 

  • van der Meij, J., & de Jong, T. (2011). The effects of directive self-explanation prompts to support active processing of multiple representations in a simulation-based learning environment. Journal of Computer Assisted Learning, 27(5), 411–423.

    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 

  • Villalta-Cerdas, A., & Sandi-Urena, S. (2016). Assessment of self-explaining effect in a large enrollment general chemistry course. EducacionQuimica, 27(2), 115–125.

    Google Scholar 

  • Weller, J. A., Levin, I. P., Rose, J. P., & Bossard, E. (2012). Assessment of decision-making competence in preadolescence. Journal of Behavioral Decision Making, 25(4), 414–426.

    Article  Google Scholar 

  • 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: Innovation and Research, 20(2), 24–31.

    Google Scholar 

  • Wylie, R., & Chi, M. T. H. (2014). The self-explanation principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge Handbook of Multiemedia Learning (pp. 413–432). Cambridge University Press.

    Chapter  Google Scholar 

  • Wylie, R., Koedinger, K. R., & Mitamura, T. (2010). Extending the self-explanation effect to second language grammar learning. In K. Gomez, L. Lyons, & J. Radinsky (Eds.), Learning in the disciplines: Proceedings of the 9th International Conference of the Learning Sciences, 1, 57–64. International Society of the Learning Sciences.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yahya İltüzer.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

İltüzer, Y., Demiraslan Çevik, Y. Effects of self-explanation on applying decision rules in an online learning environment. Educ Inf Technol 26, 4771–4794 (2021). https://doi.org/10.1007/s10639-021-10499-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-021-10499-y

Keywords

Navigation