Do Goal-Free Problems with Guided Questions Affect Retention, Transfer, and Cognitive Load?

Fika Aprillia; Endah Retnowati

doi:10.22437/edumatica.v15i3.45657

Authors

Fika Aprillia Universitas Negeri Yogyakarta
Endah Retnowati Universitas Negeri Yogyakarta

DOI:

https://doi.org/10.22437/edumatica.v15i3.45657

Keywords:

cognitive load, goal given, goal-free problems, guided questions, retention and transfer

Abstract

This study aimed to examine the effects of problem presentation and guided questions on students’ retention, transfer, and cognitive load. The study used a 2×2 factorial design with two factors: problem presentation (goal-free vs. goal given) and guided questions (with vs. without). The subjects consisted of 150 seventh-grade students from a public junior high school in Sleman Regency, who were randomly assigned to four experimental groups. The instruction was carried out in three phases: introduction, acquisition, and testing. Data were analyzed using two-way ANOVA at a significance level of 0.05. The results showed that problem presentation did not have a significant effect on retention and transfer but had a significant effect on cognitive load. Guided questions did not have a significant effect on any of the dependent variables. A significant interaction was found between problem presentation and guided questions on retention, transfer, and cognitive load in the retention test. Goal-given problems were more effective without guided questions for improving retention and transfer, while guided questions were more effective when combined with goal-free problems. Cognitive load on the retention test was higher for goal given problems with guided questions because goal given problems and guided questions led to means-end analysis and deep information processing.

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References

Allen, M. J., & Yen, W. M. (1979). Introduction to Measurement Theory. Brooks/Cole. https://doi.org/://doi.org/10.1016/b978-075065081-6/50002-3

Ayres, P. L. (1993). Why Goal-Free Problems Can Facilitate Learning. Contemporary Educational Psychology, 18(3), 376–381.

Bradshaw, A. C. (2008). Using Question Prompts to Support Ill-Structured Problem Solving in Online Peer Collaborations. 4, 148–165.

Chen, O., Castro-alonso, J. C., Paas, F., Sweller, J., & Hinze, S. (2018). Undesirable Difficulty Effects in the Learning of High-Element Interactivity Materials. Educational Psychology, 9(August), 1–7. https://doi.org/10.3389/fpsyg.2018.01483

Cowan, N. (2009). What are the differences between long-term, short-term, and working memory? Progress in Brain Research, 6123(07), 323–338. https://doi.org/10.1016/S0079-6123(07)00020-9.What

Dochy, F., Segers, M., & Buehl, M. M. (1999). The relation between assessment practices and outcomes of studies: The case of research on prior knowledge. Review of Educational Research, 69(2), 145–186. https://doi.org/10.3102/00346543069002145

Foster, C. (2021). Problem solving and prior knowledge. Mathematics in School, 6–8.

Ge, X., & Land, S. M. (2003). Scaffolding students’ problem-solving processes in an ill-structured task using question prompts and peer interactions. Educational Technology Research and Development, 1, 21–38.

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), 1–14. https://doi.org/10.20897/ejsteme/9252

Intaros, P., Inprasitha, M., & Srisawadi, N. (2014). Students’ Problem Solving Strategies in Problem Solving-mathematics Classroom. Procedia - Social and Behavioral Sciences, 116(June 2015), 4119–4123. https://doi.org/10.1016/j.sbspro.2014.01.901

Irwansyah, M. F., & Retnowati, E. (2019). Efektivitas worked example dengan strategi pengelompokan siswa ditinjau dari kemampuan pemecahan masalah dan cognitive load. Jurnal Riset Pendidikan Matematika, 6(1), 62–74. https://doi.org/10.21831/jrpm.v6i1.21452

Jones, I., Swan, M., & Pollitt, A. (2015). Assessing mathematical problem solving using comparative judgement. International Journal of Science and Mathematics Education, 13, 151–177.

Kalyuga, S. (2012). Role of Prior Knowledge in Learning Processes. Encyclopedia of the Sciences of Learning, 2886–2888. https://doi.org/10.1007/978-1-4419-1428-6_214

Kemendikbudristek BSKAP. (2022). Capaian Pembelajaran Pada Pendidikan Anak Usia Dini, Jenjang Pendidikan Dasar, Dan Jenjang Pendidikan Menengah Pada Kurikulum Merdeka. In Kemendikbudristek BSKAP RI.

King, A. (1991). Effects of Training in Strategic Questioning on Children’s Problem-Solving Performance. Journal of Educational Psychology, 83(3), 307–317. https://doi.org/10.1037/0022-0663.83.3.307

Kirschner, P., & Merrienboer, J. J. . Van. (2008). Ten steps to complex learning: A systematic approach to four-component instructional design. 1, 244–253.

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), 1058–1072. https://doi.org/10.3758/s13428-013-0334-1

Maulidya, S. R., Hasanah, R. U., & Retnowati, E. (2017). Can goal-free problems facilitating students’ flexible thinking? AIP Conference Proceedings, 1868. https://doi.org/10.1063/1.4995128

Mayer, R. E. (2002). Rote Versus Meaningful Learning. Theory Into Practice, 41(4), 226–232.

Meguerdichian, M., Walker, K., & Bajaj, K. (2016). Working memory is limited : improving knowledge transfer by optimising simulation through cognitive load theory. 131–138. https://doi.org/10.1136/bmjstel-2015-000098

Merrienboer, J. J. G. Van, & Sweller, J. (2005). Cognitive Load Theory and Complex Learning: Recent Developments and Future Directions. Educational Psychology Review, 17(2). https://doi.org/10.1007/s10648-005-3951-0

Novita, R., & Putra, M. (2016). Using task like Pisa’s problem to support student’s. Journal on Mathematics Education, 7(1), 31–42.

Paas, F., & Ayres, P. (2014). Cognitive Load Theory : A Broader View on the Role of Memory in Learning and Education. 191–195. https://doi.org/10.1007/s10648-014-9263-5

Paas, F. G. W. C. (1992). Training Strategies for Attaining Transfer of Problem-Solving Skill in Statistics: A Cognitive-Load Approach. Journal of Educational Psychology, 84(4), 429–434. https://doi.org/10.1037/0022-0663.84.4.429

Purnama, P. W., & Retnowati, E. (2020). The effectiveness of goal-free problems for studying triangle similarity in collaborative groups. JRAMathEdu (Journal of Research and Advances in Mathematics Education), 6(1), 32–45. https://doi.org/10.23917/jramathedu.v6i1.11198

Sugiman, Retnowati, E., Ayres, P., & Murdanu. (2019). Learning goal-free problems: Collaboratively or individually? Cakrawala Pendidikan, 38(3), 590–600. https://doi.org/10.21831/cp.v38i3.26914

Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. https://doi.org/10.1007/s10648-010-9128-5

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. In In Psychology of learning and motivation. https://doi.org/10.1007/978-1-4419-8126-4_5

Sweller, J., & Levine, M. (1982). Effects of goal specificity on means-ends analysis and learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8(5), 463–474. https://doi.org/10.1037/0278-7393.8.5.463

Sweller, J., Merriënboer, J. J. G., & Paas, F. (2019). Cognitive Architecture and Instructional Design : 20 Years Later.

Tanudjaya, C. P., & Doorman, M. (2020). Examining higher order thinking in Indonesian lower secondary mathematics classrooms. Journal on Mathematics Education, 11(2), 277–300. https://doi.org/10.22342/jme.11.2.11000.277-300

Wilkerson-Jerde, M. H., & Wilensky, U. J. (2011). How do mathematicians learn math?: Resources and acts for constructing and understanding mathematics. Educational Studies in Mathematics, 78(1), 21–43. https://doi.org/10.1007/s10649-011-9306-5

Wirth, J., Künsting, J., & Leutner, D. (2009). The impact of goal specificity and goal type on learning outcome and cognitive load. Computers in Human Behavior, 25(2), 299–305. https://doi.org/10.1016/j.chb.2008.12.004

Zumbach, J., Ortler, C., Deibl, I., & Moser, S. (2020). Using Prompts to Scaffold Metacognition in Case-Based Problem Solving within the Domain of Attribution Theory. 1–11.