TEORI BEBAN KOGNITIF: PETA KOGNITIF DALAM PEMECAHAN MASALAH PADA MATEMATIKA SEKOLAH

Barep Yohanes(1), Feby Indriana Yusuf(2*),

(1) Universitas PGRI Banyuwangi
(2) Universitas PGRI Banyuwangi
(*) Corresponding Author


Abstract


Penelitian ini merupakan penelitian kualitatif deskriptif yang bertujuan untuk mendeskripsikan Beban Kognitif dalam Pemecahan Masalah melalui peta kognitif pada mahasiswa. Penelitian dilakukan dengan memberikan permasalahan matematika kepada mahasiswa dan kemudian hasil kerja dianalisis berdasarkan pengetahuan, masalah, prosedur, dan konsep (peta kognitif) dalam pemecahan masalah berdasarkan beban kognitif. Hasil penelitian menunjukkan bahwa dalam peta kognitif mahasiswa dalam pemecahan masalah dapat terlihat adanya beban kognitif yang muncul. Peta kognitif yang interkoneksi antara pengetahuan, masalah, prosedur, dan konsep memiliki beban kognitif baik itu beban kognitif intrinsic maupun germane. Kesimpulan pada penelitian ini bahwa  Pemecahan Masalah selalu melibatkan kemampuan kognitif (Beban Kognitif) yang dapat ditelusuri atau dilihat dari peta kognitif peserta didik. Didalam peta kognitif terdapat beban kognitif yang mengarah untuk dapat memecahkan masalah.

Keywords


Beban Kognitif; Cognitive Map; Matematika Sekolah; Problem Solving

References


Ahmed, E., El Khoribi, R. A., Darwish, G., Muzy, A., & Bernot, G. (2020). Modeling of the development of the fetus cognitive map from the sensorimotor system. Egyptian Informatics Journal, 21(4), 191–199. https://doi.org/10.1016/j.eij.2020.01.002

Bottini, R., & Doeller, C. F. (2020). Knowledge Across Reference Frames: Cognitive Maps and Image Spaces. Trends in Cognitive Sciences, 24(8), 606–619. https://doi.org/10.1016/j.tics.2020.05.008

Choppin, J. (2011). The role of local theories: Teacher knowledge and its impact on engaging students with challenging tasks. Mathematics Education Research Journal, 23(1), 5–25. https://doi.org/10.1007/s13394-011-0001-8

de Jong, T. (2010). Cognitive Load Theory, Educational research, and instructional design: some food for thought. Instructional Science, 38(2), 105–134. https://doi.org/10.1007/s11251-009-9110-0

Friedman, N., Fekete, T., Gal, K., & Shriki, O. (2019). EEG-based prediction of cognitive load in intelligence tests. Frontiers in Human Neuroscience, 13(June). https://doi.org/10.3389/fnhum.2019.00191

Gifford, S., & Rockliffe, F. (2012). Mathematics difficulties: does one approach fit all? Research in Mathematics Education, 14(1), 1–15. https://doi.org/10.1080/14794802.2012.657436

Ginns, P., & Leppink, J. (2019). Special Issue on Cognitive Load Theory: Editorial. Educational Psychology Review, 31(2), 255–259. https://doi.org/10.1007/s10648-019-09474-4

Huang, Y. H. (2018). Influence of instructional design to manage intrinsic cognitive load on learning effectiveness. Eurasia Journal of Mathematics, Science and Technology Education, 14(6), 2653–2668. https://doi.org/10.29333/ejmste/90264

Kalyuga, S. (2011). Informing: A cognitive load perspective. Informing Science: The International Journal of an Emerging Transdiscipline, 14(1), 33–45. https://doi.org/10.28945/1349

Kaune, C., Cohors-Fresenborg, E., & Nowinska, E. (2011). Development of metacognitive and discursive activities in Indonesian maths teaching a theory based design and test of a learning environment. Journal on Mathematics Education, 2(1), 15–40. https://doi.org/10.22342/jme.2.1.777.15-40

Lange, C., & Costley, J. (2018). The moderating effects of intrinsic load on the relationship between self-regulated effort and germane load. Journal of Computer Assisted Learning, 34(6), 652–660. https://doi.org/10.1111/jcal.12269

Lin, J. J. H., & Lin, S. S. J. (2014). Cognitive Load for Configuration Comprehension in Computer-Supported Geometry Problem Solving: an Eye Movement Perspective. International Journal of Science and Mathematics Education, 12(3), 605–627. https://doi.org/10.1007/s10763-013-9479-8

Muis, K. R., Psaradellis, C., Lajoie, S. P., Di Leo, I., & Chevrier, M. (2015). The role of epistemic emotions in mathematics problem solving. Contemporary Educational Psychology, 42, 172–185. https://doi.org/10.1016/j.cedpsych.2015.06.003

Murray, S. (2011). Declining participation in post-compulsory secondary school mathematics: Students’ views of and solutions to the problem. Research in Mathematics Education, 13(3), 269–285. https://doi.org/10.1080/14794802.2011.624731

NCTM. (2000). Principle and Standarts for School Mathematics.

Plass, J. L., Moreno, R., & Brünken, R. (2010). COGNITIVE LOAD THEORY. Cambridge University Press.

Putu, N., Arilaksmi, G., & Sulandra, I. M. (2021). Kemampuan Berpikir Kreatif Mahasiswa Pendidikan Matematika dalam Memecahkan Masalah Open-Ended Trigonometri. JIPM: Jurnal Ilmiah Pendidikan Matematika, 9(2), 1–13.

Sammut-Bonnici, T., & McGee, J. (2015). Cognitive Map. Wiley Encyclopedia of Management, January, 1–3. https://doi.org/10.1002/9781118785317.weom120127

Santos-Trigo, M. (2014). Problem Solving in Mathematics Education. In Encyclopedia of Mathematics Education. https://doi.org/10.1007/978-94-007-4978-8_129

Seufert, T. (2018). The interplay between self-regulation in learning and cognitive load. Educational Research Review, 24(August 2017), 116–129. https://doi.org/10.1016/j.edurev.2018.03.004

Smith, C. (2010). Choosing more mathematics: Happiness through work? Research in Mathematics Education, 12(2), 99–115. https://doi.org/10.1080/14794802.2010.496972

Subanji. (2015). TEORI KESALAHAN KONSTRUKSI KONSEP DAN PEMECAHAN MASALAH MATEMATIKA. Universitas Negeri Malang.

Sweller, J., Ayres, P., & Kalyuga, S. (2011). COGNITIVE LOAD THEORY (Vol. 82, Issue 1). Cambridge University Press. http://www.springer.com/series/8640

Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive Architecture and Instructional Design: 20 Years Later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5

Tachie, S. A. (2019). Meta-cognitive skills and strategies application: How this helps learners in mathematics problem-solving. Eurasia Journal of Mathematics, Science and Technology Education, 15(5). https://doi.org/10.29333/ejmste/105364

Voutsina, C., & Ismail, Q. (2011). The use of additive composition in arithmetic: The case of children classified as low attainers. Research in Mathematics Education, 13(3), 287–303. https://doi.org/10.1080/14794802.2011.624750

Yayuk, E., & Husamah, H. (2020). The difficulties of prospective elementary school teachers in item problem solving for mathematics: Polya’s steps. Journal for the Education of Gifted Young Scientists, 8(1), 361–378. https://doi.org/10.17478/jegys.665833

Yohanes, B., Subanji, & Sisworo. (2016). Students’ Cognitive Load in Geometry Learning. Jurnal Pendidikan: Teori, Penelitian Dan Pengembangan, 1(2), 187–195.




DOI: http://dx.doi.org/10.24127/ajpm.v10i4.4033

Refbacks

  • There are currently no refbacks.