Psikologi Kognitif dalam Pembelajaran Matematika Modern untuk Mengatasi Math Anxiety dan Meningkatkan Kreativitas Numerik Generasi Digital

Authors

  • Adji Seputro Institut Teknologi dan Bisnis Semarang
  • Ahmad Dwi Nurdiyanto Institut Teknologi dan Bisnis Semarang

DOI:

https://doi.org/10.59031/jnts.v1i3.776

Keywords:

cognitive psychology, creativity, digital generation, math anxiety, neuroeducation

Abstract

Mathematics anxiety has long been recognized as a barrier to student achievement, particularly in an era where digital technologies are reshaping education. This study aims to investigate the role of cognitive psychology in modern mathematics learning as a means of reducing math anxiety and enhancing numerical creativity among digital generation learners. Employing an experimental classroom design informed by neuroeducation principles, the research explored how interventions rooted in cognitive regulation, emotional control, and cognitive load management influence student outcomes. Data were collected through pre-test and post-test instruments measuring levels of math anxiety and creativity in numerical problem-solving. The findings demonstrate a significant reduction in mathematics anxiety following the implementation of the neuroeducation-based intervention. Simultaneously, students exhibited marked improvements in creative approaches to numerical challenges, indicating that addressing psychological factors is essential to unlocking mathematical potential. These results highlight the importance of integrating cognitive psychology into mathematics instruction, moving beyond procedural learning toward a holistic approach that considers students’ emotional and cognitive states. The implications extend to educators, curriculum designers, and policymakers, suggesting that modern mathematics education should balance cognitive development, emotional well-being, and creative problem-solving skills to prepare students for the challenges of a rapidly evolving digital world.

References

Blair, C., & Raver, C. C. (2014). Closing the achievement gap through modification of neurocognitive and neuroendocrine function: Results from a cluster randomized controlled trial of an innovative approach to the education of children in kindergarten. PLoS ONE, 9(11), e112393. https://doi.org/10.1371/journal.pone.0112393

Borst, G. (2023). Neuroeducation _LaPsyDÉ (UMR CNRS 8240) [La Neuroéducation - LaPsyDÉ (UMR CNRS 8240)]. Année Psychologique, 123(2), 387–392. https://doi.org/10.3917/anpsy1.232.0387

Buckley, S., Reid, K., Goos, M., Lipp, O. V., & Thomson, S. (2016). Understanding and addressing mathematics anxiety using perspectives from education, psychology and neuroscience. Australian Journal of Education, 60(2), 157–170. https://doi.org/10.1177/0004944116653000

Caballero-Cobos, M., & Llorent, V. J. (2022). Teacher training on neuroeducation for improving reading, mathematical, social, emotional and moral competencies of secondary school students: A two-year quasi-experimental study. Revista de Psicodidáctica, 27(2), 158–167. https://doi.org/10.1016/j.psicod.2022.04.001

Carboni, A., Maiche, A., & Valle-Lisboa, J. C. (2021). Teaching the science in neuroscience to protect from neuromyths: From courses to fieldwork. Frontiers in Human Neuroscience, 15, 718399. https://doi.org/10.3389/fnhum.2021.718399

Carey, E., Devine, A., Hill, F., & Szucs, D. (2017). Differentiating anxiety forms and their role in academic performance from primary to secondary school. PLoS ONE, 12(3), e0174418. https://doi.org/10.1371/journal.pone.0174418

Cavanagh, S. R., Lang, J. M., Birk, J. L., Fulwiler, C. E., & Urry, H. L. (2021). A multicourse, multisemester investigation of the impact of cognitive reappraisal and mindfulness instruction on short- and long-term learning in the college classroom. Scholarship of Teaching and Learning in Psychology, 7(1), 14–38. https://doi.org/10.1037/stl0000174

Cherukunnath, D., & Singh, A. P. (2022). Exploring cognitive processes of knowledge acquisition to upgrade academic practices. Frontiers in Psychology, 13, 682628. https://doi.org/10.3389/fpsyg.2022.682628

Efremova, N., & Huseynova, A. (2023). Digital pedagogy: Opportunities and challenges of learning in the information environment. In Lecture Notes in Networks and Systems (Vol. 574, pp. 283–292). Springer. https://doi.org/10.1007/978-3-031-21432-5_29

Evangelopoulou, M., Jiménez-Fanjul, N., & Jose Madrid, M. (2023). Classroom-based mathematics anxiety among students in Greek secondary education: A perspective from math teachers. Operations Research Forum, 4(4), 74. https://doi.org/10.1007/s43069-023-00253-0

Eysenck, M. W., & Brysbaert, M. (2023). Fundamentals of cognition (pp. 1–624). Routledge. https://doi.org/10.4324/9781003384694

Garg, D., Patel, R., Patel, R., Kaka, B., Goel, P., & Patel, B. (2021). Digital learning: A new perception to learn beyond the classroom boundary. In Smart Innovation, Systems and Technologies (Vol. 195, pp. 517–527). Springer. https://doi.org/10.1007/978-981-15-7078-0_50

Hadie, S. N. H., Tan, V. P. S., Omar, N., Nik Mohd Alwi, N. A., Lim, H. L., & Ku Marsilla, K. I. (2021). COVID-19 disruptions in health professional education: Use of cognitive load theory on students' comprehension, cognitive load, engagement, and motivation. Frontiers in Medicine, 8, 739238. https://doi.org/10.3389/fmed.2021.739238

Höfler, E., Geier, G., & Zimmermann, C. (2016). How to design a mathematical learning app suitable for children: The myth of digital natives. In Digital Tools for Seamless Learning (pp. 160–178). IGI Global. https://doi.org/10.4018/978-1-5225-1692-7.ch008

Ifenthaler, D., Adcock, A. B., Erlandson, B. E., Gosper, M., Greiff, S., & Pirnay-Dummer, P. (2014). Challenges for education in a connected world: Digital learning, data rich environments, and computer-based assessment. Technology, Knowledge and Learning, 19(1–2), 121–126. https://doi.org/10.1007/s10758-014-9228-2

Jamieson, J. P., Black, A. E., Pelaia, L. E., & Reis, H. T. (2021). The impact of mathematics anxiety on stress appraisals, neuroendocrine responses, and academic performance in a community college sample. Journal of Educational Psychology, 113(6), 1164–1176. https://doi.org/10.1037/edu0000636

Justicia-Galiano, M.-J., Pelegrina, S., Lechuga, M.-T., Gutiérrez-Palma, N., Martín-Puga, E.-M., & Lendínez, C. (2016). Math anxiety and its relationship to inhibitory abilities and perceived emotional intelligence. Anales de Psicología, 32(1), 125–131. https://doi.org/10.6018/analesps.32.1.194891

Kalyuga, S., & Liu, T.-C. (2015). Managing cognitive load in technology-based learning environments. Educational Technology and Society, 18(4), 1–8.

Kirschner, P. A., Sweller, J., Kirschner, F., & Zambrano, J. R. (2018). From cognitive load theory to collaborative cognitive load theory. International Journal of Computer-Supported Collaborative Learning, 13(2), 213–233. https://doi.org/10.1007/s11412-018-9277-y

Kirste, L., & Holtbrügge, D. (2019). Experiential learning in the digital context: An experimental study of online cultural intelligence training. Journal of Teaching in International Business, 30(2), 147–174. https://doi.org/10.1080/08975930.2019.1663775

Lahuerta-Otero, E., Cordero-Gutiérrez, R., & Izquierdo-Álvarez, V. (2019). Using social media to enhance learning and motivate students in the higher education classroom. In Communications in Computer and Information Science (Vol. 1011, pp. 351–361). Springer. https://doi.org/10.1007/978-3-030-20798-4_30

Leppisaari, I., & Lee, O. (2012). Modelling digital natives' international collaboration: Finnish-Korean experiences of environmental education. Educational Technology and Society, 15(2), 244–256.

Loon, M., & Bell, R. (2018). The moderating effects of emotions on cognitive skills. Journal of Further and Higher Education, 42(5), 694–707. https://doi.org/10.1080/0309877X.2017.1311992

Meissner, R., & Köbis, L. (2020). Annotated knowledge graphs for teaching in higher education: Supporting mentors and mentees by digital systems. In Lecture Notes in Computer Science (Vol. 12128, pp. 551–555). Springer. https://doi.org/10.1007/978-3-030-50578-3_43

Mendías, J. S., Alex, I. S., & Espigares, A. M. (2022). Mathematics anxiety, achievement, and university preparatory studies of teachers in training. PNA, 16(2), 115–140. https://doi.org/10.30827/pna.v16i2.21703

Naismith, L. M., Cheung, J. J. H., Sibbald, M., Tavares, W., Cavalcanti, R. B., & Haji, F. A. (2019). Using cognitive load theory to optimize simulation design. In Clinical Simulation: Education, Operations and Engineering (pp. 129–141). Elsevier. https://doi.org/10.1016/B978-0-12-815657-5.00010-3

Nikolić, S. T., Vrgović, P., Stanković, J., & Safranj, J. (2015). Students' emotional state and educational efficiency: Temptations of modern education. New Educational Review, 39(1), 153–164. https://doi.org/10.15804/tner.2015.39.1.13

Núñez-Peña, M. I., Suárez-Pellicioni, M., & Bono, R. (2013). Effects of math anxiety on student success in higher education. International Journal of Educational Research, 58, 36–43. https://doi.org/10.1016/j.ijer.2012.12.004

Palacios, A., Hidalgo, S., Maroto, A., & Ortega, T. (2013). Causes and consequences of mathematics anxiety: A structural equation model. Enseñanza de las Ciencias, 31(2), 93–111. https://doi.org/10.5565/rev/ec/v31n2.891

Peregrina Nievas, P., & Gallardo-Montes, C. D. P. (2023). The neuroeducation training of students in the degrees of early childhood and primary education: A content analysis of public universities in Andalusia. Education Sciences, 13(10), 1006. https://doi.org/10.3390/educsci13101006

Pizzie, R. G., & Kraemer, D. J. M. (2023). Strategies for remediating the impact of math anxiety on high school math performance. npj Science of Learning, 8(1), 44. https://doi.org/10.1038/s41539-023-00188-5

Plerou, A., & Vlamos, P. (2016). Evaluation of mathematical cognitive functions with the use of EEG brain imaging. In Special and Gifted Education: Concepts, Methodologies, Tools, and Applications (pp. 2165–2186). IGI Global. https://doi.org/10.4018/978-1-5225-0034-6.ch094

Richaud, M. C., Filippetti, V. A., & Mesurado, B. (2018). Bridging cognitive, affective, and social neuroscience with education. In Psychiatry and Neuroscience Update (Vol. III, pp. 287–297). Springer. https://doi.org/10.1007/978-3-319-95360-1_23

Schroeder, P. A., Dresler, T., Bahnmueller, J., Artemenko, C., Cohen Kadosh, R., & Nuerk, H.-C. (2017). Cognitive enhancement of numerical and arithmetic capabilities: A mini-review of available transcranial electric stimulation studies. Journal of Cognitive Enhancement, 1(1), 39–47. https://doi.org/10.1007/s41465-016-0006-z

Simões, I., & da Silva, J. T. (2022). Maths anxiety: An overview of its origins, impact, and possible interventions. Revista de Estudios e Investigación en Psicología y Educación, 9(1), 19–38. https://doi.org/10.17979/reipe.2022.9.1.8691

Stella, M. (2022). Network psychometrics and cognitive network science open new ways for understanding math anxiety as a complex system. Journal of Complex Networks, 10(3), cnac022. https://doi.org/10.1093/comnet/cnac022

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Published

2023-08-31

How to Cite

Adji Seputro, & Ahmad Dwi Nurdiyanto. (2023). Psikologi Kognitif dalam Pembelajaran Matematika Modern untuk Mengatasi Math Anxiety dan Meningkatkan Kreativitas Numerik Generasi Digital. Journal of New Trends in Sciences, 1(3), 34–44. https://doi.org/10.59031/jnts.v1i3.776