Main Article Content

Mohammad Nasrul
Achmad Noor Fatirul
Djoko Adi Walujo

Abstract

The purpose of this study was to determine the test results: (1) Knowing the differences in the use of the internet on students' mathematical reasoning abilities, (2) Knowing the differences in learning motivation on students' mathematical reasoning abilities, and (3) Knowing the interaction between internet use and learning motivation on students' mathematical reasoning abilities. This experimental research was carried out using a 2x2 factorial design. The results of the calculation show that: (a) The use of internet media can increase students' ability to understand more deeply and be inspired by actual events, (b) The use of internet media students will be more motivated to understand the content of messages that can resemble facts or actual events, and (c) ) the effect in the interaction to improve students' understanding of the subject matter between the use of learning media or students' learning motivation. Based on the results of this study, it was concluded that by using Internet Media Utilization. From the results of the analysis showed that between learning media and learning motivation both contributed jointly between learning media and learning motivation, meaning that the effect of interaction between learning media and learning motivation could increase students' ability to understand both in terms of the use of internet media and without using internet media and both students who have high motivation and those who have low motivation.

Downloads

Download data is not yet available.

Article Details

How to Cite
Nasrul, M., Fatirul, A. N. and Walujo, D. A. (2022) “The Effect of Internet Media Utilization and Learning Motivation on Mathematical Reasoning Ability”, Jurnal Mantik, 6(3), pp. 3167-3174. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/3078 (Accessed: 9May2026).
References
Dawood, O., Rea, J., Decker, N., Kelley, T., & Cianciolo, A. T. (2021). Problem-Based Learning About Problem-Based Learning: Lessons Learned from a Student-Led Initiative to Improve Tutor Group Interaction. Medical Science Educator, 31(2). https://doi.org/10.1007/s40670-021-01259-1
Edwards, N., King, J., Pfeffer, S., Lovric, E., & Watling, H. (2021). Teaching disability using problem-based learning in the international context: utility for social work. European Journal of Social Work. https://doi.org/10.1080/13691457.2021.1954890
Eyal, L., & Gil, E. (2022). Hybrid Learning Spaces — A Three-Fold Evolving Perspective. https://doi.org/10.1007/978-3-030-88520-5_2
Ezaldeen, H., Misra, R., Bisoy, S. K., Alatrash, R., & Priyadarshini, R. (2022). A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis. Journal of Web Semantics, 72. https://doi.org/10.1016/j.websem.2021.100700
Gunawan, W., Mastoah, I., Septantiningtyas, N., Wiyarno, Y., & Atiqoh, A. (2022). Pengaruh Strategi PBL dan Motivasi Belajar terhadap Hasil Belajar Bahasa Inggris. Jurnal Basicedu, 6(4), 6023–6029. https://doi.org/10.31004/basicedu.v6i4.3122
Huang, J., & Zhou, Q. (2022). Partitioned hybrid learning of Bayesian network structures. Machine Learning. https://doi.org/10.1007/s10994-022-06145-4
Kaewsrisai, K. (2022). Active blended learning management in music subject based on the hybrid learning framework for primary 4 students at Ban Nong Du School, Thawat Buri District, Roi Et Province. Linguistics and Culture Review, 6. https://doi.org/10.21744/lingcure.v6ns2.2073
Khan, M., Naeem, M. R., Al-Ammar, E. A., Ko, W., Vettikalladi, H., & Ahmad, I. (2022). Power Forecasting of Regional Wind Farms via Variational Auto-Encoder and Deep Hybrid Transfer Learning. Electronics (Switzerland), 11(2). https://doi.org/10.3390/electronics11020206
Kim, S. H., Park, D. Y., & Lee, K. H. (2022). Hybrid Deep Reinforcement Learning for Pairs Trading. Applied Sciences (Switzerland), 12(3). https://doi.org/10.3390/app12030944
Morgado, M., Mendes, J. J., & Proença, L. (2021). Online problem-based learning in clinical dental education: Students’ self-perception and motivation. Healthcare (Switzerland), 9(4). https://doi.org/10.3390/healthcare9040420
Salmanpour, M. R., Shamsaei, M., Hajianfar, G., Soltanian-Zadeh, H., & Rahmim, A. (2022). Longitudinal clustering analysis and prediction of Parkinson’s disease progression using radiomics and hybrid machine learning. Quantitative Imaging in Medicine and Surgery, 12(2). https://doi.org/10.21037/qims-21-425
Sengupta, D., Ali, S. N., Bhattacharya, A., Mustafi, J., Mukhopadhyay, A., & Sengupta, K. (2022). A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology. PLoS ONE, 17(1 January). https://doi.org/10.1371/journal.pone.0261181
Xu, B., Guo, F., Xing, L., Wang, Y., & Zhang, W. A. (2022). Accelerated and Adaptive Power Scheduling for More Electric Aircraft via Hybrid Learning. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/TIE.2022.3150107.