Measuring Interest and Talent in Determining Learning Using the Quadrant Model in the Learning Process in Smart Classroom

Irfan Santiko; Tri Retnaningsih Soeprobowati; Bayu Surarso; Imam Tahyudin

doi:10.21831/jitp.v12i1.73585

Measuring Interest and Talent in Determining Learning Using the Quadrant Model in the Learning Process in Smart Classroom

Irfan Santiko, Universitas Amikom Purwokerto, Indonesia
Tri Retnaningsih Soeprobowati, Universitas Diponegoro, Indonesia
Bayu Surarso, Universitas Diponegoro, Indonesia
Imam Tahyudin, Universitas Amikom Purwokerto, Indonesia

10.21831/jitp.v12i1.73585

Abstract

Naturally, the learning process in smart classrooms is greatly impacted by the trend of individual learning, now known as personalized learning. Actually, a number of studies have demonstrated that, in relation to the problem of technological advances, the effectiveness of the anticipated results has not been fully achieved. While there are benefits to technology, some scholars link it to issues. This study aims to demonstrate it by evaluating learning interests and talents. A sample of at least 1000 students from 419 universities participated in the questionnaire experiment. Each of the three questionnaire domains—affective, cognitive, and psychomotor—was examined using ANOVA. The coefficient test uses two variables: interest and talent. With an ANOVA P-value of 0.021 for psychomotor and 0.031 for affective and cognitive, the three domains demonstrated a statistically significant connection. The coefficients of interest and talent, which average between 1 and 0.05 for P emotional and cognitive interest (0.054) and P talent (0.023) and between 0.027 and 0.055 for P psychomotor interest and P talent, demonstrate the significant values of both factors. The developed interest and talent measuring model can be used to forecast learning outcomes based on these findings. In addition to information technology, the results of this interest and talent measuring design can be utilized to define and evaluate the learning process, including appropriateness.

Keywords

Educational technology; Learning evaluation; Smart classroom; Prediction achievement

References

Achmad, R. K., & Mulyati, Y. (2023). The perceptions of high school teachers and students towards digital interest and literacy. Jurnal Inovasi Teknologi Pendidikan, 10(3), 283–297. https://doi.org/10.21831/jitp.v10i3.58804

Akbari, O., & Sahibzada, J. (2020). Students’ self-confidence and its impacts on their learning process. American International Journal of Social Science Research, 5(1), 1–15. https://doi.org/10.46281/aijssr.v5i1.462

Chen, X., Zou, D., Xie, H., & Wang, F. L. (2021). Past, present, and future of smart learning: a topic-based bibliometric analysis. International Journal of Educational Technology in Higher Education, 18(1), 2. https://doi.org/10.1186/s41239-020-00239-6

Désiron, J. C., Schmitz, M.-L., & Petko, D. (2024). Teachers as Creators of Digital Multimedia Learning Materials: Are they Aligned with Multimedia Learning Principles. Technology, Knowledge, and Learning, 29(4), 1–17. https://doi.org/10.1007/s10758-024-09770-1

Durmuşçelebi, M. (2018). Examination of students’ academic motivation, research concerns and research competency levels during the education period. Universal Journal of Educational Research, 6(10), 2115–2124. https://doi.org/10.13189/ujer.2018.061008

Ekantiningsih, P. D., & Sukirman, D. (2023). Trends of education and training teacher competency in information and communication technology. Jurnal Inovasi Teknologi Pendidikan, 10(1), 87–105. https://doi.org/10.21831/jitp.v10i1.52630

El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education, 18(1), 53. https://doi.org/10.1186/s41239-021-00289-4

Gao, X., Li, P., Shen, J., & Sun, H. (2020). Reviewing assessment of student learning in interdisciplinary STEM education. International Journal of STEM Education, 7, 1–14. https://doi.org/10.1186/s40594-020-00225-4

Georgiou, Y., & Kyza, E. A. (2018). Relations between student motivation, immersion and learning outcomes in location-based augmented reality settings. Computers in Human Behavior, 89, 173–181. https://doi.org/10.1016/j.chb.2018.08.011

Harefa, D., Sarumaha, M., Telaumbanua, K., Telaumbanua, T., Laia, B., & Hulu, F. (2023). Relationship student learning interest to the learning outcomes of natural sciences. International Journal of Educational Research &Amp, 240–246. https://doi.org/10.51601/ijersc.v4i2.614

Hell, M., Knežević, A., & Babić, Z. (2021). Multicriteria analysis of the quality of teaching process in higher education: How to evaluate implementation of critical thinking. Croatian Operational Research Review, 12(1), 15–26. https://doi.org/10.17535/crorr.2021.0002

Hsu, C. L. (2022). Applying cognitive evaluation theory to analyze the impact of gamification mechanics on user engagement in resource recycling. Information & Management, 59(2), 103602. https://doi.org/10.1016/J.IM.2022.103602

Jiao, Y. P., Liu, P., & Qi, P. Q. (2022). Quality Evaluation Method for Settlement Data Matching Based on Grey Correlation Analysis. Journal of Physics, 1–7. https://doi.org/10.1088/1742-6596/2181/1/012034

Li, M. (2024). Integrating Models in Education: Evaluating Strategies and Enhancing Student Learning Through Advanced Analytical Methods. Lecture Notes in Education Psychology and Public Media, 51(1), 29–35. https://doi.org/10.54254/2753-7048/51/20240560

Lo, C. K., & Hew, K. F. (2020). A comparison of flipped learning with gamification, traditional learning, and online independent study: the effects on students’ mathematics achievement and cognitive engagement. Interactive Learning Environments, 28(4), 464–481. https://doi.org/10.1080/10494820.2018.1541910

Mahler, D., Großschedl, J., & Harms, U. (2018). Does motivation matter?--The relationship between teachers’ self-efficacy and enthusiasm and students’ performance. PloS One, 13(11), 145–156. https://doi.org/10.1371/journal.pone.0207252

Murillo-Zamorano, L. R., López Sánchez, J. Á., Godoy-Caballero, A. L., & Bueno Muñoz, C. (2021). Gamification and active learning in higher education: is it possible to match digital society, academia and students’ interests? International Journal of Educational Technology in Higher Education, 18, 1–27. https://doi.org/10.1186/s41239-021-00249-y

Nguyen, V. A. (2022). A model to detect student’s learning styles in the blended learning course. 46–51. https://doi.org/10.1145/3545862.3545870

Oubibi, M., Zhao, W., Wang, Y., Zhou, Y., Jiang, Q., Li, Y., Xu, X., & Qiao, L. (2022). Advances in Research on Technological, Pedagogical, Didactical, and Social Competencies of Preservice TCFL Teachers. Sustainability (Switzerland), 14(4). https://doi.org/10.3390/su14042045

Putri, N. R. S., & Meilana, S. F. (2023). Effect of Experimental Learning Methods on Students’ Cognitive Abilities in Science Learning. Jurnal Penelitian Pendidikan IPA, 9(9), 7539–7546. https://doi.org/10.29303/jppipa.v9i9.4602

Reis, S. M., Renzulli, S. J., & Renzulli, J. S. (2021). Enrichment and gifted education pedagogy to develop talents, gifts, and creative productivity. Education Sciences, 11(10), 615–625. https://doi.org/10.3390/educsci11100615

Santiko, I., Soeprobowati, T. R., & Surarso, B. (2024). Experiments to Review Literature on Topic Trends in Technology Development in Educational Information Systems. 94–99. https://doi.org/10.1109/icitisee58992.2023.10404276

Santiko, I., Soeprobowati, T. R., Surarso, B., & Tahyudin, I. (2025). Traditional-Enhance-Mobile-Ubiquitous-Smart: Model Innovation in Higher Education Learning Style Classification Using Multidimensional and Machine Learning Methods. Journal of Applied Data Sciences, 6(1), 753–772. https://doi.org/https://doi.org/10.47738/jads.v6i1.598

Santiko, I., Wijaya, A. B., & Hamdi, A. (2022). Smart Campus Evaluation Monitoring Model Using Rainbow Framework Evaluation and Higher Education Quality Assurance Approach. Journal of Information Systems and Informatics, 4(2), 336–348. https://doi.org/10.51519/journalisi.v4i2.258

Shen, C., & Ho, J. (2020). Technology-enhanced learning in higher education: A bibliometric analysis with latent semantic approach. Computers in Human Behavior, 104(6), 106–117. https://doi.org/10.1016/j.chb.2019.106177

Sutarto, S., Sari, D. P., & Fathurrochman, I. (2020). Teacher strategies in online learning to increase students’ interest in learning during COVID-19 pandemic. Jurnal Konseling Dan Pendidikan, 8(3), 129–137. https://doi.org/10.29210/147800

Tussa, H., Yana, I. N., Satria, S., Pendit, D., & Kunci, K. (2024). Implementing Experimental Learning Methods on Student Learning Motivation. 8(2), 75–82. https://doi.org/10.23887/jpai.v8i2.79404

Yeşilyurt, E., Ulaş, A. H., & Akan, D. (2016). Teacher self-efficacy, academic self-efficacy, and computer self-efficacy as predictors of attitude toward applying computer-supported education. Computers in Human Behavior, 64, 591–601. https://doi.org/10.1016/j.chb.2016.07.038

Yu, R., & Singh, K. (2018). Teacher support, instructional practices, student motivation, and mathematics achievement in high school. The Journal of Educational Research, 111(1), 81–94. https://doi.org/10.1080/00220671.2016.1204260

DOI: https://doi.org/10.21831/jitp.v12i1.73585