Measuring interest and talent in determining learning using the quadrant model in the learning process in a 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 a smart classroom

Irfan Santiko, Universitas Amikom Purwokerto, Universitas Diponegoro, 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. Several studies have demonstrated that, because of the problem of technological advances, the effectiveness of the anticipated results has not been fully achieved. While there are technology benefits, some scholars link them 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 its appropriateness. Further research recommendations include a framework to measure interests and talents early, aiding admissions, curriculum, resources, methods, and learning media development.

Keywords

Educational technology; Learning evaluation; Smart classroom; Prediction achievement

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