Mixed Learning Models and IoT Devices: Effectively Increasing Competence and Training Independent Learning Students in Unnormal Situations

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Abstract

—Abnormal situations often occur, such as natural disasters and COVID-19. Educational institutions struggle to regulate learning. The web programming course aims to shape students into website programmers. Independence in learning is needed so that competence is obtained. Students are not enough to rely on learning from the lecturer. This study aims to analyze the combination of the Inquiry-Based Learning model with IoT devices based on Android mobile. As a supporter, an application is built with a mobile programming language. This type of research is quasi-experimental. Calculations using SPSS 23.0. An experimental class learns to use the Inquiry-Based Learning model with IoT devices, and a control class learns with various media. The research subjects were 60 students of Information Management. The study found differences in students' competence and learning independence in those who learned to use the inquiry-based learning model with IoT devices compared to those who studied with various media. The test results showed a higher increase in the experimental class. The experimental class's value is 14.40 for a gain of 7.5. The sig. value is .000, and the average gain is .83. Control class score is 11.87, an increase of 5.1, sig. value is .000, and the average gain is .53. Applying the inquirybased learning model with IoT devices has also proven to be effective as a model and learning media in abnormal situations and reinforced by the average gain of the experimental class, which is greater than the control class. Future research could use different methods to determine what methods are most effective.

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Purba, R. A., Simarmata, J., Limbong, T., & Damanik, R. (2024). Mixed Learning Models and IoT Devices: Effectively Increasing Competence and Training Independent Learning Students in Unnormal Situations. International Journal on Informatics Visualization, 8(4), 2502–2510. https://doi.org/10.62527/joiv.8.4.2553

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