A blended learning model with IoT-based technology

In 2021, the COVID-19 pandemic is still not over. Thailand is the one that is facing the second wave of new coronavirus. Schools and universities were closed, and faculties need to mostly teach with Online pedagogy, including the graduate students' courses. This study proposes to focus on the Ubiquitous area of the Blended Learning model with IoT-based to solve a problem of graduate students and their advisors by the qualitative focus-group technique. The mobile application draft was synthesized and designed to track and monitor graduate students' research activities on smartphones by built-in sensors. They should stay active along while researching the advisor’s assignments on their smartphone. Non-active periods are implied when several behaviors are detected. Virtualize dashboards are processed to report the total active learning period of students for the advisor's evaluation.Moreover, students can continually monitor their self-efficacy to improve the online learning process. Besides, this study proposes to confirm the model’s quality by twelve experts with the questionnaire. The results show average scores of Propriety, Utility, Feasibility, and Accuracy standard are 4.32, 4.41, 4.37, and 4.21, respectively. Therefore, the Blended Learning model's overall qualities with IoT-based smartphones are extremely high and proper to implement.</p

Blended Learning Model with IoT-based by Smartphone

In 2021, the COVID-19 pandemic is still not over. Thailand is the one that is facing the second wave of new coronavirus. Schools and universities were closed, and faculties need to mostly teach with Online pedagogy, including the graduate students' courses. This study proposes to focus on the Ubiquitous area of the Blended Learning model with IoT-based to solve a problem of graduate students and their advisors by the qualitative focus-group technique. The mobile application draft was synthesized and designed to track and monitor graduate students' research activities on smartphones by built-in sensors. They should stay active along while researching the advisor’s assignments on their smartphone. Non-active periods are implied when several behaviors are detected. Virtualize dashboards are processed to report the total active learning period of students for the advisor's evaluation.Moreover, students can continually monitor their self-efficacy to improve the online learning process. Besides, this study proposes to confirm the model’s quality by twelve experts with the questionnaire. The results show average scores of Propriety, Utility, Feasibility, and Accuracy standard are 4.32, 4.41, 4.37, and 4.21, respectively. Therefore, the Blended Learning model's overall qualities with IoT-based smartphones are extremely high and proper to implement