Forecasting Graduation Schedule Model of Higher Education Learners using Feature Selection Techniques

The biggest concern for learners in the 21st century is that graduation does not fit their educational plan. This research aims to study factors affecting students' academic achievement in the Faculty of Education during the COVID-19 crisis in Thailand. The data was on the academic achievement of 90 students from the Bachelor of Art Program in Educational Technology and Communications at the Faculty of Education, Naresuan University, Phitsanulok, Thailand. The research process was analyzed using data mining techniques, including CRISP-DM procedures, decision tree algorithm, forward selection analysis, cross-validation techniques, and confusion matrix performance. This research found that the course 001211 Fundamental English was the most significant subject for delayed graduation, where the developed model has a very high level of accuracy (89.00%). Researchers can use such a model to create effective planning strategies for preventing graduation failures. © 2023 Seventh Sense Research Group®

Predictive Model for Clustering Learning Outcomes Affected by COVID-19 Using Ensemble Learning Techniques

The influence of COVID-19 has caused a sudden change in learning patterns. Therefore, this research studied the learning achievement modified by online learning patterns affected by COVID-19 at Rajabhat Maha Sarakham University. This research has three objectives. The first objective is to study the cluster of learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. The second objective is to develop a predictive model using machine learning and data mining technique for clustering learning outcomes affected by COVID-19. The third objective is to evaluate the predictive model for clustering learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. Data collection comprised 139 students from two courses selected by purposive sampling from the Faculty of Information Technology at the Rajabhat Maha Sarakham University during the academic year 2020-2021. Research tools include student educational information, machine learning model development, and data mining-based model performance testing. The research findings revealed the strengths of using educational data mining techniques for developing student relationships, which can effectively manage quality teaching and learning in online patterns. The model developed in the research has a high level of accuracy. Accordingly, the application of machine learning technology obviously supports and promotes learner quality development. © 2023 The Author(s). Open Access - This article is under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Contextual Academic Achievement Analysis Affected by COVID-19 Pandemic of Higher Education Learners in Thailand Using Machine Learning Techniques

This research aims to present the context and impact that the Thai education system has experienced from the COVID-19 pandemic in Thailand. It consists of three research objectives: (1) to study the context of the impact on academic achievement from the COVID-19 pandemic in higher education, (2) to develop a model for clustering the academic achievement of students in higher education during the COVID-19 pandemic in Thailand, and (3) to compare the academic achievement of students in higher education during the COVID-19 pandemic in Thailand. The research data were 43,230 transactions (1961 students) from four educational programs at the Faculty of Business Administration and Information Technology, Rajamangala University of Technology Tawan-ok, the results showed that the context of the impact on the education system among tertiary learners has decreased in the number of graduates during the COVID-19 pandemic. However, students graduating during the COVID-19 pandemic in Thailand had higher levels of academic achievement than those in normal circumstances. The findings reflect those learners who achieved academic achievement during the COVID-19 pandemic were more persevering and tolerant than those in the traditional system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Implementation of Data Analytics and Machine Learning in Thailand Education Sector

Since the global epidemic of the coronavirus disease 2019 (COVID-19) over the past few years, Thailand education sector has been affected by the requisites for a digitization system and distance education. This sudden change has affected the quality of learning and statistical evaluations in the long term. Consequently, data analysis and categorization in learning quality assessment are critical for predicting the number of future students and learning performance after the COVID-19 outbreak. However, vast data analytics might be applied to the education sector in many aspects. In addition, machine learning can influence the categorization of students that are useful for analyzing the performance of different educational systems. Therefore, this study reviews the perspective and usability of data analytics and machine learning that influences current situations in Thailand education sector © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.

A Predictive Model for Depression Risk in Thai Youth during COVID-19

The risk of depression in youth affects future development of the learning process. Therefore, it is important to study on preventing the risk of depression in youth. The purpose of this research was (1) to study the risk situation of youth’ depression in Thailand, and (2) to develop a model for predicting depression among youth in Thailand. The data used in the research were 1,413 samples from 9 faculties at the Rajabhat Maha Sarakham University, and Phadungnaree School at Mueang District of Maha Sarakham Province, Thailand. Research tools and procedures used were the data mining principles to analyze and develop prototype models. It includes the decision tree, naïve bayes, and artificial neural networks techniques. The results showed that the majority of the respondents had no depressive risk conditions with 1,059 samples (74.95%). However, there are still three risk groups that need to be monitored: mild level with 260 samples (18.40%) moderate level with 78 samples (5.52%), and severe level with 16 samples (1.13%). The observations were taken to develop a prototype model. It was found that the highest accuracy model was the artificial neural networks technique with an accuracy value of 97.88%. Based on such success, the researchers hope to develop a future application in preventing youth’ risk depression. © 2022 J. Adv. Inf. Technol.

Learning Management Impacted with COVID-19 at Higher Education in Thailand Learning Strategies for Lifelong Learning

The COVID-19 situation has a serious global impact on the education system. Thus, the research purpose is aimed to construct the models of online learning strategies for Thailand students on learning management in the coronavirus 2019 scenario. The research methodology was conducted according to the process of the cross-industry standard process for data mining, known as the CRISP-DM model for developing the best research. The data collected 487 students from the University of Phayao (UP), and Rajabhat Maha Sarakham University (RMU) from the 1st semester in academic year 2020. The collected data has been agreed upon in accordance with research ethics. The results of the study revealed that the factors influencing the model consisted of 8 out of 38 attributes, with a high predictive accuracy (85.14%). Finally, the researchers can plan for the management of teaching and learning for students at the University of Phayao to solve the Coronavirus 2019 Scenario in the academic year 2021 and the future.