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.
<span style="font-size: 10.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;">The purpose of the research is to identify the risk of dropping out in tertiary students with an application. The components of the research goal aim (1) to develop the students’ achievement prediction model and (2) to construct a prototype application for the predictions of the tertiary students dropping out. <a name="_Hlk71439748"></a>The research tools consisted of three parts, (1) tool for developing predictive prototypes uses a tool called the CRISP-DM process with Decision Tree Classification, Feature Selection methods, Confusion Matrix performance, Cross-Validation methods, Accuracy, Precision and Recall measurements, (2) tool for application development used the SDLC with V-method, and (3) tool to assess application satisfaction used questionnaires and statistical analysis. Data sample were collected from 401 students enrolled in the Business Computer Program at the School of Information and Communication Technology, University of Phayao during the academic year 2012-2016. The results showed that the prediction model had a very high percentage of accuracy (82.29%). The prototype test results with the data gathered had a very high score level (84.04%; correct 337 out of 401 training examples). An overview of the underlying application with the utmost integrity by the researchers planned to put the application to the test in the first semester of the academic year 2021 at the School of Information Technology and Communication, University of Phayao. For future research, the researchers plan to create a mobile application for mentors in the University of Phayao to monitor learner on both Android and iOS systems.</span>
This research presents a chatbot application to provide educational information for university students. There are three objectives: 1) to study the problem of providing information to university students with chatbots, 2) to develop a model and construct a chatbot to predict the interest of university students, and 3) to assess the satisfaction of the information provided by the chatbot application. The research datasets were the conversations from the Messenger Facebook Page of the Faculty of Information Technology, Rajabhat Maha Sarakham University, during the academic year 2020-2021. In total, there were 1,094 transactions used in this research work. Furthermore, data mining and machine learning techniques, including CRISP-DM, Naïve Bayes, K-Nearest Neighbors, and Neural Network, were used as the research tools. The cross-validation and confusion matrix techniques were used to test the model performance. Moreover, a questionnaire was the application satisfaction assessment tool for 30 respondents. As a result, it showed that the developed model provided high-level results, which are 88.73% accuracy and an average of 3.97 for application satisfaction. In the future, the researchers plan to apply the results for the next academic year and expand into other academic programs.
Youth depression is a silent threat threatening students around the world. Therefore, the purpose of the research was (1) to cluster students' risk of adolescent depression during COVID-19 and (2) to compare the predictive cluster to the standard depression rating scale. The samples used in the analysis were 687 samples from three institutions with two levels of education. It was 470 samples (68.41%) from Rajabhat Maha Sarakham University (RMU) at the university level, 33 samples (4.80%) from Mahasarakham University (MSU) at the university level, and 184 samples (26.78%) from Phadungnaree School (PS) at the high school level. The research tool is a data mining analysis technique. It consists of k-Means clustering and k-Determination. The results of the data mining analysis showed that the cluster analyzed by data mining was a little dissimilar from the normal process. It discovered that there were 120 different data samples (17.47%). Therefore, it can be concluded that the models studied by the researchers are consistent with the 5th Diagnostic and Statistical Manual of Mental Disorders (Dsm5). For future work, the researchers aim to develop forecasting prototypes and develop mobile applications to facilitate further work.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.