Development of an AI predictive model to categorize and predict online learning behaviors of students in Thailand.

This qualitative study has three objectives: (1) to develop a predictive AI model to categorize the online learning behavior of Thai students who study through a Thai Massive Open Online Course (MOOC); (2) to categorize students' online behavior in a Thai MOOC; and (3) to evaluate the prediction accuracy of the developed predictive AI models. Data were collected from 8000 learners enrolled in the KMUTT015 course on the Thai MOOC platform. The k-means clustering algorithm classified learners enrolled in the Thai MOOC platform based on their online learning behaviors. The decision tree algorithm was used to assess the accuracy of the AI model prediction capability. The study finds the predictive AI model successfully categorizes learners based on their learning behaviors and predicts their future online learning behaviors in the online learning environment. The k-means clustering algorithm yields three groups of learners in the Thai MOOC platform: High Active Participants (HAP), Medium Active Participants (MAP), and Lurking participants. The findings also indicate high predictive accuracy rates for each behavioral group (HAP cluster = 0.98475, Lurking participants cluster = 0.967625, and MAP cluster = 0.955375), indicating the proficiency of the AI predictive model in forecasting learner behavior. The results of this study will benefit the design of online courses that respond to the needs of students with different online learning characteristics and help them achieve a high level of academic performance.

Effectiveness of Program to Slow Progression of Chronic Kidney Disease Among T2DM With HT With CKD 3 in the Community: A Randomized Controlled Trial.

Chronic kidney disease (CKD) is a significant threat to the quality of life of the global population. Thus, slowing kidney disease progression is considered vital in the treatment of CKD patients. The study aimed to evaluate the effectiveness of a program designed to slow the progression of CKD among Type 2 diabetes mellitus patients with HT with stage 3 CKD (CKD 3) in Thailand by adopting the information-motivation-behavioral skills (IMB) model and applying technology. This study was conducted as a cluster randomized controlled trial. The program activities comprised: (i) providing disease information and teaching behaviors to slow the progression of kidney disease to patients and family members via a 2-h session; (ii) teaching practical behavioral skills for appropriate diet and exercise to patients and family members via a 2-h session; (iii) enhancing personal motivation via the Line application and motivational interviewing via telephone calls; (iv) enhancing social motivation with the support of a family member; (v) employing technology to monitor behaviors and increase patient motivation; and (vi) assessing behaviors that were not practical, exploring barriers to behavior modification, and teaching further practical behavioral skills via the Line application. The data were analyzed using percentages, means, standard deviations, chi-square tests, t-tests, and a 2-way repeated-measures analysis of variance. The results showed that after the experiment, the experimental group had significantly higher mean scores in terms of knowledge, perceived benefits, perceived severity, self-efficacy, and behaviors to slow the progression of kidney disease than the control group (P < .05). Further, the experimental group had significantly better clinical outcomes (SBP, DBP, eGFR, BUN, creatinine, albuminuria, FBS, and HbA1c) than the control group (P < .05). In summary, the findings indicate that the program improved clinical outcomes, especially kidney function, effectively slowing the progression of kidney disease.