Analysis of AI Precision Education Strategy for Small Private Online Courses

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.

The application and influence of "Small Private Online Course" based on flipped classroom teaching model in the course of fundamental operations in surgery in China

To investigate the effect of "Small Private Online Course" (SPOC) based on flipped classroom teaching model on the students in the course of fundamental operations in surgery. A prospective study. 8-year program students (juniors) majored in clinical medicine in Navy medical university. The mastery of theoretical knowledge and operational skill of the students, the comparison of final test examination score between traditional teaching method and "SPOC + flipped classroom" model and the feedback completed by students. Our study found that SPOC + flipped classroom could significantly increase the efficacy of the class and enhance the ability of the students compared with the traditional method. The new teaching model could have a positive influence for medical students on their basic knowledge and operational skill.

Precision Medical Education

Medical schools and residency programs are increasingly incorporating personalization of content, pathways, and assessments to align with a competency-based model. Yet, such efforts face challenges involving large amounts of data, sometimes struggling to deliver insights in a timely fashion for trainees, coaches, and programs. In this article, the authors argue that the emerging paradigm of precision medical education (PME) may ameliorate some of these challenges. However, PME lacks a widely accepted definition and a shared model of guiding principles and capacities, limiting widespread adoption. The authors propose defining PME as a systematic approach that integrates longitudinal data and analytics to drive precise educational interventions that address each individual learner's needs and goals in a continuous, timely, and cyclical fashion, ultimately improving meaningful educational, clinical, or system outcomes. Borrowing from precision medicine, they offer an adapted shared framework. In the P4 medical education framework, PME should (1) take a proactive approach to acquiring and using trainee data; (2) generate timely personalized insights through precision analytics (including artificial intelligence and decision-support tools); (3) design precision educational interventions (learning, assessment, coaching, pathways) in a participatory fashion, with trainees at the center as co-producers; and (4) ensure interventions are predictive of meaningful educational, professional, or clinical outcomes. Implementing PME will require new foundational capacities: flexible educational pathways and programs responsive to PME-guided dynamic and competency-based progression; comprehensive longitudinal data on trainees linked to educational and clinical outcomes; shared development of requisite technologies and analytics to effect educational decision-making; and a culture that embraces a precision approach, with research to gather validity evidence for this approach and development efforts targeting new skills needed by learners, coaches, and educational leaders. Anticipating pitfalls in the use of this approach will be important, as will ensuring it deepens, rather than replaces, the interaction of trainees and their coaches.

Visualizing Commognitive Responsibility Shift in Collaborative Problem-Solving During Computer-Supported One-to-One Math Tutoring

The aim of this study is to use a commognitive responsibility framework to visualize responsibility shift in collaborative problem solving (CPS) during computer-supported one-to-one tutoring. Commognitive responsibility shift means that individuals' cognitive responsibility shift can be reflected by the discourse in communication. For our sample, we chose a 15-year-old Chinese boy and his mathematics teacher with 6 years of teaching experience, both of whom have experienced computer-supported learning and teaching mathematics, respectively. We collected four tutoring videos (each 60-90 min; in total, more than 330 min) online, and a 45-min interview video from the teacher. We found that the third type of commognitive responsibility shift in both the teacher's and student's CPS behavior online is not only teacher-student comparison but also alternating-led, which includes teacher-student-led (TS) and student-teacher-led (ST).