In response to pedagogical challenges in higher education, blended learning has become a prevalent practice in colleges and universities. Flipped learning (FL) represents a newly emerging form of blended learning, where students individually watch online lectures prior to class and then engage in classroom learning activities interacting with peers and instructors. Although the conceptual framework of FL may be intuitively appealing, its design and implementation involve considerable intricacy. The purpose of this study was to develop a FL design model for higher education that could systematically guide instructors or designers in creating an appropriate blend of individualized online lectures and collaborative face-to-face learning activities. Using an established method for model development research, a theoretically constructed initial model was iteratively improved and underwent internal and external validation through model usability tests, expert review, and field evaluation. The implementation of an algebra class in a Korean university designed by the final model resulted in significant increases in the maturity of mathematical views, quality of reflections, and satisfactions of students. Features of the model are discussed, along with theoretical and practical implications and suggestions for further research.

This paper explores the potential of Large Language Models (LLMs), specifically GPT-4, to enhance the precision and effectiveness of Automated Assessment Systems (AAS) for open-ended mathematics problems. While LLMs have demonstrated transformative capabilities across various disciplines, their application in AAS, particularly for mathematical logic and open-ended problem-solving, still needs to be explored. Our research addresses this gap by developing and critically evaluating a GPT-4-based AAS. We analyzed 4,180 responses to open-ended mathematics questions from 380 6th-grade primary school students. Three human experts and the GPT-4 model independently assessed these responses using a pre-established rubric. Our findings reveal high consistency between human and GPT-4 assessments in most instances, highlighting the potential of integrating GPT-4 into AAS. We categorized scoring discrepancies from GPT-4 and human raters by error type and identified specific mathematical content areas where automated assessment faced limitations. We evaluated two strategies to enhance GPT-4’s assessment capabilities: (1) using elaborate prompts and (2) implementing advanced prompt engineering techniques such as Chain-of-thought, Self-consistency, and Tree-of-thought. While comprehensive prompts significantly improved assessment quality, applying advanced prompt engineering techniques directly produced suboptimal results, indicating a need for further refinement. This study contributes to the emerging body of research evaluating GPT-4 in the context of AAS for open-ended mathematics problems, shedding light on both the strengths and limitations of this approach. Our findings provide valuable insights and a foundation for future research to refine the integration of LLMs in AAS, particularly in mathematics education.

This study examined three cases in which an online support system was utilized to enhance creative problem solving (CPS) in a higher education setting. This study aimed to review instructional design strategies that allow the integration of an online support system into a university course. Creativity, which is defined as the ability to generate a new and useful idea, is a key competency for students to possess as it can assist them in taking effective actions in the future society. Considered to be a representative model for developing creative thinking abilities, the CPS model is known to effectively stimulate learners’ divergent and convergent thinking while supporting a systematic approach to find creative solutions and enabling meaningful learning by utilizing its supporting tools. Considering the advantages derived from using the Online Support System for CPS, three cases across diverse colleges were reviewed: the College of Education, Fine Arts, and Engineering. Through comprehensive analysis, this study proposes major design strategies to achieve the integrated application of the Online Support System to university courses that encourage problem solving abilities. The study also suggests future research directions for CPS using the Online Support System.

This preliminary study explores how GPT-4 Vision (GPT-4V) technology can be integrated into teacher analytics through observational assessment, aiming to improve reflective teaching practice. Our study develops a Video-based Automatic Assessment System (VidAAS) powered by GPT-4V. This approach uses Generative Artificial Intelligence (GenAI) to provide detailed insights into classroom dynamics. Our study encompasses various methods with multiple steps: a comprehensive literature review, prototype development of the VidAAS, and usability testing with in-service teachers. The study findings reveal that VidAAS demonstrates high accuracy in evaluating skills in the behavioral (psychomotor) domain and offers comprehensive explanations for each assessment. While showing promise in these areas, the system also indicates potential for further enhancement in processing speed and refinement in assessing cognitive and affective domains. We discuss how VidAAS supports teachers’ reflection-in-action and reflection-on-action, emphasizing the need to balance AI-driven insights and human judgment. Our study findings also guide future research avenues for VidAAS design, implementation, and integration in teacher analytics, underscoring GPT-4V’s potential for real-time, scalable feedback and a deeper classroom understanding.