Large Language Model-based System to Provide Immediate Feedback to Students in Flipped Classroom Preparation Learning
This addresses the problem of student engagement and motivation in flipped classrooms for educators and learners, but it is incremental as it builds on existing video-watching support systems and LLM technology.
The paper tackles the challenge of providing immediate feedback to students in flipped classrooms by developing a system using large language models, specifically the ChatGPT API, to answer student questions and align responses with context, while also incorporating teacher answers as additional guides.
This paper proposes a system that uses large language models to provide immediate feedback to students in flipped classroom preparation learning. This study aimed to solve challenges in the flipped classroom model, such as ensuring that students are emotionally engaged and motivated to learn. Students often have questions about the content of lecture videos in the preparation of flipped classrooms, but it is difficult for teachers to answer them immediately. The proposed system was developed using the ChatGPT API on a video-watching support system for preparation learning that is being used in real practice. Answers from ChatGPT often do not align with the context of the student's question. Therefore, this paper also proposes a method to align the answer with the context. This paper also proposes a method to collect the teacher's answers to the students' questions and use them as additional guides for the students. This paper discusses the design and implementation of the proposed system.