Kyoto University · 컴퓨터과학
알버트 C. 박사의 연구실은 인공지능 기반 형성평가 및 정밀학습을 핵심으로 하여, 학생의 학습 행동, 지속성, 자기평가 행동 등에 대한 데이터 기반 분석을 통해 교육 성과를 향상시키는 데 초점을 맞추고 있습니다. 특히 컴퓨터 적응형 평가(CAT), 테스트 효과, 텍스트 마킹 분석, 챗지피티 기반 지능형 튜터링 시스템(PyTutor) 등을 활용한 실증 연구를 통해 학습자 중심의 정밀교육 시스템을 구축하고자 합니다. 다양한 교육 환경(예:会계 수업, 프로그래밍 교육)에서의 실시간 피드백과 지속적인 자기점검을 가능하게 하는 AI 기반 평가 기술 개발이 핵심 과제입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Previous studies have found that the frequency and the regularity of taking the self-assessment are positively correlated with learning performance. However, as artificial intelligence is widely used for self-assessment in various educational contexts, numerous behaviors have been identified, including nonstandard behaviors that can negatively impact learning. Therefore, more analysis regarding students' self-assessment behaviors in different contexts and their influence on learning is required.
Computerized adaptive testing (CAT) can effectively facilitate student assessment by dynamically selecting questions on the basis of learner knowledge and item difficulty. However, most CAT models are designed for one-time evaluation rather than improving learning through formative assessment. Since students cannot remember everything, encouraging them to repeatedly evaluate their knowledge state and identify their weaknesses is critical when developing an adaptive formative assessment system in
Programming is regarded as a focal point in the current rapidly evolving educational landscape. To aid learning in this domain, we developed PyTutor, an innovative intelligent tutoring system (ITS) that is designed to assist beginners in Python programming. PyTutor utilizes the ChatGPT model to offer continuous guidance, problem-solving hints, and detailed code explanations. It features a structured hint system for each question, covering pseudocode, cloze, basic, and advanced coding solutions.
Reviewing learned knowledge is critical in the learning process. Testing the learning content instead of restudying, which is known as the testing effect, has been demonstrated to be an effective review strategy. However, education research recommends that instructors generate practice tests, but this burdens teachers and may also hinder teaching quality. To resolve this issue, the current study applied a modern artificial intelligence technique (BERT) to automate the generation of tests and eva
Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students’ learning process. Text marking is an essential learning skill in reading. In this study, we proposed a model that leverages the state-of-the-art text summarization technique, Bidirectional Encoder Representations from Transformers (BERT)
Abstract Persistence represents a crucial trait in learning. A lack of persistence prevents learners from fully mastering their current skills and makes it difficult for them to acquire new skills. It further hinders the administration of effective interventions by learning systems. Although most studies have focused on identifying non-persistence and unproductive persistence behaviors, few have attempted to model students’ persistence propensity in learning. In the present study, we evaluated s
28th International Conference on Computers in Education, 23-27 November 2020, Web conference.
28th International Conference on Computers in Education, 23-27 November 2020, Web conference.
