Kyoto University · Computer Science
Professor Albert C. M. Yang's research lab specializes in leveraging artificial intelligence, machine learning, and learning analytics to advance precision education and formative assessment in educational settings. The lab focuses on developing intelligent tutoring systems, adaptive assessment frameworks, and AI-driven tools that enhance student learning through personalized feedback, automated test generation, and behavioral analysis. Key research directions include modeling student persistence and self-assessment behaviors, integrating large language models like GPT and BERT into educational applications, and applying cognitive theories such as the learning memory cycle to improve learning outcomes. The lab emphasizes practical, data-informed solutions to support diverse learners, especially in STEM and programming education.
Figures are computed from collected data and may differ slightly.
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.
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