Kyoto University · 컴퓨터과학
브래던 플라너건 교수의 연구실은 학습 분석(Learning Analytics)과 인공지능 기반 교육 데이터 분석을 중심으로, 학생의 학습 행동, 성취도, 참여도를 정량적으로 분석하고 예측하는 데에 초점을 맞추고 있습니다. 특히 학습 로그, 디지털 교재 사용 패턴, 성과 예측 모델링을 바탕으로 개인화된 피드백 및 협업 그룹 구성 전략을 개발하고 있으며, 데이터 프라이버시 문제를 해결하기 위한 합성 데이터 생성 기법의 응용에도 주력하고 있습니다. 연구는 실제 교육 현장의 데이터 기반 의사결정과 학습자 맞춤형 지원을 목표로 하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
In recent years, learning analytics has become a hot topic with many institutes deploying learning management systems and learning analytics tools. In this paper, we introduce learning analytics platforms that have been established in two top national Japanese universities. These initiatives are part of a broader research project into creating wide-reaching learning analytics frameworks. The aim of the project is to support education and learning through research into educational big data accumu
Abstract Digitized learning materials are a core part of modern education, and analysis of the use can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open-book test from the perspective of
While data privacy is a key aspect of Learning Analytics, it often creates difficulty when promoting research into underexplored contexts as it limits data sharing. To overcome this problem, the generation of synthetic data has been proposed and discussed within the LA community. However, there has been little work that has explored the use of synthetic data in real-world situations. This research examines the effectiveness of using synthetic data for training academic performance prediction mod
In recent years, machine learning of increasing complexity is being applied to problems in education. However, there is an increasing call for transparency and understanding into how the results of complex models are derived, leading to explainable AI gaining attention. The application of machine learning to automated group formation for collaborative work from learning system logs and other data has been progressing. Building on previous research in this field, we propose a group formation meth
[The 9th International Learning Analytics and Knowledge (LAK) Conference] March 4-8, 2019, Tempe, Arizona, USA
This article contends that the profile of a foreign language learner can contain valuable information about possible problems they will face during the learning process, and could be used to help personalize feedback. A particularly important attribute of a foreign language learner is their native language background as it defines their known language knowledge. Native language identification serves two purposes: to classify a learners' unknown native language; and to identify characteristic fea
In order to overcome mistakes, learners need feedback to prompt reflection on their errors. This is a particularly important issue in education systems as the system effectiveness in finding errors or mistakes could have an impact on learning. Finding errors is essential to providing appropriate guidance in order for learners to overcome their flaws. Traditionally the task of finding errors in writing takes time and effort. The authors of this paper have a long-term research goal of creating too
This research introduces the self-explanation-based automated feedback (SEAF) system, aimed at alleviating the teaching burden through real-time, automated feedback while aligning with SDG 4’s sustainability goals for quality education. The system specifically targets the enhancement of self-explanation, a proven but challenging cognitive strategy that bolsters both conceptual and procedural knowledge. Utilizing a triad of core feedback mechanisms—customized messages, quality assessments, and pe
In the realm of mathematics education, self-explanation stands as a crucial learning mechanism, allowing learners to articulate their comprehension of intricate mathematical concepts and strategies. As digital learning platforms grow in prominence, there are mounting opportunities to collect and utilize mathematical self-explanations. However, these opportunities are met with challenges in automated evaluation. Automatic scoring of mathematical self-explanations is crucial for preprocessing task
