Christopher C. Y. Yang 교수

Research has revealed the positive effects of flipped classroom approaches on students’ learning engagement and performance compared with conventional lecture-based classrooms. However, because of a lack of out-of-class learning support, many students fail to comprehensively prepare the provided lecture materials before class. One promising solution to this problem is recommendation systems in the educational area, which have been instrumental in helping learners identify useful and relevant lec

The advancement in network technology has stimulated the proliferation of online learning. Online learning platforms, such as the learning management systems (LMS) and e-book reading systems, are widely used in higher education to enhance students' reflection and planning of the learning process. Although many studies have explored the relationships between students' reading patterns and learning performances, few have examined the effects of self-regulated learning, learning strategy, and self-

E-book reader supports users to create digital learning footprints in many forms like highlighting sentences or taking memos. Nowadays, it also allows an instructor to update their e-books in the e-book reader. However, e-book users often face problems when trying to find learning footprints they made in a new version e-book. Thus, users' reading experience continuity across e-book revisions is hard to be maintained and seems to become a shortcoming within the e-book system. In this paper, in or

[The 9th International Learning Analytics and Knowledge (LAK) Conference] March 4-8, 2019, Tempe, Arizona, USA

In this paper, we propose an E-Book Page Ranking (EBPR) method to rank e-book pages from the original learning material automatically. The proposed method ranks all the e-book pages by the class probabilities retrieved from machine learning models. The top-ranked e-book pages are then selected to form the pre-class reading (preview) recommendation. The proposed method extracts image features and text features from e-book page contents as well as the e-book usage features from students’ previous

This study examines how Retrieval-Augmented Generation (RAG) enhances the effectiveness of generative artificial intelligence in programming education. This study compares commercial and open-source large language models within a RAG system, examining how retrieval design and prompt engineering affect response quality. By creating a database specific to machine learning courses and using the RAGAS (Retrieval-Augmented Generation Assessment) evaluation framework, a comparative analysis of five pr