Poster Presentation Time: 1225-1400; 1500-1600

Venue: C2, Tai Po-Shek-O Room, Lower Level I

Presenter(s)

– Professor Bin LI, Associate Professor, Department of Linguistics and Translation, City University of Hong Kong

– Dr Yee Na LI, Part-time Research Associate, Department of Linguistics and Translation, City University of Hong Kong

Abstract

Our ten years’ evidence-based study revealed that the alternative pathway from sub-degree to degree studies is viable while challenging for Senior Year Admission (SYA) students. The inadequate alignment between sub-degree and university programmes is the main determinant of their heavy academic workload. Their transitional challenges call for an examination of the existing programme articulation process and academic advising to SYA students. In response to their needs, the project sustains our previous work from UGC-funded and TDG sustainability projects to promote best practices to support SYA students. An online Cross Institutional Credit-transfer Information System (CICIS) was launched to enhance the transparency of credit transfer and facilitate smooth transition of SYA students. Another student-centred Resources Website was developed to provide cross-institutional support to SYA students from the first point of transferring to universities. In addition, a series of collaborative activities, such as an international Credit Transfer Conference, local and overseas webinar sessions and the Internationalisation-at-Home (IaH) programme, has been organised. The project provides implications to administrators and policy makers in higher education, informing policies and practices to optimize the transfer experience for students’ greater success in universities and in the society.

Theme: 1. Showcase Project Achievements

Sub-theme: 1.1 Teaching Development and Language Enhancement Grant (TDLEG)

Poster Presentation Time: 1500-1600; 1700-1800

Venue: G1, Tai Po-Shek-O Room, Lower Level I

Presenter(s)

– Dr Yuk Ming TANG, Senior Lecturer, Department of Indusial and System Engineering, The Hong Kong Polytechnic University

Abstract

STEM education is essential in today’s curriculum even for university students. However, traditional classroom-based instruction methods often lack interactivity and tailored experiences that foster student engagement and comprehension. The integration of Virtual Reality (VR) and Artificial Intelligence (AI) generative chatbots has emerged as a transformative influence on the teaching and learning process. Despite this, limited research has explored the impact of advanced technology on STEM learning outcomes. This study explores the potential of employing VR and AI as tools to facilitate teaching to enhance students’ learning outcomes. 120 university students are involved in this study to examine the difference in learning outcomes by utilizing three instructional approaches for learning projectile motion: (1) a traditional didactic classroom, (2) a game-based VR metaverse, and (3) a game-based VR metaverse enriched with a generative chatbot-based pedagogical agent. The study prudently evaluated alterations in student motivation, cognitive benefit, and learning outcomes. Preliminary findings suggest that incorporating VR and AI into teaching considerably enhances student engagement and cognitive participation. This study demonstrates how the integration of VR with AI can elevate student engagement, comprehension, and skill acquisition in STEM education, paving the way for a more captivating and effective learning environment in the Edu-metaverse.

Theme: 2. Thematic Exploration

Sub-theme: 2.1 Community of Practice (CoP)

Poster Presentation Time: 1225-1400; 1500-1600

Venue: G4, Tai Po-Shek-O Room, Lower Level I

Presenter(s)

– Dr Hung-lin CHI, Associate Professor, Department of Building and Real Estate, The Hong Kong Polytechnic University

– Ms Junyu CHEN, Ph.D Student, Department of Building and Real Estate, The Hong Kong Polytechnic University

– Mr Haolei LIN, Ph.D Student, Department of Building and Real Estate, The Hong Kong Polytechnic University

Abstract

KnowLearn is an interactive learning assistant system designed for architecture, engineering, and construction (AEC) education, where personalized recommendations for students in virtual learning environments remain under-explored. An educational knowledge graph (KG) was constructed to contain multifaceted information by connecting pedagogical, learning performance, and learning feedback data as sub-graphs. A heterogeneous graph attention network (HAN) was implemented to infer latent information in the educational KG and identified essential factors shaping students’ acceptance of virtual learning environments. Based on sampling data of 107 students from the Hong Kong Polytechnic University, Department of Building and Real Estate, we found students’ self-efficacy, intention to use, and in-class quiz performance were significant predictors of final learning outcomes in subjects that adopt virtual learning environments. This project further deployed a local-based large language model (LLM) Qwen-7B and built an interactive graphical user interface (GUI) with Gradio. Utilizing the information preserved in the educational KG and learned from HAN as the basis, this LLM facilitated conversations between students and KnowLearn, enhancing personalized recommendations while securing student privacy. The developed system contributed to helping improve the learning experiences and performances of AEC students within virtual learning environments.

Theme: 1: Showcase Project Achievements

Sub-theme: 1.1  Teaching Development and Language Enhancement Grant (TDLEG)