Poster Presentation Time: 1225-1400; 1500-1600

Venue: H2, Tai Po-Shek-O Room, Lower Level I

Presenter(s)

– Dr Pauli LAI, Lecturer, Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University

– Dr Julia CHEN, Director, Educational Development Centre, The Hong Kong Polytechnic University

Abstract

A common assessment in university is the oral presentation, and students are often required to deliver presentations in English. Two challenges arise. First, many students mainly focus on the discipline content in the assessment preparation process rather than the communication or use of English in their presentations. Second, lecturers of large classes (e.g. around 200 engineering students in one course) hardly have time to give feedback to each student on the English communication aspect of their oral presentations. A baseline survey reveals students’ need for assistance with presentation skills and a hope for having AI-generated feedback among both students and discipline teachers. To address these needs and hope, a team of educators from PolyU and BU with expertise in language and AI technology collaboratively developed an online English oral presentation platform called SmartPresenter. SmartPresenter provides students with presentation tips, learning materials, and extensive AI-generated feedback on the communication-related aspects of delivering oral presentations in English, including eye contact, facial expressions, vocal fillers, pronunciation, and fluency. This presentation describes the development and features of SmartPresenter, and the evaluation results of the effectiveness of the platform in facilitating independent learning practices for English oral presentations and assisting teachers in grading presentation assessment.

Theme: 1: Showcase Project Achievements

Sub-theme: 1.3  Special UGC Grant for Strategic Development of Virtual Teaching and Learning (VTL)

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)

Oral Presentation Time: 1400-1500

Venue: Camomile Room, Lower Level II

Team member(s)

– Professor Alvin Chung Man LEUNG, Associate Head & Associate Professor, Department of Information Systems, City University of Hong Kong

Abstract

The COVID-19 pandemic has highlighted the critical importance of online learning, where learners must engage in self-regulated learning (SRL) to achieve optimal outcomes. Gamification interventions have been implemented to improve SRL engagement in online environments, but the mixed results of these efforts have raised doubts about their efficacy. This study investigates whether the inconsistent findings can be attributed to a lack of consideration for individual learner characteristics during gamification design. Focusing on Massive Open Online Courses (MOOCs), we examined how gamified performance feedback interacted with learners’ goal orientation, an individual trait known to influence SRL and learning. By tracking the SRL engagement of 760 college students over five weeks using learning analytics, we found that positively framed performance feedback without social comparisons increased SRL engagement and learning outcomes for participants with a strong performance-avoidance goal orientation. Conversely, the same feedback had a negative impact on participants with a strong mastery goal orientation. These findings contribute to SRL theory by demonstrating that the effectiveness of gamification in online learning is contingent on aligning the design elements with individual learner characteristics and highlight the importance of personalized gamification approaches to optimize SRL and learning in MOOC.

Theme: 1. Showcase Project Achievements

Sub-theme: Teaching Development and Language Enhancement

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)