Unlock the Potential of AI in Feedback Designs

The emergence of GenAI has brought a paradigm shift to various industries, in particular education. Educators and researchers are working hard to harness the power of this transformative technology and actively exploring the potential of GenAI in various aspects of teaching and learning. Dr Jessica To is one of the pioneers examining the role of GenAI in developing student feedback literacy. She investigates how GenAI could be better applied to increase students’ capability to interpret and enact feedback. Her extensive research experience in assessment feedback, peer and self-assessment, dialogic use of exemplars, and educational innovations has laid a strong foundation for exploring the role of GenAI in feedback designs. Her research works have been published in various high-impact academic journals, for instance, Assessment and Evaluation in Higher Education, Higher Education Research and Development, Teaching and Teacher Education, etc.

Practical Advice in the Age of AI

GenAI, in particular, ChatGPT is a useful tool to support student learning. It offers personalised feedback, recommends learning resources according to individual students’ progress, and encourages their seeking of academic assistance in a psychologically safe environment. It could also promote critical thinking and self-assessment skills when they are guided to compare their drafts with AI-generated materials and reflect on their own performance. Personalised feedback caters for individuals’ learning needs and empowers them to actively engage in their own learning journey. Despite the immense benefits of GenAI, Dr To emphasises that the considerable potential of GenAI may not be realised unless teachers possess the knowledge and capacity to incorporate GenAI in the curriculum. In the age of AI, it is advisable for teachers to:

  1. understand the constraints on one’s existing pedagogical context and selecting suitable GenAI tools to circumvent the limitations;
  2. learn the pedagogical use of GenAI through trial and error;
  3. participate in GenAI-related forums, workshops or seminars to exchange experience; and
  4. realise and address the ethical concerns about using GenAI.

Dr. To is committed to support HKU’s teaching fraternity in designing innovative and effective pedagogical, assessment and feedback practices, and promoting evidence-based initiatives to improve teaching and learning. She is eager to share her expertise and collaborate with academic staff in the University to strive for excellence in teaching learning and feedback practices.    

Dr. Jessica Kar Yan TO

Lecturer
Teaching and Learning Innovation Centre 

Here is the Evidence

Event Details

Date : 19 Feb, 19 Mar, 2 Apr & 17 Apr 2025
Time : 1:00pm – 2:00pm
Venue: Zoom
Speaker: Prof. Luke Fryer, Assistant Director / Associate Professor, TALIC, HKU
Register Now!

Abstract

This Seminar Series will present the current evidence for both popular and lesser known (but important) areas of teaching and learning in higher education. Each seminar will have three components:

  1. A short overview of the topic
  2. Evidence for/against its implementation in university courses – drawing chiefly on current reviews and meta/meta-meta-analyses
  3. Straight forward suggestions for instruction
The seminars will conclude with a brief discussion focusing on attendee’s experiences and questions.

Date : 19 Feb 2025 (Wed)
Time : 1:00pm – 2:00pm
Venue : Zoom

Abstract

Cognitive Load Theory describes how our cognitive architecture mediates learning. Based on a considerable body of experimental research, several straight-forward implications for instruction are well established and stand ready for integration into HKU courses. This seminar will introduce the theory, its essential instructional implications, and practical suggestions for application.
Date : 19 Mar 2025 (Wed)
Time : 1:00pm – 2:00pm
Venue : Zoom

Abstract

Flipped learning is a popular but poorly understood instructional strategy pervading higher education internationally. Recorded lectures are a contentious addition to almost any course. Videos are a powerful learning resource in specific learning situations. This seminar will introduce evidence for and against each of these strategies/tools and invite discussion regarding current uses here at HKU going forward.
Date : 2 Apr 2025 (Wed)
Time : 1:00pm – 2:00pm
Venue : Zoom

Abstract

Despite decades of repudiation from international experts, learning styles (and the match hypothesis: e.g., matching learners to a supposed disposition or preference such as auditory, visual and kinesthetic styles of learning) are still popular across levels of education and national borders. This seminar will start by clarifying the difference between the commonly conflated styles, preferences, and strategies. Then, reflecting recent reviews in this area, the longstanding evidence demonstrating why teaching to this perceived individual difference is not a good investment will be reviewed. Other high impact aspects of the student learning experience will be highlighted for interested instructors.

Date : 17 Apr 2025 (Thu)

Time : 1:00pm – 2:00pm

Venue : Zoom

Speakers :

  • Dr. Weijiao Huang, Postdoctoral Fellow, TALIC, HKU
  • Prof. Luke Fryer, Assistant Director / Associate Professor, TALIC, HKU

Abstract

Chatbots are increasingly being used in higher education and potentially change the way students access and engage with learning materials. These tools fulfill important pedagogical roles by providing students with personalised content and real-time feedback. Recognising the growing presence of chatbots, including generative AI, and their natural influence on student learning, there is an urgent need to understand their impact on student motivation, which influences how and why students engage with learning materials.

This seminar will start by distinguishing between student (short-term) engagement and (long-term developmental) motivation in the context of chatbot-supported learning. It will then review current evidence, focusing on the underlying theoretical frameworks, the impact on student learning outcomes and motivational dimensions, and the characteristics of chatbot design. Practical suggestions for the reflective use of chatbots will be discussed.

For information, please contact:

Ms. Miffy LEUNG

Teaching and Learning Innovation Centre

Beyond Gaming, DISCORD for Student Learning

In today’s dynamic learning environment, effective collaboration and communication among students and faculty is crucial. Space, time, student mobility, class size, and classroom settings are prime factors to consider when conducting collaborative group activities in a traditional classroom. Integrating technology is a great way to address these factors and create an active and collaborative learning space in the modern classroom.

This blog post explores the potential of Discord, a platform initially known for gaming communities, as a powerful tool for fostering collaborative learning and discussion within higher education settings. We will examine its features and demonstrate its applicability for various academic activities, drawing on specific examples to highlight its effectiveness.

Discord: A Versatile Platform Beyond Gaming

In a recent Discord workshop, Dr. Carson Hung of TALIC introduced the functionalities of Discord that make it ideal for creating virtual learning environments for both large-scale and small-scale group activities, as well as discussions that cater to diverse learning experiences and preferences. Discord, often perceived as only a gaming platform, offers robust communicative features and versatile functionalities stemming from its capacity to create customised servers and organize channels for specific roles and purposes. Generally, a Discord server enables users to create up to 500 voice and text channels and as many as 50 different categories. These versatile capabilities offer a dynamic environment that facilitates both synchronous and asynchronous interactions (Uong et al., 2022).

Discord’s meticulous categories and channels help maintain clarity and facilitate easy navigation for students immersed in a large pool of information. For instance, the HKU Admission Office has a Discord server that answers questions from incoming and new students and helps them find information from an array of supports and services, from applying to HKU to student housing, academic support, student activities, and more.

Furthermore, Discord’s versatile communication channels and easily managed role-based permissions system provide robust support for large-scale university projects such as the GenAI Hackathon for the Social Good, which utilizes Discord to engage students across different universities in a cross-institutional collaboration project. 

Moreover, the integration of artificial intelligent apps and chatbots significantly expands Discord’s capabilities. For instance, Discord integrates the popular text-to-image generative AI bot, Midjourney, to generate images. Other chatbots, such as ChatGPT, writing, and summary assistants, can potentially provide a comprehensive learning experience in their communicative group projects.

Facilitating Collaborative Learning Activities

In the Discord Workshop, Ellen Seto of TALIC leverages the diverse functionalities of the Discord platform to engage teachers and participants to role-play two group activities. These include a synchronous small group debate and an asynchronous class discussion. Teachers quite often engage students in similar types of group activities in face-to-face format in a classroom.

Conducting synchronous group activity on the Discord server requires online platform management. Discord’s role-based permissions system enables teachers to manage and facilitate student participation through setting and assigning multiple functional roles and access specifications for students’ group work. Teachers can monitor synchronous and asynchronous activities when students post, moderate, or delete messages on Discord channels and discussion threads, encouraging a safe and inclusive learning environment.

In a synchronous group debate activity in the workshop, the participants first take a stance on whether they agree or disagree that the use of AI will diminish students’ reading and writing proficiencies and join the discussion channel on Discord that supports their stance. Customized text channels in Discord facilitate the participants engagements and interactions with their peers in a synchronous discussion, allowing them to present and share text, audio, and graphic files, and give instant feedback to peers in their designated text and voice channels. In the process, students also engage in information search, sorting, organizing, critically evaluating evidence, and correctly citing references; these are all important aspects of evidence-based learning in collaborative argumentation (Mayweg-Paus et al., 2021). A GPT chatbot is integrated into the Discord server to support real-time collaborative discourse.

Asynchronous multi-channel discussion of an essay-grading activity for peer review and feedback provides a structured environment for constructive criticism. Furthermore, the integration of a ‘Summarize’ chatbot enabled participants to recount key ideas and findings of the group and class discussions in a logical and organized manner. This can facilitate further learning and help to identify future areas of interest or exploration.

Future Potential

Discord offers a powerful and flexible environment for collaborative learning and discussion. While first-time users of a multi-channel Discord server may find it overwhelming, with proper planning and moderation, these challenges can be overcome, revealing its potential to transform the future academic experience.

Reference

  • Park, E.L., Choi, B.K. Transformation of classroom spaces: traditional versus active learning classroom in colleges. High Educ 68, 749–771 (2014). https://doi.org/10.1007/s10734-014-9742-0
  • Uong, T. G. T., Nguyen, D. K., & Nguyen, H. N. (2022). Teachers’ feedback on using Discord as an online learning platform. International Journal of TESOL & Education, 2(4), 84-104.

Ellen SETO

Senior Lecturer / Senior Instructional Designer
Teaching and Learning Innovation Centre

Unlocking the Power of Data and Analytics in Enhancing Teaching and Learning

The digital revolution has transformed our lives, providing unprecedented access to vast amounts of data. In today’s data-driven world, higher education institutions are increasingly using data analytics to improve teaching and learning experiences. This article explores how data analytics at TALIC is being leveraged to provide actionable insights to support teaching and learning strategies, quality assurance, and quality enhancement at the University of Hong Kong.

Institutional Surveys

Launched in 2008, the annual Student Learning Experience Questionnaire (SLEQ) has collected data on students’ perceptions of their learning experiences, covering aspects such as teaching and learning environments, achievement of University Educational Aims, and overall satisfaction. SLEQ data is analysed using various strategies like statistical and psychometric analyses, text mining, and data visualisation techniques, allowing Faculties, Committees, and Programme Directors to identify areas for improvement and enhance the overall learning experience. Over the past 16 years, the SLEQ has revealed an upward trajectory, indicating a growing positive learning experience among students. The Student Feedback on Teaching and Learning (SFTL) is a course feedback survey that gathers data on course and teaching effectiveness. SFTL data informs teaching and pedagogical practices for teachers and supports continuous improvement in teaching and learning. Recently, a revamp has been made to enhance the SFTL online survey and reporting system, leading to a significant increase in response rates. 

Data Analytics for Teaching and Learning

The analysis of data collected from these surveys, along with other quantitative and qualitative studies, yields actionable insights and serves as vital sources of data for assuring and enhancing teaching and learning practices in various ways. Examples of how institutional data has been utilised include annual discussions with Faculties to address issues from survey findings and facilitate teaching and learning action plan developments; tracking students’ perceived learning gains during university studies and after graduation to assess the value-addedness brought by the University Educational Aims; providing teaching and learning related performance indicators to support the identification of areas of improvement in the Teaching and Learning Strategy; examining student characteristics across diverse student populations; and incorporating survey data into institution-wide focus reviews to enhance aspects of teaching and learning, such as e-learning, experiential learning, Common Core, academic advising, and more. 

Data Analytics and Student Assessment in Higher Education

Data analytics has become a crucial component in higher education across the globe. The growing trend of data analytics emphasises the importance of harnessing data to make informed decisions and drive improvements in education. Adopting an evidence-based approach, Dr Maggie Zhao, Assistant Director at TALIC, has been leading a dedicated team that aims to uphold high standards in their data support for teaching and learning through rigorous learning analytics and meticulous research efforts. Consequently, their discoveries have been published in reputable academic journals, and their innovative developments in student assessment have been shared at distinguished international conferences and forums, including UNESCO. This exemplifies TALIC’s dedication to harnessing the power of data analytics to enhance teaching and learning experiences. 

As data analytics continues to evolve and shape the future of higher education, institutions worldwide will benefit from embracing data-driven approaches to improve their teaching and learning practices. Dr Zhao emphasises that with the ongoing evolution of technology, including artificial intelligence, the capacity for data-driven practices will expand even further. She highlights TALIC’s data and analytics initiatives, such as SLEQ and SFTL, as demonstrations of the immense potential of harnessing data to enrich student learning experiences. Dr Zhao concludes that embracing the power of data and analytics provides actionable insights into students’ learning, enabling teachers to design and implement evidence-based strategies that promote more personalised and engaging learning experiences for students, ultimately unleashing students’ full potential and nurturing future readiness.

Dr. Maggie Yue ZHAO

Assistant Director / Senior Lecturer
Teaching and Learning Innovation Centre 

Future-Ready Education: Prompt Engineering, an Emerging Competency

The rapid advancement of artificial intelligence (AI) is reshaping numerous aspects of our lives, and higher education is no exception. The generation of students entering universities today will witness more technological change than ever before. Yet, how can we help our teachers —the guides of this generation— be better prepared for this evolving landscape? To this aim, HKU TALIC presented a series of edTech and AI workshops on emerging technologies with a focus on practical strategies and resources. The workshops were created to provide teachers and the university community more options to enhance student learning, and to cultivate AI literacy and ICT skills. The first workshop held in Spring 2024 focused on the power of prompt engineering, as well as providing tactics and practical examples in AI prompting for teaching practice.

Harnessing the Power of HKU ChatGPT Service

HKU ITS provides staff and students with the ChatGPT and DALL∙E web apps (https://chatgpt.hku.hk/), which are powered by Microsoft’s Azure OpenAI services to improve teaching and learning effectiveness, productivity, and the overall educational experience, The recently released DeepSeek-R1 and DeepSeek-V3 models, as well as models like GPT-4, GPT-4o, and GPT-3.5, are all included in the ChatGPT online app.

How does the ChatGPT web application react to user input? Generally, the AI chatbot produces a response when users input a prompt. Additionally, the ChatGPT web app organizes discussion history into many “topic” groups. In contrast to more basic chatbot models, newer chatbots permit content preservation and more complex, multi-turn interactions.

Performance is important. The most recent model of the web application, GPT-4o, outperforms DeepSeek R1 and V3 in terms of output speeds. For effective classroom use, this speed performance advantage can be helpful and pertinent.

The ChatGPT app, like any AI tool, has usage limits. Users can upload files up to a maximum file size of 3 MB, and only one file can be uploaded per minute. For larger documents, educators need to strategize, perhaps splitting documents into smaller parts or prioritizing uploads.

Prompting Techniques

Prompt engineering is the art of crafting effective prompts to elicit desired outputs from AI models. Cain (2023) describes prompt engineering as a “steering mechanism” of GenAI users. A 2024 article by McKinsey & Company defines it as “the practice of designing inputs for AI tools that will produce optimal outputs.” By strategically crafting prompts, a user not only improves the relevance of the information provided by an AI model, but also enhances the productivity and satisfaction of human-AI collaboration experience as one masters effective prompting techniques.

In AI prompting workshop, Dr. Carson Hung of TALIC discussed six key strategies for effective prompt engineering.

  • Write clear instructions: Be specific and unambiguous in your instructions.
    Provide reference text: Supplement your prompt with relevant context or examples.
  • Split complex tasks: Break down large tasks into smaller, more manageable subtasks.
  • Give the model time to “think”: Allow sufficient processing time for complex tasks.
  • Use external tools: Integrate external resources/tools to enhance the AI’s capabilities.
  • Test changes systematically: Experiment and refine the prompts for optimal
    results.

Beyond these strategies, effective prompting entails various tactics. These include: providing detailed context and specific information in your prompts; adopting a persona: (i.e., a historian, a research professor); and employing delimiters (i.e., quotes, double quotes, triple quotes and tags) to structure and clarify different parts of the prompt.

  • Context Information: Including detailed context and specific information in your prompt.
  • Adopting a Persona: Instructing the AI to respond as a specific persona (e.g., a historian, a journalist).
  • Using Delimiters: Employing delimiters (e.g., quotes, tags) to structure and clarify different parts of the prompt. [Insert a slide image showcasing delimiter use here].

Integrating Prompt Engineering to Enhance Lesson Design

Prompt engineering can be integrated into the classroom to enhance the teaching and learning experience. By carefully crafting prompts, teachers can guide Al to produce tailored, relevant, and engaging content. ChatGPT is a powerful language model that can handle varied tasks from drafting, analyzing, and summarizing textual content, to translating, performing grammar checking, and more. However, to leverage Al effectively, it is important to provide clear and specific instructions detailing the context and specifics of the educational goal in crafting meaningful AI prompts.

In the prompting workshop, Ellen Seto of TALIC engaged teachers in two hands-on activities including collaboration with participants in writing an abstract for a student research paper, and preparing a summary for a grant proposal. The teachers who participated in the workshop were provided pre-designed “standardized prompts” to guide them to tailor their prompts to their own T&L context. The teachers experimented with different descriptive words, made changes to the AI persona, and applied delimiters to modify their prompts in ChatGPT based on the results they observed. They are able to evaluate whether the wordings that they applied in their prompts effectively express the intended results they desired to generate. Employing carefully crafted standard prompts supplemented with precise word definitions, roles and context descriptions as stated by Spasié and Jankovic (2023), has the potential to enhance the strategy design and lesson preparation of teachers who are working in collaboration with AI.

Challenges and Future Outlooks

ChatGPT is an evolving language model, it is not perfect and there are limitations to consider. ChatGPT’s answers are based directly on the prompt that a user provides, it is important to note that there may be potential biases based on how one phrases the prompt. It is important to fact check ChatGPT’s response because it can provide inaccurate, biased or outdated answers. In addition, the chatbot models also may not be trained with specific expert knowledge on highly specialised subjects. Hence, references and citations should always be checked for accuracy. AI Chatbot models also may not be able to provide the same level of human insight as a person on understanding cultural practices, human habits, slang and idioms etc. For those queries, human subjects are a source of more sensible answers.

AI tools such as ChatGPT, when used effectively, offer significant potential for enhancing teaching and learning. Even with the given restrictions associated with the use of artificial intelligence, effective prompt engineering gives teachers and students the ability to harness the immense potential of AI, which may elevate the learners interest and improve teaching and learning outcomes. What we are interested in learning most in the future is: How can we best enable teaching and learning through building AI-human partnerships?

Reference

Ellen SETO

Senior Lecturer / Senior Instructional Designer
Teaching and Learning Innovation Centre

Hands-on Training and Overview of Gradescope, an AI-assisted Grading Tool

Event Details

Date : 12 February 2025 (Wednesday)
Time : 10:00am – 11:30am
Venue : Learning Lab (RRS 321, 3/F, Run Run Shaw Building, Main Campus, HKU)
On-line Speakers:
  • Lyn Riverstone, Senior Solutions Engineer, Gradescope
  • Callan Rose, Senior Onboarding Consultant, Gradescope
On-Site Facilitators :
  • Dr. Law Ka Ho, Lecturer, Department of Mathematics, Faculty of Science, HKU
  • Dr. Vindya Bhat, Demonstrator, Department of Mathematics, Faculty of Science, HKU
  • Dr. Marian Choy, Lecturer, Department of Mechanical Engineering, Faculty of Engineering, HKU
Video Recording

Abstract

Gradescope is an AI-assisted grading platform that can cut grading time by up to 80%. Do you want to give personalized feedback to your students in a fraction of the time you currently spend grading assignments and exams? Would you like to use analytics on assessments to inform your students’ learning and your teaching? Register for this workshop to learn more and participate in the Gradescope pilot at HKU.

In this workshop, Gradescope will give a high-level demo of their product’s features as well as a hands-on training to use the tool’s features. These features include customizable rubrics, answer groups, bulk and shared grading, and submission and feedback management. Participants should bring a laptop to the workshop and may signup for a Gradescope account in advance with their HKU email address at www.gradescope.com.

With funding from the Faculty of Science, Gradescope is currently accessible to teachers across all faculties at HKU. Gradescope can support multiple choice, fill-in-the-blank, and free-response type questions. STEM departments and any department that uses bubble sheet assessments are encouraged to try it. Gradescope is integrated with Moodle to allow for ease in roster and gradebook syncing.

About the speakers

Lyn Riverstone has been a Senior Solutions Engineer at Turnitin for the past four years. Before joining Turnitin, she taught mathematics for over 20 years and used Gradescope in her own teaching. She also worked as an instructional technologist on her university’s academic technology team for three years, where she supported educators in their use of Gradescope.
Callan Rose has been a Senior Onboarding Consultant at Turnitin for two years, specialising in supporting educators and institutions across the Asia Pacific region. Before joining Turnitin, he spent a decade at a leading Australian university, where he held various roles focused on instructional design, educational technology, and staff development.
For information, please contact:

Ms. Miffy LEUNG

Teaching and Learning Innovation Centre

HKU Teaching and Learning Fellow Sharing Seminar

Event Details

Date : 15 January 2025 (Wednesday)
Time : 12:30pm – 2:00pm
Venue : Learning Lab (RRS 321, 3/F, Run Run Shaw Building, Main Campus, HKU)
Speakers :
  • Dr. Vincent Tam, Principal Lecturer, Department of Electrical and Electronic Engineering, Faculty of Engineering
  • Ms. Nicole J. Tavares, Senior Lecturer, Academic Unit of Language and Literacy Education, Faculty of Education
  • Dr. Dana Vackova, Principal Lecturer, School of Public Health, Li Ka Shing Faculty of Medicine
  • Dr. Jian Yang, Principal Lecturer, School of Biochemical Sciences, Li Ka Shing Faculty of Medicine
Facilitator : Dr. Jannie Roed, Director, TALIC
Register

Abstract

Under the aegis of the University Grant’s Committee’s (UGC) Virtual Teaching and Learning (VTL) initiative, the Teaching and Learning Innovation Centre (TALIC) in collaboration with Faculty Associate Deans (Teaching and Learning) and the Director of the Common Core have been launching the HKU Teaching and Learning Fellows programme. In this seminar, four HKU Teaching and Learning Fellows will share the work carried out in the faculty as a result of the secondment to TALIC.

About the speakers

Dr. Vincent Tam is a Principal Lecturer in the Department of Electrical and Electronic Engineering, Faculty of Engineering. He teaches a General Engineering course as well as core/elective courses in Computer Engineering.

Vincent was awarded with the Faculty Best Teacher Award (2010), Faculty Outstanding Teaching (Team) Award (2013), Faculty Outstanding Teaching (Individual) Award (2017), the Faculty Outstanding Teaching (Team) Award (2019) in HKU. In addition, he was awarded with the Fellowship (FHEA) of the AdvanceHE (formerly as the Higher Education Academy) in 2019, and has been serving as both the mentor and reviewer for the HKU Advance HE Fellowship Scheme since 2020.

His research interests include artificial intelligence, e-learning, learning analytics, mobile computing, and information visualization.

Ms. Nicole Tavares (FHEA) is Senior Lecturer in the academic unit of Language and Literacy Education at the Faculty of Education. She teaches on BA&BEd, PGDE, MEd and MA programmes, specialising in English language teaching (ELT) methodology and Content and Language Integrated Learning (CLIL). She is currently MA(TESOL) Programme Director.

Nicole has published on COIL, online teaching and learning, 21st-century skills, using educational technologies to promote teacher development, good ELT and CLIL practices, and Interactive Assessment.

Nicole has received multiple teaching awards, notably the HKU Teaching Innovation Award (2020), her Faculty’s Emergency Remote Teaching Award (2020), and HKU Outstanding Teaching Award (2015). She is keen on experimenting with innovative pedagogies and is acknowledged for her creative design of collaborative activities that maximise student voice. She enjoys professional dialogues with educators on how research informs practice and has initiated several professional development activities within her Faculty and beyond.

Dr. Dana Vackova, MD, MBA, FHKAM, FHKCCM, SFHEA is a Principal Lecturer at the School of Public Health (SPH) in the LKS Faculty of Medicine of the University of Hong Kong (HKUMed). She is a coordinator of the HKUMed’s MBBS Enrichment Year (EY) Humanitarian Services, member of the EY committee, BIMHSE Taskforce for Inter-professional education and a member of the HKUMed Clinical Curriculum Reform committee. She is responsible for coordinating SPH undergraduate courses, planning and developing the MBBS curriculum, and managing SPH MBBS courses and Interprofessional Education (IPE). She developed MBBS courses such as interdisciplinary HRP, Occupational Medicine, Challenges in Health Care Management and online Induction course for EY students.

Dr. Vackova received grants for medical education research and presented her research results at the international medical education conferences. She is an author of many cases for MBBS Problem-Based Learning (PBL) and case studies for MBBS students.

Dr. Vackova is a Fellow of the Hong Kong College of Community Medicine, HKAM (Community Medicine) and a Senior Fellow of the Higher Education Academy.

Dr. Jian Yang graduated from Peking University Health Science Center and went on to pursue a PhD in neuroscience at University of Bristol. He joined the Department of Anatomy, the University of Hong Kong after graduation in 2007, and has devoted his passion to anatomy and histology education since 2012. He is now a Principal Lecturer in the School of Biomedical Sciences, lead in anatomy education, Coordinator of HKU Body Donation Programme, Deputy Director of Education Technology, LKS Faculty of Medicine, HKU, and Vice Chairman of Research and Development Subcommittee, Jockey Club Institute for Medical Education and Development.

He dedicates his effort to converting instructional anatomy and histology classes into indispensable active-learning experiences. His research focuses on integrating digital/mobile technology to enhance active learning in anatomy and histology labs. He is now leading the Virtual Reality Anatomy Lab and the TechMezz learning space. The current projects include creating digital active learning and peer learning platform and integrating VR-enriched tasks (VRETs) into gross anatomy classroom.

For information, please contact:

Ms. Canice MOK

Teaching and Learning Innovation Centre

Exploring Motivation and Learning Strategies for Student Success

Professor Fryer, a researcher and educator, has dedicated his career to exploring the realms of motivation to learn, learning strategies, and educational technology. With over 20 years of experience in education, Professor Fryer’s research projects aim to bridge these areas, uncovering meaningful synergies, exploring the role of artificial intelligence (AI) as a learning partner and investigating how students process and integrate new knowledge. His work has both theoretical and practical implications, shedding light on student persistence, deep versus surface processing, and the integration of technology in educational settings.

Exploring the Intersection

Throughout his academic journey, Professor Fryer has delved into various themes that have shaped his research trajectory. Two enduring themes that have captivated his interest are the potential of AI as a learning partner and the limited understanding of how students process and integrate new knowledge. His early exploration in 2006, investigating students’ engagement and benefits in AI learning partners, laid the foundation for subsequent studies that examined the value of chatbots in education. With the rise of GenAI, Professor Fryer’s recent work has focused on reviews and theoretical contributions, while also spearheading large-scale research projects centered on GenAI tutors.

Probing Learning Strategies

Professor Fryer’s fascination with learning strategies emerged from his keen interest in deep versus surface processing, inspired by the Student Approaches to Learning literature. Dissatisfied with the field’s poor conceptualisation and measurement, he sought better approaches, drawing upon Patricia Alexander’s Models of Domain Learning. Collaborating with distinguished researchers such as Professor Jan Vermunt and Professor Daniel Dinsmore, Professor Fryer has published theoretical papers that harmonise these models, aiming to extract the best from both. By bridging these theoretical frameworks, his work contributes to a deeper understanding of effective learning strategies.

Unravelling Student Persistence

At the heart of Professor Fryer’s current research lies a focus on student persistence. Recognising the multitude of factors that influence this crucial aspect of education, he strives to identify those that explain the majority of variance in student persistence while being grounded in robust learning theories. Drawing upon the Four Phase Model of Interest, Social Cognitive Theory, the Model of Domain Learning, and the role of prior knowledge, Professor Fryer has undertaken empirical, large-scale studies exploring middle school students’ motivations to learn math native and foreign language. His research sheds light on the reciprocal relationship between interest, self-efficacy, self-concept, utility value, and knowledge. His work has emphasised the essential connection between student interest and self-efficacy, and how specific, often social, tasks drive interest in learning.

Leadership and International Collaboration

Collaborating with colleagues at HKU and in Japan, he has been involved in the development of software for formative testing, classroom feedback, and reading skills development. Under his supervision, Dr Alex Shum’s PhD research has explored how students can be supported in establishing and maintaining learning goals using the GEAR platform. Professor Fryer’s work in this area also includes knowledge exchange efforts, collaborating with primary schools in Japan to integrate the software into their classrooms.

In addition to leading research projects and initiatives within his field, Professor Fryer actively supports research postgraduate students in the Faculty of Education, HKU through the development of the “Starter Research Seminar Series.” This series equips research postgraduate students with essential research skills, setting them on the path to independent mastery. Professor Fryer’s leadership extends to regional and international projects. He is seeking to enhance quality assurance in Asia Pacific universities and address camp-ism in educational research. He fosters collaboration and integration among scholars from various backgrounds and recently edited a special issue in Educational Psychology Review on the topic “Hybridising our Educational Psychology Theories”, to be published in Spring 2024.

Recognised as a Top Producing Early Career Scholar

Professor Fryer’s contributions to the field have garnered recognition. He has been cited as a top producing early career scholar in educational psychology journals from 2015 to 2021 (Rank #19) and was included in the top 2% of cited researchers across all fields of study in the World’s Top 2% Scientists published by Stanford University. His research journey has positioned him as a pioneering figure in the domains of motivation to learn, learning strategies, and educational technology. His commitment to uncovering meaningful synergies, harnessing the potential of AI, and understanding student persistence has led to significant contributions to the field.

Prof. Luke Kutszik FRYER

Assistant Director / Associate Professor
Teaching and Learning Innovation Centre