Dr BAI, Shurui Tiffany    白書瑞 博士
Assistant Professor
Department of Mathematics and Information Technology
Contact
ORCiD
0000-0003-2004-7810
Phone
(852) 2948 8247
Email
tstbai@eduhk.hk
Address
10 Lo Ping Road, Tai Po, New Territories, Hong Kong
Scopus ID
57213191256
Research Interests

Technology-enhanced learning, gamification, and intrinsic motivation to learn.

Teaching Interests

Foundation of ICT. Coding and Computational Thinking.

External Appointments

HKAECT 2022-2024 organization committee member and editor

Personal Profile

Dr. Bai received her Ph.D. in e-learning environment from the University of Hong Kong (HKU) in 2022. Before that, she got a master degree in English linguistics from Renmin University of China (RUC) in 2018 and two bachelor degrees (English literature and economics) from Shandong University (SDU) in 2015. Her research interests lie in AI-empowered learning, gamification, scenario-based learning, digital storytelling, and intrinsic motivation to learn.

Research Interests

Technology-enhanced learning, gamification, and intrinsic motivation to learn.

Teaching Interests

Foundation of ICT. Coding and Computational Thinking.

External Appointments

HKAECT 2022-2024 organization committee member and editor

Research Outputs

Scholarly Books, Monographs and Chapters
Tiffany Shurui Bai, Yingxue Liu, Yue Qiu (2023). Evaluation of good practices of using rewards in online classrooms based on the five principles of motivation. Springer. https://doi.org/10.1007/978-981-99-7559-4_8
Anna Wing Bo TSO, Wendy Wing Lam CHAN, Steven Kwan Keung NG, Tiffany Shurui BAI, Noble Po Kan LO (2023). Critical reflections on ICT and education: Selected papers from the HKAECT 2023 International Conference. Hong Kong: Springer. https://doi.org/10.1007/978-981-99-7559-4
Tso, A. W. B., NG, S. K. K., Law, L., & BAI, T. S. (2023). The Post-pandemic Landscape of Education and Beyond: Innovation and Transformation Selected Papers from the HKAECT 2022 International Conference.

Journal Publications
Shurui Bai, Donn Emmanuel Gonda, & Khe Foon Hew (2024). Write-Curate-Verify: A Case Study of Leveraging Generative AI for Scenario Writing in Scenario-Based Learning. IEEE Transactions on Learning Technologies, 1, 1-10. https://doi.org/10.1109/TLT.2024.3378306
Bai, S., Hew, K.F., Gonda, D.E. et al. (2022). Incorporating fantasy into gamification promotes student learning and quality of online interaction. Int J Educ Technol High Educ, 19, 29. https://doi.org/10.1186/s41239-022-00335-9.
Hew, K. F., Bai, S., Huang, W., Dawson, P., Du, J., Huang, G., Jia, C., & Thankrit, K. (2021). On the use of flipped classroom across various disciplines: Insights from a second-order meta-analysis. Australasian Journal of Educational Technology37(2), 132–151. https://doi.org/10.14742/ajet.6475
Bai, S., Hew, K. F., Sailer, M., & Jia, C. (2021). From top to bottom: How positions on different types of leaderboard may affect fully online student learning performance, intrinsic motivation, and course engagement. Computers and Education, 173 Doi:10.1016/j.compedu.2021.104297.
Hew, K.F., Bai, S., Dawson, P., & Lo, C.K. (2021). Meta-analyses of flipped classroom studies: A review of methodology. Educational Research Review, 33, 100393.
Chengyuan Jia, Khe Foon Hew, Shurui Bai & Weijiao Huang (2022). Adaptation of a conventional flipped course to an online flipped format during the Covid-19 pandemic: Student learning performance and engagement. Journal of Research on Technology in Education, 54(2), 281-301 Doi:10.1080/15391523.2020.1847220.
Khe Foon Hew, Chengyuan Jia, Donn Emmanuel Gonda & Shurui Bai (2020). Transitioning to the “new normal” of learning in unpredictable times: Pedagogical practices and learning performance in fully online flipped classrooms. International Journal of Educational Technology in Higher Education, 17 Doi:10.1186/s41239-020-00234-x.
Shurui Bai, Khe Foon Hew, Biyun Huang (2020). Does gamification improve student learning outcome? Evidence from a meta-analysis and synthesis of qualitative data in educational contexts. Educational Research Review, 30 Doi:10.1016/j.edurev.2020.100322.

Conference Papers
Bai, S., Liu, Y., Song, Y., & Cross, J. S. (2023). Exploring the Effects of Digital Storytelling-Enhanced Scenario-Based Learning on Students’ Learning Outcomes. 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), 1–7. https://doi.org/10.1109/TALE56641.2023.10398298
Shurui Bai, Yingxue Liu, Yanjie Song, & Jeffrey Scott Cross (2023, November). Exploring the Effects of Digital Storytelling-Enhanced Scenario-Based Learning on Students' Learning Outcomes. 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), Auckland, New Zealand. https://doi.org/10.1109/TALE56641.2023.10398298
Leminen, Felix & Bai, Shurui (2023, September). Applying visual mnemonics enhances Chinese characters learning for Chinese as second language learners: A mixed-method study. Proceedings of the 7th International Conference on Digital Technology in Education, ICDTE 2023, Hangzhou China. https://doi.org/10.1145/3626686.3631646
S. Bai, K. F. Hew, S. Zhang, W. Huang and D. E. Gonda, "Examining Effects of Science Flipped Classrooms A Meta-analysis," 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), Hung Hom, Hong Kong, 2022, pp. 276-283, doi: 10.1109/TALE54877.2022.00053.
Bai, S., Hew, K. F., & Gonda, D. E. (2022, November). How Fantasy May Affect Student Engagement in Gamified Fully Online Classes: A Mixed-Method Study. In 2022 20th International Conference on Information Technology Based Higher Education and Training (ITHET) (pp. 1-6). IEEE.
S. Bai, K. F. Hew and D. E. Gonda, "Examining Effects of Different Leaderboards on Students' Learning Performance, Intrinsic Motivation, and Perception in Gamified Online Learning Setting," 2021 IEEE International Conference on Educational Technology (ICET), Beijing, China, 2021, pp. 36-41, doi: 10.1109/ICET52293.2021.9563130.
S. Bai, D. E. Gonda and K. F. Hew, "Effects of Tangible Rewards on Student Learning Performance, Knowledge Construction, and Perception in Fully Online Gamified Learning," 2021 IEEE International Conference on Engineering, Technology & Education (TALE), Wuhan, Hubei Province, China, 2021, pp. 899-904, doi: 10.1109/TALE52509.2021.9678741.
Hew,K.F., Bai,S., Huang,W., Du,J., Huang,G., Jia,C., & Khongjan,T. (2020). Does flipped classroom improve student cognitive and behavioral outcomes in STEM subjects? Evidence from a second-order meta-analysis and validation study. Blended learning: Education in a smart learning environment: 13th International Conference, ICBL 2020, Bangkok, Thailand, August 24–27, 2020, proceedings, 264-275 Doi:10.1007/978-3-030-51968-1_22.
Shurui BAI, Khe Foon HEW (2020, 4). Gamification enhances student academic performance in educational contexts: Evidence from meta-analysis. Paper presented at 2020 Annual Meeting of American Educational Research Association: "The Power and Possibilities for the Public Good When Researchers and Organizational Stakeholders Collaborate", San Francisco
Shurui Bai (2019, November). Examining the effects of leaderboards in gamified learning environment. Proceedings of the 27th International Conference on Computers in Education, Taiwan. http://www.scopus.com/inward/record.url?scp=85077683974&partnerID=8YFLogxK Scopus publicationLink to publication in Scopus, https://apsce.net/icce/icce2019/04_Proceedings.htmlpublished version
Shurui Bai & Khe Foon Hew (2019, 11). Examining the effect of gamification in information science, computer and engineering education: A meta-analysis of student learning performance. Proceedings of the 27th International Conference on Computers in Education, Taiwan.

Projects

Impact of Artificial Intelligence-enabled Automated Adaptive Feedback on Students’ Motivational and Cognitive Learning Outcomes in Scenario-based Learning
Research Objective(s):
1. To examine the different specific features of adaptive feedback.
2. To investigate the effects of adaptive and static feedback on students’ motivational and cognitive learning outcomes.
Artificial intelligence (AI) development makes the automation of adaptive feedback feasible and efficient. However, studies about automated adaptive feedback on student motivation are scant. These relevant questions are unanswered. What are the different specific features of adaptive feedback? What elements should the feedback adapt to for enhanced learning (Bernacki et al., 2021)? How does adaptive feedback affect learners’ motivation and cognitive learning performance? This study aims to address these important questions.
In this research, we apply automatic adaptive feedback in chatbot format in an SBL setting. The primary aim of this research is to examine the effects of automated adaptive feedback on student intrinsic motivation in scenario-based learning compared with static feedback. This work may help explore how adaptive feedback may affect students’ different aspects of motivation (i.e., affective, behavioural and cognitive). Second, we will provide easy-to-follow guidance for teachers on using AI to automate adaptative feedback and design SBL tasks for improved student motivation.

Project Start Year: 2024, Principal Investigator(s): BAI, Shurui, Tiffany
 
Impact of Artificial Intelligence-enabled Automated Adaptive Feedback on Students’ Motivational and Cognitive Learning Outcomes in Scenario-based Learning
A quasi-experimental design and a mixed method will be adopted in this study. Mixed-method research entails the utilisation of quantitative and qualitative data. We will apply the static feedback in Experiment 1 (Jan–Feb 2024) and the adaptive feedback in Experiment 2 (Mar–Apr 2024) in the same course. The course is offered in a master programme at a university in Hong Kong (the PI is the teacher). This course discusses coding strategies to develop students’ computational thinking in educational contexts.
Project Start Year: 2023, Principal Investigator(s): BAI, Shurui, Tiffany
 
Developing learners’ problem-solving competence using digital storytelling-driven gamification approach
To cope with the challenges of the Twenty-first Century, our students need to be equipped with problem-solving skills. By contextualizing the learning content in a story supported by digital multimedia and visualizing step-by-step process of solving a problem, digital storytelling presents its effectiveness in fostering students’ problem-solving competence. But feedback of students’ learning tasks performance and less-motivated students are often neglected in solely digital storytelling strategy. Gamification can provide instant feedback on one’s actions or performance and keep one motivated by awarding game elements such as points, badges and ranks on leaderboards. Hence, we suggest combining digital storytelling with gamification mechanism to better guide students in solutions formulation, implementation and evaluation process. This project aims to explore the application of the digital storytelling-driven gamification (DSDG) approach to promote students’ problem-solving skills in real-world scenarios. Through the process of working with teammates, demonstrating and evaluating the solution, students could also develop their collaborative, communicative and research competencies. The final outputs will develop DSDG design guidelines based on the research findings. A digital toolkit and a series of sandbox workshops of DSDG using this toolkit to scaffold the design process will also be generated.
Project Start Year: 2023, Principal Investigator(s): BAI, Shurui, Tiffany
 
Toward an Effective Gamification Design in Higher Education: Validating a Theoretical GAFCC-F Model Using Design-based Research
To examine how each motivational element in the GAFCC-F model (Goal, Access, Feedback, Challenge, Collaboration, Fantasy) and its relevant game design elements (e.g., points, badges, leaderboards) affect students’ intrinsic motivation in doing learning tasks. PI will also identify the challenges students, teachers, and instructional designers face when experiencing and designing gamification. To refine the GAFCC-F model based on participants’ suggestions for tackling challenges and improving the learning experience in gamified learning, with a set of proposed design principles corresponding to each motivational element.
Project Start Year: 2022, Principal Investigator(s): BAI, Shurui, Tiffany