Aligned with its strategic vision, the Center for STEM and AI Education at the University of Macau is committed to advancing integrated and inclusive approaches to STEM and AI learning. The Center’s research agenda involves but is not limited to: advancing teaching, learning, and assessment in STEM and AI education, supporting teacher professional development, promoting effective technology integration, and informing evidence-based policy innovation. Its research achieves global benchmarks of scholarly quality while simultaneously addressing urgent educational needs and broader social demands within both the local and national contexts.

Faculty members contribute actively to leading international journals, including Review of Educational Research, Educational Research Review, Educational Psychology Review, Journal of Educational Psychology, Teaching and Teacher Education, Journal for Research in Mathematics Education, Educational Studies in Mathematics, ZDM–Mathematics Education, International Journal of STEM Education, Science Education, Journal of Research in Science Teaching, Computers & Education, Computers in Human Behavior, and the British Journal of Educational Technology. These publications underscore the Center’s interdisciplinary expertise and its commitment to generating robust empirical evidence.

Representative Journal Publications of Centre Members

1. Lianghuo FAN

• Fan, L., Wijayanti, D., Meng, D., Li, K., & Mailizar, M. (2025). The role of textbooks in the implementation of curriculum development: a comparative study through the lens of Chinese and Indonesian teachers’ views. ZDM-Mathematics Education, 57(5), 935-949. https://doi.org/10.1007/s11858-025-01692-1
• Fan, L., Xie, S., Luo, L., Li, L., Tang, J., & Li, S. (2023). Teachers’ perceptions of less successfully organized professional development practices in mathematics: A study of nine secondary schools in Shanghai, China. Journal of Mathematics Teacher Education, 26(5), 667–697. https://doi.org/10.1007/s10857-023-09591-6
• Fan, L., Qi. C., Liu. X, Wang. Yi, & Lin. M. (2017). Does a transformation approach improve students’ ability in constructing auxiliary lines for solving geometric problems? An intervention-based study with two Chinese classrooms. Educational Studies in Mathematics, 96(2), 229–248. https://doi.org/10.1007/s10649-017-9772-5
• Fan, L. (2013). Textbook research as scientific research: towards a common ground on issues and methods of research on mathematics textbooks. ZDM-International Journal on Mathematics Education, 45(5), 765-777. https://doi.org/10.1007/s11858-013-0530-6
• Fan, L. (2003). A generalization of synthetic division and a general theorem of division of polynomials. Mathematical Medley, 30(1), 30–37. http://eprints.soton.ac.uk/168861/1/FLH_article_on_polynomial_division.pdf

2. Chunlian JIANG

• Cai, J., Koichu, B., Rott, B., & Jiang, C. (2024). Advances in research on mathematical problem posing: Focus on task variables. Journal of Mathematical Behavior, 76, 101186. https://doi.org/10.1016/j.jmathb.2024.101186.
• Li, Y., Jiang, C., Chen, Z., Fang, J., Wang, C., & He, X. (2023). Peer tutoring models in cooperative learning of mathematical problem-solving in flipped classroom and their influence on group achievement. Education and Information Technologies, 28, 6595-6618. https://doi.org/10.1007/s10639-022-11429-2.
• Wang, F., Jiang, C., King, R. B., & Leung, S. O. (2023). Motivated Strategies for Learning Questionnaire (MSLQ): Adaptation, validation, and development of a short form in the Chinese context for mathematics. Psychology in the Schools, 60, 2018-2040. https://doi.org/10.1002/pits.22845.
• Cai, J. & Jiang, C. (2017). An analysis of problem-posing tasks in Chinese and US elementary mathematics textbooks. International Journal of Science and Mathematics Education, 15(8), 1521-1540. https://doi.org/10.1007/s10763-016-9758-2.
• Jiang, C., Hwang, S., & Cai, J. (2014). Chinese and Singaporean sixth-grade students’ strategies for solving problems about speed. Educational Studies in Mathematics, 87(1), 27-50. https://doi.org/10.1007/s10649-014-9559-x.

3. Xuhua SUN

• Sun, X. H. (2019). Bridging whole numbers and fractions: Problem variations in Chinese mathematics textbook examples. ZDM Mathematics Education, 51(1), 109-123.
• Bartolini Bussi, M. G., Bertolini, C., Ramploud, A., & Sun, X. H. (2017). Cultural transposition of Chinese lesson study to Italy: An exploratory study on fractions in a fourth-grade classroom. International Journal for Lesson and Learning Studies, 6(4), 380-395.
• Sun, X. H., Teo, T., & Chan, T. C. (2015). Application of the open-class approach to pre-service teacher training in Macao: A qualitative assessment. Research Papers in Education, 30(5), 567-584.
• Sun, X. H. (2011). “Variation problems” and their roles in the topic of fraction division in Chinese mathematics textbook examples. Educational Studies in Mathematics, 76(1), 65-85.
• Sun, X. H., Xin, Y. P., & Huang, R. (2019). A complementary survey on the current state of teaching and learning of Whole Number Arithmetic and connections to later mathematical content. ZDM–Mathematics Education, 51(1), 1-12.

4. Xinrong YANG

• Krawitz, J., Schukajlow, S., Yang, X., & Geiger, V. (2025). A Systematic Review of International Perspectives on Mathematical Modelling: Modelling Goals and Task Characteristics. ZDM–Mathematics Education, 57(2-3), 193–212.
• Yang, X., Chan, M. C. E., Kaur, B., Deng, J., Luo, J., & Wen, Y. (2025). International comparative studies in mathematics education: a scoping review of the literature from 2014 to 2023. ZDM–Mathematics Education, 57(4), 745–761.
• Yang, X., Li, X., Deng, Z., & Kaiser, G. (2025). Obstacles to in-service Chinese high school mathematics teachers’ implementation of mathematical modelling in classrooms: An empirical investigation of teachers’ perspectives. ZDM–Mathematics Education, 57(2-3), 535–551.
• Zhang, Y., Yang, X., & Kaiser, G (2023). The reciprocal relationship among Chinese senior secondary students’ motivation, cognitive engagement, and mathematics achievement: A three-wave cross-lagged study. ZDM–Mathematics Education, 55(2), 399–412.
• Yang, X., Schwarz, B., & Leung, I. (2022). Pre-service mathematics teachers’ professional modelling competencies: A comparative study between Germany, Mainland China and Hong Kong. Educational Studies in Mathematics, 109(2), 409-429.

5. Kwok Cheung CHEUNG

• Zheng, J. Q., Cheung, K. C., Sit, P. S., Lam, C. C. (2025). Unfolding key factors of resilience in ICT cognitive-motivational engagement: Global evidence from machine learning techniques. International Journal of Educational Research, 131, 102607.
• Cheung, K. C., Sit, P. S., Zheng, J. Q., Lam, C. C., Mak, S. K., Ieong, M. K. (2024). A machine learning model of academic resilience in the times of the COVID-19 pandemic: Evidences drawn from 79 countries/economies in the PISA 2022 mathematics study. British Journal of Educational Psychology, 94(4), 1224-1244.
• Zheng, J. Q., Cheung, K. C., & Sit, P. S. (2024). A systematic review of academic resilience in East Asia: Evidence from the large-scale assessment research. Psychology in the Schools, 61(3), 1238-1254.
• Zheng, J. Q., Cheung, K. C., & Sit, P. S. (2023). Identifying key features of resilient students in digital reading: Insights from a machine learning approach. Education and Information Technologies, 29(2), 2277-2301.
• Zheng, J. Q., Cheung, K. C., & Sit, P. S. (2024). The effects of perceptions toward Interpersonal relationships on collaborative problem-solving competence: comparing four ethnic Chinese communities assessed in PISA 2015. The Asia-Pacific Education Researcher, 33(2), 481-493.

6. Emily Pey-Tee OON

• Li, Z., Hu, W., & Oon, P. T. (2024). Unveiling pre-service teachers’ competency and challenges in 5E inquiry-based STEM lessons: A quantitative ethnography approach. International Journal of Science and Mathematics Education.
• Li, Z., Oon, P. T., (2024). The transfer effect of computational thinking (CT)-STEM: A systematic literature review and meta-analysis. International Journal of STEM Education, 11, 44.
• Li, Z., Oon, P. T. E., & Chai, S. (2024). Examining the impact of teacher scaffolding in the knowledge building environment: Insights from students’ interaction patterns, social epistemic networks, and academic performance. Education and Information Technologies, 29(14), 18501-18532.
• Zhang, L., Lin, Y., & Oon, P. T.(2025). The implementation of engineering design-based STEM learning and its impact on primary students’ scientific creativity. Research in Science & Technological Education, 43(2), 568-588.
• Oon, P. T., Cheng, M. M. W., & Wong, A. S. L. (2020). Gender differences in attitude towards science: Methodology for prioritising contributing factors. International Journal of Science Education, 42(1), 89-112.

7. Xiaowei TANG

• Jin, X., Tang, X., Yu, B., Li, Z., Chen, J., Zhu, Z., …, & Ding, B. (2025). Professional Learning in a Web‐Based Community of Practice Of, By, and For Chinese Primary Science Teachers: A Narrative Inquiry. Science Education, 109, 928-946.
• Tang, X., & Hammer, D. (2024). “I think of it that way and it helps me understand”: Anthropomorphism in elementary students’ mechanistic stories. Science Education, 108(3), 661-679.
• Tang, X., Shu, G., Wei, B., & Levin, D. (2024). Emergent learning about measurement and uncertainty in an inquiry context: A case from an elementary classroom. Science Education, 108(1), 308-331.
• Tang, X., Levin, D. M., Chumbley, A. K., & Elby, A. (2022). Arguing about argument and evidence: Disagreements and ambiguities in science education research and practice. Science Education, 106(2), 285-311.
• Tang, X., Elby, A., & Hammer, D. (2020). The tension between pattern‐seeking and mechanistic reasoning in explanation construction: A case from Chinese elementary science classroom. Science Education, 104(6), 1071-1099.

8. Bing WEI

• Tan, L., Wei, B., & Chen, F. (2025). An exploratory process mining on students’ complex problem-solving behavior: The distinct patterns and related factors. Computers & Education, 238, 105398.
• Jiang, Z. & Wei, B.(2025). Understanding science identity development among college students: A systematic literature review. Science & Education, 34 (3), 1797-1824.
• Lam, S. F., Vong, K. I. P., & Wei, B. (2025). The malaise of preserving a minority language in multilingual homes in southwest China: A family language policy perspective. Journal of Multilingual and Multicultural Development, 1-17.
• Su, R. & Wei, B.(2025). Representation of the views of nature and human-nature relationships in chemistry textbooks: A comparative analysis. International Journal of Science Education. 47 (10), 1304-1327.
• Tan, L., & Wei, B. (2025). How science teachers deal with STEM education: An explorative study from the lens of curriculum ideology. Science Education, 109(1), 82-105.

9. Fu CHEN

• Li, X., Chen, F., & Lu, C. (2025). Internet usage inequality among high school students: Patterns, motivational predictors, and educational outcomes. Computers & Education. https://doi.org/10.1016/j.compedu.2025.105417
• Chen, F., Lu, C., & Cui, Y. (2024). Using learners’ problem-solving processes in computer-based assessments for enhanced learner modeling: A deep learning approach. Education and Information Technologies, 29, 13713–13733. https://doi.org/10.1007/s10639-023-12389-x
• Chen, F., Liu, Y., Lu, C., Gao, Y., & Cui, Y. (2025). Does ICT matter for student complex problem-solving competency? A multilevel analysis of 33 countries and economies. Thinking Skills and Creativity, 57, 101805. https://doi.org/10.1016/j.tsc.2025.101805
• Chen, F., Lu, C., Cui, Y., & Gao, Y. (2023). Learning outcome modeling in computer-based assessments for learning: A sequential deep collaborative filtering approach. IEEE Transactions on Learning Technologies, 16(2), 243– https://doi.org/10.1109/TLT.2022.3224075
• Chen, F., Sakyi, A., & Cui, Y. (2022). Identifying key contextual factors of top performers in digital reading through a machine learning approach. Journal of Educational Computing Research,60(7), 1763–1795. https://doi.org/10.1177/07356331221083215

10. Kan Kan CHAN

• Lin, X., Luo, G., Luo, S., Liu, J. Chan, K., Chen, H., Zhou, W., & Li, Z. (2024). Promoting pre-service teachers’ learning performance and perceptions of inclusive education: An augmented reality-based training through learning by design approach. Teaching and Teacher Education, 148, 104661.
• Chan, K. K. (2020). Using tangible objects in early childhood classrooms: A study of Macau pre-service teachers. Early Childhood Education Journal, 48(4), 441-450.
• Chen, R. W., & Chan, K. K. (2019). Using augmented reality flashcards to learn vocabulary in early childhood education. Journal of Educational Computing Research, 57(7), 1812-1831.
• Zhou, M., Chan, K. K., & Teo, T. (2016). Modelling Mathematics Teachers’ Intention to Use the Dynamic Geometry Environments in Macau: An SEM Approach. Journal of Educational Technology & Society, 19(3), 181-193.
• Chan, K. K. (2015). Salient Beliefs of Mathematics Teachers Using Dynamic Geometry Software. EURASIA Journal of Mathematics, Science & Technology Education, 11(1), 139-148.

11. Andy Chun Wai FAN

• Dai, Y., Xiao, J.-Y., Huang, Y., Zhai, X., Wai, F.-C., & Zhang, M. (2025). How generative AI enables an online project-based learning platform: An applied study of learning behavior analysis in undergraduate students. Applied Sciences, 15(5), 2369.
• Dai, Y., Xiao, J.-Y., Huang, Y., Zhai, X., Wai, F.-C., & Zhang, M. (2025). How generative AI enables an online project-based learning platform: An applied study of learning behavior analysis in undergraduate students. Applied Sciences, 15(5), 2369.
• Zeng, J., Sun, D., Looi, C. K., & Fan, A. C. W. (2024). Exploring the impact of gamification on students’ academic performance: A comprehensive meta‐analysis of studies from the year 2008 to 2023. British Journal of Educational Technology, 55(6), 2478–2502.
• Dai, Y., Xiao, J.-Y., Huang, Y., Zhai, X., Wai, F.-C., & Zhang, M. (2025). How generative AI enables an online project-based learning platform: An applied study of learning behavior analysis in undergraduate students. Applied Sciences, 15(5), 2369.
• Fan, W. A. (2014). To explore the effectiveness of computer games to improve junior primary students in drawing. International Journal of Information and Education Technology, 4(4), 373.

12. Lijia LIN

• Xu, K. M., & Lin, L., Gorter, M., Davis, R. O., Schneider, S., Weidlich, J. Kreijns, K., de Groot, R. (2025). Social presence: A key factor in embedding a pedagogical agent into online learning in primary education. British Journal of Educational Technology, 00:1-16.
• Lin, L., King, R., Fu, L., & Leung, S. O. (2024). Information and communication technology engagement and digital reading: How meta-cognitive strategies impact their relationship. British Journal of Educational Technology, 55(1), 277-296.
• Lin, L., Lin., X., Zhang, X., & Ginns, P. (2024). The personalized learning by interest effect on interest, cognitive load, retention, and transfer: A meta-analysis. Educational Psychology Review, 36(3), 88.
• Xu, K. M., Koorn, P., de Koning, B., Skuballa, I., Lin, L., Henderikx, M., Marsh, H. W., Sweller, J., & Paas, F. (2021). A growth mindset lowers perceived cognitive load and improved learning: Integrating motivation to cognitive load. Journal of Educational Psychology, 113(6), 1177–1191.
• Lin, L., Ginns, P., Wang, T., & Zhang, P. (2020). Using a pedagogical agent to deliver conversational style instruction: What benefits can you obtain? Computers & Education, 143, 103658.

13. Tongxi LlU

• Liu, T. (2024). Relationships between executive functions and computational thinking. Journal of Educational Computing Research, 62(5), 1267–1301.
• Liu, T. (2024). Assessing implicit computational thinking in game-based learning: A logical puzzle game study. British Journal of Educational Technology, 55, 2357–2382.
• Liu, T. (2024). Examining the impact of variations in executive functions on students’ problem-solving behaviors. IEEE Access: IEEE Education Society Section. vol. 12, pp. 185894-185904.
• Ishida, T., Liu, T., & Wang, H. (2024, November). Facilitating holistic evaluations with LLMs: insights from scenario-based experiments. In The 32nd International Conference on Computers in Education, ICCE 2024. Asia-Pacific Society for Computers in Education.
• Liu, T., & Israel, M. (2022). Uncovering students’ problem-solving processes in game-based learning environments. Computers & Education, 104462.

14. Lele SHA

• Li, Y., Sha, L., Yan, L., Lin, J., Raković, M., Galbraith, K., Lyons, K., Gašević, D. and Chen, G., 2023. Can large language models write reflectively. Computers and Education: Artificial Intelligence, 4, p.100140.

Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2023). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112.

• Sha, L., Raković, M., Lin, J., Guan, Q., Whitelock-Wainwright, A., Gašević, D. and Chen, G. (2022). Is the latest the greatest? A comparative study of automatic approaches for classifying educational forum posts. IEEE Transactions on Learning Technologies, 16(3), pp.339-352.
• Sha, L., Rakovic, M., Das, A., Gasevic, D., & Chen, G. (2022). Leveraging class balancing techniques to alleviate algorithmic bias for predictive tasks in education. IEEE Transactions on Learning Technologies, 15(4), 481–492.
• Sha, L., Rakovic, M., Whitelock-Wainwright, A., Carroll, D., Yew, V. M., Gasevic, D., & Chen, G. (2021). Assessing algorithmic fairness in automatic classifiers of educational forum posts. In Lecture notes in computer science (pp. 381–394).

15. Jiahong SU

• Su, J., Chen, X., Chu, S. K. W., & Hu, X. (2025). A scoping review of empirical research on AI literacy assessments. Educational technology research and development, 1-26.
• Su, J.(2024). Development and validation of an artificial intelligence literacy assessment for kindergarten children. Education and Information Technologies, 29(16), 21811-21831.
• Su, J., Guo, K., Chen, X., & Chu, S. K. W. (2024). Teaching artificial intelligence in K–12 classrooms: a scoping review. Interactive Learning Environments, 32(9), 5207-5226.
• Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial intelligence (AI) literacy in early childhood education: The challenges and opportunities. Computers and Education: Artificial Intelligence, 4, 100124.
• Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355-366.