Professor LIU, Lukas Z.

Computational thinking: My current work focuses on developing both students’ and teachers’ computational thinking. I am currently working with a team at University of Massachusetts Dartmouth on developing a professional learning model to promote K-12 teachers’ computational thinking skills as well their ability to integrate computational thinking into STEM classrooms.

Methodological focus: I use various quantitative methods (e.g., hidden Markov model, Bayesian network, sequential analysis, social network analysis, and natural language processing) to understand learners’ behavior, competence, interaction, and discourse. I am also interested in bridging the gap between quantitative and qualitative methods (e.g., quantitative ethnography, design-based implementation research).

Instructional design focus: I have been designing and developing interactive game-based learning environments for learners to promote active learning through problem solving. The goal of such environment is to help learners develop transferrable skills beyond the performance within the system.

• Gagne-Briggs Outstanding PhD Student Award, Florida State University (2020)
• Ruby Diamond Future Professor Award, Florida State University (2019)
• Liliana Muhlman Masoner Award for Excellent Performance as an International Student in the ISLT Program, Florida State University (2019)
• Gagne-Briggs Outstanding Service Award, Florida State University (2018)
• Outstanding Contribution in Reviewing, The Internet and Higher Education (2017)
• Best presentation, 2015 Finalists, PacifiCorp Design and Development Award, Association of Educational Communication and Technology (2015)

​I am available to supervise PhD/EdD/MPhil students and would welcome enquiries for collaboration.

Liu, Z., & Fulwider, G. C (in press). Balancing granularity and context in designing evidence models for stealth assessment: A case of pause distribution in digital game-based learning environment. In M. P. McCreery, & S. K., Krach (Eds.), Games as Stealth Assessments. DIO Press.

Shute, V. J., Fulwider, G. C., Liu, Z., & Rahimi, S. (in press). Machine learning. In R. Tierney, F. Rizvi, & K. Ercikan (Eds.), International Encyclopedia of Education (4th Edition). Elsevier Publishers.

Liu, Z., & Jeong, A. (2022). Connecting learning and playing: The effects of in-game cognitive supports on the development and transfer of computational thinking. Educational Technology Research & Development. https://doi.org/10.1007/s11423-022-10145-5

Bong, J., & Liu, Z. (2022). Media usage behaviors of learners in OODE. In O. Zawacki-Richter, & I. Jung (Eds.), Handbook of Open, Distance and Digital Education. Springer. https://doi.org/10.1007/978-981-19-0351-9_54-1

Bainbridge, K., Shute, V., Rahimi, S., Liu, Z., Slater, S., Baker, R. S., & D'Mello, S. K. (2022). Does embedding learning supports enhance transfer during game-based learning?. Learning and Instruction, 77, 1-11. https://doi.org/10.1016/j.learninstruc.2021.101547

Dennen, V. P., Bagdy, L., Arslan, O., Liu, Z., & Choi, H. (2021) Supporting new online instructors and engaging remote learners during COVID-19: A distributed team-based approach. Journal of Research on Technology in Education. https://doi.org/10.1080/15391523.2021.1924093

Tlili, A., Chang, M., Moon, J., Liu, Z., Burgos, Chen, N., & Kinshuk. (2021) Literature review of empirical studies on learning analytics in educational games: From 2010 to 2018. International Journal of Interactive Multimedia and Artificial Intelligence. http://dx.doi.org/10.9781/ijimai.2021.03.003

Liu, Z., Moon, J., Kim, B., & Dai, C. (2020) Integrating adaptivity to educational games: A combination of bibliometric analysis and meta-analysis review. Educational Technology Research and Development, 68(4), 1931-1959. https://doi.org/10.1007/s11423-020-09791-4

Shute, V. J., Rahimi S., Smith, G., Ke, F., Almond, R., Dai, C, Kuba, R., Liu, Z., Yang, X., & Sun, C. (2020). Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity, and learning supports in Physics Playground. Journal of Computer-Assisted Learning, 37(1). https://doi.org/10.1111/jcal.12473

Shute, V. J., Smith, G., Kuba, R., Dai, C., Rahimi, S., Liu, Z., & Almond, R. G. (2020) The design, development, and testing of learning supports for the physics playground game. International Journal of Artificial Intelligence in Education, 31. https://doi.org/10.1007/s40593-020-00196-1

Moon, J., & Liu, Z. (2019). Rich representations for analyzing learning trajectories: Systematic review on sequential-data analytics in game-based learning research. In A, Tlili., & M, Chang. (Eds.), Data Analytics Approaches in Educational Games and Gamification Systems. Springer. https://doi.org/10.1007/978-981-32-9335-9_2

Jeong, A., & Liu, Z. (2017). he effects of prior beliefs on student interactions in online debates. TechTrends, 61(2), 115-120. https://doi.org/10.1007/s11528-016-0133-5

Lin, X., Hu, X., Hu, Q., & Liu, Z. (2014). A social network analysis of teaching and research collaboration in a teachers' virtual learning community. British Journal of Educational Technology, 47(2). https://doi.org/10.1111/bjet.12234

Community Service

• Communication Officer, American Educational Research Association, Division of Emerging Learning Technologies, 2020 – Present
• Member at Large, American Educational Research Association, Division of Emerging Learning Technologies, 2019 – 2020
• Planning committee member of Florida State University Alumni Student Knowledge Exchange (ASKe) Conference, 2017 – 2019
• President, Florida State University Instructional Systems Students Association, 2017 - 2018

Reviewer

• Reviewer for Journal of Computer Assisted Learning, 2022 - Present
• Reviewer for British Journal of Educational Technology, 2020 – Present
• Reviewer for Journal of Research in Educational Technology, 2020 – Present
• Reviewer for Educational Technology Research & Technology, 2019 – Present
• Reviewer for Internet and Higher Education, 2016 – Present