Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions


  • MIng Ming Chiu Department of Special Education and Counseling The Education University of Hong Kong



Time, multilevel modelling, hierarchicaly linear modelling, mathematical proof, sequential analysis.


Learning analysts often consider whether learning processes across time are related 1) to one another or 2) to learning outcomes at higher levels. For example, are a group's temporal sequences of talk (e.g., correct evaluation -> correct, new idea) during its problem solving related to its group solution? I show how to address these issues with 1) a higher-level outcome regression and 2) a lower-level process regression, applying both to 3,234 turns of talk by 80 students working in 20 groups to solve an algebra problem. The easy-to-use, outcome-level analysis of group solution score has the following problems: multicollinearity, possibly low statistical power, cannot test for links among sequence components, and cannot model outcomes at multiple levels. The complex, process-level analysis for turns of talk overcomes these shortcomings with multilevel analysis, vector auto-regression, and outcome-level regression residuals. These results suggest a combined procedure. First, run an outcome-level analysis. If the results are significant, then the outcome-level results suffice. Otherwise, non-significant results might reflect multicollinearity, which then requires a process-level analysis. This procedure can help test a comprehensive model of how learning processes or their temporal sequences are related to learning outcomes at the turn-, time period-, individual-, group-, class-, and school-levels.


Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models. London: Sage.

Chen, G., & Chiu, M. M. (2008). Online discussion processes: Effects of earlier messages’ evaluations, knowledge content, social cues and personal information on later messages. Computers and Education, 50, 678-692.

Chiu, M. M. (2008). Flowing toward correct contributions during groups' mathematics problem solving: A statistical discourse analysis. Journal of the Learning Sciences, 17(3), 415-463.

Chiu, M. M., & Chow, B. W. Y. (2015). Classmate characteristics and student achievement in 33 countries: Classmates’ past achievement, family SES, educational resources and attitudes toward reading. Journal of Educational Psychology, 107(1), 152-169.

Chiu, M. M., & Fujita, N. (2014). Statistical discourse analysis: A method for modelling online discussion processes. Journal of Learning Analytics, 1(3), 61–83.

Chiu, M. M., & Khoo, L. (2005). A new method for analyzing sequential processes: Dynamic multi-level analysis. Small Group Research, 36, 600-631.

Chiu, M. M., & Klassen, R. M. (2010). Relations of mathematics self-concept and its calibration with mathematics achievement. Learning and Instruction, 20, 2-17.

Chiu, M. M., & Kuo, S. W. (2009). From metacognition to social metacognition: Similarities, differences, and learning. Journal of Education Research, 3, 4, 1-19.

Chiu, M. M., & Lehmann-Willenbrock, N. (in press). Statistical discourse analysis: Modeling sequences of individual behaviors during group interactions across time. Group Dynamics: Theory, Research, and Practice.

Cohen, J., West, S. G., Aiken, L., & Cohen, P. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Mahwah, NJ: Lawrence Erlbaum.

Goldstein, H. (2011). Multilevel Statistical Models. Sydney: Edward Arnold.

Hacker, D. J., & Bol, L. (2004). Metacognitive theory: Considering the social-cognitive influences. In D. M. McInerney & S. Van Etten (Eds.), Big theories revisited: Vol. 4 (pp. 275-297). Greenwich, Connecticut: Information Age Publishing, Inc.

Kennedy, P. (2008). A Guide to Econometrics. Cambridge: Blackwell.

Molenaar, I., & Chiu, M. M. (2014). Dissecting sequences of regulation and cognition: Statistical discourse analysis of primary school children's collaborative learning. Metacognition and Learning, 9, 137-160.




How to Cite

Chiu, M. M. (2018). Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions. Journal of Learning Analytics, 5(1), 75–91.



Special Section: It's About Time: Temporal Analysis of Learning Data Part 2