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. Copyright © 2018 UTS ePRESS.
CitationChiu, 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. doi: 10.18608/jla.2018.51.6
- Multilevel modelling
- Hierarchicaly linear modelling
- Mathematical proof
- Sequential analysis