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ERIC Number: EJ1176038
Record Type: Journal
Publication Date: 2018
Pages: 17
Abstractor: As Provided
ISSN: EISSN-1929-7750
Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions
Chiu, Ming Ming
Journal of Learning Analytics, v5 n1 p75-91 2018
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 [right arrow] 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.
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Publication Type: Journal Articles; Reports - Research
Education Level: Grade 9
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: California
Grant or Contract Numbers: N/A