PERC 2016 Abstract Detail Page
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| Abstract Title: | Data, Variables, and Evidence: Specifying Theoretically Sound Predictive Models |
|---|---|
| Abstract: | In conducting large-scale research efforts which seek to determine the effect of active learning and Learning Assistant support on student outcomes, we routinely collect massive amounts of data from a variety of measurement instruments. Each of these data sources carries with it implicit assumptions about learning. For example, pre-post concept inventory and gain scores assume a cognitive theory of learning, where the latent construct resides in an individual's singular mind. On the other hand, characterizations of student interactions within a collective classroom network assume at least a socio-cognitive (if not sociocultural) view on learning, where interactions between individuals contribute to development of understanding or sophistication. Using different data sources such as these to define distinct variables within the same quantitative model requires theoretical justification and articulation of an explicit learning theoretical framework. In this presentation we describe our work in dealing with these issues. |
| Abstract Type: | Symposium Talk |
| Parallel Session: | Investigating the Impact of Learning Assistant Model Adoption on Students and Learning Assistants |
Author/Organizer Information | |
| Primary Contact: |
Robert M. Talbot School of Education and Human Development, University of Colorado Denver |
| Co-Author(s) and Co-Presenter(s) |
Leanne Doughty, School of Education and Human Development, University of Colorado Denver Amreen Nasim, School of Education and Human Development, University of Colorado Denver Paul Le, Department of Integrative Biology, University of Colorado Denver Laurel Hartley, Department of Integrative Biology, University of Colorado Denver |
