Nokes - Dialectical Interaction and Robust Learning
Dialectical Interaction and Robust Learning
|PIs||Timothy Nokes, John Levine|
|Other Contributers||Daniel Belenky, Soniya Gadgil|
|Study Start Date||Sep. 1, 2009|
|Study End Date||May. 31, 2010|
|Site||University of Pittsburgh|
|Number of Students||N = ~180|
|Total Participant Hours||~360.|
|DataShop||no data yet|
Background & Significance
This work, which lies at the intersection of motivation, affect, social interaction and conceptual learning, studies the role of affect in a learning situation in which it is hypothesized to play a particularly prominent role. We focus on dialectical interaction, in which two or more people with roughly equal status but alternative viewpoints work together to solve a problem, perform a task, or reach agreement on an issue. The term “alternative viewpoints” is used broadly to include different stances on a controversial issue and different strategies for solving a problem. We assume that dialectical interaction affects participants’ cognitive activity in large part through its impact on their motivational states / goals and affective responses during discussion.
How do students learn when engaged in a debate? Do they integrate their own viewpoint with that of their opponent, or focus only on their own side? Does the format of the debate affect this? Also, what motivational and affective factors play into this? How do student goals (like performance or mastery goals) influence what information gets processed? Do different affective experiences lead to different patterns of learning?
Our first study has a 2 x 2 design.
Factor 1: Debate Format - Alternating Turns (1 minute each) or Free-Form
Factor 2: Debate Criterion - Substance or Rhetoric
Our dependent variables will consist of various measures of learning, gathered after the debate. These measures include a multiple-choice test, as well as an essay. Both of these will be evaluated in terms of how well a student has learned his side of the debate, as well as how well he has learned the other side, and how well he has integrated the two.
We will also gather measures of affect during the debate. This will be used as a dependent variable to see if our manipulations relating to the debate structure influence the affective reactions experienced. We will also be able to affective response as a predictor of learning, or as mediating variable between debate and learning. These affective measures will come from analysis of the vocal parameters of speech of each participant, as well as by analysis of facial cues.
We predict that focusing the debate on the substance of the arguments will produce a more coherent representation of both sides of the argument. We also predict that the free-form debate will lead to better learning of both sides, as the participants must be engaging with what a participant is saying more actively, and respond more immediately and thoroughly than when they have a minute between speaking turns.
In terms of affect, we expect that positive affective reactions to cognitive conflict will produce systematic processing of an opponent’s arguments, which in turn will facilitate learning these arguments and developing a more complex cognitive representation of the discussion topic. In contrast, negative affective reactions will produce superficial processing of the opponent’s arguments coupled with rehearsal of one’s own arguments. When negative affect is mild, interactants are unlikely to learn the opponent’s arguments or to develop a complex representation of the topic. Moreover, when negative affect is strong, interactants may actually show cognitive regression -- less complex representations of the topic after interaction than before.