Hausmann Study2

The Effects of Interaction on Robust Learning

Robert Hausmann and Kurt VanLehn

Abstract

It is widely assumed that an interactive learning resource is more effective in producing learning gains than non-interactive sources. It turns out, however, that this assumption may not be completely accurate. For instance, research on human tutoring suggests that human tutoring (i.e., interactive) is just as effective as reading a textbook (i.e., non-interactive) under very particular circumstances (VanLehn et al., in press). This rises the question, under which conditions should we expect to observe strong learning gains from interactive learning situations? The current project seeks to address this question by contrasting interactive learning (i.e., jointly constructing explanations) with non-interactive learning (i.e., individually constructing explanations).

Background and Significance

Several studies on collaborative learning have shown that it is more effective in producing learning gains than learning the same material alone. This finding has been replicated in many different configurations of students and across several different domains. Once the effect was established, the field moved into a more interesting phase, which was to accurately describe the interactions themselves and their impact on student learning (Dillenbourg, 1999). One of the hot topics in collaborative research is on the "co-construction" of new knowledge. Co-construction has been defined in many different ways. Therefore, the present study limits the scope of co-constructed ideas to jointly constructed explanations.

Evidence of the impacton jointly constructed explanations is sparse, but can be found in a study by McGregor and Chi (2002). They found that collaborative peers are able to not only jointly constructed ideas, but they will also reuse the ideas in a later problem-solving session. One of the limitations of their study is that it did not assess impact of jointly constructed ideas on individual, robust learning. In a related study, Hausmann and Chi (2004) found correlational evidence for learning from co-construction. To provide more stringent evidence for the impact of jointly constructed explanations, the present sutdy will manipulate the types of conversations dyads have by promtping for jointly constructed explanations and measuring the effect on robust learning.

Glossary

Jointly constructed explanation: a statement or set of statements, spread across two (or more) speakers, that makes explicit the causal or relational connection between concepts. Here is an example of a jointly constructed explanation in the domain of kinematics (from Hausmann & Chi, 2004):

Jill:	Force acting on block B, is different from force acting on block A.
Sara:	Ok. Because their mass, is different.
Jill:	Yeah. Because-yeah.

Jill presents a proposition (i.e., the forces are different) and Sara supplies the justification (i.e., because the masses are different).

Prompting: an explicit verbal reminder to engage in a specific interactive process, such as explaining.

Research question

How is robust learning affected by self-explanation vs. jointly constructed explanations?

Independent variables

Two variables were crossed:

• Interaction: singleton vs. dyad
• Engagement: natural vs. prompted

The interaction variable manipulates whether the participants works individually (i.e., singleton) or with a collaborative peer (i.e., dyad).

Crossed with the first variable, the engagement variable manipulates if the participants are prompted to keep working together (i.e., natural) or reminds them to jointly construct an explanation of the step they just observed (i.e., prompted). Prompting for natural speech is to control for Hawthorn effects. Prompting for jointly construct explanations increases the probability that the dyad will traverse that particular learning event path.

Hypothesis

The Interactive Hypothesis: collaborative peers will learn more than the individual learners because they benefit from the process of negotiating meaning with a peer, of appropriating part of the peers’ perspective, of building and maintaining common ground, and of articulating their knowledge and clarifying it when the peer misunderstands. In terms of the Intearctive Communication cluster, the hypothesis states that, even when controlling for the amount of knowledge components covered, the dyads will learn more than the individuals.

The Coverage Hypothesis: if both peers and singletons cover the same knowledge components, then they will learn the same amount.

Dependent variables & Results

• Near transfer, immediate: problems solved during the laboratory period.

• Near transfer, retention: homework preformance on electrodynamics problems that are isomorphic to the problems solved during the laboratory period.

• Far transfer, retention: homework preformance on electrodynamics problems that are not isomorphic to the problems solved during the laboratory period.

• Acceleration of future learning: homework preformance on magnetisim problems.

The data for this study have not been collected (target date = January, 2008). Our predicted results are:

Main Effect (Engagement): The coverage hypothesis (CI) makes the predictions shown in the Table below. First, there should be a main effect for interaction with dyads performing better than the singletons because, as the earlier studies on collaboration suggest, peer learning increases the probability of covering the set of knowledge applications compared to solo learning. The interaction hypothesis (IH) makes a similar prediction, but for a different reason. The interaction hypothesis states that the dyads will outperform the singletons because of the interaction itself.

Main Effect (Prompting): The coverage hypothesis predicts a main effect for prompting such that the prompted condition will cover more knowledge components and thus learn more than the natural condition. Evidence for this prediction can be found in studies examining individual and collaborative learning. In terms of individual learning, Chi et al. (1994) successfully increased the rate of self-explanations through the use of content-free prompts, which then led to greater amounts of learning (especially on more difficult items). For collaborative learning, Coleman (1998) was able to prompt groups of students to produce explanations. Therefore, we expect that the prompting will raise the singletons up to the level of the dyads. The interaction hypothesis does not make a clear prediction for the effect of engagement. However, it would probably predict a null effect because the main effect of prompting combines or collapses over the beneficial effects of interaction; thus, washing out any effects of the prompting procedure.

Interaction: The coverage hypothesis predicts that the prompted-explanation dyads should learn the same amount as the prompted-explanation singletons. The interaction hypothesis would predict that the dyads should learn more than the singletons. This assumes that both groups cover the same knowledge applications, as intended by the prompting manipulation. However, if the solos have less prior knowledge at their disposal than the dyads, then they may not be able to cover as many knowledge applications as the dyads. In this case, both the coverage hypothesis and the interaction hypothesis predict that dyads will gain more. The manipulation check mentioned earlier will be used to determine whether the solos or dyads cover roughly the same number of knowledge components. The materials will be designed to make this as likely as possible.

		Interaction
		Singletons	Dyads
Prompting	Natural	CH: Low Gain IH: Low Gain	CH: High Gain IH: High Gain
	Explain	CH: High Gain IH: Low Gain	CH: High Gain IH: High Gain

Explanation

This study is part of the Interactive Communication cluster, and its hypothesis is

References

VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rose, C. P. (in press). When are tutorial dialogues more effective than reading? Cognitive Science.