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In this paper we attempt to identify which peer collaboration characteristics may be accountable for conceptual change through interaction. We focus on different socio-cognitive aspects of the peer dialog and relate these with learning gains on the dyadic as well as the individual level. The scientific topic that was used for this study concerns natural selection, a topic for which students' intuitive conceptions have been shown to be particularly robust. Learning tasks were designed according to the socio-cognitive conflict instructional paradigm. After receiving a short instructional intervention on natural selection, paired students were asked to collaboratively construct explanations for certain evolutionary phenomena while engaging in dialectical argumentation. Two quantitative coding schemes were developed, each with a different granularity. The first assessed discrete dialog moves that pertained to dialectical argumentation and to consensual explanation development. The second scheme characterized the dialog as a whole on a number of socio-cognitive dimensions. Results from analyses on the dyadic as well as the individual level revealed that the engagement in dialectical argumentation predicted conceptual learning gains, whereas consensual explanation development did not. These findings open up new venues for research on the mechanisms of learning in and from peer collaboration.

In this study the effects of argumentation-eliciting interventions on conceptual understanding in evolution were investigated. Two experiments were conducted: In the first, 76 undergraduates were randomly assigned to dyads to collaboratively solve and answer items in evolution; half of them were instructed to conduct an argumentative discussion, whereas control dyads were only asked to collaborate. In the second experiment, 42 singletons participated in one of two conditions: Experimental students engaged in monological argumentation on their own and a confederate's solution in response to prompts read by the confederate, whereas in the control condition they merely shared their solutions. Conceptual gains were assessed on immediate and delayed post-tests. In both experiments, students in the argumentative conditions showed larger learning gains on the delayed post-test than control students. Students in argumentative conditions were able to preserve gains that were obtained immediately following the intervention, whereas control subjects either lost immediate gains (dialogical condition) or did not improve their conceptual understanding at any time (monological condition).

Data collection from online platforms, such as Amazon’s Mechanical Turk (MTurk), has become popular in clinical research. However, there are also concerns about the representativeness and the quality of these data for clinical studies. The present work explores these issues in the specific case of major depression. Analyses of two large data sets gathered from MTurk (Sample 1: N = 2,692; Sample 2: N = 2,354) revealed two major findings: First, failing to screen for inattentive and fake respondents inflates the rates of major depression artificially and significantly (by 18.5%–27.5%). Second, after cleaning the data sets, depression in MTurk is still 1.6 to 3.6 times higher than general population estimates. Approximately half of this difference can be attributed to differences in the composition of MTurk samples and the general population (i.e., sociodemographics, health, and physical activity lifestyle). Several explanations for the other half are proposed, and practical data-quality tools are provided.

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