Experience and Meaning in Small-Group Contexts

Abstract

Self-report data have contributed to a rich understanding of learning and motivation; yet, self-report measures present challenges to researchers studying students' experiences in small-group contexts. Rather than using self-report data alone, we argue that fusing self-report and observational data can yield a broader understanding of students' small-group dynamics. We provide evidence for this assertion by presenting mixed-methods findings in three sections: (a) self-report data alone, (b) observational data alone, and (c) the fusion of both data sources. We rely on 101 students' self-reported experiences as well as observational (i.e., audio) data of students working in their group (N = 24 groups). In section order, we found that (1) students' self-reported small-group behavior predicted their end-of-study reported anxiety and emotion; (2) coded observational data captured five types of group dynamics that students can engage in; and (3) students' initial group-level characteristics predicted their real-time group dynamics, and observed group regulation activity predicted students' self-reported anxiety, emotion, and regulation moving forward. Thus, while self-report and observational data alone can each increase our understanding of student motivation and learning processes, pursuing both in tandem more effectively captures the give-and-take among students, how these experiences evolve over time, and the personal meanings they can afford.

Tracking Patterns in Self-Regulated Learning Using Students’ Self-Reports and Online Trace Data

“…This is particularly pressing since existing research suggests that learners tend to use ineffective learning strategies (Jamieson-Noel & Winne, 2003), and do not make effective use of available resources to optimize their learning, even in those environments that build on effective learning designs (Ellis, et al, 2005;Lust, et al, 2013). As discussed in this special issue, potential reasons are students' cognitive processing capacity (Chauliac, et al, 2020), exerted effort (Iaconelli & Wolters, 2020), and student characteristics (Vriesema & McCaslin, 2020). Comparing self-reported data with online traces of learning behaviour taps right into a potential upward bias in self-reports on motivation and strategy use.…”

Self-Report is Indispensable to Assess Students’ Learning

“…Extending the perspective beyond individual learning, Vriesema and McCaslin (2020) used selfreport to assess secondary school students' general test anxiety, their attitudes towards school, and their behavior and emotions during group work in mathematics. There were clear links between self-reported behaviour and emotions related to the group work situation, but less so with the test anxiety measure.…”

A Proposed Remedy for Grievances about Self-Report Methodologies

“…van Halem, et al, (2020) administered the well-known Motivated Strategies for Learning Questionnaire which includes various subscales of Likert items describing constructs within the arenas of motivation, cognition and metacognition. Participants in Vriesema and McCaslin's (2020) study responded to a survey including Likert response items about anxiety and selected from among a set of 20 sentences ones that described perceptions about participation in a small group activity.…”

“…States may be internal, for example, a mood or physical condition. Vriesema and McCaslin's (2020) participants made a dichotomous decision whether their "stomach felt funny" or "head hurt" during a group activity. States also can be external, such as a characteristic of a learning situation or the availability of needed information.…”

“…Second, it is often the case that self-report data are among other predictors used to predict an outcome. Multiple regression and similar models are common statistical methods applied in this case, as was true for studies in this special issue (Chauliac et al, 2020;Durik & Jenkins, 2020;van Halem et al, 2020;Moeller et al, 2020;Vriesema & McCaslin, 2020). In these types of analysis, each predictor is residualized for every other predictor.…”