The main goal of this research is to understand and reduce students’ failures to attend to, and learn from, classroom lectures in science, technology, engineering, and mathematics (STEM) disciplines by examining the occurrences of “mind-wandering” during learning and its influence on the learning and retention of the lecture materials. In a series of 6 studies varying in methodology (lab-based experiments, individual differences analyses, classroom observations), this research specifies the effects of common classroom activities (e.g., note-taking, media-multitasking) on mind-wandering, the individual differences factors (e.g., cognitive, motivational) affecting the frequency of mind-wandering, and possible ways to minimize the negative influence of mind-wandering on STEM learning.
University of Colorado Boulder and University of North Carolina, Greensboro. Each of the six planned studies will be run simultaneously at these two locations in part to examine the robustness of the results across two research locations.
The project uses a cross-sectional research design and will generate evidence that is descriptive [observational], associative/correlational [quasi-experimental], and causal [experimental, quasi-experimental, statistical modeling]. Original data are being collected on beginning undergraduate students using assessments of learning, survey research [self-completion questionnaires], and laboratory based experiments. Interventions being tested include attentional scaffolding (Study 5) and writing-based self-relevance (Study 6).
Instruments or measures include self-rating-based questionnaires, responses to probes to assess the occurrence of mind-wandering during learning, computer-administered measures of cognitive abilities (such as working memory capacity and executive functions), responses to comprehension questions assessing the understanding and retention of lecture materials, and course grades and exam scores. The project is employing correlation/regression analysis using multilevel modeling, multivariate analysis such as confirmatory factor analysis and structural equation modeling.
Findings will be posted as they become available.