Understanding Work With Data in Summer STEM Programs Through An Experience Sampling Method Approach
These are files associated with Joshua Rosenberg's dissertation, completed as part of the Educational Psychology and Educational Technology (EPET) degree program at Michigan State University (MSU). Right now, everything except for the data is included here.
- A link to the PDF here
- A link to the (PPT) presentation here
- An online book (Gitbook; with links to PDF and EPUB) here
- An EPUB book here
Data-rich activities provide an opportunity to develop core competencies in both science and mathematics identified in curricular standards. Research on work with data has focused on cognitive outcomes and the development of specific practices at the student and classroom levels, and yet, little research has considered learners’ engagement and the conditions that facilitate it. The present study explores learners engagement in work with data in the context of summer STEM programs. The aspects of work with data that are the focus of this study are: asking questions, observing phenomena, constructing measures and generating data, data modeling, and interpreting findings. Data from measures of learners' engagement is collected through the Experience Sampling Method (ESM) that involves asking learners at random intervals to answer short questions about their engagement to discover profiles of learners' engagement.
Data was collected from nine summer STEM programs over four weeks in the Northeastern United States. 203 learners reported 2,970 responses via short ESM surveys of how engaged they were (cognitively, behaviorally, and affectively, assessed through separate items) and of their perceptions of themselves (their competence) and of the activity (its challenge). These data were used to examine five specific research questions: 1) What is the frequency and nature of opportunities for youth to engage in each of the five aspects of work with data in summer STEM programs? 2) What profiles of engagement emerge from data collected via ESM in the programs? 3) What are sources of variability for the profiles of engagement? 4) How do the five aspects of work with data relate to profiles of engagement? 5) How do youth characteristics relate to profiles of engagement.
Findings show that aspects of work with data were fairly common overall, but that work with data was enacted out in varying ways, including some that were superficial in nature. Six profiles of youth engagement were identified, representing distinct configurations of the five indicators of engagement. Substantial variability in the profiles was present at the youth level, with less explained by the program youth were in or the nature of the particular instructional episode present at the times youth were signaled. Relations between the profiles of engagement and each of the aspects of work with data were somewhat small: Notable exceptions were the generating data and data modeling were significantly associated with full engagement. Youth with higher pre-program interest in STEM were more likely to be engaged and competent but not challenged, though other youth characteristics were not highly related to the profiles.
Key findings as regards work with data in summer STEM programs and other informal learning environments, the nature of youths' engagement, and what factors can predict engagement are discussed. The design and goals of summer STEM programs, which are not (necessarily) focused on activities related to work with data, as well as other limitations including the measures for work with data used and the analytic approach, are identified and described. The role of generating data and modeling data as well as attention to the specifics of how work with data are enacted are presented as implications for practice with relevance to both formal and informal learning environments.
Understanding Work With Data in Summer STEM Programs Through An Experience Sampling Method Approach is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
