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IntroductionElementary teachers face many challenges when including reform-based science instruction in their classrooms, and some teachers have chosen to enhance their science instruction by introducing students to citizen science (CS) projects. When CS projects are incorporated in formal school settings, students have an opportunity to engage in real-world projects as they collect and make sense of data, yet relatively few CS projects offer substantial guidance for teachers seeking to implement the projects, placing a heavy burden on teacher learning. MethodsFramed in theory on teacher relationships with curricula, we prepared science standards-aligned educative support materials for two CS projects. We present convergent mixed methods research that examines two teachers’ contrasting approaches to including school-based citizen science (SBCS) in their fifth-grade classrooms, each using support materials for one of the two CS projects. Both are veteran teachers at under-resourced Title 1 (an indicator of the high percentage of the students identified as economically disadvantaged) rural schools in the southeastern United States. We document the teachers’ interpretations and use of SBCS materials for the CS projects with data from classroom observations, instructional logs, teacher interviews, and student focus groups. ResultsOne teacher adapted the materials to include scaffolding to position students for success in data collection and analysis. In contrast, the second teacher adapted the SBCS support materials to maintain a teacher-centered approach to instruction, identifying perceptions of students’ limited abilities and limited instructional time as constraining factors. DiscussionWe discuss the intersection of CS projects in formal education and opportunities for engaging students in authentic science data collection, analysis, and sense-making. The two teachers’ stories identify the influences of school context and the need for teacher support to encourage elementary teachers’ use of SBCS instruction to supplement their science instruction.
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This content will become publicly available on February 27, 2026
Using a wider digital ecosystem to improve self-regulated learning
IntroductionSelf-regulated learning skills are necessary for academic success. While not all students entering post-secondary education are proficient at many of these critical skills, they can be improved upon when practiced. However, self-regulation tends to be highly internal, making it difficult to measure. One form of measurement comes from using data traces collected from educational software. These allow researchers to make strong empirical inferences about a student's internal state. Automatically captured data traces also make it possible to provide automated interventions that help students practice and master self-regulated learning skills. Methods/resultsUsing an experimental methodology we created a set of promising data traces that are grounded in theory to study self-regulated learning within a typical Computer Science course. Extra attention is given to studying the skill of help-seeking, which is both a key to success in CS and requires unobtrusive observation to properly measure. DiscussionWe also make the case for taking a broader perspective with our data collection efforts. The traces identified in this paper are not from one source, but the full ecosystem of software tools common to CS courses.
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- Award ID(s):
- 2213790
- PAR ID:
- 10601203
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Education
- Volume:
- 10
- ISSN:
- 2504-284X
- Subject(s) / Keyword(s):
- self-regulated learning learning analytics computing education measurement models data traces unobtrusive
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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