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In response to reform efforts to center students’ interest and identity in the context of assessment, we pilot tested a set of three-dimensional, phenomenon-driven assessment tasks and asked students to respond to additional items that prompted them to reflect on their experience with the tasks. Using a qualitative approach, we analyzed written responses from 502 middle school students across the United States. Through inductive and deductive coding, we developed a comprehensive framework that captures students' experiences across five dimensions: cognitive engagement, affective engagement, relevance to students’ lives, beyond-classroom connections, and assessment design and features. By exploring these dimensions, the framework aims to provide a comprehensive lens for understanding how students experience science assessments, revealing key insights into the factors that influence their engagement and learning. Ultimately, this framework can serve as a practical tool for educators and researchers to analyze and improve science assessments by centering students' voices, thereby fostering deeper learning, promoting student agency, and supporting all learners.
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Using intensive longitudinal methods to quantify the sources of variability for situational engagement in science learning environments
Abstract BackgroundSituational engagement in science is often described as context-sensitive and varying over time due to the impact of situational factors. But this type of engagement is often studied using data that are collected and analyzed in ways that do not readily permit an understanding of the situational nature of engagement. The purpose of this study is to understand—and quantify—the sources of variability for learners’ situational engagement in science, to better set the stage for future work that measures situational factors and accounts for these factors in models. ResultsWe examined how learners' situational cognitive, behavioral, and affective engagement varies at the situational, individual learner, and classroom levels in three science learning environments (classrooms and an out-of-school program). Through the analysis of 12,244 self-reports of engagement collected using intensive longitudinal methods from 1173 youths, we found that the greatest source of variation in situational engagement was attributable to individual learners, with less being attributable to—in order—situational and classroom sources. Cognitive engagement varied relatively more between individuals, and affective engagement varied more between situations. ConclusionsGiven the observed variability of situational engagement across learners and contexts, it is vital for studies targeting dynamic psychological and social constructs in science learning settings to appropriately account for situational fluctuations when collecting and analyzing data.
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- Award ID(s):
- 1937700
- PAR ID:
- 10487803
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- International Journal of STEM Education
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2196-7822
- Page Range / eLocation ID:
- 1-14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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PurposeThis study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms. Design/methodology/approachThis paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning. FindingsFindings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables. Originality/valueData science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.more » « less
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Background: Math anxiety (MA) and math achievement are generally negatively associated. Aims: This study investigated whether and how classroom engagement behaviors mediate the negative association between MA and math achievement. Sample: Data were drawn from an ongoing longitudinal study that examines the roles of affective factors in math learning. Participants consisted of 207 students from 4th through 6th grade (50% female). Methods: Math anxiety was measured by self-report using the Mathematics Anxiety Scale for Children (Chiu & Henry, 1990, Measurement and valuation in Counseling and Development, 23, 121). Students self-reported their engagement in math classrooms using a modified version of the Math and Science Engagement Scale (Wang et al., 2016, Learning and Instruction, 43, 16). Math achievement was assessed using the Applied Problem, Calculations, and Number Matrices subtests from the Woodcock-Johnson IV Tests of Achievement (Schrank et al., 2014, Woodcock-Johnson IV Tests of Achievement. Riverside). Mediation analyses were conducted to examine the mediating role of classroom engagement in the association between MA and math achievement. Results: Students with higher MA demonstrated less cognitive-behavioral and emotional engagement compared to students with lower MA. Achievement differences among students with various levels of MA were partly accounted for by their cognitive-behavioral engagement in the math classroom. Conclusions: Overall, students with high MA exhibit avoidance patterns in everyday learning, which may act as a potential mechanism for explaining why high MA students underperform their low MA peers.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1186/s40594-023-00449-0
