Measuring Student Engagement in Science Classrooms: Creating Optimal Experiences for Learning

• In Event: 63.018 - Promising Scholarship in Education: Dissertation Fellows and Their Research

Abstract

As U.S. student achievement in science remains around the international average (OECD, 2013), and student engagement in science remains at or below international averages (OECD, 2007), more closely examining how students experience science in high school can help to shed light on how learning experiences can be optimized, which can help strengthen the pipeline into the STEM field. While “increasing engagement” is a current priority and worthwhile pursuit in education policies than can often elicit images of eager students participating in a collaborative dialogue or perhaps working in their science lab— engagement may be different for each student, teacher, classroom, and subject—and there is no single instructional approach or reform that can universally improve how a student is engaged. The experience of engagement may also vary depending on the contextual elements that comprise daily activities in the classroom, such as the company one is with or the value of a particular activity in relation to future goals. Thus, engagement should be investigated by considering multiple dimensions that contribute to optimal learning, which is the motivation for this research design.

This study analyzes data from the first two waves of HSLS:09. The analytic framework and approach to analyzing HSLS:09 is informed by additional data collected from a separate National Science Foundation study that is examining student engagement in science classes using the Experience Sampling Method (ESM) to capture the daily lives of students and provides in-the-moment measurements of tasks/activities and affective states . Using this ESM data to complement HSLS:09 allows for the articulation of engagement measures to be further explored by a weighted sample, which can be generalized to a national population of students. The variables used to measure the components of engagement in HSLS:09 include challenge, skill, and interest in the student’s science class in 2009 as well as additional contextual variables for student background, teacher characteristics, instructional features, and school-level variables. The HSLS:09 dataset also allows a state-representative sample to be created for a comparison to the state from which the ESM data were collected.

Author

• Justina Judy Spicer, Michigan State University