– All aspects of science engagement were statistically significantly and positively related to science achievement, and nearly all showed medium or large effect sizes.
– Each aspect was positively associated with one of the four practices (strategies) of science teaching.
– Focus on applications or models was positively related to the most aspects of science engagement (science self-concept, enjoyment of science, instrumental motivation for science, general value of science, and personal value of science).
– Hands-on activities were positively related to additional aspects of science engagement (science self-efficacy and general interest in learning science) and also showed a positive relationship with science achievement.
– School mean SES has a positive and significant effect on students’ future motivation in science and on science achievement.
2017 - Science Engagement and Science Achievement in the Context of Science Instruction: A Multilevel Analysis of U.S. Students and Schools
The authors adopted the input-process-output (IPO) model (Ilgen, Hollenbeck, Johnson, & Jundt, 2005) that has been widely applied in school effects research to guide the selection of variables and specification of statistical models (Ma, Ma, & Bradley, 2008). In the IPO model, students bring different individual and family characteristics and different cognitive and affective conditions into their schools. Schools then process, by means of context and climate, students with different backgrounds into different categories of outcome measures (e.g. attitude, achievement).
Data on 15-year-old U.S. students were acquired from the 2006 PISA dataset (OECD, 2007). Multi-stage stratified random sampling was used to sample the U.S. 15-year-old student population (OECD, 2009). The U.S. sample included 4456 students in 132 schools.
DV: science self-efficacy, science self-concept, enjoyment of science, general interest in science, instrumental motivation for science, future-oriented science motivation, general value of science, personal value of science, and science-related activities (measured with a scale consisting of several items on the student questionnaire (OECD, 2007); science achievement (combined literacy scale of using scientific evidence, identifying scientific issues, and explaining phenomena scientifically (OECD, 2007)
IV:
Student: gender (dichotomous with 1 as male), age, father’s socioeconomic status (SES) and mother’s SES (PISA standardised indices), immigration background (dichotomous with 1 as at least one parent born in the U.S.), and language spoken at home (dichotomous with 1 as English)
School context: school size (total enrolment), school type (dichotomous with 1 as public), proportion of girls, school mean father’s SES and school mean mother’s SES (aggregated from SES of students within a school), proportion of teachers certified, student-teacher ratio, teacher shortage (PISA index), and quality of educational resources (PISA index)
School climate: school responsibility (autonomy) for resource allocation (PISA index), school responsibility (autonomy) for curriculum and assessment (PISA index), ability grouping (dichotomous with 1 as grouping either between or within classes), parent influence (count of ‘yes’ to four items about parent groups exerting direct influences on educational decisions), teacher influence (count of ‘yes’ to four items about teacher groups exerting direct influences on educational decisions), school activities to promote the learning of science (count of ‘yes’ to five school activities), school mean science teaching – focus on models or applications, school mean science teaching – hands-on activities, school mean science teaching – interaction, and school mean science teaching – student investigations.
To address the data hierarchy of students nested within schools in the PISA dataset, hierarchical linear modeling (HLM) or multilevel modeling was employed.