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2014 - Characteristics of Schools Successful in STEM: Evidence from Two States’ Longitudinal Data

Attribution: Hansen, Michael
Researchers: Michael Hansen
University Affiliation: American Institutes for Research
Email: mhansen@air.org
Research Question:
This report estimates school effectiveness in science and mathematics to identify and describe both successful and un-successful schools in STEM fields.
Published: Yes
Journal Name or Institutional Affiliation: The Journal of Educational Research
Journal Entry: Vol. 107, Pp. 374-391
Year: 2014
Findings:

– Surprisingly, a negative association between students’ STEM course participation and success in STEM is consistently documented across both states, in addition to low participation of underrepresented minority students in successful schools in STEM.
– A plausible explanation could be a quantity-quality tradeoff when schools offer STEM courses- the participation measures presented here are quantity measures and schools may compensate for low-quality courses with a higher quantity of them.
– Socioeconomic variables such as the percentage of minority students, and the percentage of students eligible for lunch assistance showed a generally consistent relationship with success in STEM in both states.
– Variables capturing turnover among the student body and turnover among STEM teachers also showed a consistent relationship across states in which success was associated with stability in either variable.
– Indices on mathematics and science instruction also showed a strong association with success in both mathematics and science outcomes. Additionally, some of the STEM variables included in the analysis showed no clear relationship across states or tests; such was the case with the variable on specialized STEM schools, the percentage of STEM teachers who are certified, and departmentalization at the elementary level.
– A troubling association revealed in this analysis is the low level of participation in STEM courses among URM students in successful schools, in addition to the lower levels of URM students in these schools to begin with. This negative association is at odds with policy interests that seek to both encourage STEM achievement overall and expand STEM participation for URM students.

 

Scholarship Types: Journal Article Reporting Empirical ResearchKeywords: Academic AchievementRaceRacial CompositionSocioeconomic StatusSTEM SchoolRegions: SouthMethodologies: QuantitativeResearch Designs: Administrative DataAnalysis Methods: Value-Added Regression Sampling Frame:Students in NC & FL
Sampling Types: Population of a StateAnalysis Units: SchoolStudentData Types: Quantitative-Longitudinal
Data Description:

The author used administrative educational data from longitudinal databases in Florida and North Carolina, which span the 2006-2007 through 2008-2009 school years. The longitudinal data tracked students over time across all schools in the public education system in both states, allowing for the estimation of school value-added estimates in STEM subjects.

Lists of STEM schools available from external sources were sought out; second, websites for all magnet schools in the states were visited to determine whether the magnet focus of the school was on STEM; and third, a search for STEM-related keywords in school names (e.g., science, career, academy) was conducted and the websites of schools with these keywords were visited to determine whether it had a STEM focus. A school identified as STEM by any of these three methods was considered a STEM school in the data.

In Florida, students in Grades 3-10 are tested at the end of each school year in both reading and mathematics; because of the focus on STEM performance, only test scores in mathematics are used in this report. These standardized test scores are the dependent variable in the value-added analysis.

The Florida data contain information on all teachers and tested students in the public education system. Student-level covariates used in estimating the value-added model include indicators on gender, race or ethnicity, eligibility for the free or reduced-price lunch program, and limited English proficiency. An additional indicator on student mobility was created and used as a covariate in the model. School-level metrics on the percentage of teachers teaching any STEM courses who are certified in a STEM field and the percentage of teachers new to the school (a year-specific measure of turnover among STEM faculty) are also calculated in the data. Additionally, course membership files document the actual courses students are observed taking over time.

In North Carolina, mathematics and reading tests are given annually to all students in Grades 3-8. In addition to the mathematics and reading tests, North Carolina has also administered an EOG science test to students in Grades 5 and 8 only since the 2007-2008 school year, which means 2 years of these scores are available for use in the analysis. Finally, North Carolina tests middle and high school students using standardized End-of-Course (EOC) tests for core courses required for graduation; those used in this study are from algebra 1, geometry, algebra 2, biology, physical science, chemistry, and physics.

Similar to the data from Florida, the longitudinal data in North Carolina also document student demographics, eligibility for free or reduced-price lunch, and limited English proficiency. Course membership data are likewise used to create measures of course offerings and student participation.
Finally, teacher-level certification data are used to ascertain which STEM teachers are certified to teach STEM courses, and school-level measures on the percentage of STEM teachers who are new to the school are also computed.

Five different samples were employed in the analysis, each corresponding to a different test score as an outcome:
1. Florida schools with valid FCAT-SSS scores in mathematics,
2. North Carolina schools with valid EOG scores in mathematics,
3. North Carolina schools with valid EOG scores in science,
4. North Carolina schools with valid EOC scores in mathematics subjects (algebra 1, geometry, and algebra 2), and
5. North Carolina schools with valid EOC scores in science subjects

The method used here cannot be interpreted to provide answers on causal relationships due to nonrandom student sorting that may not be fully captured by covariates included in the model.

Theoretical Framework:
Relevance:STEM-focused Schools
Archives: K-16 STEM Abstracts
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