학교수준의 부가가치모델을 통한 학교 효율성 척도 연구
Measuring school effectiveness via VAMs and examining its psychometrics
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
본 연구에서는 한국교육종단연구 1-3차년도 데이터를 사용하여 중학생들의 학업성취도에 영향을 미치는 학교단위 변인들을 탐색하였다. 학교단위의 변인들을 바탕으로 네 가지의 학교단위 부가가치모델(value-added model)을 구현함으로써 학교 효율성 척도를 산출하였다. 특히 전체학생들과 학업성취도 수준이 낮은 학생들에 대한 학교의 효율성(effectiveness)을 구분함으로써 모집단이 달라짐에 따른 학교 효율성 차이를 확인하고자 하였다. 연구 결과, 제시된 네 가지 부가가치모델은 통계적으로 유의미한 차이를 보였다. 같은 과목 내에서의 모델 간 학교 효율성 척도 상관관계는 대부분 0.8 이상으로 높았지만, 각 모델 내에서 영어와 수학에 대한 학교 효율성 척도 상관관계는 0.39부터 0.44로 낮았다. 이는 부가가치모델이 학교 간의 차이를 나타내는 다양한 변인들을 포함할 수 있음을 보여준다. 또한 과목 간의 낮은 상관관계를 통해 알 수 있듯이 부가가치모델을 통한 효율성 척도는 각 과목별로 해석되고 활용되어져야 함을 의미한다.
Abstract
As student learning gains depend on the quality of school, effective school must be a primary measure of its quality. However, the evaluation of school is at best inconsistent, which is mainly because of the lack of reliable and valid evaluation tools. This study examines four value-added models (VAMs) to measure school accountability by using panel data from the first, second, and third waves of the Korean Education Longitudinal Study. VAM estimates yields the academic growth rates of groups of all students in middle school and low performing students, respectively, from year to year. The results indicate that VAM estimates from four different VAMs are significantly different from one another. In particular, correlations between Math and Reading in each VAM vary between 0.39 and 0.44, while correlations among VAM estimates within each subject are above 0.8. The findings suggest that critical school variables could be taken differently to compare the school effectiveness in Math and Reading, respectively. VAM estimates also need to be interpreted by subjects, Math and Reading, separately. Recommendations for policy associated with VAMs and future research topics are discussed.
참고문헌 (22)
- 강상진, "학교교육의 책무성 제고를 위한 학교평가지수 산출모형 비교: 학업성취도를 중심으로", 교육평가연구, 22(1), pp.79~106 (2009), 한국교육평가학회
- 김준엽, "학생 종단자료를 이용한 학교 부가가치지수 산출의 적용 타당성", 교육평가연구, 25(2), pp.213~239 (2012), 한국교육평가학회
- 신혜숙, "배경변수의 통제에 따른 학교별 향상도의 변동", 교육평가연구, 27(5), pp.1217~1241 (2014), 한국교육평가학회
- 신혜숙, "다층잠재변인회귀모형을 적용한 고등학교 학교효과 추정 및 효과적인 학교 특성 분석", 교육평가연구, 25(1), pp.23~48 (2012), 한국교육평가학회
- ) in the Houston Independent School District (HSD): Intended and Unintended, "Education Policy Analysis Archives", 2012, 1~28
- Bates, D., "Fitting linear mixed-effects model using {lme4}", Journal of Statistical Software, 67, pp.1~48 (2015)
- Briggs, D. C., "Improving Large-Scale Assessment in Education: Theory, Issues and Practice" (2012), Routledge
- Briggs, D. C, "Is growth in student achievement scale dependent?", Paper presented at the annual meeting of the National Council on Measurement in Education (2009)
- Briggs, D. C., "The impact of vertical scaling decisions on growth interpretations", Education Measurement: Issues and Practice, 28, pp.3~14 (2009)
- Briggs, D. C., "The persistence of value-added school effects", Journal of Educational and Behavioral Statistics, 36, pp.616~637 (2011)
- Cantrell, S. M., "The measures of effective teaching project: An experiment to build evidence and trust", Education Finance and Policy, 7(2), pp.203~218 (2012)
- Ferrão, M., "Adjusting for measurement error in the value added model: evidence from Portugal", Quality & Quantity, 43(6), pp.951~963 (2009)
- Kupermintz, H., "Teacher effects and teacher effectiveness: A validity investigation of the Tennessee Value Added Assessment System", Educational Evaluation and Policy Analysis, 25(3), pp.287~298 (2003)
- Laird, N. M., "Random-effects models for longituindal data", Biometrics, 38, pp.963~974 (1982)
- Lockwood, J. R., "Bayesian methods for scalable value-added assessment", Journal of Educational and Behavioral Statistics, 32, pp.125~150 (2007)
- McCaffrey, D. F., "Let’s see more empirical studies on value-added modeling of teacher effects: A reply to Raudenbush, Rubin, Stuart, and Zanutto, and Reckase", Journal of Educational and Behavioral Statistics, 29(1), pp.139~143 (2004)
- Petersen, N. S., "Scaling, norming, and equating", Educational measurement, 3, pp.221~262 (1989)
- "R: A language and environment for statistical computing" (2016)[URL]
- Raudenbush. S. W., "What are value-added models estimating and what does this imply for statistical practice?", Journal of Educational and Behavioral Statistics, 29, pp.121~129 (2004)
- Rubin, D. B., "A Potential Outcomes View of Value-Added Assessment in Education", Journal of Educational and Behavioral Statistics, 29(1), pp.103~116 (2004)
- Sanders, W. L., "Grading teachers, grading schools: Is student achievement a valid evaluation measure?" (1997), Corwin Press
- NYC teacher data initiative: Technical report on the NYC Value-Added Model 2010, "Value-Added Research Center", New York City Department of Education, ~ (2010)