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Investigating Content Multidimensionality in a Large-Scale Science Assessment: A Mixed Methods Approach
Investigating Content Multidimensionality in a Large-Scale Science Assessment: A Mixed Methods Approach
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151941
- ISBN
- 9798383565285
- DDC
- 371
- 서명/저자
- Investigating Content Multidimensionality in a Large-Scale Science Assessment: A Mixed Methods Approach
- 발행사항
- [Sl] : University of Oregon, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 218 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
- 주기사항
- Advisor: Scalise, Kathleen.
- 학위논문주기
- Thesis (Ph.D.)--University of Oregon, 2024.
- 초록/해제
- 요약Science, Technology, Engineering, and Math (STEM) skills are increasingly required of students to be successful in higher education and the workforce. Therefore, modeling assessment outcomes accurately, often using more types of student data to get a complete picture of student learning, is increasingly relevant. The Program for International Student Assessment (PISA) is promoted as a summative assessment opportunity that includes a science framework. As with many science assessments, the framework includes Life, Physical, and Earth science, which alone seems to imply multidimensionality, and also there are other sources of dimensionality that seem to be described conceptually in the framework. Using data from the 2015 PISA science assessment, a multidimensional item response theory (MIRT) model was fit to see how a multidimensional model operates with the data. Before developing the MIRT model, a qualitative review of the framework for multidimensionality took place and exploratory analyses were implemented for the quantitative data, including a data science technique to explore multidimensionality and some factor analysis techniques. After fitting the MIRT model, it was compared to several unidimensional IRT (UIRT) models to determine the model that explains the most variation. The qualitative analyses generated evidence of multidimensional science content domains in the 2015 PISA science framework, which should require a MIRT model, but quantitative analyses indicate a unidimensional model is more practically significant. Once quantitative results were triangulated with the qualitative review of the framework for multidimensionality, the implications on equity and history of harm with regards to science assessments were discussed. Findings from the qualitative and quantitative aspects of the study were used to generate recommendations for different stakeholders.
- 일반주제명
- Secondary education
- 일반주제명
- Statistics
- 일반주제명
- Science education
- 일반주제명
- Educational philosophy
- 키워드
- STEM education
- 기타저자
- University of Oregon Department of Educational Methodology Policy and Leadership
- 기본자료저록
- Dissertations Abstracts International. 86-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798383565285
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■1001 ▼aMalcom, Cassandra N.▼0(orcid)0009-0000-9415-1421
■24510▼aInvestigating Content Multidimensionality in a Large-Scale Science Assessment: A Mixed Methods Approach
■260 ▼a[Sl]▼bUniversity of Oregon▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a218 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-01, Section: B.
■500 ▼aAdvisor: Scalise, Kathleen.
■5021 ▼aThesis (Ph.D.)--University of Oregon, 2024.
■520 ▼aScience, Technology, Engineering, and Math (STEM) skills are increasingly required of students to be successful in higher education and the workforce. Therefore, modeling assessment outcomes accurately, often using more types of student data to get a complete picture of student learning, is increasingly relevant. The Program for International Student Assessment (PISA) is promoted as a summative assessment opportunity that includes a science framework. As with many science assessments, the framework includes Life, Physical, and Earth science, which alone seems to imply multidimensionality, and also there are other sources of dimensionality that seem to be described conceptually in the framework. Using data from the 2015 PISA science assessment, a multidimensional item response theory (MIRT) model was fit to see how a multidimensional model operates with the data. Before developing the MIRT model, a qualitative review of the framework for multidimensionality took place and exploratory analyses were implemented for the quantitative data, including a data science technique to explore multidimensionality and some factor analysis techniques. After fitting the MIRT model, it was compared to several unidimensional IRT (UIRT) models to determine the model that explains the most variation. The qualitative analyses generated evidence of multidimensional science content domains in the 2015 PISA science framework, which should require a MIRT model, but quantitative analyses indicate a unidimensional model is more practically significant. Once quantitative results were triangulated with the qualitative review of the framework for multidimensionality, the implications on equity and history of harm with regards to science assessments were discussed. Findings from the qualitative and quantitative aspects of the study were used to generate recommendations for different stakeholders.
■590 ▼aSchool code: 0171.
■650 4▼aEducational tests & measurements
■650 4▼aSecondary education
■650 4▼aStatistics
■650 4▼aScience education
■650 4▼aEducational philosophy
■653 ▼aItem response theory
■653 ▼aLarge-scale assessment
■653 ▼aMultidimensionality
■653 ▼aQualitative framework review
■653 ▼aSTEM education
■653 ▼aSummative assessment
■690 ▼a0288
■690 ▼a0533
■690 ▼a0463
■690 ▼a0998
■690 ▼a0714
■71020▼aUniversity of Oregon▼bDepartment of Educational Methodology, Policy, and Leadership.
■7730 ▼tDissertations Abstracts International▼g86-01B.
■790 ▼a0171
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162173▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
