Hyesun You
Biographic Data
| ID | 5877218 |
|---|---|
| NAME | Hyesun You |
| GIVEN NAMES | Hyesun |
| FAMILY NAME | You |
| SIGNATURE | YOU H |
| AFFILIATIONS | University of Iowa |
| ORCID | 0000-0002-5835-5053 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Advancing Interdisciplinary Science Education: Validating the Interdisciplinary Science Assessment of Carbon Cycling II Through Internal Structure and External Factors
The Interdisciplinary Science Assessment of Carbon Cycling II (ISACC II) addresses the growing need for effective interdisciplinary learning assessments in science education. ISACC II expanded the foundational work of ISACC I by targeting a more appropriate grade level, enhancing psychometric evidence, and incorporating new items that address sustainability and real-life challenges. We reported evidence of validity for ISACC II in this study by e…
Next-gen assessment for interdisciplinary learning: Development and validation of the interdisciplinary science assessment of carbon cycling II
Interdisciplinary understanding is critical for reasoning about complex socio-environmental phenomena, yet validated assessments of interdisciplinary learning remain limited. This study reports the development and validation of the Interdisciplinary Science Assessment of Carbon Cycling II (ISACC II), designed to measure college students’ integration of concepts across multiple scientific disciplines. Guided by a construct-modelling framework, we …
Predictive insights into U.S. students’ mathematics performance on Pisa 2022 using ensemble tree-based machine learning models
Unveiling effectiveness: A meta‐analysis of professional development programs in science education
Teacher professional development (PD) is essential to continuously improve teaching skills, to adapt to diverse student needs, and to promote equity and inclusion. Only a few studies to date have synthesized how PD programs improve teachers' content knowledge and instructional quality, as well as students' academic performance. In this meta‐analysis, we aim to evaluate the impact of PD programs on science teachers and their students. We calculate…
Machine Learning Mixed Methods Text Analysis: An Illustration From Automated Scoring Models of Student Writing in Biology Education
Assessing student knowledge based on their writing using traditional qualitative methods is time-consuming. To improve speed and consistency of text analysis, we present our mixed methods development of a machine learning predictive model to analyze student writing. Our approach involves two stages: first an exploratory sequential design, and second an iterative complex design. We first trained our predictive model using qualitative coding of cat…
A closer look at US schools: What characteristics are associated with scientific literacy? A multivariate multilevel analysis using Pisa 2015
The purpose of this study is to examine the characteristics of US schools associated with two measures of scientific literacy (content knowledge and “procedural and epistemic” knowledge) using the 2015 Programme for International Student Assessment (PISA) data. Because outcomes are nested within students, and students within schools, a multivariate three‐level modeling method was employed. About 21% of the total variance in science performance li…
Machine Learning Mixed Methods Text Analysis: An Illustration From Automated Scoring Models of Student Writing in Biology Education
Assessing student knowledge based on their writing using traditional qualitative methods is time-consuming. To improve speed and consistency of text analysis, we present our mixed methods development of a machine learning predictive model to analyze student writing. Our approach involves two stages: first an exploratory sequential design, and second an iterative complex design. We first trained our predictive model using qualitative coding of cat…
A closer look at US schools: What characteristics are associated with scientific literacy? A multivariate multilevel analysis using Pisa 2015
The purpose of this study is to examine the characteristics of US schools associated with two measures of scientific literacy (content knowledge and “procedural and epistemic” knowledge) using the 2015 Programme for International Student Assessment (PISA) data. Because outcomes are nested within students, and students within schools, a multivariate three‐level modeling method was employed. About 21% of the total variance in science performance li…
Machine Learning Mixed Methods Text Analysis: An Illustration From Automated Scoring Models of Student Writing in Biology Education
Assessing student knowledge based on their writing using traditional qualitative methods is time-consuming. To improve speed and consistency of text analysis, we present our mixed methods development of a machine learning predictive model to analyze student writing. Our approach involves two stages: first an exploratory sequential design, and second an iterative complex design. We first trained our predictive model using qualitative coding of cat…
Predictive insights into U.S. students’ mathematics performance on Pisa 2022 using ensemble tree-based machine learning models
Unveiling effectiveness: A meta‐analysis of professional development programs in science education
Teacher professional development (PD) is essential to continuously improve teaching skills, to adapt to diverse student needs, and to promote equity and inclusion. Only a few studies to date have synthesized how PD programs improve teachers' content knowledge and instructional quality, as well as students' academic performance. In this meta‐analysis, we aim to evaluate the impact of PD programs on science teachers and their students. We calculate…
Advancing Interdisciplinary Science Education: Validating the Interdisciplinary Science Assessment of Carbon Cycling II Through Internal Structure and External Factors
The Interdisciplinary Science Assessment of Carbon Cycling II (ISACC II) addresses the growing need for effective interdisciplinary learning assessments in science education. ISACC II expanded the foundational work of ISACC I by targeting a more appropriate grade level, enhancing psychometric evidence, and incorporating new items that address sustainability and real-life challenges. We reported evidence of validity for ISACC II in this study by e…
Next-gen assessment for interdisciplinary learning: Development and validation of the interdisciplinary science assessment of carbon cycling II
Interdisciplinary understanding is critical for reasoning about complex socio-environmental phenomena, yet validated assessments of interdisciplinary learning remain limited. This study reports the development and validation of the Interdisciplinary Science Assessment of Carbon Cycling II (ISACC II), designed to measure college students’ integration of concepts across multiple scientific disciplines. Guided by a construct-modelling framework, we …
Mathematics education (4 works) · Psychology (4 works) · Science education (4 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Educational Assessment and Pedagogy (2 works) · Interdisciplinary Research and Collaboration (2 works) · Machine learning (2 works) · Mathematics (2 works) · Pedagogy (2 works)