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Research paper thumbnail of Lost in the Transition: The Cost of College-Readiness English Standards Misalignment for Students Initially Classified as English Learners

The Journal of Higher Education

ABSTRACT Using linked, individual-level data from a large metropolitan K-12 district and a large ... more ABSTRACT Using linked, individual-level data from a large metropolitan K-12 district and a large urban community college district in California, we examine whether indicators of college-readiness for graduating high school students who were initially classified as English Learners (ELs) are honored in community college course placement. In particular, we explore (1) whether there is misalignment in course assignment levels among former EL students between the K-12 and community college sector based on high school readiness metrics; and (2) if misalignment exists, we investigate its extent among various levels and identities of EL students. We employ an integrated capitals and funds of knowledge framework to emphasize students’ assets in the form of resources, knowledge, and skills while also acknowledging the role of education systems in reproducing social inequities. We find that along with students who speak Spanish at home, Latina/o, and Black students, former ELs who met college-readiness standards experienced the highest levels of inter-sector English misalignment (over 75%). Our results show a negative association between experiencing inter-sector misalignment and college credit accumulation. After controlling for course placement, background, and academic characteristics our analysis suggests that despite facing higher levels of inter-sector misalignment relative to other students, former ELs manage to accumulate five to six more transferable degree units than their monolingual English-speaking peers. The findings suggest a need for even greater inter-sector communication and accountability between legislators, K-12, and postsecondary leaders to ensure that state-level college-readiness standards are properly defined and consistently used in the transition from high school to college.

Research paper thumbnail of Faculty Perspectives on Using High School Data in an Era of Placement Testing Reform

Community College Review, 2021

Objective: Community colleges across the country are making dramatic shifts away from traditional... more Objective: Community colleges across the country are making dramatic shifts away from traditional reliance on placement testing for developmental education and toward using high school measures to assess college-readiness. Yet the views of faculty dealing with these changes, including their perspectives on the quality and usefulness of high school data, are not well-understood. We explore faculty views of high school transcript and placement testing data, attributions made with the data, and beliefs about the extent to which these data are useful for instruction. Methods: We conducted a survey and semi-structured interviews with math faculty in one community college math department ( n = 21). We used real high school records to develop a Personalized Student Profile of student math backgrounds to engage faculty in sensemaking about high school and placement testing data. Results: Faculty did not appear to readily trust high school data, tending only to do so when it fit their existi...

Research paper thumbnail of Inside the Math Trap: Chronic Math Tracking From High School to Community College

Urban Education, 2020

Examining linked academic transcripts from urban community colleges and their feeder high schools... more Examining linked academic transcripts from urban community colleges and their feeder high schools, we identify math course-taking patterns that span sectors. We highlight stifled mobility and chronic repetition of math coursework in the transition to college, and we identify “math traps” from which students do not escape. Math mobility was limited, math repetition was rampant, and nearly half of students found themselves in math traps. All else equal, being trapped in math was significantly linked to race/ethnicity, suggesting that these forms of chronic math tracking across sectors expose previously undocumented forms of inequality in educational experiences.

Research paper thumbnail of Selección de características relevantes usando información mutua

Dyna, 2006

RESUMEN: Un nuevo método para la selección de atributos relevantes basado en Información Mutua es... more RESUMEN: Un nuevo método para la selección de atributos relevantes basado en Información Mutua es presentado. Este se basa en el concepto de probabilidad de relevancia de cada atributo, el cual es medido a través de una prueba de permutación, y permite descartar variables irrelevantes así como ordenar por importancia aquellas relevantes. La metodología propuesta es probada usando tres problemas de clasificación bien conocidos. Igualmente, se realiza una investigación con miras a esclarecer su robustez cuando las variables relevantes están contaminadas con ruido, o existen variables aleatorias artificiales irrelevantes. Los resultados indican las bondades de la metodología propuesta, por lo que se sugiere que ella debe ser una parte integral de las herramientas usadas en la selección de características relevantes.

Research paper thumbnail of Lost in the Transition: The Cost of College-Readiness English Standards Misalignment for Students Initially Classified as English Learners

The Journal of Higher Education

ABSTRACT Using linked, individual-level data from a large metropolitan K-12 district and a large ... more ABSTRACT Using linked, individual-level data from a large metropolitan K-12 district and a large urban community college district in California, we examine whether indicators of college-readiness for graduating high school students who were initially classified as English Learners (ELs) are honored in community college course placement. In particular, we explore (1) whether there is misalignment in course assignment levels among former EL students between the K-12 and community college sector based on high school readiness metrics; and (2) if misalignment exists, we investigate its extent among various levels and identities of EL students. We employ an integrated capitals and funds of knowledge framework to emphasize students’ assets in the form of resources, knowledge, and skills while also acknowledging the role of education systems in reproducing social inequities. We find that along with students who speak Spanish at home, Latina/o, and Black students, former ELs who met college-readiness standards experienced the highest levels of inter-sector English misalignment (over 75%). Our results show a negative association between experiencing inter-sector misalignment and college credit accumulation. After controlling for course placement, background, and academic characteristics our analysis suggests that despite facing higher levels of inter-sector misalignment relative to other students, former ELs manage to accumulate five to six more transferable degree units than their monolingual English-speaking peers. The findings suggest a need for even greater inter-sector communication and accountability between legislators, K-12, and postsecondary leaders to ensure that state-level college-readiness standards are properly defined and consistently used in the transition from high school to college.

Research paper thumbnail of Faculty Perspectives on Using High School Data in an Era of Placement Testing Reform

Community College Review, 2021

Objective: Community colleges across the country are making dramatic shifts away from traditional... more Objective: Community colleges across the country are making dramatic shifts away from traditional reliance on placement testing for developmental education and toward using high school measures to assess college-readiness. Yet the views of faculty dealing with these changes, including their perspectives on the quality and usefulness of high school data, are not well-understood. We explore faculty views of high school transcript and placement testing data, attributions made with the data, and beliefs about the extent to which these data are useful for instruction. Methods: We conducted a survey and semi-structured interviews with math faculty in one community college math department ( n = 21). We used real high school records to develop a Personalized Student Profile of student math backgrounds to engage faculty in sensemaking about high school and placement testing data. Results: Faculty did not appear to readily trust high school data, tending only to do so when it fit their existi...

Research paper thumbnail of Inside the Math Trap: Chronic Math Tracking From High School to Community College

Urban Education, 2020

Examining linked academic transcripts from urban community colleges and their feeder high schools... more Examining linked academic transcripts from urban community colleges and their feeder high schools, we identify math course-taking patterns that span sectors. We highlight stifled mobility and chronic repetition of math coursework in the transition to college, and we identify “math traps” from which students do not escape. Math mobility was limited, math repetition was rampant, and nearly half of students found themselves in math traps. All else equal, being trapped in math was significantly linked to race/ethnicity, suggesting that these forms of chronic math tracking across sectors expose previously undocumented forms of inequality in educational experiences.

Research paper thumbnail of Selección de características relevantes usando información mutua

Dyna, 2006

RESUMEN: Un nuevo método para la selección de atributos relevantes basado en Información Mutua es... more RESUMEN: Un nuevo método para la selección de atributos relevantes basado en Información Mutua es presentado. Este se basa en el concepto de probabilidad de relevancia de cada atributo, el cual es medido a través de una prueba de permutación, y permite descartar variables irrelevantes así como ordenar por importancia aquellas relevantes. La metodología propuesta es probada usando tres problemas de clasificación bien conocidos. Igualmente, se realiza una investigación con miras a esclarecer su robustez cuando las variables relevantes están contaminadas con ruido, o existen variables aleatorias artificiales irrelevantes. Los resultados indican las bondades de la metodología propuesta, por lo que se sugiere que ella debe ser una parte integral de las herramientas usadas en la selección de características relevantes.