Please join us Monday, February 17 at 10:00 a.m. in 232M Baker Hall for this talk given by Elizabeth Hirshorn, a post-doctoral fellow in the Learning Research and Development Center at the Pittsburgh Science of Learning Center.
Title: Reading off the beaten pathway: Investigating alternate pathways to literacy
Being able to read and comprehend text is an essential skill in our society, from filling out tax forms, to understanding contracts, to reading the news. Researchers have made great gains in understanding how typical readers achieve literacy, but there are still many unanswered questions about whether alternate routes to literacy exist, and if they do, how they work. Research has demonstrated that certain skills are very important for and predictive of reading ability in the typical pathway to literacy. One such skill is phonological awareness, or knowledge of smaller units of speech sounds, which facilitates the mapping between speech and written letters. Phonological knowledge is thought to support high quality lexical representations that facilitate comprehension. However, not all learners are created with an equal ability to attain these foundational skills. I will present some of my past, current, and future research that examines important individual differences in reading ability and potential atypical pathways to literacy through two main strands of investigation: 1) internal factors due to an individual’s cognitive abilities, like phonological awareness, and 2) external factors due to the writing system or language itself.
Please join us Monday, February 10 at 10:00 a.m. in 232M Baker Hall for this talk given by Jason Imbrogno, a Ph.D. candidate in Economics in the Tepper School of Business at CMU.
The Efficacy of a Pre-Algebra Cognitive Tutor in Chile and Mexico
A math cognitive tutor (MCT) system widely used throughout the U.S. was adapted for use in Chilean and Mexican public middle schools. The curriculum requires large changes in pedagogy, including the use of computers for individual students to progress through an extended pre-algebra program. The study was conducted over a 6-month time period. Using a hierarchical linear model (HLM), we show that students enrolled in schools which were randomly assigned to adopt the MCT significantly improved their standardized math test scores as compared to control group peers. However, the implementation of the changes in the schools and classrooms was not perfect. Those schools which were better prepared to make changes, especially those with sufficient computers and technical support services, saw their students master more of the software part of the curriculum. Students and teachers generally viewed the MCT positively. The results on math performance and attitudes are promising for further propagation of the MCT curriculum. Knowledge from this study regarding the structure and implementation required for schools to successfully exploit the unique teaching capabilities of the MCT should guide the future diffusion of this specific technology.
Please join us on Monday, January 27 at 10:00 a.m. in 232M Baker Hall for this talk given by J.R. Lockwood, a researcher at the Educational Testing Service.
Title: Recent Results on Weighting and Matching Methods for Estimating Causal Effects Using Error-Prone Covariates
Abstract: Weighting and matching estimators using observed covariates to adjust for differences among non-equivalent groups are commonly used to estimate causal effects with observational data. The theorems supporting these approaches require that covariates necessary to adjust for group differences be measured without error. However, covariate measurement error is common in many applications, including education research where test scores are typically important covariates but measure latent achievement constructs with error. This presentation will review recent work on approaches that can be used to correct weighting and matching estimators for covariate measurement error. It argues that a general strategy exists to correct weighting estimators, but matching estimators cannot generally be fixed.
This is joint work with Daniel F. McCaffrey
Please join us on Monday, December 16 at 10:00 a.m. in 232M Baker Hall for this talk given by Lindsay Page, a professor at the University of Pittsburgh School of Education.
Title: Summer Nudging: Can Personalized Text Messages and Peer Mentor Outreach Increase College Going Among Low-Income High School Graduates?
Abstract: Despite decades of policy intervention to increase college entry among low-income students, substantial disparities in college participation by family income persist. Policymakers have largely overlooked the summer after high school as an important time period in students’ transition to college, yet recent research documents summer attrition rates ranging from 10 – 40 percent among students who had been accepted to college and declared an intention to enroll in college as of high school graduation. Encouragingly, several experimental interventions demonstrate that students’ postsecondary plans are quite responsive to additional outreach during the summer months. Questions nonetheless remain about how to maximize the impact and cost-effectiveness of summer support. Text messaging and peer mentor outreach programs are two promising approaches both to inform students of college-related summer tasks and to connect them to professional support when they need help. In this paper, we report on two randomized trials to investigate the role of technology and peer mentor outreach in mitigating summer attrition and helping students enroll and succeed in college. We find that an automated and personalized text messaging campaign to remind students of required pre-matriculation tasks substantially increased college enrollment among students who had less access to college planning supports. A peer mentor intervention increased four-year college enrollment. At a cost of $7 per participant for the text message campaign and $80 per participant for the peer mentor campaign, both strategies—and particularly the text outreach—are promising and cost-effective approaches to increase college entry among populations traditionally underrepresented in higher education. This is joint work with Benjamin Castleman (University of Virginia).
Please join us on Monday, December 2 at 10:00 a.m. in 232M Baker Hall for this talk given by Layla Unger, a doctoral student in Psychology at CMU.
Title: Tracking the Development of Semantic Organization
Abstract: Over the course of development, children learn a substantial amount of information about things in the world around them. However, they must also learn about relationships between things in order to convert the individual collections of attributes known about each thing into a structured, interconnected web of semantic knowledge. Prior research has revealed that children’s ability to recognize different types of relationships expands considerably over the course of development. In contrast, research on the way that semantic knowledge is organized on the basis of these relationships has revealed a static developmental trajectory, in which the growing body of information that children learn with increasing age does not appear to be accompanied by any changes in the way that this knowledge is organized. This discrepancy may be due to limitations of the methodology and analyses used in semantic organization research. The aim of my current project is to explore ways of overcoming these limitations in order to illuminate the development of semantic organization. During this talk, I will focus on the analyses that I am attempting to apply to this question, and the challenges that I have encountered along the way.
Please join us on Monday, November 18 at 10:00 a.m. in 232M Baker Hall for this talk given by Adam Sales, a Post-doc at CMU Statistics and RAND.
Title: Adjusting for Many Covariates in a Matching-Based Educational Program Evaluation
Authors: Adam Sales, Ben Hansen
Abstract: When experiments are impossible, education researchers often attempt to estimate the effect of an intervention by adjusting for available pre-treatment covariates. One popular adjustment method is to estimate propensity scores: a subject’s propensity score is its probability of being assigned to treatment, conditional on its covariates. However, propensity scores are difficult or impossible to estimate when the number of available covariates is large compared to the number of subjects. For instance, many states provide hundreds or thousands of school-level variables, many of which can be used as covariates in a propensity score model. We propose a solution to this problem that combines traditional propensity scoring with high-dimensional predictive models of the outcome of interest. We will apply our method to evaluate a school-level intervention on six Kentucky high schools, for which over 700 school-level variables are available.
Please join us on Monday, November 4 at 10:00 a.m. in 232M Baker Hall for this talk given by Sean Kelly, an assistant professor in the Department of Administrative and Policy Studies at the University of Pittsburgh.
Title: Accounting for the Relationship between Initial Status and Growth in Regression Models
Authors: Sean Kelly, Feifei Ye
Abstract: Educational analysts studying achievement and other educational outcomes frequently encounter an association between initial status and growth, which has important implications for the analysis of covariate effects, including group differences in growth. As explicated by Allison (1990), where only two time points of data are available, it can be difficult or impossible to identify a preferred model. In this paper we extend Allison’s inquiry by considering multiple sources of the association between initial status and growth simultaneously, including measurement error but also intrinsic associations between initial status and growth. We illustrate the potential tradeoffs between the change-score model specifications (models without a control for initial status) and regressor-variable specifications (with a control for initial status) using simulated data.
Please join us on Monday, October 21 at 10:00 a.m. in 232M Baker Hall for this talk given by Kyle Siler-Evans (Research Analyst with the Project on College and Career Readiness in Pittsburgh Public Schools).
Title: Early Indicators of College Readiness in Pittsburgh Public Schools
Abstract: While Pittsburgh Public Schools (PPS) has the ambitious goal of getting 80% of its student to and through college, currently only 26% of PPS graduates complete a 2- or 4- year degree within five years of high school graduation. Closing the gap between our goals and the current reality requires identifying and intervening with off-track students as early as possible. To that end, this work explores 9th grade indicators of college readiness using data on 5000+ Pittsburgh Public School students and college enrollment data from the National Student Clearinghouse. Logistic regressions are used to explore the relationship between 9th grade characteristics—including GPA, behavior and attendance, test scores, demographics, and socioeconomics—and (1) high school graduation, (2) college enrollment, and (3) college persistence. Lastly, we use on-track indicators to identify students who are likely to fall off-track during the transition to high school.
Please join us on Monday, October 7 at 10:00 a.m. in 232M Baker Hall for this talk given by Nathan VanHoudnos (Ph.D. student, Statistics and Public Policy)
Title: Progress On Correcting a Significance Test for Model Misspecification
Abstract: Meta-analysis of educational interventions is sometimes complicated by errors in the statistical analysis of the primary studies. Recently, a series of papers in the education research literature (Hedges, 2007a, 2009; Hedges and Rhoads, 2011) have derived post-hoc corrections to misspecified test statistics so that the corrected versions can be used in a meta-analysis. However, these corrections are currently limited to special cases of simple models. This talk will outline progress made in extending the Hedges family of corrections to a wider class of models. We will study the current policy of the What Works Clearinghouse as an example.
hnm etc for serg (2) (Click here to access slides from this talk)
Please join us on Monday,September 23 at 10:00 a.m. in 232M Baker Hall for this talk given by Brian Junker (CMU Professor of Statistics).
Title: Statistical Models for Ensembles of Social Networks in Education Research
Joint work with Tracy Sweet (College of Education, Univ of Maryland) and Andrew Thomas (Statistics, CMU).
Experimental and observational studies in education are sometimes focused not on the effects of changing curriculum, teaching and learning materials, or classroom technique, but rather on changes in the way students—or teachers, teaching coaches and administrators—interact with one another. Many whole school initiatives encourage some type of social structural change, be it an increase in collaboration, distribution of leadership or a push toward small learning communities: in short, they encourage changes in the social networks of students and of professionals in school systems. In this talk I will give a overview of a new modeling framework, the Hierarchical Network Model (HNM) framework (Sweet, Junker & Thomas, 2013, JEBS), that is particularly suited to social network data in education research. I will discuss some specific models in this framework, focusing on useful interpretations, with some data analysis examples. If time permits, I will also discuss some design and power issues for experiments using these models (Sweet & Junker, technical report), as well as a related set of computationally convenient models, the Conditionally Independent Dyad (CID, Thomas, Dabbs, Sweet, Sadinle & Junker, in progress) models.