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- All Subjects: Mathematics
- Status: Published
Description
More than 30% of college entrants are placed in remedial mathematics (RM). Given that an explicit relationship exists between students' high school mathematics and college success in science, technology, engineering, and mathematical (STEM) fields, it is important to understand RM students' characteristics in high school. Using the Education Longitudinal Survey 2002/2006 data, this study evaluated more than 130 variables for statistical and practical significance. The variables included standard demographic data, prior achievement and transcript data, family and teacher perceptions, school characteristics, and student attitudinal variables, all of which are identified as influential in mathematical success. These variables were analyzed using logistic regression models to estimate the likelihood that a student would be placed into RM. As might be expected, student test scores, highest mathematics course taken, and high school grade point average were the strongest predictors of success in college mathematics courses. Attitude variables had a marginal effect on the most advantaged students, but their effect cannot be evaluated for disadvantaged students, due to a non-random pattern of missing data. Further research should concentrate on obtaining answers to the attitudinal questions and investigating their influence and interaction with academic indicators.
ContributorsBarber, Rebecca (Author) / Garcia, David R. (Thesis advisor) / Powers, Jeanne (Committee member) / Rodrigue Mcintyre, Lisa (Committee member) / Arizona State University (Publisher)
Created2011
Description
The deficiency of American primary and secondary schools as compared to schools worldwide has long been documented. The Teaching Gap highlights exactly where our problems might lie, and lays out a plan for how to deal with it. Progress would be slow, but tangible. Our country, however, seems to prefer vast and immediate overhauls that have historically failed (see: New math, etc.). If we had implemented the changes in The Teaching Gap in the decade in which it was written, we would be seeing results by now. Instead, every change we make gets reverted. The Common Core State Standards will prove over the next few years to be either another one of these attempts or a large step in the right direction. It might finally be the latter, as its creation was informed by practices that work best in every state in the US, as well as high- performing countries around the world.
ContributorsMcKee, Emily (Author) / Sande, V. Carla (Thesis director) / Ashbrook, Mark (Committee member) / Schroeder, Darcy (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12
Description
Identifying associations between genotypes and gene expression levels using next-generation technology has enabled systematic interrogation of regulatory variation underlying complex phenotypes. Understanding the source of expression variation has important implications for disease susceptibility, phenotypic diversity, and adaptation (Main, 2009). Interest in the existence of allele-specific expression in autosomal genes evolved with the increased awareness of the important role that variation in non-coding DNA sequences can play in determining phenotypic diversity, and the essential role parent-of-origin expression has in early development (Knight, 2004). As new implications of high-throughput sequencing are conceived, it is becoming increasingly important to develop statistical methods tailored to large and formidably complex data sets in order to maximize the biological insights derived from next-generation sequencing experiments. Here, a Bayesian hierarchical probability model based on the beta-binomial distribution is proposed as a possible approach for quantifying allele-specific expression from whole genome (WGS) and whole transcriptome (RNA-seq) data. Pipeline for the analysis of WGS and RNA-seq data sets from ten samples was developed and implemented, while allele-specific expression (ASE) was quantified from both haplotypes using individuals heterozygous at the tested variants utilizing the described methodology. Both computational and statistical framework applied accurately quantified ASE, achieving high reproducibility of already described allele-specific genes in the literature. In conclusion, described methodology provides a solid starting point for quantifying allele specific expression across whole genomes.
ContributorsMalenica, Ivana (Author) / Craig, David (Thesis director) / Rosenberg, Michael (Committee member) / Szelinger, Szabolcs (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12