Leadership Practices that Affect Student Achievement: Creating a Supportive Organization for Learning

Thesis advisor: Diana PullinIt is widely accepted that school leadership has both a direct and indirect impact on student achievement. Hitt and Tucker’s (2016) Unified Leadership framework summarized a decade of work by numerous researchers identifying the five most effective leadership domains that influence student learning. Using that work as a conceptual framework, this qualitative case study analyzed one of the five interdependent leadership domains in an urban elementary school that succeeded in educating traditionally marginalized students and outperformed other schools with similar demographics in the district. This study focused on the fourth of Hitt and Tucker’s (2016) key leadership domains or practices: creating a supportive organization for learning. Creating a supportive organization for learning is important because just as teachers need to establish a sense of well-being and trust for students to learn in their classroom, administrators must establish the same sense of trust and comfort to create an environment where teachers can teach to their highest capacity. This study explored whether the key leadership practices of creating a supportive organization for learning were present in a school and whether the school leaders believed that presence of the attributes contributed to the effectiveness of the school. This study found that the five attributes of creating a supportive organization for learning were present at the school in that the principal built capacity in her building, the school resources targeted student achievement and there was a belief that all students can learn. Importantly, the superintendent also highlighted the principal’s ability to push her staff to continuous results without pushing so hard that they lost trust in her and love for the students they serve. There were, however, opportunities for improvement including creating a clear set of district supports for schools and improving cultural proficiency at the school level. We also found that administrators in the district believe that school leaders have made the school successful by setting high expectations for the students, no matter their situation, and created a culture of productive collaboration that was focused on continuously improving student achievement, key components of creating a supportive organization for learning.Thesis (EdD) — Boston College, 2018.Submitted to: Boston College. Lynch School of Education.Discipline: Educational Leadership and Higher Education

Analysis of Affymetrix GeneChip Data Using Amplified RNA

The standard method of target synthesis for hybridization to Affymetrix GeneChip® expression microarrays requires a relatively large amount of input total RNA (1-15 micrograms). When small biological samples are collected by microdissection or other methods, amplification techniques are required to provide sufficient target for hybridization to expression arrays. One amplification technique used is to perform two successive rounds of T7-based in vitro transcription. However, the use of random primers required to re-generate cDNA from the first round transcription reaction results in shortened copies of the cDNA, and ultimately the cRNA, transcripts from which the 5\u27 end is missing. In this paper we describe an experiment designed to compare the quality of data obtained from labeling small RNA samples using the Affymetrix Small Sample Target Labeling Protocol V 2 to that of data obtained using the standard protocol. We utilized different preprocessing algorithms to compare the data generated using both labeling methods and present a new algorithm that improves upon existing ones in this setting

cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate

Cost-effective oligonucleotide genotyping arrays like the Affymetrix SNP 6.0 are still the predominant technique to measure DNA copy number variations (CNVs). However, CNV detection methods for microarrays overestimate both the number and the size of CNV regions and, consequently, suffer from a high false discovery rate (FDR). A high FDR means that many CNVs are wrongly detected and therefore not associated with a disease in a clinical study, though correction for multiple testing takes them into account and thereby decreases the study's discovery power. For controlling the FDR, we propose a probabilistic latent variable model, ‘cn.FARMS’, which is optimized by a Bayesian maximum a posteriori approach. cn.FARMS controls the FDR through the information gain of the posterior over the prior. The prior represents the null hypothesis of copy number 2 for all samples from which the posterior can only deviate by strong and consistent signals in the data. On HapMap data, cn.FARMS clearly outperformed the two most prevalent methods with respect to sensitivity and FDR. The software cn.FARMS is publicly available as a R package at http://www.bioinf.jku.at/software/cnfarms/cnfarms.html

Graphical exploration of gene expression data: a comparative study of three multivariate methods

This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. Proper weighting is important, since it enables less reliable data to be down-weighted and more reliable information to be emphasized.status: publishe

Graphical Exploration of Gene Expression Data: A Comparative Study of Three Multivariate Methods

This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. Proper weighting is important, since it enables less reliable data to be down-weighted and more reliable information to be emphasized.status: publishe