The Pragmatist in Context of a National Science Foundation Supported Grant Program Evaluation: Guidelines and Paradigms

Background:  The philosophical underpinnings of evaluation guidelines set forth by a funding agency can sometimes seem inconsistent with that of the intervention. Purpose: Our purpose is to introduce questions pertaining to the contrast between the instructional program’s underlying philosophical beliefs and assumptions and those underlying our evaluation approach. Drawing heavily on Scriven, we discuss these from a pragmatist evaluation stance in light of issues defined by Lincoln and Guba (2000). The discussion is couched in the evaluation of an innovative approach to teaching computer science. Setting: Auburn University, Auburn, AL Intervention: The evaluation is designed to investigate the effects of a studio-based teaching approach in computer science education. The evaluation framework employs a rigorous design that seeks to provide evidence to support or refute some assumed truth about the object (or construct) investigated. The program evaluated is steeped in a constructivist framework which assumes that no universal truth or reality exists, but rather, is constructed by the individual. Research Design: Our evaluation design, to a good extent, reflects a post-positivist, quasi-experimental position. We also include a qualitative component using student interviews. Data Collection and Analysis: Evidence of the effectiveness of the instructional approach for learning is assessed quantitatively using pre- and post-test and pre- and post-survey data group comparisons (mixed design ANOVA). Interviews provide the basis for qualitative theme analysis. Findings: Quantitative results were somewhat weak but consistent in support of the studio-based teaching. Interview data suggest that most students did find working in groups enjoyable and a valuable experience

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Constructive and Collaborative Learning of Algorithms

This research began by investigating the literature on student learning from algorithm animations and conducting experimental studies of an existing hypermedia algorithm visualization system. Results of these efforts led us to develop a system, CAROUSEL (Collaborative Algorithm Representations Of Undergraduates for Self-Enhanced Learning), using which students created expository representations of algorithms, shared their representations with others, evaluated each other's representations and discussed them. The system and the activities of representation creation, sharing, evaluation and discussion that it supports were then studied in three experiments, which are summarized. They show a significant positive relationship between these constructive and collaborative activities and algorithm learning, which suggests that this is a beneficial pedagogical approach for introductory courses on algorithms

A methodology for knowledge acquisition and reasoning in failure analysis of systems

The problem of modeling knowledge about the fault behavior of a system and utilizing this model for reasoning about and diagnosing failures is addressed. A solution that merges graph and fault-tree-based failure analysis with rule-oriented reasoning is presented. Failure analysis is divided into two phases, a failure source location phase and a failure cause identification phase. Each phase consists of a failure model and a process that operates on it. The failure models for the first and second phases are based on lesel-structured fault propagation digraphs and augmented fault trees, respectively. The augmented fault tree (AFT) is a conceptual structure that encodes probabilistic, temporal, and heuristic information in addition to the causal aspects of failures modeled by conventional fault trees. The two models are combined to form a novel hierarchical failure knowledge representation scheme. Upper levels of this hierarchy are made up of the fault propagation digraphs. Each level represents a view of the system under a particular granularity, and the granularity increases with levels. This feature permits control over the resolution of fault diagnosis. The lowest level consists of a set of cause-consequence knowledge bases containing production rules. These production rules are derived from augmented fault trees and represent the cause-effect relations among failure events that lead to the corresponding subsystem's failure. A knowledge acquisition procedure to generate these failure models and failure analysis processes that operate on them are described. The methodology proposed is inherently parallel as the processes may operate on different levels independently

Facilitating Students' Collaboration and Learning in a Question and Answer System

Abstract Green Dolphin (GD) is a question and answer system for students learning programming, with a social web interface. It crowd-sources the task of answering technical questions to the peers of students who ask questions. GD has several original features that make it different from existing systems. It automatically identifies students who are knowledgeable based on their activity, and tags them as experts to whom other students can ask questions. GD provides students with automatic feedback of the quality of code they submit. Thus, students get fast and high quality answers from their peers and the system, freeing up time for teachers. After a student posts a question in GD, it delays making visible answers from instructors and teaching assistants so that other students are encouraged to participate, and have time to answer the question. We believe that this can significantly increase student participation, collaboration and sense of ownership. Students gain new knowledge from the flow of questions and answers in the system. They develop communication skills by asking and answering questions as well as programming and debugging skills