3 resultados para Inside-Outside Algorithm
em Brock University, Canada
Resumo:
Globalization has resulted in large-scale international and local assessments closely tied to notions of accountability and competitiveness in a globalized economy. Although policy makers seek to ensure citizens meet the demands of a global knowledge-based economy, such assessments may also impede the development of requisite 21st century skills. While standardization currently is viewed as the most effective measurement of student achievement, several Canadian and international jurisdictions are moving toward assessment for learning (AfL). This conceptual study sought to identify whether AfL or standardized assessment most effectively meets 21st century learning goals in the wake of rapid global change. It applies a Story Model theoretical framework to understand the current, the new emerging, and the future ideal story of education from a personal, cultural, and global lens. The study examines the main critiques and/or challenges of standardized testing, the benefits of AfL for student learning, and new teaching and assessment approaches to the development of 21st century learning goals. The study applies the Story Model’s inside-outside/past-future approach to determine the future direction of assessment. Results show that the new story of assessment will most likely entail a model that integrates both standardized testing and in-class assessments in the form of AfL and PBL.
Resumo:
Abstract The main focus of this qualitative research was to explore how parents from different national backgrounds see their role in their children’s education inside and outside of school. Although greater recruitment was described and sought after, this qualitative research gathered data from two immigrant female parents from a community parents’ group located in Ontario, Canada. Data were collected through face-to-face interviews with each participant using open-ended questions asking about the different ways these mothers, along with their spouses, were involved in their children’s education. Moreover, questions were designed to find out what alternatives parents use to support their children’s learning. The main question driving this research was “How are immigrant families currently involved with their children’s education inside and outside of school?” NVivo, 10 was used to code the transcripts giving rise to themes which could then be utilized to explain and explore the research question. The findings of this research are congruent with past research and demonstrate that immigrant mothers are more involved than the fathers are in their children’s education (Grolnick & Slowiaczek 1994; Peters, Seeds, Goldstein, & Coleman, 2008). A specifically important finding in this research is that schools are perceived by the immigrant mothers in this study as not doing enough to actively engage immigrant parents in their children’s education. On the other hand, findings also show that parents are eager to find different avenues to get involved and help their children succeed.
Resumo:
Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.