Professional Development For Educators In Order To Support English Learner Students From Oral Cultures

This capstone project addresses the question, How can I create professional development for educators in order to support English Learner (EL) students from oral cultures? This project uses a series of frameworks and theories to provide the best comprehensive two-part professional development for educators supporting EL students from oral cultures. It is tailored to educators by recognizing their unique needs as adult learners who receive ongoing training in their field. By catering to adult learners and drawing from both EL best practices and culturally responsive teaching, this professional development offers educators insight and tools to implement when working with EL students from oral cultures. Students from oral cultures experience education differently due to their instinct to gravitate toward people, utilizing relationships and language to connect to others, instead of print. In order to properly support students from oral cultures, the PD offers a resource folder filled with different types of tools to utilize when working with students from oral cultures. The PD is structured to support educators as much as it is structured to support students, through direct modeling that educators can implement in their own classrooms. Key influences of this work are Walter J. Ong’s (2002) Orality and Literacy in addition to Dr. Andrea DeCapua’s (2011, 2009) work on EL students and Students with Limited or Interrupted Formal Education (SLIFE). This professional development is set up so educators can obtain skills that support students from oral cultures, implement said skills, collect data through student surveys, reflect upon their practices and set goals for future improvement

Competitive Intelligence Practices in Small Business: a Social Media Analytics Approach

With the recent advances in the development of Information and Communication Technologies (ICTs), a lot of small businesses and micro-enterprises achieve their business goals and reach economic development. While Competitive Intelligence (CI) is the practice of studying competitors and competitive environment, its purpose is to provide actionable intelligence for informative organizational decision making. In this study, we propose a social media analytics approach to understand the CI of the small businesses and micro-enterprises. Three research questions are proposed to further guide the future research. Based on Chen (1996)’s framework, case study will be conducted from micro-enterprises. The work contributes to the CI body of knowledge by introducing advanced text mining techniques and social mediate data into CI analysis in the context of small businesses. Evaluation experiment will be conducted in the future

Kernel-based distance metric learning for microarray data classification

BACKGROUND: The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with traditional pattern classifications, gene expression-based data classification is typically characterized by high dimensionality and small sample size, which make the task quite challenging. RESULTS: In this paper, we present a modified K-nearest-neighbor (KNN) scheme, which is based on learning an adaptive distance metric in the data space, for cancer classification using microarray data. The distance metric, derived from the procedure of a data-dependent kernel optimization, can substantially increase the class separability of the data and, consequently, lead to a significant improvement in the performance of the KNN classifier. Intensive experiments show that the performance of the proposed kernel-based KNN scheme is competitive to those of some sophisticated classifiers such as support vector machines (SVMs) and the uncorrelated linear discriminant analysis (ULDA) in classifying the gene expression data. CONCLUSION: A novel distance metric is developed and incorporated into the KNN scheme for cancer classification. This metric can substantially increase the class separability of the data in the feature space and, hence, lead to a significant improvement in the performance of the KNN classifier