Elements of Successful Education Abroad Programs for STEM and Vocational Students: Lessons from the University of Wisconsin – Stout, Polytechnic

In a globalized economy, wherein manufacturing and knowledge trade are international, STEM – science, technology, engineering, math – and vocational degrees lead to international career paths. International experience through education abroad is relevant, therefore, to preparing STEM and vocational students preparing for these international paths. The University of Wisconsin – Stout, Polytechnic (Stout) serves as a case-study for the question: “What elements or structures of an education abroad program should be emphasized to build programs which best serve STEM and vocational students’ unique needs, creating an optimal environment for learning and growth, while preparing them for global careers?” Data collection to address this question includes interviews with the staff of Stout’s Office of International Education (OIE), drawing on all digitally available student records, surveying Stout study abroad student alumni, and follow-up interviews with a number of those students. Findings from each step of this process are analyzed against each other, seeking over-arching trends. Data analysis of program trends match to reported student experiences, and student responses reveal six elements key to programmatic success: academic fit, preparation in academics and mindset, structures which promote growth, location relevance to their field, having a balanced and enjoyable experience, and reflection connecting that experience to their future. These elements suggest application across all education abroad programs of the OIE as well as similar institutions. Beyond these findings, mis-matches between researcher expectations and quantitative and qualitative findings also reveal many opportunities for further investigation into the research question

Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments

Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we propose the Robust Personalized Emotion Recognition Framework with the Adaptive Data Boosting Algorithm to solve the cold-start problem. The proposed framework incrementally provides a customized training model for the target user by reinforcing the dataset by combining the acquired target user speech with speech from other users, followed by applying SMOTE (Synthetic Minority Over-sampling Technique)-based data augmentation. The proposed method proved to be adaptive across a small number of target user datasets and emotionally-imbalanced data environments through iterative experiments using the IEMOCAP (Interactive Emotional Dyadic Motion Capture) database.This research was supported by an Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2017-0-00655). This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information & communications Technology Promotion). This research was supported by the MIST (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW supervised by the IITP (Institute for Information & communications Technology Promotion) (2017-0-00093)

espida Bibliography

This is the bibliography pulled together during research for the espida Project

Principles of forming a modern accounting and analytical model of commercial organization in digital economy

Purpose: The article presents basic methodological approaches to the creation of a new model of forming and functioning of the accounting and analytical system to meet the information needs of internal and external stakeholders of organizations. Design/Approach/Methodology: Substantiation of the principles of building a system for accounting and analytical information management that meets current conditions for the business functioning using modern hardware and software. Findings: The developed model of cascade functioning of organization’s information support system optimizes the structure and content of accounting and analytical modules, contributes to the effective implementation of management functions, timely control and rapid response to the impact of negative factors. Practical implications: The principles of information flow management system constructing formulated in the article contribute to optimization of expenses for organization of accounting and analytical functions, improvement of quality of financial and non-financial reporting, realistic assessment and forecasting of business efficiency. Originality/Value: The proposed new model for constructing an accounting and analytical information base allows to improve the procedures of collection, processing, storage and disclosure of financial and non-financial information, to create a balanced structure of the database on the basis of cascade digitization of primary and derived data.peer-reviewe

Double Bottom Line Progress Report: Assessing Social Impact in Double Bottom Line Ventures, Methods Catalog

Outlines methods for social entrepreneurs and their investors to define, measure and communicate social impact and return in early-stage ventures

Automating biomedical data science through tree-based pipeline optimization

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators---such as synthetic feature constructors---that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.Comment: 16 pages, 5 figures, to appear in EvoBIO 2016 proceeding

An Unfinished Canvas: A Review of Large-Scale Assessment in K-12 Arts Education

Reviews the status of and current practices in statewide standards-based arts assessment for K-12 education accountability. Examines the approaches and criteria of several models of large-scale arts assessment and five states' assessment programs

Supporting Success: Why and How to Improve Quality in After-School Programs

This report examines the program improvement strategies, step-by-step, that allowed The James Irvine Foundation's CORAL initiative to achieve the levels of quality needed to boost the academic success of participating students. And, it makes specific policy and funding suggestions for improving program performance. Communities Organizing Resources to Advance Learning (CORAL) is an eight-year, $58 million after-school initiative to improve educational achievement in low-performing schools in five California cities