Abstract-There is no doubt choosing specific language teaching materials can influence the quality of teaching and learning procedures. The textbooks can often play an essential role in students' success or failure as a part of the materials used in the language classrooms. Consequently, special care should be taken in evaluating educational materials based on dependable and valid instruments. Some of the usual instruments to evaluate the English Language Teaching materials are the checklists. An evaluation checklist is an instrument that allows the evaluator with a number of features of successful teaching and learning materials. Regarding this, the present study is an attempt to evaluate the recent general English textbook by Richards and Bohlke (2012) titled "Four Corners" using Daoud and Celce-Murcia's (1979) evaluation checklist. The finding of the study supports the strengths of the aforementioned textbook putting it in one of the reliable available textbooks.
Population growth can lead to public school capacity issues as well as increased school bus utilization, which, in turn, can result in longer school bus transport times for regular and special needs students. Special needs or medically fragile students are children with special health care needs who are at increased health and safety risk. It is common practice to provide special needs students with specially-equipped buses and/or special classroom environments with specific facilities or services. However, the assignment of student services to schools is regularly made without regard to bus transportation considerations for special needs students. Considering the potentially negative impact of long school bus rides on these students, we present the first sys tematic, integrated analyses of special needs student busing and classroom assign ments. We provide models and algorithms for maintaining administration-based transportation financial performance measures while simultaneously designing smarter transportation networks considering both student geographical location and service needs.
In the aftermath of a mass-casualty incident, one of the first steps in the response is to triage the casualties. Triage systems categorize the casualties based on criticality, and then prioritize casualties for transfer to hospitals for further treatment. The prioritization is usually based on simply ordering the casualty types without considering the available resources to transport them and the scale of the disaster. These factors can significantly affect the outcome of the rescue efforts. In this research we study a mathematical model to incorporate the above mentioned factors in the triage process. We assume a disaster location with a set of casualties, categorized by criticality and care requirements, that must be transported to hospitals in the region using a fleet of available ambulances. The goal is to maximize the expected number of survivors. We analyze the structure of the optimal solution to this problem, and compare the performance of the model with the current practice and other related models in the literature.
Private giving represents more than three fourths of all U.S. charitable donations, about 2% of total Gross Domestic Product (GDP). Private giving is a significant factor in funding the nonprofit sector of the U.S. economy, which accounts for more than 10% of total GDP. Despite the abundance of data available through tax forms and other sources, it is unclear which factors influence private donation, and a reliable predictive mechanism remains elusive. This study aims to develop predictive models to accurately estimate future charitable giving based on a set of potentially influential factors. We have selected several factors, including unemployment rate, household income, poverty level, population, sex, age, ethnicity, education level, and number of vehicles per household. This study sheds light on the relationship between donation and these variables. We use Stepwise Regression to identify the most influential variables among the available variables, based on which predictive models are developed. Multiple Linear Regression (MLR) and machine learning techniques, including Artificial Neural Networks (ANN) and Support Vector Regression (SVR) are used to develop the predictive models. The results suggest that population, education level, and the amount of charitable giving in the previous year are the most significant, independent variables. We propose three predictive models (MLR, ANN, and SVR) and validate them using 10-fold cross-validation method, then evaluate the performance using 9 different measuring criteria. All three models are capable of predicting the amount of future donations in a given region with good accuracy. Based on the evaluation criteria, using a test data set, ANN outperforms SVR and MLR in predicting the amount of charitable giving in the following year.
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