Missing Data in the Context of Student Growth

One property of student growth data that is often overlooked despite widespread prevalence is incomplete or missing observations. As students migrate in and out of school districts, opt out of standardized testing, or are absent on test days, there are many reasons student records are fractured. Missing data in growth models can bias model estimates and growth inferences. This study presents empirical explorations of how well missing data methodologies recover attributes of would-be complete student data used for teacher evaluation. Missing data methods are compared in the context of a Student Growth Percentiles (SGP) model used by several school systems for accountability purposes. Using a real longitudinal dataset, we evaluate the sensitivity of growth estimates to missing data and compare the following missing data methods: listwise deletion, likelihood-based imputation using an expectation-maximization algorithm, multiple imputation using a Markov Chain Monte Carlo method, multiple imputation using a predictive mean matching method, and inverse probability weighting. Methodological and practical consequences of missing data are discussed

DATA-DRIVEN TECHNIQUES FOR DIAGNOSING BEARING DEFECTS IN INDUCTION MOTORS

Induction motors are frequently used in many automated systems as a major driving force, and thus, their reliable performances are of predominant concerns. Induction motors are subject to different types of faults and an early detection of faults can reduce maintenance costs and prevent unscheduled downtime. Motor faults are generally related to three components: the stator, the rotor and/or the bearings. This study focuses on the fault diagnosis of the bearings, which is the major reason for failures in induction motors. Data-driven fault diagnosis systems usually include a classification model which is supported by an efficient pre-processing unit. Various classifiers, which aim to diagnose multiple bearing defects (i.e., ball, inner race and outer race defects of different diameters), require well-processed data. The pre-processing tasks plays a vital role for extracting informative features from the vibration signal, reducing the dimensionality of the features and selecting the best features from the feature pool. Once the vibration signal is perfectly analyzed and a proper feature subset is created, then fault classifiers can be trained. However, classification task can be difficult if the training dataset is not balanced. Induction motors usually operate under healthy condition (than faulty situation), thus the monitored vibration samples relate to the normal state of the system expected to be more than the samples of the faulty state. Here, in this work, this challenge is also considered so that the classification model needs to deal with class imbalance problem

The Impact of Student Performance on Large-Scale Assessments: A View of Long-Term Health, Career, and Societal Outcomes

This study examined the predictive power of student growth for large-scale assessments on meaningful life outcomes, focusing on the three categories of health, career, and societal involvement. Analysis was conducted using the NELS:88/00 dataset–a longitudinal study that followed a nationally-representative sample of over 12,000 eighth grade students from 1988 to 2000, until the students were 26 years old and entered into the work force. The large-scale assessment variables included math and reading performance in the 1988 cognitive batteries administered by NELS. To gauge growth levels, I generated Student Growth Percentiles (SGP) from tests administered by NELS from 1988 to 1992. Measurable outcomes related to health included binge drinking and cigarette use. Career outcomes included yearly income and job satisfaction. Outcomes related to societal involvement included voting habits, social integration, and the frequency of obtaining information from the outside world. This quantitative study revealed that student growth on large-scale assessments is meaningfully predictive for three of seven outcome variables: binge drinking, cigarette smoking, and social involvement. Interestingly, I found that students’ performance growth on large-scale exams did not yield more desirable outcomes linearly. For occurrences of binge drinking at age 26, only low reading growth increased the likelihood of binge drinking. Typical and high growths in reading performance were statistically identical in reducing binge-drinking occurrences. The use of cigarettes at age 26 saw similar results for both reading and math growth: only low growth on the large-scale assessments increased the likelihood of the respondent smoking as a young adult. Finally, only respondents who had exhibited typical growth in math performance were more likely to be highly socially involved as young adults. From the methods and conclusions of this study, I support two major recommendations. First, I recommend that policymakers and school leaders make a habit of collecting longitudinal data along with large-scale assessment results in order to allow researchers and school personnel to investigate long-term program effectiveness. Second, I recommend that a philosophical shift occur among educational researchers in the interest of tracking long-term outcomes that benefit the adult lives of students and society instead of short-lived gains in performance scores and signals

An Examination of Adult Learners, Learning Outcomes, and Selected Learning Environments at a Land-Grant Research I University.

Population projections predicting dramatic increases in demand for higher education, and the explosive increase in technologies which may be essential to meet demand, are motivating changes in the culture of higher education. The purpose of this study was to describe characteristics of courses offered and learners served by the Louisiana State University Evening School in different learning environments; and to compare characteristics of learners enrolled in the course by the medium through which the course was delivered (defined as on-campus, telecourse, and off-campus). Data were collected from institutional records, course and instructor evaluations, and from the course syllabi provided by the instructors. A single introductory course in psychology was used in this study in order to minimize error due to subject matter effects. There were 213 learners enrolled in eight sections of the course. Selected characteristics of learners, instructors, learning materials, enrollments, and learning outcomes were described; and selected learner, instructor and outcome information were compared across learning environments. Over 90% of learners were single, and almost 70% were women. The majority of learners were under the age of 33, white, and more likely to be enrolled as undergraduates than as adult special students. Recommendations included tracking learners to aid outreach to underserved populations, providing learners with detailed course information to decrease obstacles to taking courses, and working with other organizational units to assess the needs of all learners

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute

Classroom observation, self-assessment of efficacy, and student perceptions of engagement as predictors of value-added scores

The purpose of this study was to determine which has the strongest correlation to student achievement as measured by value-added test scores: Principal Observations, Teachers Self-Efficacy Ratings, or Student Perceptions of Teacher Effectiveness. 68 teachers from a K-12 public school in the southeast region of the United States agreed to participate in the study. The Teachers’ Sense of Efficacy Scale (TSES) was utilized to measure the teachers in terms of their own reports of self efficacy. The School Improvement Model (SIM) of Iowa State University instruments were used to measure the students’ perceptions of teacher effectiveness. The Teacher Advancement Program (TAP) model observation protocol was used to measure the principals/supervisors’ observation scores. These three measures were run in a multiple regression correlation to determine which of the three was the strongest predictor of student outcomes. An analysis of Pearson’s Moment Correlation among all three variables revealed that the principal observation scores were the only statistically significantly correlated measures that could be inferred to have any predictive impact on student achievement as measured by Value Added Scores

Ambient and Indoor Air Pollution in Pregnancy and the risk of Low birth weight and Ensuing Effects in Infants (APPLE): A cohort study in Bangalore, South India

Background: Exposure to air pollution (IAP) from the combustion of solid fuels is a significant cause of morbidity and mortality in developing countries. Pregnant women exposed to higher pollutant levels are at higher risk of delivering a low-birth-weight (LBW) baby. There is a lack of standardized data regarding the levels and types of specific pollutants and how they impact LBW. We aim to prospectively assess the association between ambient and indoor air pollution levels in pregnancy and low birth weight and understand the subsequent risk of adiposity in these infants. Methods: We will conduct a prospective cohort study of 516 pregnant women recruited before 18 weeks of gestation in the urban slums of Bangalore, who have voluntarily consented to participate. We will estimate the level of air pollutants including coarse particulate matter 10 ug/m3 (PM10 ), fine particulate matter 2.5 ug/m3(PM2.5) and carbon monoxide (CO) parts per million (ppm) levels in both indoor and ambient environment. The follow-up of the delivered children will be done at delivery until the infant is two years old. The association between pollutants and LBW will be evaluated using logistic regression adjusting for potential confounders.Further, we will explore the mediation role of LBW in the hypothesized causal chain of air pollution and adiposity. Nested within a larger Maternal Antecedents of Adiposity and Studying the Transgenerational role of Hyperglycemia and Insulin (MAASTHI) cohort, we can estimate the absolute risk of having low birth weight caused by air pollution and other variables. Discussion: Understanding the association between exposures to ambient and indoor air pollution and low birth weight is essential in India. LBW babies have a higher risk of developing obesity and Non-Communicable Diseases (NCDs) during adulthood. The results from this study can inform the efforts for controlling the air pollution-related chronic diseases in India.</ns4:p