Use of Standardized Mastery Content Assessments Given During the First Year of a Baccalaureate Nursing Program For Predicting NCLEX-RN Outcomes

The purpose of this study was to evaluate the relationship between standardized content specific mastery assessments and NCLEX-RN outcomes. Three content-specific standardized assessments testing Fundamentals, Pharmacology and Mental Health concepts were used to explain the dichotomous NCLEX-RN outcome of pass or fail. The three assessments were developed by Assessment Technologies Institute, LLC (ATI). The assessments were administered to baccalaureate nursing students (N = 119) during the first year of a nursing program in one public university over a period of five consecutive semesters. Group comparisons between those passing and those failing NCLEX-RN on the first attempt and correlations were calculated using SAS, Version 9.2. Multivariate analysis of the quantitative data was completed using the logistic regression procedure. The Stepwise iterative method to determine the most accurate model revealed the Pharmacology assessment score predicted the NCLEX-RN outcome of the sample with 73.7% accuracy. Use of the Pharmacology content assessment can assist nurse educators in early identification of at risk students for implementation of a comprehensive remediation plan to decrease NCLEX-RN failures

Standardization and Control for Confounding in Observational Studies: A Historical Perspective

Control for confounders in observational studies was generally handled through stratification and standardization until the 1960s. Standardization typically reweights the stratum-specific rates so that exposure categories become comparable. With the development first of loglinear models, soon also of nonlinear regression techniques (logistic regression, failure time regression) that the emerging computers could handle, regression modelling became the preferred approach, just as was already the case with multiple regression analysis for continuous outcomes. Since the mid 1990s it has become increasingly obvious that weighting methods are still often useful, sometimes even necessary. On this background we aim at describing the emergence of the modelling approach and the refinement of the weighting approach for confounder control.Comment: Published in at http://dx.doi.org/10.1214/13-STS453 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

The effect of duration, serialposition and competitive clutter on consumer memory for television advertising: An extension and a test of generalizability

Advertising;Consumer Behaviour;Television;marketing

Four Decades of the Journal \u3ci\u3eLaw and Human Behavior\u3c/i\u3e: A Content Analysis

Although still relatively young, the journal Law and Human Behavior (LHB) has amassed a publication history of more than 1300 full-length articles over four decades. Yet, no systematic analysis of the journal has been done until now. The current research coded all full-length articles to examine trends over time, predictors of the number of Google Scholar citations, and predictors of whether an article was cited by a court case. The predictors of interest included article organization, research topics, areas of law, areas of psychology, first-author gender, first-author country of institutional affiliation, and samples employed. Results revealed a vast and varied field that has shown marked diversification over the years. First authors have consistently become more diversified in both gender and country of institutional affiliation. Overall, the most common research topics were jury/judicial decision-making and eyewitness/memory, the most common legal connections were to criminal law and mental health law, and the most common psychology connection was to social-cognitive psychology. Research in psychology and law has the potential to impact both academic researchers and the legal system. Articles published in LHB appear to accomplish both

Application of the EM Algorithm for Mixture Models

A developmental trajectory describes the course of behaviour over time. Iden­ tifying multiple trajectories within an overall developmental process permits a focus on subgroups of particular interest. This research introduces a SAS macro program that identifies trajectories by using the Expectation-Maximization (EM) algorithm to fit semi-parametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algo­ rithms (e.g. SAS procedure PROC TRAJ) and standard EM, as well as two other EM-based algorithms for speeding up convergence. The simulation study shows that our EM methods produce more accurate parameter estimates than the full maximization methods. The EM-based methodology is illustrated with a longitudinal data set involving adolescents smoking behaviours

“I h 8 u”: Findings from a five-year study of text and e-mail bullying

Copyright @ 2010 British Educational Research Association. The final version of this article is available at the link below.This study charts reports of nasty or threatening text and e-mail messages received by students in academic years 7 and 8 (11-13 years of age) attending 13 secondary schools in the North of England between 2002-2006. Annual surveys were undertaken on behalf of the local education authority (LEA) to monitor bullying. Results indicated that, over five years, the number of pupils receiving one or more nasty or threatening text messages or e-mails increased significantly, particularly among girls. However, receipt of frequent nasty or threatening text and e-mail messages remained relatively stable. For boys, being a victim of direct-physical bullying was associated with receiving nasty or threatening text and e-mail messages; for girls it was being unpopular among peers. Boys received more hate-related messages and girls were primarily the victims of name-calling, Findings are discussed with respect to theoretical and policy developments, and recommendations for future research are offered

A systematic review on machine learning models for online learning and examination systems

Examinations or assessments play a vital role in every student’s life; they determine their future and career paths. The COVID pandemic has left adverse impacts in all areas, including the academic field. The regularized classroom learning and face-to-face real-time examinations were not feasible to avoid widespread infection and ensure safety. During these desperate times, technological advancements stepped in to aid students in continuing their education without any academic breaks. Machine learning is a key to this digital transformation of schools or colleges from real-time to online mode. Online learning and examination during lockdown were made possible by Machine learning methods. In this article, a systematic review of the role of Machine learning in Lockdown Exam Management Systems was conducted by evaluating 135 studies over the last five years. The significance of Machine learning in the entire exam cycle from pre-exam preparation, conduction of examination, and evaluation were studied and discussed. The unsupervised or supervised Machine learning algorithms were identified and categorized in each process. The primary aspects of examinations, such as authentication, scheduling, proctoring, and cheat or fraud detection, are investigated in detail with Machine learning perspectives. The main attributes, such as prediction of at-risk students, adaptive learning, and monitoring of students, are integrated for more understanding of the role of machine learning in exam preparation, followed by its management of the post-examination process. Finally, this review concludes with issues and challenges that machine learning imposes on the examination system, and these issues are discussed with solutions