Download or read book Testing an ADN Predictive Model of Student Retention written by Judith E. Miller and published by . This book was released on 2006 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modeling Student Retention in an Environment with Delayed Testing written by Shoujing Li and published by . This book was released on 2013 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Over the last two decades, the field of educational data mining (EDM) has been focusing on predicting the correctness of the next student response to the question (e.g., [2, 6] and the 2010 KDD Cup), in other words, predicting student short-term performance. Student modeling has been widely used for making such inferences. Although performing well on the immediate next problem is an indicator of mastery, it is by far not the only criteria. For example, the Pittsburgh Science of Learning Center's theoretic framework focuses on robust learning (e.g., [7, 10]), which includes the ability to transfer knowledge to new contexts, preparation for future learning of related skills, and retention - the ability of students to remember the knowledge they learned over a long time period. Especially for a cumulative subject such as mathematics, robust learning, particularly retention, is more important than short-term indicators of mastery. The Automatic Reassessment and Relearning System (ARRS) is a platform we developed and deployed on September 1st, 2012, which is mainly used by middle-school math teachers and their students. This system can help students better retain knowledge through automatically assigning tests to students, giving students opportunity to relearn the skill when necessary and generating reports to teachers. After we deployed and tested the system for about seven months, we have collected 287,424 data points from 6,292 students. We have created several models that predict students' retention performance using a variety of features, and discovered which were important for predicting correctness on a delayed test. We found that the strongest predictor of retention was a student's initial speed of mastering the content. The most striking finding was that students who struggled to master the content (took over 8 practice attempts) showed very poor retention, only 55% correct, after just one week. Our results will help us advance our understanding of learning and potentially improve ITS.
Download or read book Nursing Student Retention written by Marianne R. Jeffreys and published by Springer Publishing Company. This book was released on 2004 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the current nursing shortage, student retention is a priority concern for nurse educators, health care institutions, and the patients they serve. This book presents an organizing framework for understanding student retention, identifying at-risk students, and developing both diagnostic-prescriptive strategies to facilitate success and innovations in teaching and educational research. The author's conceptual model for student retention, "Nursing Undergraduate Retention and Success," is interwoven throughout, along with essential information for developing, implementing, and evaluating retention strategies. An entire chapter is devoted to how to set up a Student Resource Center. Most chapters conclude with "Educator-in-Action" vignettes, which help illustrate practical application of strategies discussed. Nurse educators at all levels will find this an important resource.
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2007 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Development of a Predictive Model for First year Student Retention at Mercyhurst College written by Marc Gattie and published by . This book was released on 2009 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Associate Degree Nursing Education written by Patricia T. Haase and published by Duke University Press. This book was released on 1990 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume offers a comprehensive listing, from the development of the Associate Degree Nursing (ADN) program in 1948 to the present, of all literature related to the ADN program. Any item related to the degree programs and their contributions, the AD nurses, their relation to nurses trained in other programs, and their role in the health care system is included. Published and unpublished items as well as dissertations, research reports and monographs, state and federal government documents, materials issued by state and national nursing groups, journal articles, and books are listed.
Download or read book Developing a Hybrid Model to Predict Student First Year Retention and Academic Success in STEM Disciplines Using Neural Networks written by Ruba Alkhasawneh and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the reasoning behind the low enrollment and retention rates of Underrepresented Minority (URM) students (African Americans, Hispanic Americans, and Native Americans) in the disciplines of science, technology, engineering, and mathematics (STEM) has concerned many researchers for decades. Numerous studies have used traditional statistical methods to identify factors that affect and predict student retention. Recently, researchers have relied on using data mining techniques for modeling student retention in higher education [1]. This research has used neural networks for performance modeling in order to obtain an adequate understanding of factors related to first year academic success and retention of URM at Virginia Commonwealth University. This research used feed forward back-propagation architecture for modeling. The student retention model was developed based on fall to fall retention in STEM majors. The overall freshman year GPA was used to model student academic success. Each model was built in two different ways: the first was built using all available student inputs, and the second using an optimized subset of student inputs. The optimized subset of the most relevant features that comes with the student, such as demographic attributes, high school rank, and SAT test scores was formed using genetic algorithms. A further step towards understanding the retention of URM groups in STEM fields was taken by conducting a series of focus groups with participants of an intervention program at VCU. Focus groups were designed to elicit responses from participants for identifying factors that affect their retention the most and provide more knowledge about their first year experiences, academically and socially. Results of the genetic algorithm and focus groups were incorporated into building a hybrid model using the most relevant student inputs. The developed hybrid model is shown to be a valuable tool in analyzing and predicting student academic success and retention. In particular, we have shown that identifying the most relevant student inputs from the student's perspective can be incorporated with quantitative methodologies to build a tool that can be used and interpreted effectively by people who are related to the field of STEM retention and education. Further, the hybrid model performed comparable to the model developed using the optimized set of inputs that resulted from the genetic algorithm. The GPA prediction hybrid model was tested to determine how well it would predict the GPA for all students, majority students and URM students. The root mean squared error (RM. S.E) on a 4.0 scale was 0.45 for all students, 0.47 for majority students, and 0.45 for URM students. The hybrid retention model was able to predict student retention correctly for 74% of all students, 79% of majority students and 60% of URM students. The hybrid model's accuracy was increased 3% compared to the model which used the optimized set of inputs.
Download or read book Opportunities and Challenges in Predictive Modelling for Student Retention written by and published by . This book was released on 2018 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Strategically Using University Student Records to Build a Student Retention Model written by Emma J. Crabtree and published by . This book was released on 2019 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, Strategic Enrollment Management (SEM) initiatives have become integral in the programs that colleges and universities develop to recruit and retain students. With 73.0% of full-time freshmen at Wichita State University in 2017 returning for their second year of study in fall of 2018, there is a need to implement interventions designed to identify and provide services to students at-risk of not being retained. Based on research of academic attrition and models of student retention, a conceptual model of student retention was developed. Wichita State University has developed collaborations across multiple SEM offices to increase their capacity to strategically use student record data to create data-driven programs and policies. This study utilized this capacity to develop a predictive model of student retention for three of the university's main student populations: first-time-in-college students, transfer students, and returning adult students. The availability of student data for each population is impacted by university admissions and data monitoring practices, requiring the conceptual model to be tailored to each student group. Bivariate comparisons between the students who were retained and who were not retained in each population revealed significant differences between the groups, so a logistic regression was used to predict retention risk. The logistic regression equations for each population were able to predict student retention with at least 70% accuracy. Implications, limitations, and suggestions for future research will be discussed.
Download or read book Predicting the Risk of Attrition for Undergraduate Students with Time Based Modelling written by Kevin E. K. Chai and published by . This book was released on 2015 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improving student retention is an important and challenging problem for universities. This paper reports on the development of a student attrition model for predicting which first year students are most at-risk of leaving at various points in time during their first semester of study. The objective of developing such a model is to assist universities by proactively supporting and retaining these students as their situations and risk change over time. The study evaluated different models for predicting student attrition at four different time periods throughout a semester study period: pre-enrolment, enrolment, in-semester and end-of-semester models. A dataset of 23,291 students who enrolled in their first semester between 2011-2013 was extracted from various data sources. Three supervised machine learning techniques were tested to develop the predictive models: logistic regression, decision trees and random forests. The performance of these models were evaluated using the precision and recall metrics. The model achieved the best performance and user utility using logistic regression (67% precision, 29% recall). A web application was developed for users to visualise and interact with the model results to assist in the targeting of student intervention responses and programs. [For the full proceedings, see ED562093.].
Download or read book Resources in Education written by and published by . This book was released on 1992-12 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Predicting and Improving First Year Engineering Student Retention Through Lean Thinking and Quality Management Concepts written by Thomas Bereza and published by . This book was released on 2017 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: While the percentage of undergraduate engineering degrees awarded has increased over the past decade, it has been outpaced by the overall growth in bachelor degree attainment. With this, the amount of enrollment in engineering programs has increased, but still a significant number of engineering students choose to drop out or pursue other educational paths. Universities and policy makers are motivated to increase the retention of engineering students to graduation. This thesis explores the quantitative data that makes up a first year engineering student's profile. The data is used to develop an ordinal logistic regression model to predict 2nd year student retention. Ideas to improve retention are discussed with a focus of applying Lean Manufacturing techniques in conjunction with the proposed prediction model. Data from a college of engineering within a public land-grant research university is used to test for significance as indicators for freshman retention. Data used in this study is from 2010 and 2011 freshman engineering cohorts. Using collected student data, a prediction model is developed that assesses the probability of a first year engineering student either i) returning to engineering in their second year, ii) leaving engineering but remaining at the university, or iii) leaving the university altogether. Then, using concepts from lean manufacturing and quality management this prediction model is incorporated in a proposed engineering education quality system.This study creates a prediction model to identify students that are likely to be: retained in engineering, switch majors out of engineering, and drop out of the university. This prediction model is then incorporated into the proposed engineering education quality management system to assist with identifying; where and when students may not persist in engineering curriculum, and ideas to promote student persistence using the prediction results.
Download or read book Journal of Developmental Education written by and published by . This book was released on 2013 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Resources in Education written by and published by . This book was released on 1992 with total page 1072 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Comprehensive Toxicology written by and published by Elsevier. This book was released on 2017-12-01 with total page 8639 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Toxicology, Third Edition, Fifteen Volume Set discusses chemical effects on biological systems, with a focus on understanding the mechanisms by which chemicals induce adverse health effects. Organized by organ system, this comprehensive reference work addresses the toxicological effects of chemicals on the immune system, the hematopoietic system, cardiovascular system, respiratory system, hepatic toxicology, renal toxicology, gastrointestinal toxicology, reproductive and endocrine toxicology, neuro and behavioral toxicology, developmental toxicology and carcinogenesis, also including critical sections that cover the general principles of toxicology, cellular and molecular toxicology, biotransformation and toxicology testing and evaluation. Each section is examined in state-of-the-art chapters written by domain experts, providing key information to support the investigations of researchers across the medical, veterinary, food, environment and chemical research industries, and national and international regulatory agencies. Thoroughly revised and expanded to 15 volumes that include the latest advances in research, and uniquely organized by organ system for ease of reference and diagnosis, this new edition is an essential reference for researchers of toxicology. Organized to cover both the fundamental principles of toxicology and unique aspects of major organ systems Thoroughly revised to include the latest advances in the toxicological effects of chemicals on the immune system Features additional coverage throughout and a new volume on toxicology of the hematopoietic system Presents in-depth, comprehensive coverage from an international author base of domain experts
Download or read book Index Medicus written by and published by . This book was released on 2004 with total page 2160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings.
Download or read book Teaching and Learning in Nursing written by Gregor Stiglic and published by BoD – Books on Demand. This book was released on 2017-05-17 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: A significant body of knowledge is the basis for a holistic, caring and scientific evidence-based nursing education in practice for professional development. Quality teaching leads to good learning and both aspects are two of the main issues of quality assurance in nursing education today. To begin with, not all nursing students have the same levels of motivation or learning abilities. It is with cognisance of providing quality care for patients that the role of the nurse educator has to be to enhance nursing students' learning using scientific evidence based teaching. Research around teaching and learning processes is an important part of the delivery of quality education, which in turn impacts on students' learning results and experiences, thereby, ensuring holistic biopsychosocial care to patients. The main aim of teaching and learning in nursing, at all levels, is to enhance the nurses' contribution to assist the individuals, families and communities in promoting and preserving health, well-being and to efficiently respond to illnesses. We hope that this book can be used as a resource to increase the body of knowledge in teaching and learning in nursing, thereby enhancing the role and contribution of health care professionals to clinical practice.
