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Winkler, Bea; Kiszl, Péter – New Review of Academic Librarianship, 2022
Artificial intelligence (AI) is a defining technology of the 21st century, creating new opportunities for academic libraries. The goal of this paper is to provide a much-needed analysis, interpreted in an international context, on what the leaders of academic libraries in East-Central Europe, and specifically in Hungary, think about AI and its…
Descriptors: Foreign Countries, Academic Libraries, Administrators, Artificial Intelligence
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Christine Ladwig; Taylor Webber; Dana Schwieger – Information Systems Education Journal, 2023
Data is a powerful tool for the healthcare industry to use for managing, analyzing, and reporting on critical events in the field. The analysis of broad, salient data files aids healthcare businesses in uncovering hidden patterns, market trends, and customer preferences; these details may then be used to improve the quality and delivery of care to…
Descriptors: Rural Areas, Health Services, Data Analysis, Learning Activities
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Chance, Beth; Reynolds, Shea – Journal of Statistics Education, 2019
Through a series of explorations, this article will demonstrate how the Kentucky Derby winning times dataset provides various opportunities for introductory and advanced topics, from data processing to model building. Although the final goal may be a prediction interval, the dataset is rich enough for it to appear in several places in an…
Descriptors: Prediction, Statistics, Data Processing, Homework
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Cominole, Melissa; Ritchie, Nichole Smith; Cooney, Jennifer – National Center for Education Statistics, 2021
This publication describes the methods and procedures used for the 2008/18 Baccalaureate and Beyond Longitudinal Study (B&B:08/18). The B&B graduates, who completed the requirements for a bachelor's degree during the 2007-08 academic year, were first surveyed as part of the 2008 National Postsecondary Student Aid Study (NPSAS:08), and then…
Descriptors: Bachelors Degrees, College Graduates, Longitudinal Studies, Data Collection
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Snyder, Johnny – Information Systems Education Journal, 2019
Quantitative decision making (management science, business statistics) textbooks rarely address data cleansing issues, rather, these textbooks come with neat, clean, well-formatted data sets for the student to perform analysis on. However, with a majority of the data analyst's time spent on gathering, cleaning, and pre-conditioning data, students…
Descriptors: Data Analysis, Error Patterns, Data Collection, Spreadsheets
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Conrad, Colin; Bliemel, Michael; Ali-Hassan, Hossam – Journal of Information Systems Education, 2019
The expansion of technical concepts into everyday business practices suggests a need for effectively teaching difficult subjects to non-technical users. This paper describes hands-on analogy, an innovative method for teaching technically difficult concepts using interactive, experiential learning activities and a gamified exercise. We demonstrate…
Descriptors: Experiential Learning, Business Administration Education, Data Processing, Logical Thinking
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Chopra, Shivangi; Gautreau, Hannah; Khan, Abeer; Mirsafian, Melicaalsadat; Golab, Lukasz – International Educational Data Mining Society, 2018
It is well known that post-secondary science and engineering programs attract fewer female students. In this paper, we analyze gender differences through text mining of over 30,000 applications to the engineering faculty of a large North American university. We use syntactic and semantic analysis methods to highlight differences in motivation,…
Descriptors: Gender Differences, Undergraduate Students, Engineering Education, STEM Education
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Pouchard, Line; Bracke, Marianne Stowell – Issues in Science and Technology Librarianship, 2016
This paper describes a survey of data practices given to the Purdue College of Agriculture. Data practices are a concern for many researchers with new governmental funding mandates that require data management plans, and for the institution providing resources to comply with these mandates. The survey attempted to answer these questions: What are…
Descriptors: Agricultural Education, Case Studies, Data Processing, Data
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Wiley, Christie; Mischo, William H. – Issues in Science and Technology Librarianship, 2016
This article analyzes 21 in-depth interviews of engineering and atmospheric science faculty at the University of Illinois Urbana-Champaign (UIUC) to determine faculty data management practices and needs within the context of their research activities. A detailed literature review of previous large-scale and institutional surveys and interviews…
Descriptors: Data Processing, College Faculty, Scientists, Engineering Education
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Nord, C.; Hicks, L.; Hoover, K.; Jones, M.; Lin, A.; Lyons, M.; Perkins, R.; Roey, S.; Rust, K.; Sickles, D. – National Center for Education Statistics, 2011
This user's guide documents the procedures used to collect, process, and summarize data from the 2009 High School Transcript Study (HSTS 2009). Chapters detail the sampling of schools and graduates (chapters 2 and 3), data collection procedures (chapter 4), data processing procedures (chapter 5), and weighting procedures (chapter 6). Chapter 7…
Descriptors: High School Graduates, Academic Records, National Competency Tests, Questionnaires
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Strizek, Gregory A.; Tourkin, Steve; Erberber, Ebru – National Center for Education Statistics, 2014
This technical report is designed to provide researchers with an overview of the design and implementation of the Teaching and Learning International Survey (TALIS) 2013. This information is meant to supplement that presented in OECD publications by describing those aspects of TALIS 2013 that are unique to the United States. Chapter 2 provides…
Descriptors: Learning, Instruction, Research Design, Program Implementation
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Rupp, Andre A.; Levy, Roy; Dicerbo, Kristen E.; Sweet, Shauna J.; Crawford, Aaron V.; Calico, Tiago; Benson, Martin; Fay, Derek; Kunze, Katie L.; Mislevy, Robert J.; Behrens, John T. – Journal of Educational Data Mining, 2012
In this paper we describe the development and refinement of "evidence rules" and "measurement models" within the "evidence model" of the "evidence-centered design" (ECD) framework in the context of the "Packet Tracer" digital learning environment of the "Cisco Networking Academy." Using…
Descriptors: Computer Networks, Evidence Based Practice, Design, Instructional Design
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Schmitt, Carl M.; O'Shae, Patricia; Vaden, Kaleen – National Center for Education Statistics, 2007
This manual describes the methods, procedures, techniques, and activities that were used to produce the Academic Library Survey of 2004 (ALS:2004). This manual is designed to provide guidance and documentation for users of the ALS data. Included in the manual are the following: (1) an overview of the study and its predecessor studies; (2) an…
Descriptors: Academic Libraries, Surveys, Data Collection, Response Rates (Questionnaires)
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Ingels, Steven J.; Pratt, Daniel J.; Herget, Deborah R.; Burns, Laura J.; Dever, Jill A.; Ottem, Randolph; Rogers, James E.; Jin, Ying; Leinwand, Steve – National Center for Education Statistics, 2011
The High School Longitudinal Study of 2009 (HSLS:09) is the fifth in a series of National Center for Education Statistics (NCES) secondary longitudinal studies. The core research questions for HSLS:09 explore secondary to postsecondary transition plans and the evolution of those plans; the paths into and out of science, technology, engineering,…
Descriptors: High Schools, Longitudinal Studies, Secondary Education, School Statistics
Shettle, Carolyn; Cubell, Michele; Hoover, Katylee; Kastberg, David; Legum, Stan; Lyons, Marsha; Perkins, Robert; Rizzo, Lou; Roey, Stephen; Sickles, Diane – US Department of Education, 2008
This technical report documents the procedures used to collect and summarize data from the 2005 High School Transcript Study (HSTS 2005). The transcript studies serve as a barometer for changes in high school graduates' course-taking patterns; these patterns provide information about the rigor of high school curricula followed across the nation.…
Descriptors: Check Lists, High Schools, School Activities, Course Selection (Students)
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