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Welker, Josh – Computers in Libraries, 2012
Any librarian who has managed electronic resources has experienced the--for want of words--"joy" of gathering and analyzing usage statistics. Such statistics are important for evaluating the effectiveness of resources and for making important budgeting decisions. Unfortunately, the data are usually tedious to collect, inconsistently organized, of…
Descriptors: Library Services, Databases, Academic Libraries, Electronic Libraries
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Greenhoot, Andrea Follmer; Dowsett, Chantelle J. – Journal of Cognition and Development, 2012
Existing data sets can be an efficient, powerful, and readily available resource for addressing questions about developmental science. Many of the available databases contain hundreds of variables of interest to developmental psychologists, track participants longitudinally, and have representative samples. In this article, the authors discuss the…
Descriptors: Data Analysis, Developmental Psychology, Research Methodology, Best Practices
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Martinez-Maldonado, Roberto; Shum, Simon Buckingham; Schneider, Bertrand; Charleer, Sven; Klerkx, Joris; Duval, Erik – Journal of Learning Analytics, 2017
The continuous advancement of natural user interfaces (NUIs) allows for the development of novel and creative ways to support collocated collaborative work in a wide range of areas, including teaching and learning. The use of NUIs, such as those based on interactive multi-touch surfaces and tangible user interfaces (TUIs), can offer unique…
Descriptors: Computer Interfaces, Computer System Design, Visualization, Technology Uses in Education
Waters, John K. – Campus Technology, 2012
In the case of higher education, the hills are more like mountains of data that "we're accumulating at a ferocious rate," according to Gerry McCartney, CIO of Purdue University (Indiana). "Every higher education institution has this data, but it just sits there like gold in the ground," complains McCartney. Big Data and the new tools people are…
Descriptors: Higher Education, Educational Change, Data, Data Processing
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Stapel, Martin; Zheng, Zhilin; Pinkwart, Niels – International Educational Data Mining Society, 2016
The number of e-learning platforms and blended learning environments is continuously increasing and has sparked a lot of research around improvements of educational processes. Here, the ability to accurately predict student performance plays a vital role. Previous studies commonly focused on the construction of predictors tailored to a formal…
Descriptors: Teaching Methods, Academic Achievement, Electronic Learning, Mathematics Instruction
Niemi, David; Gitin, Elena – International Association for Development of the Information Society, 2012
An underlying theme of this paper is that it can be easier and more efficient to conduct valid and effective research studies in online environments than in traditional classrooms. Taking advantage of the "big data" available in an online university, we conducted a study in which a massive online database was used to predict student…
Descriptors: Higher Education, Online Courses, Academic Persistence, Identification
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Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
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Burns, Shelley, Ed.; Wang, Xiaolei, Ed.; Henning, Alexandra, Ed. – National Center for Education Statistics, 2011
Since its inception, the National Center for Education Statistics (NCES) has been committed to the practice of documenting its statistical methods for its customers and of seeking to avoid misinterpretation of its published data. The reason for this policy is to assure customers that proper statistical standards and techniques have been observed,…
Descriptors: National Surveys, Data Processing, Statistical Data, Data Collection
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Lee, In Heok – Career and Technical Education Research, 2012
Researchers in career and technical education often ignore more effective ways of reporting and treating missing data and instead implement traditional, but ineffective, missing data methods (Gemici, Rojewski, & Lee, 2012). The recent methodological, and even the non-methodological, literature has increasingly emphasized the importance of…
Descriptors: Vocational Education, Data Collection, Maximum Likelihood Statistics, Educational Research
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Ingels, Steven J.; Pratt, Daniel J.; Jewell, Donna M.; Mattox, Tiffany; Dalton, Ben; Rosen, Jeffrey; Lauff, Erich; Hill, Jason – National Center for Education Statistics, 2012
This report describes the methodologies and results of the third follow-up Education Longitudinal Study of 2002 (ELS:2002/12) field test which was conducted in the summer of 2011. The field test report is divided into six chapters: (1) Introduction; (2) Field Test Survey Design and Preparation; (3) Data Collection Procedures and Results; (4) Field…
Descriptors: Longitudinal Studies, Field Tests, Followup Studies, Surveys
Koh, Byungwan – ProQuest LLC, 2011
The advent of information technology has enabled firms to collect significant amounts of data about individuals and mine the data for developing their strategies. Profiling of individuals is one common use of data collected about them. It refers to using known or inferred information to categorize the type of an individual and to tailor specific…
Descriptors: Screening Tests, Program Effectiveness, Information Technology, Data Collection
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Sangasubana, Nisaratana – Qualitative Report, 2011
The purpose of this paper is to describe the process of conducting ethnographic research. Methodology definition and key characteristics are given. The stages of the research process are described including preparation, data gathering and recording, and analysis. Important issues such as reliability and validity are also discussed.
Descriptors: Student Research, Ethnography, Research Methodology, Data Analysis
Yukselturk, Erman; Ozekes, Serhat; Turel, Yalin Kilic – European Journal of Open, Distance and E-Learning, 2014
This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies…
Descriptors: Online Courses, Distance Education, Dropout Characteristics, Prediction
Vander Does, Susan Lubow – ProQuest LLC, 2012
Teachers' observations of student performance in reading are abundant and insightful but often remain internal and unarticulated. As a result, such observations are an underutilized and undervalued source of data. Given the gaps in knowledge about students' reading comprehension that exist in formal assessments, the frequent calls for teachers'…
Descriptors: Reading Instruction, Reading Comprehension, Teacher Student Relationship, Observation
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Huebner, Richard A. – Research in Higher Education Journal, 2013
Educational data mining (EDM) is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. A literature review on educational data mining topics…
Descriptors: Educational Research, Data Processing, Data Analysis, Organizational Change
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