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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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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
West, Darrell M. – Brookings Institution, 2012
Twelve-year-old Susan took a course designed to improve her reading skills. She read short stories and the teacher would give her and her fellow students a written test every other week measuring vocabulary and reading comprehension. A few days later, Susan's instructor graded the paper and returned her exam. The test showed that she did well on…
Descriptors: Data Processing, Internet, Pattern Recognition, Data Analysis
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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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Nguyen, Bao-An; Yang, Don-Lin – International Review of Research in Open and Distance Learning, 2012
An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with…
Descriptors: Language Processing, Information Retrieval, Instructional Materials, Semantics
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Karatepe, Akif – Educational Research and Reviews, 2012
Today's important geo-spatial technologies, GIS (Geographic Information Systems), GPS (Global Positioning Systems) and Google Earth have been widely used in geography education. Transferring spatially oriented data taken by GPS to the GIS and Google Earth has provided great benefits in terms of showing the usage of spatial technologies for field…
Descriptors: Geography Instruction, Geographic Information Systems, Field Experience Programs, Field Instruction
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Vitinš, Maris; Rasnacs, Oskars – Informatics in Education, 2012
Information and communications technologies today are used in virtually any university course when students prepare their papers. ICT is also needed after people are graduated from university and enter the job market. This author is an instructor in the field of informatics related to health care and social sciences at the Riga Stradins…
Descriptors: Educational Technology, Assignments, Information Science, Data Processing
Zhang, Bin – ProQuest LLC, 2012
Social scientists usually are more interested in consumers' dichotomous choice, such as purchase a product or not, adopt a technology or not, etc. However, up to date, there is nearly no model can help us solve the problem of multi-network effects comparison with a dichotomous dependent variable. Furthermore, the study of multi-network…
Descriptors: Social Networks, Network Analysis, Comparative Analysis, Population Groups
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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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Mislevy, Robert J.; Behrens, John T.; Dicerbo, Kristen E.; Levy, Roy – Journal of Educational Data Mining, 2012
"Evidence-centered design" (ECD) is a comprehensive framework for describing the conceptual, computational and inferential elements of educational assessment. It emphasizes the importance of articulating inferences one wants to make and the evidence needed to support those inferences. At first blush, ECD and "educational data…
Descriptors: Educational Assessment, Psychometrics, Evidence, Computer Games
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Faulkner, Robert; Davidson, Jane W.; McPherson, Gary E. – International Journal of Music Education, 2010
The use of data mining for the analysis of data collected in natural settings is increasingly recognized as a legitimate mode of enquiry. This rule-inductive paradigm is an effective means of discovering relationships within large datasets--especially in research that has limited experimental design--and for the subsequent formulation of…
Descriptors: Foreign Countries, Data Processing, Pattern Recognition, Data Analysis
Vialardi, Cesar; Bravo, Javier; Shafti, Leila; Ortigosa, Alvaro – International Working Group on Educational Data Mining, 2009
One of the main problems faced by university students is to take the right decision in relation to their academic itinerary based on available information (for example courses, schedules, sections, classrooms and professors). In this context, this work proposes the use of a recommendation system based on data mining techniques to help students to…
Descriptors: Data Analysis, Higher Education, Course Selection (Students), Enrollment
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Hourigan, Clare – Journal of Institutional Research, 2011
Academic standards and performance outcomes are a major focus of the current Cycle 2 Australian Universities Quality Agency (AUQA) audits. AUQA has clearly stated that universities will need to provide "evidence of setting, maintaining, and reviewing institutional academic standards and outcomes" (2010, p. 27). To do this, universities…
Descriptors: Decision Making, Admission Criteria, Program Improvement, Data Analysis
Michalski, Greg V. – Association for Institutional Research (NJ1), 2011
Excessive college course withdrawals are costly to the student and the institution in terms of time to degree completion, available classroom space, and other resources. Although generally well quantified, detailed analysis of the reasons given by students for course withdrawal is less common. To address this, a text mining analysis was performed…
Descriptors: College Instruction, Courses, Withdrawal (Education), College Students
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Jafar, Musa J. – Journal of Information Technology Education, 2010
Data mining is an emerging field of study in Information Systems programs. Although the course content has been streamlined, the underlying technology is still in a state of flux. The purpose of this paper is to describe how we utilized Microsoft Excel's data mining add-ins as a front-end to Microsoft's Cloud Computing and SQL Server 2008 Business…
Descriptors: Information Systems, Data Analysis, Teaching Methods, Integrated Curriculum
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