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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
Delen, Dursun – Journal of College Student Retention: Research, Theory & Practice, 2012
Affecting university rankings, school reputation, and financial well-being, student retention has become one of the most important measures of success for higher education institutions. From the institutional perspective, improving student retention starts with a thorough understanding of the causes behind the attrition. Such an understanding is…
Descriptors: Higher Education, Student Attrition, School Holding Power, Prediction
Chen, Ling; Liu, Yang; Gallagher, Marcus; Pailthorpe, Bernard; Sadiq, Shazia; Shen, Heng Tao; Li, Xue – Journal of Information Systems Education, 2012
The demand for graduates with exposure in Cloud Computing is on the rise. For many educational institutions, the challenge is to decide on how to incorporate appropriate cloud-based technologies into their curricula. In this paper, we describe our design and experiences of integrating Cloud Computing components into seven third/fourth-year…
Descriptors: Computers, Schools, Foreign Countries, Computer Science
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
Boyer, Kristy Elizabeth; Phillips, Robert; Ingram, Amy; Ha, Eun Young; Wallis, Michael; Vouk, Mladen; Lester, James – International Journal of Artificial Intelligence in Education, 2011
Identifying effective tutorial dialogue strategies is a key issue for intelligent tutoring systems research. Human-human tutoring offers a valuable model for identifying effective tutorial strategies, but extracting them is a challenge because of the richness of human dialogue. This article addresses that challenge through a machine learning…
Descriptors: Markov Processes, Intelligent Tutoring Systems, Tutoring, Program Effectiveness
D'Allegro, Mary Lou; Kerns, Stefanie – Journal of College Student Retention: Research, Theory & Practice, 2011
Data mining and statistical analyses at a less selective institution reveal that the relationships between parents' educational level and some first year success indicators are not linear. Specifically, students who report that either parent or guardian(s) have an educational level beyond a baccalaureate degree or do not report parent education…
Descriptors: First Generation College Students, Educational Attainment, Data Processing, Pattern Recognition
Banta, Trudy W., Ed. – Assessment Update, 2012
This issue of "Assessment Update" presents the following articles: (1) Expectations for Assessment Reports: A Descriptive Analysis (Keston H. Fulcher, Matthew Swain, and Chris D. Orem); (2) Editor's Notes: A Surprising Reaction (Trudy W. Banta); (3) Getting SMART with Assessment: ACTION Steps to Institutional Effectiveness (Eric Daffron…
Descriptors: Higher Education, Educational Practices, Educational Trends, Educational Development
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
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
Grover, Lovleen Kumar; Mehra, Rajni – Journal of Statistics Education, 2008
The field of Data Mining like Statistics concerns itself with "learning from data" or "turning data into information". For statisticians the term "Data mining" has a pejorative meaning. Instead of finding useful patterns in large volumes of data as in the case of Statistics, data mining has the connotation of searching for data to fit preconceived…
Descriptors: Statistics, Data Processing, Differences, Mathematics Education
Lazarony, Paul J.; Driscoll, Donna A. – American Journal of Business Education, 2011
Researchers are often distressed to discover that the data they wanted to use in their landmark study is not configured in a way that is usable by a Statistical Analysis Software Package (SASP). For example, the data needed may come from two or more sources and it may not be clear to the researcher how to get them combined into one analyzable…
Descriptors: Data Processing, Statistical Analysis, Database Management Systems, Experiential Learning
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
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
Hamblin, David J.; Phoenix, David A. – Journal of Higher Education Policy and Management, 2012
There are increasing demands for higher levels of data assurance in higher education. This paper explores some of the drivers for this trend, and then explains what stakeholders mean by the concept of data assurance, since this has not been well defined previously. The paper captures insights from existing literature, stakeholders, auditors, and…
Descriptors: Higher Education, Educational Technology, Stakeholders, Quality Assurance
Chen, Chen-Su; Sable, Jennifer; Mitchell, Lindsey; Liu, Fei – National Center for Education Statistics, 2012
The Common Core of Data (CCD) nonfiscal surveys consist of data submitted annually to the National Center for Education Statistics (NCES) by state education agencies (SEAs) in the 50 states, the District of Columbia, Puerto Rico, the four U.S. Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin…
Descriptors: School Surveys, Documentation, State Surveys, Annual Reports

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