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Rienties, Bart; Cross, Simon; Marsh, Vicky; Ullmann, Thomas – Open Learning, 2017
Most distance learning institutions collect vast amounts of learning data. Making sense of this 'Big Data' can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, the Open University UK instigated…
Descriptors: Foreign Countries, Distance Education, Data Collection, Data Interpretation
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Ma, Lia – Information Research: An International Electronic Journal, 2013
Introduction: The term "information" in information science does not share the characteristics of those of a nomenclature: it does not bear a generally accepted definition and it does not serve as the bases and assumptions for research studies. As the data deluge has arrived, is the concept of information still relevant for information…
Descriptors: Relevance (Education), Information Science, Information Science Education, Concept Formation
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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
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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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
Dorans, Neil J.; Moses, Tim P.; Eignor, Daniel R. – Educational Testing Service, 2010
Score equating is essential for any testing program that continually produces new editions of a test and for which the expectation is that scores from these editions have the same meaning over time. Particularly in testing programs that help make high-stakes decisions, it is extremely important that test equating be done carefully and accurately.…
Descriptors: Equated Scores, Methods, Data Collection, Data Processing
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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
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Cocea, M.; Weibelzahl, S. – IEEE Transactions on Learning Technologies, 2011
Learning environments aim to deliver efficacious instruction, but rarely take into consideration the motivational factors involved in the learning process. However, motivational aspects like engagement play an important role in effective learning-engaged learners gain more. E-Learning systems could be improved by tracking students' disengagement…
Descriptors: Prediction, Electronic Learning, Online Courses, Delivery Systems