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Koedinger, Kenneth R.; McLaughlin, Elizabeth A. – International Educational Data Mining Society, 2016
Many educational data mining studies have explored methods for discovering cognitive models and have emphasized improving prediction accuracy. Too few studies have "closed the loop" by applying discovered models toward improving instruction and testing whether proposed improvements achieve higher student outcomes. We claim that such…
Descriptors: Educational Research, Data Collection, Task Analysis, Cognitive Processes
Lei, Wu; Qing, Fang; Zhou, Jin – International Journal of Distance Education Technologies, 2016
There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules.…
Descriptors: Causal Models, Attribution Theory, Correlation, Evaluation Methods
Statistical Reporting Service (USDA), Washington, DC. – 1974
Intended as a reference for the convenience of students in sampling, this monograph attempts to express relevant, introductory mathematics and probability in the context of sample surveys. Although some proofs are presented, the emphasis is more on exposition of mathematical language and concepts than on the mathematics per se and rigorous proofs.…
Descriptors: Agriculture, Algebra, Classification, Data Collection
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection

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