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Download full textSaarela, Mirka; Kärkkäinen, Tommi – International Educational Data Mining Society, 2015
Certain stereotypes can be associated with people from different countries. For example, the Italians are expected to be emotional, the Germans functional, and the Chinese hard-working. In this study, we cluster all 15-year-old students representing the 68 different nations and territories that participated in the latest Programme for…
Descriptors: Weighted Scores, Stereotypes, Standardized Tests, Student Characteristics
Madhyastha, Tara; Hunt, Earl – Journal of Educational Data Mining, 2009
This paper introduces a method for mining multiple-choice assessment data for similarity of the concepts represented by the multiple choice responses. The resulting similarity matrix can be used to visualize the distance between concepts in a lower-dimensional space. This gives an instructor a visualization of the relative difficulty of concepts…
Descriptors: Diagnostic Tests, Multiple Choice Tests, Concept Formation, Schematic Studies
Brown, R. L. – 1984
The plotting of multivariate data using computer line-printers has become a popular means of quickly representing multidimensional data. While many plotting programs are available, there is a paucity of research regarding the validity and reliability of interpretations made by viewing such graphics. This study explores the validity of four…
Descriptors: Cluster Grouping, Computer Graphics, Computer Simulation, Data Analysis
Peer reviewedRuocco, Anthony S.; Frieder, Ophir – Journal of the American Society for Information Science, 1997
Proposes use of parallel computing systems to overcome the computationally intense clustering process. Results show some near linear speed up in higher threshold clustering applications, meeting the requirements to classify, group and process large document sets within nonprohibitive execution times. Includes graphs and charts. (JAK)
Descriptors: Access to Information, Classification, Cluster Analysis, Cluster Grouping
Peer reviewedLarson, Ray R. – Journal of the American Society for Information Science, 1992
Presents the results of research into the automatic selection of Library of Congress Classification numbers based on the titles and subject headings in MARC records from a test database at the University of California at Berkeley Library School library. Classification clustering and matching techniques are described. (44 references) (LRW)
Descriptors: Academic Libraries, Bibliographic Databases, Bibliographic Records, Classification
Becker, David S.; Pyrce, Sharon R. – 1977
The goal of this project was to find ways of enhancing the efficiency of searching machine readable data bases. Ways are sought to transfer to the computer some of the tasks that are normally performed by the user, i.e., to further automate information retrieval. Four experiments were conducted to test the feasibility of a sequential processing…
Descriptors: Algorithms, Bibliographic Coupling, Cluster Grouping, Computers
