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Chen, Ming-yu – ProQuest LLC, 2010
Surveillance video recording is becoming ubiquitous in daily life for public areas such as supermarkets, banks, and airports. The rate at which surveillance video is being generated has accelerated demand for machine understanding to enable better content-based search capabilities. Analyzing human activity is one of the key tasks to understand and…
Descriptors: Video Technology, Online Searching, Data Collection, Telecommunications
Peer reviewed Peer reviewed
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Amershi, Saleema; Conati, Cristina – Journal of Educational Data Mining, 2009
In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…
Descriptors: Supervision, Classification, Models, Educational Environment
Peer reviewed Peer reviewed
Rosenberg, Seymour; Kim, Moonja Park – Multivariate Behavioral Research, 1975
Compares two basic variants of the sorting method: single-sort and multiple sort. The nature of individual differences in sorting, as well as sex differences, were also investigated. Stimulus materials were the 15 mutually exclusive kinship terms selected by Wallace and Atkins (1960). (RC)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, College Students
Luan, Jing – Online Submission, 2004
This explorative data mining project used distance based clustering algorithm to study 3 indicators, called OIndex, of student behavioral data and stabilized at a 6-cluster scenario following an exhaustive explorative study of 4, 5, and 6 cluster scenarios produced by K-Means and TwoStep algorithms. Using principles in data mining, the study…
Descriptors: Educational Strategies, Evaluation Methods, Student Behavior, College Students
McKinlay, Donald Bruce – 1971
The need for more and better manpower information is hampered by the lack of adequate occupational data classification systems. The diversity of interests in occupations probably accounts for the absence of consensus regarding either the general outlines or the specific details of a standardized occupational classification system which would…
Descriptors: Classification, Cluster Grouping, Data Collection, Data Processing