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Brusco, Michael J.; Kohn, Hans-Friedrich – Psychometrika, 2009
Several authors have touted the p-median model as a plausible alternative to within-cluster sums of squares (i.e., K-means) partitioning. Purported advantages of the p-median model include the provision of "exemplars" as cluster centers, robustness with respect to outliers, and the accommodation of a diverse range of similarity data. We developed…
Descriptors: Teaching Methods, Experiments, Computational Linguistics, Simulation
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Van Ryzin, Mark J.; Dishion, Thomas J. – Journal of Child Psychology and Psychiatry, 2014
Background: Early substance use co-occurs with youths' self-organization into deviant peer groups in which substance use is central to social interaction. We hypothesized that the social dynamics of deviant peer groups amplify the risk of progressing from early use to later dependence, and that this influence occurs over and above escalations…
Descriptors: Substance Abuse, Smoking, Drinking, Marijuana
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Yang, Diyi; Wen, Miaomiao; Kumar, Abhimanu; Xing, Eric P.; Rose, Carolyn Penstein – International Review of Research in Open and Distance Learning, 2014
In this paper, we describe a novel methodology, grounded in techniques from the field of machine learning, for modeling emerging social structure as it develops in threaded discussion forums, with an eye towards application in the threaded discussions of massive open online courses (MOOCs). This modeling approach integrates two simpler, well…
Descriptors: Online Courses, Electronic Learning, Social Structure, Discussion Groups
Weiss, Michael J.; Lockwood, J. R.; McCaffrey, Daniel F. – MDRC, 2014
In many experimental evaluations in the social and medical sciences, individuals are randomly assigned to a treatment arm or a control arm of the experiment. After treatment assignment is determined, individuals within one or both experimental arms are frequently grouped together (e.g., within classrooms or schools, through shared case managers,…
Descriptors: Error of Measurement, Randomized Controlled Trials, Correlation, Computation
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Schneider, Ray G.; Ross, Sally R.; Fisher, Morgan – College Student Journal, 2010
Although journalists and reporters have written about academic clustering among college student-athletes, there has been a dearth of scholarly analysis devoted to the subject. This study explored football players' academic major selections to determine if academic clustering actually existed. The seasons 1996, 2001, and 2006 were selected for…
Descriptors: Team Sports, College Athletics, Athletes, Statistical Significance
Trivedi, Shubhendu; Pardos, Zachary A.; Sarkozy, Gabor N.; Heffernan, Neil T. – International Educational Data Mining Society, 2012
Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…
Descriptors: Homogeneous Grouping, Prediction, Tutors, Cluster Grouping
Lopez, M. I.; Luna, J. M.; Romero, C.; Ventura, S. – International Educational Data Mining Society, 2012
This paper proposes a classification via clustering approach to predict the final marks in a university course on the basis of forum data. The objective is twofold: to determine if student participation in the course forum can be a good predictor of the final marks for the course and to examine whether the proposed classification via clustering…
Descriptors: Classification, Prediction, Grades (Scholastic), College Freshmen
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Hwang, Heungsun; Dillon, William R. – Multivariate Behavioral Research, 2010
A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which "k"-means is…
Descriptors: Data Analysis, Multivariate Analysis, Classification, Monte Carlo Methods
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Valsamidis, Stavros; Kontogiannis, Sotirios; Kazanidis, Ioannis; Theodosiou, Theodosios; Karakos, Alexandros – Educational Technology & Society, 2012
Learning Management Systems (LMS) collect large amounts of data. Data mining techniques can be applied to analyse their web data log files. The instructors may use this data for assessing and measuring their courses. In this respect, we have proposed a methodology for analysing LMS courses and students' activity. This methodology uses a Markov…
Descriptors: Foreign Countries, Electronic Learning, College Mathematics, Integrated Learning Systems
Chahine, Firas Safwan – ProQuest LLC, 2012
Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…
Descriptors: Mathematics, Artificial Intelligence, Multivariate Analysis
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Subramaniam, Aarti; Dasher, Harry Steve; Young, Jane Chin – Journal of Extension, 2012
In response to budgetary constraints, a new staffing structure, the Pilot Leadership Plan, was proposed for California's 4-H Youth Development Program. County clusters were formed, each led by a coordinator. The plan was piloted for 2 years to provide insight into how county clustering could support Extension staff to increase and enhance program…
Descriptors: Youth Programs, Counties, Extension Education, Extension Agents
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Xu, Beijie; Recker, Mimi – Educational Technology & Society, 2012
Teachers and students increasingly enjoy unprecedented access to abundant web resources and digital libraries to enhance and enrich their classroom experiences. However, due to the distributed nature of such systems, conventional educational research methods, such as surveys and observations, provide only limited snapshots. In addition,…
Descriptors: Electronic Libraries, Instructional Design, Mixed Methods Research, Use Studies
Salman, Raied – ProQuest LLC, 2012
The dissertation deals with clustering algorithms and transforming regression problems into classification problems. The main contributions of the dissertation are twofold; first, to improve (speed up) the clustering algorithms and second, to develop a strict learning environment for solving regression problems as classification tasks by using…
Descriptors: Classification, Mathematics, Regression (Statistics), Problem Solving
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Xu, Beijie; Recker, Mimi; Qi, Xiaojun; Flann, Nicholas; Ye, Lei – Journal of Educational Data Mining, 2013
This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data…
Descriptors: Electronic Libraries, Use Studies, Multivariate Analysis, Data Analysis
Eagle, Michael; Johnson, Matthew; Barnes, Tiffany – International Educational Data Mining Society, 2012
We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…
Descriptors: Data Analysis, Interaction, Network Analysis, Problem Solving
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