Dialogism provides the grounds for building a comprehensive model of discourse and it is focused on the multiplicity of perspectives (i.e., voices). Dialogism can be present in any type of text, while voices become themes or recurrent topics emerging from the discourse. In this study, we examine the extent that differences between…

This research connects several data-driven educational data mining approaches to a framework for interaction developed in educational research. In particular, 10 million usage data points collected by a Learning Management System used by students and teachers in 450 online undergraduate courses were analyzed with this framework. A range of…

This study provides the results of a latent class analysis (LCA) using data from the Trends in International Mathematics and Science Study -- 2011 (TIMSS-2011) with a focus on the 8th grade mathematics section. The study presents the analysis of item data with Mplus 7.31 to determine if results obtained yielded distinct latent subgroups. The data…

Assessment of groups in CSCL context is a challenging task fraught with many confounding factors collected and measured. Previous documented studies are by and large summative in nature and some process-oriented methods require time-intensive coding of qualitative data. This study attempts to resolve these problems for teachers to assess groups…

The development of Web 2.0 has ushered in a new set of web-based tools, including blogs. This study focused on how teachers use and manage their blogs. A sample of 165 teachers' blogs in Taiwan was analyzed by factor analysis, cluster analysis and qualitative content analysis. First, the teachers' blogs were analyzed according to six criteria…

The purpose of this study is to empirically reveal strategies of students' organization of learning-related digital materials within an online personal information archive. Research population included 518 students who utilized the personal Web space allocated to them on the university servers for archiving information items, and data describing…

A brief historical background of discriminant analysis is given, with a description of the variety of roles that discriminant analysis can perform. Focus is on the classification role of discriminant analysis and how it can be performed by using Fisher's classification functions or the canonical discriminant functions. A small hypothetical data…

This paper focuses on three aspects related to the conceptualization and application of canonical correlation analysis as a dominant statistical model: (1) partial canonical correlation analysis and its application in statistical testing; (2) the relation between canonical correlation analysis and discriminant analysis; and (3) the relation…

The issues in the interpretation of discriminant analyses presented are restricted to the typical uses of discriminant analysis by behavioral science researchers. Because behavioral researchers use computer programs packages, the issues discussed deal with information obtainable from the package discriminant analysis programs. The following issues…

This paper discusses the principles underlying discriminant analysis and constructs a simulated data set to illustrate its methods. Discriminant analysis is a multivariate technique for identifying the best combination of variables to maximally discriminate between groups. Discriminant functions are established on existing groups and used to…

Linear classification functions (LCFs) arise in a predictive discriminant analysis for the purpose of classifying experimental units into criterion groups. The relative contribution of the response variables to classification accuracy may be based on LCF-variable correlations for each group. It is proved that, if the raw response measures are…

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…

A technique for classifying hospitals on a multidimensional basis was developed. Three major sets of attributes were examined: patient case mix, facility mix, and personnel mix. Using multivariate techniques (factor analysis, cluster analysis, and discriminant analysis), 83 variables were examined for 547 hospitals. A total of six factors were…