scholarly journals Incidencia de factores personales y contextuales sobre el uso de los recursos tecnológicos por el alumnado en América Latina

2021 ◽  
Vol 29 ◽  
pp. 6 ◽  
Author(s):  
Sara Cebrián-Cifuentes ◽  
Gonzalo Almerich ◽  
Jesús Suárez-Rodríguez ◽  
Francesc Pedró
Keyword(s):  
Principal Components Analysis ◽  
Information And Communication Technologies ◽  
Principal Components ◽  
Contextual Factors ◽  
Sixth Grade ◽  
Descriptive Statistics ◽  
Ict Use ◽  
Components Analysis ◽  
Categorical Principal Components Analysis ◽  
America Latina

The use of information and communication technologies (ICT) by students reflects the appropriateness of the ICT integration process. However, the typology of ICT use by students has not been established empirically based on their use at home and at the school. Thus, the purpose of the article is to determine the structure of ICT use by students, together with the influence of personal and contextual factors. A correlational design has been used, with the sample being the sixth-grade students in Latin America who answered the questionnaire on ICT use in the Third Regional Comparative and Explanatory Study (TERCE). The data analysis performed is descriptive statistics, Multivariate Analysis of Variance (MANOVA) and Categorical Principal Components analysis (CATPCA). The results obtained through the descriptive statistics show how the students make a greater use of technological resources in the area of free time than in the academic area. It has been found, by means of a categorical principal components analysis (CATPCA), that student use is structured in three planes: personal, non-school academic and school academic. In addition, the results obtained from the MANOVA indicate that the personal and contextual factors influence the use of ICT, essentially the availability of technological devices and the Internet connection. In the light of the results, it is recommended to implement programs that favour non-school academic use of ICT by students.

scholarly journals An Analysis of the Perceptions and Resources of Large University Classes

2017 ◽  
Vol 16 (2) ◽  
pp. ar33 ◽  
Author(s):  
Ceilidh Barlow Cash ◽  
Jessa Letargo ◽  
Steffen P. Graether ◽  
Shoshanah R. Jacobs
Keyword(s):  
Large Class ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Principal Component ◽  
First Year Experience ◽  
Research Approach ◽  
Course Structure ◽  
Large Classes ◽  
Components Analysis ◽  
Categorical Principal Components Analysis

Large class learning is a reality that is not exclusive to the first-year experience at midsized, comprehensive universities; upper-year courses have similarly high enrollment, with many class sizes greater than 200 students. Research into the efficacy and deficiencies of large undergraduate classes has been ongoing for more than 100 years, with most research associating large classes with weak student engagement, decreased depth of learning, and ineffective interactions. This study used a multidimensional research approach to survey student and instructor perceptions of large biology classes and to characterize the courses offered by a department according to resources and course structure using a categorical principal components analysis. Both student and instructor survey results indicated that a large class begins around 240 students. Large classes were identified as impersonal and classified using extrinsic qualifiers; however, students did identify techniques that made the classes feel smaller. In addition to the qualitative survey, we also attempted to quantify courses by collecting data from course outlines and analyzed the data using categorical principal component analysis. The analysis maps institutional change in resource allocation and teaching structure from 2010 through 2014 and validates the use of categorical principal components analysis in educational research. We examine what perceptions and factors are involved in a large class that is perceived to feel small. Our analysis suggests that it is not the addition of resources or difference in the lecturing method, but it is the instructor that determines whether a large class can feel small.

scholarly journals ITEM DIMENSIONALITY EXPLORATION BY MEANS OF CONSTRUCT MAP AND CATEGORICAL PRINCIPAL COMPONENTS ANALYSIS

2019 ◽  
Vol 18 (2) ◽  
pp. 209-226 ◽  
Author(s):  
Gabriela Deliu ◽  
Cristina Miron ◽  
Cristian Opariuc-Dan
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Data Driven ◽  
National Assessment ◽  
Assessment Tests ◽  
Irt Models ◽  
Multidimensional Irt Models ◽  
Components Analysis ◽  
Multiple Choice Items ◽  
Categorical Principal Components Analysis

The aim of this research is to study the merits and complementarity of Construct Mapping and Categorical Principal Components Analysis as two approaches that explore the dimensionality of multiple-choice items in achievement tests. Data from the two forms of the Romanian National Assessment Tests on Science were used to explore the dimensionality of items and to identify potentially problematic items that affect the equivalence of the two parallel forms. The findings confirm that the two tests have at best partial equivalence, but while the two methods both agree on test unidimensionality, they flag in part different items as potentially problematic. The results enable researchers and practitioners to make coherent data-driven decision regarding the use of unidimensional vs multidimensional IRT models. Keywords: categorical principal components analysis, construct map, item response theory, unidimensionality.

Black Girls in Mathematics: (Re)envisioning an Inclusive Parent Involvement Measure

2021 ◽  
pp. 089590482110156
Author(s):  
Nicol R. Howard ◽  
Nicole M. Joseph
Keyword(s):  
Parent Involvement ◽  
Parental Involvement ◽  
Principal Components Analysis ◽  
Family Involvement ◽  
Principal Components ◽  
Black Girls ◽  
Federal State ◽  
Analysis Program ◽  
Components Analysis ◽  
Categorical Principal Components Analysis

Building upon research utilizing Martin’s Mathematical Socialization and Identity Framework, we examine factors related to community and family involvement to advance the current discourse that informs policies. Data from the High School Longitudinal Study (HSLS:09) public-use file provided a sample of 1,029 Black girls for our analyses. We developed a theoretically-sound inclusive measure, as defined by Black girls, titled the Community and Family Involvement Predictive Scale for Mathematics Outcomes utilizing Nonlinear Principal Components Analysis with a Categorical Principal Components Analysis program. Results are an intersectional measure that considers family, peers, and teachers. Implications for policy include a need for federal, state, and district policymakers to consider a wider variety of contexts, specifically for Black girls, in which community and family partnerships are empowered and prioritized in policies focused on parental involvement.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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