Aplicación de minería de datos para la clasificación de programas universitarios de ingeniería industrial acreditados en alta calidad en Colombia

The present research article proposes a method to classify University engineering programs, placing special attention to relations between the subjects of the curriculum and the 12 areas of knowledge established in the body of competencies published by the Institute of industrial and System Engineers (IIES). Techniques of unsupervised data analysis such as Principal Components Analysis (PCA) and cluster analysis were used for the proposed classification. Twenty-one programs, accredited by high quality in Industrial Engineering in Colombia, are used as units of study. The results show that factors such as international accreditation, size of the faculties of engineering and University profile, influence the grouping of the programs of study. The research allowed to classify three large main components and profiles of accredited programs. © 2018 Centro de Informacion Tecnologica. All Rights Reserved.Center for Outcomes Research and Evaluation, Yale School of Medicin

Metodología de Aprendizaje Automático para la Clasificación y Predicción de Usuarios en Ambientes Virtuales de Educación

A methodology to classify and predict users in virtual education environments, studying the interaction of students with the platform and their performance in exams is proposed. For this, the machine learning tools, main components, clustering, fuzzy and the algorithm of the K nearest neighbor were used. The methodology first relates the users according to the study variables, to then implement a cluster analysis that identifies the formation of groups. Finally uses a machine learning algorithm to classify the users according to their level of knowledge. The results show how the time a student stays in the platform is not related to belonging to the high knowledge group. Three categories of users were identified, applying the Fuzzy K-means methodology to determine transition zones between levels of knowledge. The k nearest neighbor algorithm presents the best prediction results with 91%. © 2019 Centro de Informacion Tecnologica. All Rights Reserved

A decision support system for curricula design

A curriculum is a set of related courses that constitutes the basis of a degree program. The required courses of a curriculum generally build student knowledge and skills particular to the field. In most cases, these are cumulative, meaning that as students go through their studies, they put their new knowledge on top of earlier ones, hence leading to the notion of prerequisite courses that must precede a given course. As accreditation practices gain widespread acceptance, and as uniformity among peer institutions is promoted to facilitate mobility, each course is assigned a set of learning outcomes. The learning outcomes of a prerequisite course are seen to encapsulate the skills necessary to take the downstream course. This study follows our efforts regarding the substantial revision of engineering courses throughout our college. As the task is quite involved, we developed a flexible linear programming-based tool to help the decision-making process by quickly evaluating alternative curricula. This study aims to provide an effective decision-making tool to accommodate many “what if” scenarios which would provide options to the decision-makers and help them detecting inconsistencies and oversights. This paper describes our approach and our experiences

Report on Phase One: The social work curriculum.

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Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions