Open University of Catalonia

About: Open University of Catalonia is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Collaborative learning & Educational technology. The organization has 1943 authors who have published 4646 publications receiving 64200 citations. The organization is also known as: Universitat Oberta de Catalunya & UOC.

Repositories of Open Educational Resources: An Assessment of Reuse and Educational Aspects

Abstract: This article provides an overview of the current state of repositories of open educational resources (ROER) in higher education at international level. It analyses a series of educational indicators to determine whether ROER can meet the specific needs of the education context, and to clarify understanding of the reuse of open educational resources (OER) provided by ROER. The aim of the study is to assess ROER by combining these two perspectives, and to form a basis for discussion among the universities that are responsible for these repositories. The method was based on content analysis and consisted of two phases: an exploration of international sources, and an analysis of 110 ROER using the proposed set of indicators. The results focus on data from the analysis of ROER websites and some models of good practices. They are presented according to three core dimensions for evaluating ROER: general factors to establish types of ROER, a focus on drivers for OER reuse, and a focus on educational aspects. It was found that most of the ROER that included one or more of the proposed reuse indicators were created exclusively for educational resources. Educational aspects are not yet firmly embedded into ROER. The few repositories that seem to have successfully included them are those that provide other educational metadata and use educational standards.

Rethinking Dropout in Online Higher Education: The Case of the Universitat Oberta De Catalunya.

Abstract: In recent years, several studies have been carried out into the reasons why students drop out of online higher education, following the rise in the relative weight of this form of education. However, more effort has gone into analyzing the causes of this phenomenon than into trying to characterize students who drop out, that is defining what a dropout student is. But obtaining a proper definition of dropout is just as important as describing its causes. It also appears that the definition of dropout is very sensitive to context. As one of the main findings of this article, we reach a pure empirical definition, at a programme level, of students who drop out of an online higher education context with non-mandatory enrollment. This definition is based on the probability of students not continuing a specific academic programme following several consecutive semesters of “theoretical break”, and is highly adaptable to institutions offering distance education with no permanence requirements, that is ones offering the possibility of taking breaks. Our findings show that there are differences regarding the number of consecutive semesters that define dropout depending on whether the programme requires previous experience or not. Additionally, we observe significant differences in the dropout rate between specific programmes, as well as a higher level of dropout in the first semesters. Analyzing the reasons behind these facts should help higher education institutions to make more sound and efficient decisions.

Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits

Abstract: We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly difficult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, \(p=N(N-1)/2\) pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is affordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge.

Constraint support in MDA tools: a survey

Abstract: The growing interest in the MDA (Model-Driven Architecture) and MDD (Model-Driven Development) approaches has largely increased the number of tools and methods including code-generation capabilities. Given a platform-independent model (PIM) of an application, these tools generate (part of) the application code either by defining first a platform-specific model or by executing a direct PIM to code transformation. However, current tools present several limitations regarding code generation of the integrity constraints defined in the PIMs. This paper compares these tools and shows that they lack expressiveness in the kind of constraints they can handle or efficiency in the code generated to verify them. Based on this evaluation, the features of an ideal code-generation method for integrity constraints are established. We believe such a method is required to extend MDA adoption in the development of industrial projects, where constraints play a key role.