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.

Early Prediction of Dropout and Final Exam Performance in an Online Statistics Course

Abstract: Higher education students who either do not complete the courses they have enrolled on or interrupt their studies indefinitely remain a major concern for practitioners and researchers. Within each course, early prediction of student dropout helps teachers to intervene in time to reduce dropout rates. Early prediction of course achievement helps teachers suggest new learning materials aimed at preventing at-risk students from failing or not completing the course. Several machine learning techniques have been used to classify or predict at-risk students, including tree-based methods, which, though not the best performers, are easy to interpret. This study presents two procedures for identifying at-risk students (dropout-prone and non-achievers) early on in an online university statistics course. These enable us to understand how classifiers work. We found that student dropout and course performance prediction was only determined by their performance in the first half of the formative quizzes. Nevertheless, other elements of participation on the virtual campus were initially considered. The classifiers will serve as a reference for intervention, despite their moderate performance metrics.

Using the Critical Incident Technique to Identify Factors of Service Quality in Online Higher Education

Abstract: Information technologies are changing the way in which higher education is delivered. In this regard, there is a necessity for developing information systems that help university managers measure the quality of online services offered to their students. This paper discusses the importance of considering students’ perception of service quality. The authors then identify key factors of service quality, as perceived by students, in online higher education. To this end, the Critical Incident Technique (CIT) is proposed as an effective qualitative methodology. Some benefits of this methodology are highlighted and an exploratory research is carried out in a real environment to illustrate this approach. Results from this research explain which quality dimensions are considered the most valuable to online students. Information provided by this methodology can significantly improve strategic decision-making processes in online universities worldwide.

Proton pump inhibitors and the risk of Alzheimer's disease and non-Alzheimer's dementias.

Abstract: Proton pump inhibitors (PPIs) are among the most prescribed medications. Previous epidemiological studies have presented contradictory results about PPIs and the risk of dementia. Our objective was to investigate the association between the use of PPIs and an increasing risk of incident AD or non-AD dementias. A community-based retrospective cohort study was conducted based on the data available from 1st January 2002 to 31st December 2015 in the Catalan health service (CatSalut) system. This cohort included all PPI users (N = 36,360) and non-users (N = 99,362). A lag window of 5 years was considered between the beginning of the PPI treatment and the diagnosis of dementia. PPI use was not associated with the risk of AD (adjusted odds ratio (OR) 1.06) (95% CI 0.93–1.21; p = 0.408). A weakly but significantly increased risk of non-AD dementias was observed among PPI users (adjusted OR 1.20, 95% CI 1.05–1.37; p = 0.007). A higher dose of PPIs was not associated with an increased risk of either AD or non-AD dementias (OR 1.20; 95% CI 0.91–1.61 and OR 0.95; 95% CI 0.74–1.22, respectively). Regarding the number of PPIs used, we observed an increased risk of AD (OR 1.47; 95% CI 1.18–1.83) and non-AD dementias (OR 1.38; 95% CI 1.12–1.70) in users of two types of PPIs compared with those who used only one type. We did not find a higher incidence of AD among PPI users, but a weak increase in the risk of non-AD dementias among PPI users was observed.

Data Analytics for Smart Parking Applications.

Abstract: We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset.