Risk markers for disappearance of pediatric Web resources.

OBJECTIVES: The authors sought to find out whether certain Webometric indexes of a sample of pediatric Web resources, and some tests based on them, could be helpful predictors of their disappearance. METHODS: The authors performed a retrospective study of a sample of 363 pediatric Websites and pages they had followed for 4 years. Main measurements included: number of resources that disappeared, number of inbound links and their annual increment, average daily visits to the resources in the sample, sample compliance with the quality criteria of 3 international organizations, and online time of the Web resources. RESULTS: On average, 11% of the sample disappeared annually. However, 13% of these were available again at the end of follow up. Disappearing and surviving Websites did not show differences in the variables studied. However, surviving Web pages had a higher number of inbound links and higher annual increment in inbound links. Similarly, Web pages that survived showed higher compliance with recognized sets of quality criteria than those that disappeared. A subset of 14 quality criteria whose compliance accounted for 90% of the probability of online permanence was identified. Finally, a progressive increment of inbound links was found to be a marker of good prognosis, showing high specificity and positive predictive value (88% and 94%, respectively). CONCLUSIONS: The number of inbound links and annual increment of inbound links could be useful markers of the permanence probability for pediatric Web pages. Strategies that assure the Web editors' awareness of their Web resources' popularity could stimulate them to improve the quality of their Websites.     

The use of augmented reality to improve the development of activities of daily living in students with ASD

Education and Information Technologies - In today's society, technology plays a fundamental role. More and more activities of daily life are being developed with the help of technology. One of...     

Faculty Employment Trends in Criminology and Criminal Justice

The academic job search process is perhaps one of the most important and stressful events for scholars hoping to gain full‐time employment in academia. The stress that this process induces is due, at least in part, to the fact that candidates are forced to make important, life‐changing decisions with very little information. The goal of the present study is to provide an update on faculty employment trends in criminology and criminal justice by examining faculty position announcements posted in The Chronicle of Higher Education (CHE) from August 2004 to July 2009. Findings reveal that candidates seeking assistant professor positions will have the most choice, as positions posted at this rank make up the largest category of advertisements. Furthermore, when a specialization was included in the announcement, most departments sought those with a law enforcement focus, followed by generalists.     

Relevance-based language modelling for recommender systems

Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the pseudo relevance feedback task. On the other hand, the field of recommender systems is a fertile research area where users are provided with personalised recommendations in several applications. In this paper, we propose an adaptation of the Relevance Modelling framework to effectively suggest recommendations to a user. We also propose a probabilistic clustering technique to perform the neighbour selection process as a way to achieve a better approximation of the set of relevant items in the pseudo relevance feedback process. These techniques, although well known in the Information Retrieval field, have not been applied yet to recommender systems, and, as the empirical evaluation results show, both proposals outperform individually several baseline methods. Furthermore, by combining both approaches even larger effectiveness improvements are achieved.     

Measuring Personal Networks and Their Relationship with Scientific Production

The analysis of social networks has remained a crucial and yet understudied aspect of the efforts to measure Triple Helix linkages. The Triple Helix model aims to explain, among other aspects of knowledge-based societies, “the current research system in its social context” (Etzkowitz and Leydesdorff 2000:109). This paper develops a novel approach to study the research system from the perspective of the individual, through the analysis of the relationships among researchers, and between them and other social actors. We develop a new set of techniques and show how they can be applied to the study of a specific case (a group of academics within a university department). We analyse their informal social networks and show how a relationship exists between the characteristics of an individual’s network of social links and his or her research output.     

The politics of educational success: a realist evaluation of early school leaving policies in Catalonia (Spain)

The objective of the article is to examine what is ‘taken for granted’ in the policies designed to combat Early School Leaving (ESL) in Catalonia, identifying their implicit ontologies, causal assumptions and logics of action. In order to do so, the realist evaluation is applied. Specifically, this methodology has been applied to compare the discourses, priorities and policies of two key agents in defining and implementing policies to combat ESL in Catalonia: the Catalan Department for Education (Regional Government body) and the Barcelona Education Consortium (Local Government body). In each case, systematic documentary analysis has been conducted as well as in-depth interviews with educational experts and policymakers. The results of the analysis allows opening up the black box of the political priorities defined by this two political agents, identifying two highly controversial logics to understand educational success and to consequently address ESL     

PISA in Spain: Expectations,impact and debate

PISA, which was launched by OECD, is one of the most significant and successful initiatives on which education systems have recently collectively embarked. However, although it is a well‐coordinated international programme, its reception differs according to country. There is therefore a need to analyse specific national circumstances in order to gain a deeper understanding of the undertaking as a whole. This article specifically considers Spain's participation in PISA and focuses on a number of aspects: a) the expectations created when it joined the programme, in parallel to the implementation of its own national education evaluation system; b) the impact PISA has had, both in the media and in political and discursive spheres; and c) the technical and scientific debates generated in Spanish academic media. Finally, it is argued that, in the last few years, PISA has met with a certain disenchantment among specialists and the public opinion because of its limitations as a ranking tool, the difficulty in explaining its findings, and its inability to prescribe education policies that are suitable for very different contexts.     

Building user profiles based on sequences for content and collaborative filtering

Modeling user profiles is a necessary step for most information filtering systems – such as recommender systems – to provide personalized recommendations. However, most of them work with users or items as vectors, by applying different types of mathematical operations between them and neglecting sequential or content-based information. Hence, in this paper we study how to propose an adaptive mechanism to obtain user sequences using different sources of information, allowing the generation of hybrid recommendations as a seamless, transparent technique from the system viewpoint. As a proof of concept, we develop the Longest Common Subsequence (LCS) algorithm as a similarity metric to compare the user sequences, where, in the process of adapting this algorithm to recommendation, we include different parameters to control the efficiency by reducing the information used in the algorithm (preference filter), to decide when a neighbor is considered useful enough to be included in the process (confidence filter), to identify whether two interactions are equivalent (δ-matching threshold), and to normalize the length of the LCS in a bounded interval (normalization functions). These parameters can be extended to work with any type of sequential algorithm.We evaluate our approach with several state-of-the-art recommendation algorithms using different evaluation metrics measuring the accuracy, diversity, and novelty of the recommendations, and analyze the impact of the proposed parameters. We have found that our approach offers a competitive performance, outperforming content, collaborative, and hybrid baselines, and producing positive results when either content- or rating-based information is exploited.