High levels of school dropout are a major burden on the educational and professional development of a country’s inhabitants. A country’s prosperity depends, among other factors, on its ability to produce higher education graduates capable of moving a country forward. To alleviate the dropout problem, more and more institutions are turning to the possibilities that artificial intelligence can provide to predict dropout as early as possible. The difficulty of accessing personal data and privacy issues that it entails force the institutions to rely on the Academic Data of their students to create accurate and reliable predictive systems. This work focuses on creating the best possible predictive model based solely on academic data, and accordingly, its capacity to infer knowledge must be maximised. Thus, Feature Engineering and Instance Engineering techniques such as dealing with redundancy, significance of the features, correlation, cardinality features, missing values, creation or elimination of features, data fusion, removal of unuseful instances, binning, resampling, normalisation, or encoding are applied in detail before the construction of well-known models such as Gradient Boosting , Random Forest , and Support Vector Machine along with an Ensemble of them at different stages: prior to enrolment, at the end of the first semester, at the end of the second semester, at the end of the third semester, and at the end of the fourth semester. Through the construction of these predictive models that serve as inputs to a decision support system, the application of effective dropout prevention policies can be applied.

Modern Web solutions resemble desktop applications, enabling sophisticated user interactions, client-side processing, asynchronous communications, and multimedia. A pure HTTP/HTML architecture fails to support these required capabilities in several respects. The "network as platform computing" idea, strengthened by Web 2.0's emergence, has accentuated HTML/HTTP's limits. This is the reason why many developers are switching to novel technologies, known under the collective name of rich Internet applications (RIAs). RIAs combine the Web's lightweight distribution architecture with desktop applications' interface interactivity and computation power, and the resulting combination improves all the elements of a Web application (data, business logic, communication, and presentation). This special issue briefly introduces the main characteristics and benefits of RIAs and highlights the research challenges in their development. The issue features two articles that address some of these open problems. One focuses on language and architecture issues, whereas the other deals with the methodological principles at the base of a model-driven approach to RIA development. Despite these efforts, the research community must continue investigating to propose novel methods and tools to make their development more systematic and efficient.

Higher Education plays a principal role in the changing and complex world of today, and there has been rapid growth in the scientific literature dedicated to predicting students’ academic success or risk of dropout thanks to advances in Data Mining techniques. Degrees such as Computer Science in particular are in ever greater demand. Although the number of students has increased, the number graduating is still not enough to provide society with as many as it requires. This study contributes to reversing this situation by introducing an approach that not only predicts the dropout risk or students’ performance but takes action to help both students and educational institutions. The focus is on maximizing graduation rates by constructing a Recommender System to assist students with their selection of subjects. In particular, the challenge is addressed of constructing reliable Recommender Systems on the basis of data which are both sparse and few in quantity, imbalanced, and anonymized, and which might have been stored under imperfect conditions. This approach is successfully applied to create a Recommender System using a real-world dataset from a public Spanish university containing performance data of a Computer Science degree course, demonstrating its successful application in real environments. The construction of a support system based on that approach is described, its results are evaluated, and its implications for students’ academic achievement, and for institutions’ graduation rates are discussed. Through the construction of this decision support system for students, we intend to increase the graduation rates and lower the dropout rate.

Several methodologies and tools have been proposed for Web applications design and development. However, traditional Web applications are still inadequate to support the interaction and presentation functionalities demanded by the users. Recently, rich Internet applications (RIAs) have been proposed as an answer to these problems providing new levels of interactivity and presentation. The use of RIAs is growing exponentially; nevertheless there is a lack of full development methodologies in this sense. This document outlines the main features, which should be modeled in RIAs and proposes an evaluation process in order to obtain the suitability of a methodology to accomplish this goal. We also use this process to evaluate the suitability of several existing Web, multimedia and hypermedia methodologies to demonstrate that each one accomplishes only few RIA features, so new methodologies or extensions of the actual methodologies become necessary.

A smart water network consists of a large number of devices that measure a wide range of parameters present in distribution networks in an automatic and continuous way. Among these data, you can find the flow, pressure, or totalizer measurements that, when processed with appropriate algorithms, allow for leakage detection at an early stage. These algorithms are mainly based on water demand forecasting. Different approaches for the prediction of water demand are available in the literature. Although they present successful results at different levels, they have two main drawbacks: the inclusion of several seasonalities is quite cumbersome, and the fitting horizons are not very large. With the aim of solving these problems, we present the application of pattern similarity-based techniques to the water demand forecasting problem. The use of these techniques removes the need to determine the annual seasonality and, at the same time, extends the horizon of prediction to 24 h. The algorithm has been tested in the context of a real project for the detection and location of leaks at an early stage by means of demand forecasting, and good results were obtained, which are also presented in this paper.

Nowadays, Rich Internet Applications are gaining ground thanks to the facilities they provide to develop Web applications with multimedia, high levels of interactivity, collaborative work, and/or homogeneous presentation requirements at the client side. However, this new kind of Web applications currently lacks complete methodologies and models which aid its design and development. This paper introduces the concepts at the base of Rich Internet Applications to take full advantage of their new capacities, and proposes an integrated Web Engineering approach based on the WebML and the RUX-Model conceptual models for supporting a high-level design of these applications and their automatic code generation.