The use of Domain-Specific Languages (DSLs) is a promising field for the development of tools tailored to specific problem spaces, effectively diminishing the complexity of hand-made software. With the goal of making models as precise, simple and reusable as possible, we augment DSLs with concepts from multilevel modelling, where the number of abstraction levels are not limited. This is particularly useful for DSL definitions with behaviour, whose concepts inherently belong to different levels of abstraction. Here, models can represent the state of the modelled system and evolve using model transformations. These transformations can benefit from a multilevel setting, becoming a precise and reusable definition of the semantics for behavioural modelling languages. We present in this paper the concept of Multilevel Coupled Model Transformations, together with examples, formal definitions and tools to assess their conceptual soundness and practical value.

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 realworld 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.

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Abstract. There is a current trend in the industry to migrate its traditional Web applications to Rich Internet Applications (RIAs). To face this migration, traditional Web methodologies are being extended with new RIA modeling primitives. However, this re-engineering process is being figured out in an adhoc manner by introducing directly these new features in the models, crosscutting the old functionality and compromising the readability, reusability and maintainability of the whole system. With the aim of performing this reengineering process more systematic and less error prone we propose in this paper an approach based on separation of concerns applied to the specific case of WebML.

Abstract. In the last years one of the main concerns of the software industry has been to reengineer their legacy Web Applications (WAs) to take advantage of the benefits introduced by Rich Internet Applications (RIAs), such as enhanced user interaction and network bandwith optimization. However, those reengineering processes have been traditionally performed in an ad-hoc manner, resulting in very expensive and errorprone projects. This situation is partly motivated by the fact that most of the legacy WAs were developed before Model-Driven Development (MDD) approaches became mainstream. Then maintenance activities of those legacy WAs have not been yet incorporated to a MDA development lifecycle. OMG Architecture Driven Modernization (ADM) advocates for applying MDD principles to formalize and standardize those reengineering processes with modernization purposes. In this paper we outline an ADM-based WA-to-RIA modernization process, highlighting the special characteristics of this modernization scenario.

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