Service-based cloud applications are software systems that continuously evolve to satisfy new user requirements and technological changes. This kind of applications also require elasticity, scalability, and high availability, which means that deployment of new functionalities or architectural adaptations to fulfill service level agreements (SLAs) should be performed while the application is in execution. Dynamic architectural reconfiguration is essential to minimize system disruptions while new or modified services are being integrated into existing cloud applications. Thus, cloud applications should be developed following principles that support dynamic reconfiguration of services, and also tools to automate these reconfigurations at runtime are needed. This paper presents an extension of a model-driven method for dynamic and incremental architecture reconfiguration of cloud services that allows developers to specify new services as software increments, and the tool to generate the implementation code for the services integration logic and the deployment and architectural reconfiguration scripts specific to the cloud environment in which the service will be deployed (e.g., Microsoft Azure). We also report the results of a quasi-experiment that empirically validate our method. It was conducted to evaluate their perceived ease of use, perceived usefulness, and perceived intention to use. The results show that the participants perceive the method to be useful, and they also expressed their intention to use the method in the future. Although further experiments must be carried out to corroborate these results, the method has proven to be a promising architectural reconfiguration process for cloud applications in the context of agile and incremental development processes.

Her research interests include Self-Regulatory Learning, learning analytics, blended learning, mobile learning and computer-supported collaborative learning. Nyi Nyi Htun is a postdoctoral researcher at the department of Computer Science at KU Leuven. His research interests include interactive recommender and information retrieval systems, human-computer interaction, digital health and business information systems. Martijn Millecamp is a PhD student at Augment, the Human-Computer Interaction group of the Department of Computer Science, KU Leuven. His research interest is user modelling for explainable recommender system interfaces and dashboards for learning analytics.

Despite the success of academic advising dashboards in several higher educational institutions (HEI), these dashboards are still under-explored in Latin American HEI's. To close this gap, three different Latin American universities adapted an existing advising dashboard, originally deployed at the KU Leuven to their own context. In all three cases, the context was the main ruling factor to these adaptations. In this paper, we describe these adaptions using a framework that focuses on four different elements of the context: Objectives, Stakeholders, Key moment and Interactions. Evaluation of the adapted dashboards in the three different Latin American universities are conducted through pilots. This evaluation shows the value of the dashboard approach in different contexts in terms of satisfaction, usefulness and impact in academic decision-making and advising tasks. The main contribution of this paper is the systematic reporting of the adaptations to an academic advising dashboard and showing the value of an academic advising dashboard on academic decision-making and advising tasks.

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