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The research project presented in this paper concerns an ontology-based recommender for teacher training and support. An important issue in Computer-Assisted Language Learning education is the complexity of exploiting technology in order to enhance language teaching. The integration of technology into language education is a rather complex achievement, which implies the ability to understand the potential of a tool for language development in different settings. Thus, the aim of the project is to exploit content-based techniques to recommend the most suited instructional design solutions given the mentioned conditions. The tool provides step-by-step recommendations and explanations and is also able to generate scenarios so that it can be used as a valuable training tool. In this context, the paper presents an example of step-by-step recommendation and the semantic modeling of learning tasks, competences and activities. Finally it reports the results of a study aimed to evaluate the correctness of the recommended solutions and explore the potential impact on the end users

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Online educational processes make use of different tools and platforms through which learning resources can be shared and exchanged. Nowadays mobile platforms are gaining momentum and learners can experience mobile ubiquitous learning. This heterogeneity of resources and devices makes the design of e-learning content a relevant issue. In this direction, approaches to design websites and applications that are responsive and adaptive to different kinds of devices in different contexts is growing in importance. In this paper we analyze this issue and discuss both the responsive and the adaptive approaches to e-learning content design in the domain of language learning. Through an experimental evaluation, we will try to demonstrate that, given the nature of language learning, responsive web design approach alone seems not sufficient to provide an effective adaptation of content, especially of learning activities, to fit the device features, while adaptive techniques have better chances t

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