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Test authors can generate test items (semi-) automatically with different approaches. On the one hand, bottom-up approaches consist of generating items from sources such as texts or domain models. However, relating the generated items to competence models, which define required knowledge and skills on a proficiency scale remains a challenge. On the other hand, top-down approaches use cognitive models and competence constructs to specify the knowledge and skills to be assessed. Unfortunately, on this high abstraction level it is impossible to identify which item elements can actually be generated automatically. In this paper we present a hybrid process which integrates both approaches. It aims at securing a traceability between the specification levels and making it possible to influence item generation during runtime, i.e., after designing all the intermediate models. In the context of the European project EAGLE, we use this process to generate items for information literacy with a focus on text comprehension.

Tourists need tools that can help them to select locations in which they can spend their holidays. We have multiple social networks in which we find information about hotels and about users’ experiences. The problem is how tourists can use this information to build their proper opinion about a particular location to decide if they should go to that place or not. We try in this paper to present a design of a solution that can be used to achieve this task. In this paper, we propose a framework for a recommender system that bases on opinions of persons on the one hand and on of users’ preferences on the other hand to generate recommendations. Indeed, opinions of tourists are extracted from different sources and analyzed to finally extract how the hotels are perceived by their customers in terms of features and activities. The final step consists in matching between these opinions and the users’ preferences to generate the recommendations. A prototype was developed in order to show how this framework is really working.

Assessment Item Generation (AIG) aims at creating semi-automatically many items from a template. This type of approaches has been used in various domains, including language learning and mathematics to support adaptation of tests to learners or allow the item authoring process to scale through decreasing the cost of items. We illustrate in this paper the automatic creation of inline choice items for reading comprehension skills using state-of-the-art approaches. However we show how the AIG process can be implemented to support the creation of items in multiple languages (English, French, and German) and how it can be complemented by the creation of item quality metrics to improve the selection of the generated items.

The Open Data movement has mainly been a data provision movement. The release of Open Data is usually motivated by (i) government transparency (citizen access to government data), (ii) the development of services by third parties for the benefit for citizens and companies (typically smart city approach), or (iii) the development of new services that stimulate the economy. The success of the Open Data movement and its return on investment should therefore be assessed among other criteria by the number and impact of the services created based on those data. In this paper, we study the development of services based on open data and means to make the data opening process more effective.

The development of open data requires a better reusability of data. Indeed, the catalogs listing data dispersed in different countries have a crucial role. However, the degree of openness is also a key success factor for open data. In this paper, we study the PublicData.eu catalogue, which allows accessing open datasets from European countries and analyse the metadata recorded for each dataset. The objectives are to (i) identify the quality of a sample of metadata properties, which are critical to enable data reuse and to (ii) study the stated level of data openness. The study uses the Tim Berners-Lee’s five star evaluation scale.