LearningQ: a large-scale dataset for educational question generation

We present LearningQ, a challenging educational question generation dataset containing over 230K document-question pairs. It includes 7K instructor-designed questions assessing knowledge concepts being taught and 223K learner-generated questions seeking in-depth understanding of the taught concepts. We show that, compared to existing datasets that can be used to generate educational questions, LearningQ (i) covers a wide range of educational topics and (ii) contains long and cognitively demanding documents for which question generation requires reasoning over the relationships between sentences and paragraphs. As a result, a significant percentage of LearningQ questions (~30%) require higher-order cognitive skills to solve (such as applying, analyzing), in contrast to existing question-generation datasets that are designed mostly for the lowest cognitive skill level (i.e. remembering). To understand the effectiveness of existing question generation methods in producing educational questions, we evaluate both rule-based and deep neural network based methods on LearningQ. Extensive experiments show that state-of-the-art methods which perform well on existing datasets cannot generate useful educational questions. This implies that LearningQ is a challenging test bed for the generation of high-quality educational questions and worth further investigation. We open-source the dataset and our codes at https://dataverse.mpi-sws.org/dataverse/icwsm18

Guest Editorial to the Theme Section on Model-Driven Web Engineering

Model-Driven Engineering (MDE) is becoming a widely accepted paradigm for the design and development of complex distributed applications. MDE advocates the use of models and model transformations as key artefacts in all phases of the software process, from system specification and analysis, to design, development and testing. Each model usually addresses one concern, independently from the rest of the issues involved in the construction of the system. Thus, the basic functionality of the system can be separated from its final implementation, and the business logic can be separated from the underlying platform technology, etc. Transformations between models enable the automated implementation of a system from the different models defined for it.Facultad de Informátic

The Key Events Dose-Response Framework: A Foundation for Examining Variability in Elicitation Thresholds for Food Allergens

Food allergies are caused by immunological reactions in individuals sensitized to normal protein components of foods. For any given sensitized individual, the severity of a reaction is generally assumed to be proportional to the dose of allergenic protein. There is substantial clinical evidence that “threshold” doses exist for the elicitation of an allergic reaction; however, the threshold (i.e., lowest dose that elicits a reaction) varies substantially across the sensitized population. Current approaches to protecting sensitized individuals from exposure to food allergens are highly qualitative (i.e., they rely on food avoidance). The Key Events Dose-Response Framework is an analytical approach for refining understanding of the biological basis of the dose-response. Application of this approach to food allergy provides a foundation for a more rigorous quantitative understanding of variability in allergic response. This study reviews the allergic disease process and the current approaches to identifying thresholds for food allergens. The pathway of key biological events occurring between food intake and allergic response is considered, along with factors that may determine the nature and severity of response to food allergens. Data needs, as well as implications for identifying thresholds, and for characterizing variability in thresholds, are also discussed