In this poster we introduce NEURONE, an open source web‐based system for assessing online inquiry competences (OIC). NEURONE is a fully customizable solution that can be adapted to different research conditions through eight modules and a set of tools. To date, NEURONE has been successfully used to implement, deploy and run an online assessment contextualized in a pretest‐posttest study design, in which nearly 1400 sessions were conducted with 350 elementary school students in their early adolescence.
Predicting perceived difficulty on a web search task is an open problem in the interactive information retrieval field. A common approach to tackle it, is through features obtained from full search sessions, which are then used to train classification models. In this poster we attempt to predict perceived task difficulty at different stages of the search process. To do so, we use the spectrum kernel for support vector machine (SVM) classification. Our preliminary results suggest that by using behavioral data from the first query segment, it is possible to provide timely classifications of whether a search task is perceived as hard or easy.
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