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© ACM 2017. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Digital Library, http://dx.doi.org/10.1145/3051457.3054015.International audienceIn formative assessments, one wants to provide a useful feedback to the examinee at the end of the test. In order to reduce the number of questions asked in an assessment, adaptive testing models have been developed for cognitive diagnosis, such as the ones encountered in knowledge space theory. However, when the number of skills assessed is very huge, such methods cannot scale. In this paper, we present a new method to provide adaptive tests and useful feedback to the examinee, even with large databases of skills. It will be used in Pix, a platform for certification of digital competencies for every French citizen

This paper describes the participation of LIG lab, in the batch filtering task for the INFILE (INformation FILtering Evaluation) campaign of CLEF 2009. As opposed to the online task, where the server provides the documents one by one, all of the documents are provided beforehand in the batch task, which explains the fact that feedback is not possible in the batch task. We propose in this paper a batch algorithm to learn category specific thresholds in a multiclass environment where a document can belong to more than one class. The algorithm uses k-nearest neighbor algorithm for filtering the 100,000 documents into 50 topics. The experiments were run on the English corpus. Our experiments gave us a precision of 0.256 while the recall was 0.295. We had participated in the online task in INFILE 2008 where we had used an online algorithm using the feedbacks from the server.