Nowadays written information is still among the main communication channels in everyday life, and online education is not the exception. In fact, text is perhaps the dominant information source for e-learning systems.
Written text in e-learning systems is mostly associated with exams and evaluations. In this regard, automatic systems for the analysis of textual information are highly relevant for the sake of developing completely autonomous assessment mechanisms. Although there are efforts to develop tools for assessing education in a very high semantic level (e.g., to provide feedback in argumentative tasks, to evaluate coherence, etc.), efforts on authentication mechanisms are more promising to be implemented in e-learning platforms.
This is mainly because of the maturity of research in tasks such as authorship verification, attribution, and profiling. In these three tasks, the goal is to develop computer programs that can tell as much as possible form a person by only looking at their written documents. Specifically, verification and attribution tools aim at modelling the writing style of authors. Writing style can be exposed by looking at the lexical, syntactic and semantic patterns that people use when writing.
However, detecting these patterns is not an easy task, not even for humans. Hence, it is common to resort to machine learning techniques, which, given a bunch of sample documents written by the author of interest, discover discriminative patterns that allow computer programs to decide on the authorship of new documents. TeSLA incorporates authorship verification mechanisms that guarantee authentication of users, making reliable the evaluations of written information.
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TeSLA is coordinated by Universitat Oberta de Catalunya (UOC) and funded by the European Commission’s Horizon 2020 ICT Programme. This website reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.