The chapter presents ReaderBench, a multi-lingual and flexible environment that integrates text mining technologies for assessing a wide range of learners' productions and for supporting teachers in several ways. ReaderBench offers three main functionalities in analyzing texts: cohesionbased assessment, reading strategies identification, and textual complexity evaluation. All of these have been object to empirical validations. ReaderBench may be used during an entire educational scenario, starting from the initial complexity assessment of the reading materials, the assignment of texts to learners, the detection of reading strategies reflected in one's self-explanations, and comprehension evaluation fostering learner's self-regulation process.

This article presents Apex, a system that can automatically assess a student essay based on its content. It relies on Latent Semantic Analysis, a tool which represents the meaning of words as vectors in a high-dimensional space. By comparing an essay and the text of a given course on a semantic basis, our system can measure how well the essay matches the text. Various assessments are presented to the student regarding the topic, the outline and the coherence of the essay. Our experiments yield promising results.

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International audienceThis study aims at investigating which cues teachers detect and process from their students during instruction. This information capturing process depends on teachers' sensitivity, or awareness, to students' needs, which has been recognized as crucial for classroom management. We recorded the gaze behaviors of two pre-service teachers and two experienced teachers during a whole math lesson in primary classrooms. Thanks to a simple Learning Analyt-ics interface, the data analysis reports, firstly, which were the most often tracked students, in relation with their classroom behavior and performance; secondly, which relationships exist between teachers' attentional frequency distribution and lability, and the overall classroom climate they promote, measured by the Classroom Assessment Scoring System. Results show that participants' gaze patterns are mainly related to their experience. Learning Analytics use cases are eventually presented, enabling researchers or teacher trainers to further explore the eye-tracking data