One of the major challenges facing Massive Open Online Courses (MOOCs) is assessing the learner performances beyond traditional automated assessment methods. This leads to a bottleneck problem due to the massiveness of course participants, especially in the context of problem solving. To tackle this issue, peer assessment has been proposed as an effective method. However, the validity of this process is still under discussion, suffers from a lack of credibility and has many weaknesses, particularly with regards to group formation. This paper develops a new method of peer assessment for MOOCs to improve the accuracy and exactitude of the learner grade. Our proposition is based on three main steps: the formation of learner groups, the assessment and synthesis of the results. First, the group definition process can use different elements of the learner model and enables to build heterogeneous groups. After, each learner is required to grade a small number of peer productions. Finally, a synthesis of the various grades is proposed using both data about the ability to assess of each learner and complexity of problems. To evaluate the proposed peer assessment process, we conducted an experimentation devoted to teaching Software Quality Assurance to beginners with computer science during the first university cycle.

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Opinion mining is a fast-developing field of study with numerous applications.It raises economic and political interests, especially in decision-making processes. In this paper, we are interested in educational opinion mining. We propose an educational guidance process that aims to improve university orientation after the secondary school cycle. Unlike the ordinary orientation processes, our process integrates the opinions of pupil, parents, and teachers, collected periodically. These opinions are treated to sort out the studies' domains in a positive polarity order. An orientation score is then calculated based on these opinions, the student's aptitudes and the continuous evaluation marks to guide pupils toward the most favourable discipline. The experimentations conducted on a sample of pupils (300) give a better convergence to the pupils' explicit choices using our proposal with a κ coefficient of 0.71, against 0.29 using only marks. K E Y W O R D Seducational guidance, opinion mining, opinion mining for education, opinion synthesis, pupil's opinions

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