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The communication gap between deaf and non-deaf communities arises due to the use of distinct mother languages. A deaf student, who used to communicate in sign language, cannot read fluently materials written in spoken language. This fact causes serious difficulties to deaf students since most didactic materials in higher education are available exclusively in spoken languages. In this paper, we propose a pedagogical model to deliver educational materials in sign language aiming to provide deaf students the same conditions to succeed as the others, i.e., didactical materials available in one's mother language. Our approach involves the integration of automatic translation technology between spoken and sign language pairs into MOOCs. As a proof of concept, we used this methodology to design a course addressing digital literacy for schoolteachers. The evaluation of the inclusive MOOC and its underlying model reveals its potential, even though further improvements are required, especially regarding evaluation and usability features.

The correct grading of free text answers to exam questions during an assessment process is time consuming and subject to fluctuations in the application of evaluation criteria, particularly when the number of answers is high (in the hundreds). In consequence of these fluctuations, inherent to human nature, and largely determined by emotional factors difficult to mitigate, it is natural that small discrepancies arise in the ratings assigned to similar responses. This means that two answers with similar quality may get a different grade which may generate inequities in the assessment process. Reducing the time required by the assessment process on one hand, and grouping the answers in homogenous groups, on the other hand, are the main motivations for developing the work presented here. We believe that it is possible to reduce unintentional inequities during an assessment process of free text answers by applying text mining techniques, in particular, automatic text classification, enabling to group answers in homogeneous sets comprising answers with uniform quality. Thus, instead of grading answers in random order, the teacher may assess similar answers in sequence, one after the other. The teacher may also choose, for example, to grade free text answers in decreasing order of quality, the best first, or in ascending order of quality, starting to grade the group of the worst answers. The active learning techniques we are applying throughout the grading process generate intermediary models to automatically organize the answers still not fixed in homogeneous groups. These techniques contribute to reduce the time required for the assessment process, to reduce the occurrence of grading errors and improve detection of plagiarism. (Contains 1 figure.)

Societies are experiencing changes in work organization, in part due to the growing ease with which people can collaborate. People with common interests are interacting within heterogeneous teams composed of members from different cultural groups and with distinct skills and backgrounds. Modern economies require professionals to excel in collaboration and communication skills in an international setting. However, these competences are not usually addressed in curricula. In the diverse environment of the current labour markets, it is essential to promote teamwork and communication skills at an international level. In the Multinational Undergraduate Teamwork course, students develop a major project as members of an international team while working at their home institutions. Team members are geographically spread to ensure that the teams are heterogeneous and to promote international cooperation. It is argued that this paradigm can be applied to any project or internship course unit. The results from the pilot programmes support the authors' initial hypothesis that this innovative paradigm significantly promotes the development of soft skills in students. (Contains 3 notes, 11 tables, and 4 figures.)