Multi-rep: An e-Learning Reputation System Aggregating Information from Heterogeneous Sources

نویسندگان

  • Alessandro Grande
  • Andrea Sterbini
  • Marco Temperini
چکیده

Reputation systems are used both as a motivational and an assessment tool in cooperative and classic e-Learning. They can prove useful in accompanying learners along the paths of their didactic activities, by fostering their involvement in the socio-cooperative didactic game. A problem arises, though, when learners (and teachers) participate in different web systems and possibly in different reputation systems, as it is the case when the learners are in proper e-learning systems, such as Moodle, and/or blogs, forums, or wikis. Then, the difficulties in computing reputation, across heterogeneous platforms, may overcome the teacher, and eventually force her towards the use of a single system. We present the initial work done and the design of Multi-Rep, a reputation aggregator, able to collect data from heterogeneous sources, by tracking the participation actions of learners across diverse e-learning tools, and compute the related reputation. Being able to deal with different reputation algorithms and to merge the results of students' interaction in several arenas, appears to be a key factor in allowing more freedom for teacher and students (who can use a wider array of socio-collaborative tools). Moreover, we want to easily define different roles for the students depending on their reputation, so that we can empower some of them (e.g. letting them be co-tutors or peer teachers), rewarding their involvement with higher capabilities/responsibilities, and thus recognizing their important role in the cooperative didactic game.

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تاریخ انتشار 2011