Privacy Framework for Peer Affective Feedback

In Intelligent Tutoring System (ITS), peer affective feedback could be an effective solution to regulate the learners´ emotions when the feedback provided by the ITS is inadequate. However, this option raises many privacy problems since peer affective feedback involves the disclosure of the learner´s personal data. However, the protection of learners´ privacy is imperative to ensure an unbiased environment for learners to support each other emotionally without prejudice. Consequently, in this work, we propose a privacy framework for peer affective feedback. The framework protects learners by combining several techniques including: anonymity, assessing privacy risk, and evaluating peers´ trust levels based on personality traits and the ratings of their co-learners.