Abstract
Ensuring effective feedback for learners is an important factor in the success of the learning experience. In the context of MOOCs, instructors are unable to provide feedback to a big, heterogeneous community of participants. Different platforms and tools have adopted peer assessment to solve this problem. However, they have been faced with a large number of learners who do not have enough capacity to generate accurate assessments and meaningful feedback. This finding leads to relying on the intelligence of the mass in order to generate more valid and effective feedback. At this level, one limitation of most tools and platforms is that they create random groups of assessors without considering the individual characteristics of its members. For this reason, this article proposes an updated assessor model that focuses on the characteristics of learners related to assessment capacity and their ability to provide correct, objective and useful feedback for their peers. Based on this feedback-oriented assessor model, we consider the aforementioned characteristics in the context of an algorithm that creates groups of assessors and allocates submissions in order to optimize peer feedback.
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Abrache, MA., Megder, K., Cherkaoui, C. (2018). Feedback-Oriented Assessor Model. In: Abdelwahed, E., Bellatreche, L., Golfarelli, M., Méry, D., Ordonez, C. (eds) Model and Data Engineering. MEDI 2018. Lecture Notes in Computer Science(), vol 11163. Springer, Cham. https://doi.org/10.1007/978-3-030-00856-7_9
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