Exploring the Need for Transparency in Educational Recommender Systems

Abstract

Educational Recommender Systems (EdRecSys) are different in nature from conventional Recommender Systems (RecSys) --mostly related to e-commerce-- as the main goal of EdRecSys is supporting students learning' instead of maximizing users' satisfaction from consuming the recommended items. Thus, research on transparency for traditional RecSys is hard to transfer from e-commerce contexts to educational scenarios, as the level of knowledge of the end-user (i.e. the student) is crucial for generating and evaluating the impact of the recommendations on students' learning. In this paper I present the main idea of my thesis proposal, which aims to fill this gap by taking a user-centered approach that combines design and evaluation of personalized recommender algorithms and explanatory interfaces with students in real learning contexts.