Educational Recommender Systems

Abstract

The emergence of data science and artificial intelligence technologies has made technology-enhanced learning (TEL) a promising area of research and practice in education. One of the significant contributions of TEL has been the development of personalized learning and educational recommender systems.

Recommender systems (RecSys) have found widespread use in a variety of internet applications, including e-commerce platforms like Amazon.com and eBay, online streaming services such as YouTube, Netflix, and Spotify, and social media sites like Facebook and Twitter. The success of these applications in improving user experience and decision making by providing personalized recommendations highlights the effectiveness of RecSys. Over the past few decades, RecSys has also made its way into the field of education, which results in the development of educational recommender systems (EdRecSys). Its applications in this field include personalized learning experiences, recommending appropriate books, informal learning materials, and after-school programs, and adapting learning to context-aware or mobile environments, and so forth.

Recently, the development of RecSys has been advanced by a series of interesting and promising topics. However, these progresses made in the field of RecSys was not adequately disseminated to the education community or the development of educational recommender systems (EdRecSys). This tutorial will deliver background, motivations, knowledge and skills of educational recommender systems to the audience, as well as a summary of emerging topics and open challenges in this area.