Recommendation Systems for Personalized Technology-Enhanced Learning

Abstract

From e-commerce to e-learning, recommendation systems have given birth to an important and thriving research niche and have been deployed in a variety of application areas over the last decade. In particular, in the technologyenhanced learning (TEL) field, recommendation systems have attracted increasing interest, especially with the rise of educational data mining and big data learning analytics. Generally, TEL recommendation systems are used to support learners in locating relevant educational content according to their profiles. These systems may involve several phases, such as data acquisition and preparation, modeling, and recommendation computation, phases, which together, can describe a TEL recommendation system and distinguish it from others. However, such a description needs to be expanded and generalized in order to cover most of the TEL recommendation systems, especially, in the context of anywhere and anytime learning based on various Web-based learning environments, including Learning Object Repository (LOR), Open Courseware (OCW), Open Educational Resources (OER), Learning Management Systems (LMS), Massive Open Online Courses (MOOC), Educational Widgets, Educational Mobile applications, etc. In this chapter, we provide a generic meta-level framework for a common description of TEL recommendation systems. Then, we present an analysis of several existing TEL recommendation systems with respect to our defined framework.

Toward a New Approach to the Evaluation of a Digital Curriculum Using Learning Analytics

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