Recommender Systems for Learning

verfasst von: Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval

Verlag: Springer New York

Buchreihe : SpringerBriefs in Electrical and Computer Engineering

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction and Background

Abstract

In this chapter, we start with a short introduction to the increase that has been witnessed in the past few years in applications of recommender systems at the TEL domain. Then we provide some background on the area of recommender systems, by defining recommender systems and outlining their basic types. A comparison with relevant work in TEL is tried, particularly focusing on adaptive educational hypermedia, learning networks, educational data mining, and learning analytics. A discussion on their similarities and differences is also made, so that relevant work can be better positioned in the TEL research landscape.

Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval

Chapter 2. TEL as a Recommendation Context

Abstract

In this chapter, we define the TEL recommendation problem and identify TEL recommendation goals. More specifically, we reflect on user tasks that are supported in TEL settings, and how they compare to typical user tasks in other recommender systems. Then, we present an analysis of existing data sets that capture contextual learner interactions with tools and resources in TEL settings. These data sets can be used for a wide variety of research purposes, including experimental comparison of the performance of recommendation algorithms for learning.

Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval

Chapter 3. Survey and Analysis of TEL Recommender Systems

Abstract

In this chapter, we present a framework for the analysis of existing recommender systems. Then, we present a detailed analysis of relevant TEL recommender systems along the dimensions defined by our framework.

Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval

Chapter 4. Challenges and Outlook

Abstract

This chapter discusses the main challenges that we see as being highlighted from this study. It also outlines the directions of future work that relevant research could take. It concludes with the main contributions and lessons learnt of this work.

Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval

Titel: Recommender Systems for Learning
verfasst von: Nikos Manouselis
Hendrik Drachsler
Katrien Verbert
Erik Duval
Verlag: Springer New York
Electronic ISBN: 978-1-4614-4361-2
Print ISBN: 978-1-4614-4360-5
DOI: https://doi.org/10.1007/978-1-4614-4361-2