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BBookX: Design of an Automated Web-based Recommender System for the Creation of Open Learning Content

Published: 11 April 2016 Publication History

Abstract

We describe BBookX, a web-based tool that uses a human-computing approach to facilitate the creation of open source textbooks. The goal of BBookX is to create a system that can search various Open Educational Resource (OER) repositories such as Wikipedia, based on a set of user-generated criteria, and return various resources that can be combined, remixed, and re-used to support specific learning goals. As BBookX is a work-in-progress, we are in the midst of a design-based research study, where user testing guided multiple rounds of iteration in the design of the user interface (UI) as well as the query engine. From an interface perspective, the challenges we present are the matching of the UI to users' mental models from similar systems, as well as educating users how to best work with the algorithms in an iterative manner to find and refine content for inclusion into open textbooks.

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Cited By

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  • (2022)Educational Resources Recommender System for Teachers: Why and How?Advances in Deep Learning, Artificial Intelligence and Robotics10.1007/978-3-030-85365-5_7(71-80)Online publication date: 3-Jan-2022

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cover image ACM Other conferences
WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web
April 2016
1094 pages
ISBN:9781450341448

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  • IW3C2: International World Wide Web Conference Committee

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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Author Tags

  1. education/learning
  2. information retrieval and extraction
  3. information seeking and search
  4. personalization
  5. wikipedia

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WWW '16
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  • IW3C2
WWW '16: 25th International World Wide Web Conference
April 11 - 15, 2016
Québec, Montréal, Canada

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WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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  • (2022)Educational Resources Recommender System for Teachers: Why and How?Advances in Deep Learning, Artificial Intelligence and Robotics10.1007/978-3-030-85365-5_7(71-80)Online publication date: 3-Jan-2022

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