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What's in it for me?: Augmenting Recommended Learning Resources with Navigable Annotations

Published: 17 March 2020 Publication History

Abstract

This paper introduces an interface that enables the user to quickly identify relevant fragments within multiple long documents. The proposed method relies on a machine-generated layer of annotations that reveals the coverage of topics per fragment and document. To illustrate how the annotations double as a tool for preview as well as navigation, an example application is presented in the form of a personalised learning system that recommends relevant fragments of video lectures according to user's history. Potential implications of this approach for lifelong learning are discussed. We argue that this approach is generally applicable to recommender and information retrieval systems, across multiple knowledge domains and document types.

References

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Janez Brank, Gregor Leban, and Marko Grobelnik. 2017. Annotating Documents with Relevant Wikipedia Concepts. In Proc. of Slovenian KDD Conference on Data Mining and Data Warehouses (SiKDD).
[2]
S. Bulathwela, M. Perez-Ortiz, E. Yilmaz, and J. Shawe-Taylor. 2020. Towards an Integrative Educational Recommender for Lifelong Learners. In AAAI Conference on Artificial Intelligence.
[3]
S. Bulathwela, M. Perez-Ortiz, E. Yilmaz, and J. Shawe-Taylor. 2020. TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources. In AAAI Conference on Artificial Intelligence.
[4]
Albert T Corbett and John R Anderson. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction 4, 4 (1994), 253--278.
[5]
Philip J. Guo, Juho Kim, and Rob Rubin. 2014. How Video Production Affects Student Engagement: An Empirical Study of MOOC Videos. In Proc. of the First ACM Conf. on Learning @ Scale.
[6]
Andrew S Lan, Christopher G Brinton, Tsung-Yen Yang, and Mung Chiang. 2017. Behavior-Based Latent Variable Model for Learner Engagement. In Proc. of Int. Conf. on Educational Data Mining.
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Andy Lane. 2010. Open Information, Open Content, Open Source. In The Tower and The Cloud, Richard N. Katz (Ed.). Educause, 158--168.

Cited By

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  • (2024)Accurate Recommendation of Personalized Mobile Teaching Resources for Piano Playing and Singing Based on Collaborative Filtering AlgorithmAdvanced Hybrid Information Processing10.1007/978-3-031-50543-0_16(226-238)Online publication date: 24-Mar-2024
  • (2023)Scalable Educational Question Generation with Pre-trained Language ModelsArtificial Intelligence in Education10.1007/978-3-031-36272-9_27(327-339)Online publication date: 3-Jul-2023
  • (2022)Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?Proceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505814(90-101)Online publication date: 14-Mar-2022
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Published In

cover image ACM Conferences
IUI '20 Companion: Companion Proceedings of the 25th International Conference on Intelligent User Interfaces
March 2020
153 pages
ISBN:9781450375139
DOI:10.1145/3379336
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 March 2020

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Author Tags

  1. Future User Interfaces
  2. Information Retrieval
  3. Intelligent Tutoring Systems
  4. OER
  5. Open Education Resources
  6. Recommender Systems
  7. User Modeling

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  • Demonstration
  • Research
  • Refereed limited

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IUI '20
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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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IUI '25

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Cited By

View all
  • (2024)Accurate Recommendation of Personalized Mobile Teaching Resources for Piano Playing and Singing Based on Collaborative Filtering AlgorithmAdvanced Hybrid Information Processing10.1007/978-3-031-50543-0_16(226-238)Online publication date: 24-Mar-2024
  • (2023)Scalable Educational Question Generation with Pre-trained Language ModelsArtificial Intelligence in Education10.1007/978-3-031-36272-9_27(327-339)Online publication date: 3-Jul-2023
  • (2022)Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?Proceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505814(90-101)Online publication date: 14-Mar-2022
  • (2021)Report on the WSDM 2020 workshop on state-based user modelling (SUM'20)ACM SIGIR Forum10.1145/3451964.345196954:1(1-11)Online publication date: 19-Feb-2021
  • (2021)X5Learn: A Personalised Learning Companion at the Intersection of AI and HCICompanion Proceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397482.3450721(70-74)Online publication date: 14-Apr-2021

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