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Sebastian, Ratan J. [Author]; Ewerth, Ralph [Author]; Hoppe, Anett [Author]; Özgöbek, Özlem [Author]; Lommatzsch, Andreas [Author]; Kille, Benjamin Uwe [Author]; Liu, Peng [Author]; Malthouse, Edward C. [Author]; Gulla, Jon Atle [Author]; Yu, Ran [Author]; Liu, Jiqun [Author]

Grade Level Filtering for Learning Object Search using Entity Linking - [published Version]

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  • Media type: E-Article; Text
  • Title: Grade Level Filtering for Learning Object Search using Entity Linking
  • Contributor: Sebastian, Ratan J. [Author]; Ewerth, Ralph [Author]; Hoppe, Anett [Author]; Özgöbek, Özlem [Author]; Lommatzsch, Andreas [Author]; Kille, Benjamin Uwe [Author]; Liu, Peng [Author]; Malthouse, Edward C. [Author]; Gulla, Jon Atle [Author]; Yu, Ran [Author]; Liu, Jiqun [Author]
  • Published: Aachen, Germany : RWTH Aachen, 2023
  • Published in: INRA + IWILDS 2022: News Recommendation and Analytics + Investigating Learning During Web Search 2022 : joint proceedings of the 10th International Workshop on News Recommendation and Analytics (INRA 2022) and the 3rd International Workshop on Investigating Learning During Web Search (IWILDS 2022), co-located with 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022) ; CEUR Workshop Proceedings ; 3411
  • Issue: published Version
  • Language: English
  • DOI: https://doi.org/10.15488/16882
  • Keywords: Konferenzschrift ; learning object ; classification ; metadata enrichment ; information retrieval ; search ; machine learning
  • Origination:
  • Footnote: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Description: More and more Learning Objects like lessons, exercises, worksheets and lesson plans are available online. Finding them, however, is a challenge as they often lack metadata concerning format, content and, in the K-12 context: grade-levels or age ranges for which they are appropriate. This work studies the automatic content-based assignment of this last aspect of Learning Object metadata. For this purpose, we (a) collected a dataset of physics lessons, (b) explored a set of text-based features for their automatic analysis (derived from both dense vector representations and entity linking methods) and (c) trained a machine learning model with different subsets of these features to predict a resource’s target grade level. We compare and discuss the results.
  • Access State: Open Access
  • Rights information: Attribution (CC BY)