Teacher vs. Algorithm: Double-blind experiment of content sequencing in mathematics

Ben Levy, Arnon Hershkovitz, Odelia Tzayada, Orit Ezra, Avi Segal, Kobi Gal, Anat Cohen, Michal Tabach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We study content recommendation in an online learning environment for mathematics (N=77, 4th-5th grade student). We compare an expert teacher's recommendation to that of a neural network algorithm, implementing collaborative filtering ranking. We do so using a double-blind randomized controlled experiment. We find that when the difficulty of the teacher's sequence of recommendation was overall increasing, the teacher was superior to the algorithm regarding students' performance. Taken together, our findings indicate on how the algorithm and the expert teacher can benefit from each other.

Original languageEnglish
Title of host publicationEDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining
EditorsCollin F. Lynch, Agathe Merceron, Michel Desmarais, Roger Nkambou
PublisherInternational Educational Data Mining Society
Pages603-606
Number of pages4
ISBN (Electronic)9781733673600
StatePublished - 2019
Event12th International Conference on Educational Data Mining, EDM 2019 - Montreal, Canada
Duration: 2 Jul 20195 Jul 2019

Publication series

NameEDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining

Conference

Conference12th International Conference on Educational Data Mining, EDM 2019
Country/TerritoryCanada
CityMontreal
Period2/07/195/07/19

Keywords

  • Collaborative filtering
  • Content sequencing
  • Neural network

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