Can an algorithm prepare students for tasks without knowing what the tasks are?

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

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

1 Scopus citations

Abstract

we report on two consecutive randomized controlled studies that tested the implementation of a state-of-the-art neural network-based algorithm for personalizing the sequencing of content to learners based on predictive subjective difficulty level. Performance of the students who followed the algorithm recommendations were first compared to those of students who followed an expert teacher-based recommendations (study 1); then, based on the findings, we compared the impact of the algorithm recommendations to that of a baseline (non-personalized) sequence set-up by human experts (study 2). In the second study, the algorithm was successful in preparing the students to the post-test tasks equally well as the human experts were, however without knowing what these tasks were. We highlight the advantages and the limitations of the expert teacher, as well as the algorithm's ability to do no worse than the human experts.

Original languageEnglish
Title of host publicationProceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages754-759
Number of pages6
ISBN (Electronic)9781728155845
DOIs
StatePublished - 1 Dec 2019
Event6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019 - Las Vegas, United States
Duration: 5 Dec 20197 Dec 2019

Publication series

NameProceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019

Conference

Conference6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019
Country/TerritoryUnited States
CityLas Vegas
Period5/12/197/12/19

Keywords

  • Collaborative filtering
  • Content sequencing
  • Mathematics education
  • Neural network
  • Online learning environments

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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