Challenge Balancing for a Kanji E-Tutoring System

Marysia Winkels, D.M. Roijers, Maarten van Someren, Emi Yamamoto, Richard Pronk, Edwin Odijk, Maarten de Jonge

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


In this paper, we investigate the potential of direct challenge balancing in e-tutoring, especially in domains where there are many skills to acquire. As a case study, we create an e-tutoring system for kanji. Our system estimates the perceived challenge level using both the correctness of the answers of the students and implicit feedback, and adapts accordingly. In order to make this estimation we train a classifier on labelled data collected via the same system. We show empirically that the perceived challenge can be estimated well using implicit feedback, and that the adaptive system based on challenge balancing is preferred over a system in which the student selects a difficulty setting, indicating that directchallenge balancing is a promising research direction for e-tutoring.
Original languageEnglish
Title of host publicationARTIFICIAL INTELLIGENCE
Subtitle of host publication30th Benelux Conference, BNAIC 2018, Den Bosch November 8-9 2018, Revised Selected Papers
EditorsMartin Atzmueller, Wouter Duivesteijn
Number of pages10
ISBN (Electronic)9783030319786
ISBN (Print)9783030319779
Publication statusPublished - 2019
Event30th Benelux Conference on Artificial Intelligence (BNAIC 2018) - Den Bosch
Duration: 8 Nov 20189 Nov 2018
Conference number: 30th

Publication series

NameBelgian/Netherlands Artificial Intelligence Conference
ISSN (Print)1568-7805


Conference30th Benelux Conference on Artificial Intelligence (BNAIC 2018)
Abbreviated titleBNAIC 2018
CityDen Bosch


  • Challenge Balancing
  • E-Tutoring
  • Kanji
  • Machine Learning


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