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

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

Artificial intelligence

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

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 language	English
Title of host publication	ARTIFICIAL INTELLIGENCE
Subtitle of host publication	30th Benelux Conference, BNAIC 2018, Den Bosch November 8-9 2018, Revised Selected Papers
Editors	Martin Atzmueller, Wouter Duivesteijn
Publisher	Springer
Pages	331-340
Number of pages	10
ISBN (Electronic)	9783030319786
ISBN (Print)	9783030319779
Publication status	Published - 2019
Event	30th Benelux Conference on Artificial Intelligence (BNAIC 2018) - Den Bosch Duration: 8 Nov 2018 → 9 Nov 2018 Conference number: 30th

Publication series

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

Conference

Conference	30th Benelux Conference on Artificial Intelligence (BNAIC 2018)
Abbreviated title	BNAIC 2018
City	Den Bosch
Period	8/11/18 → 9/11/18

Keywords

Challenge Balancing
E-Tutoring
Kanji
Machine Learning

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Winkels, M., Roijers, D. M., van Someren, M., Yamamoto, E., Pronk, R., Odijk, E., & de Jonge, M. (2019). Challenge Balancing for a Kanji E-Tutoring System. In M. Atzmueller, & W. Duivesteijn (Eds.), ARTIFICIAL INTELLIGENCE: 30th Benelux Conference, BNAIC 2018, Den Bosch November 8-9 2018, Revised Selected Papers (pp. 331-340). (Belgian/Netherlands Artificial Intelligence Conference). Springer. http://roijers.info/pub/winkels2018bnaic.pdf

Winkels, Marysia ; Roijers, D.M. ; van Someren, Maarten ; Yamamoto, Emi ; Pronk, Richard ; Odijk, Edwin ; de Jonge, Maarten. / Challenge Balancing for a Kanji E-Tutoring System. ARTIFICIAL INTELLIGENCE: 30th Benelux Conference, BNAIC 2018, Den Bosch November 8-9 2018, Revised Selected Papers. editor / Martin Atzmueller ; Wouter Duivesteijn. Springer, 2019. pp. 331-340 (Belgian/Netherlands Artificial Intelligence Conference).

@inproceedings{a4671ff60b1346d998295ea4c318dcef,

title = "Challenge Balancing for a Kanji E-Tutoring System",

abstract = "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.",

keywords = "Challenge Balancing, E-Tutoring, Kanji, Machine Learning",

author = "Marysia Winkels and D.M. Roijers and {van Someren}, Maarten and Emi Yamamoto and Richard Pronk and Edwin Odijk and {de Jonge}, Maarten",

year = "2019",

language = "English",

isbn = "9783030319779",

series = "Belgian/Netherlands Artificial Intelligence Conference",

publisher = "Springer",

pages = "331--340",

editor = "Martin Atzmueller and Wouter Duivesteijn",

booktitle = "ARTIFICIAL INTELLIGENCE",

note = "30th Benelux Conference on Artificial Intelligence (BNAIC 2018), BNAIC 2018 ; Conference date: 08-11-2018 Through 09-11-2018",

}

Winkels, M, Roijers, DM, van Someren, M, Yamamoto, E, Pronk, R, Odijk, E & de Jonge, M 2019, Challenge Balancing for a Kanji E-Tutoring System. in M Atzmueller & W Duivesteijn (eds), ARTIFICIAL INTELLIGENCE: 30th Benelux Conference, BNAIC 2018, Den Bosch November 8-9 2018, Revised Selected Papers. Belgian/Netherlands Artificial Intelligence Conference, Springer, pp. 331-340, 30th Benelux Conference on Artificial Intelligence (BNAIC 2018), Den Bosch, 8/11/18. <http://roijers.info/pub/winkels2018bnaic.pdf>

Challenge Balancing for a Kanji E-Tutoring System. / Winkels, Marysia; Roijers, D.M.; van Someren, Maarten; Yamamoto, Emi; Pronk, Richard; Odijk, Edwin; de Jonge, Maarten.

ARTIFICIAL INTELLIGENCE: 30th Benelux Conference, BNAIC 2018, Den Bosch November 8-9 2018, Revised Selected Papers. ed. / Martin Atzmueller; Wouter Duivesteijn. Springer, 2019. p. 331-340 (Belgian/Netherlands Artificial Intelligence Conference).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Winkels, Marysia

AU - Roijers, D.M.

AU - van Someren, Maarten

AU - Yamamoto, Emi

AU - Pronk, Richard

AU - Odijk, Edwin

AU - de Jonge, Maarten

N1 - Conference code: 30th

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N2 - 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.

AB - 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.

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Y2 - 8 November 2018 through 9 November 2018

ER -

Challenge Balancing for a Kanji E-Tutoring System

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