Qualitative evaluation of an adaptive exercise selection algorithm

Juliet A. Okpo; Judith Masthoff; Matt Dennis

doi:10.1145/3450614.3462240

Qualitative evaluation of an adaptive exercise selection algorithm

Juliet A. Okpo, Judith Masthoff, Matt Dennis

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents a qualitative study in which we evaluate the core parts of an adaptive algorithm for next-exercise selection in an e-learning system. The algorithm was previously constructed from a series of studies where participants played the role of a teacher and chose the difficulty of a subsequent exercise for a learner based on their performance, mental effort and self esteem. In this paper, we present these findings to real teachers to gain insights into whether the algorithm is effective and appropriate for future inclusion in an intelligent tutoring system. Overall, we found that teachers believed that the recommendations from the algorithm were appropriate.

Original language	English
Title of host publication	UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
Editors	Judith Masthoff, Eelco Herder, Nava Tintarev
Publisher	Association for Computing Machinery, Inc
Pages	167-174
Number of pages	8
ISBN (Electronic)	9781450383677
DOIs	https://doi.org/10.1145/3450614.3462240
Publication status	Published - 21 Jun 2021
Event	29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021 - Virtual, Online, Netherlands Duration: 21 Jun 2020 → 25 Jun 2020

Conference

Conference	29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021
Country/Territory	Netherlands
City	Virtual, Online
Period	21/06/20 → 25/06/20

Keywords

eLearning
Exercise selection
Personality
Personalization
Self-esteem

Access to Document

10.1145/3450614.3462240

Cite this

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title = "Qualitative evaluation of an adaptive exercise selection algorithm",

abstract = "This paper presents a qualitative study in which we evaluate the core parts of an adaptive algorithm for next-exercise selection in an e-learning system. The algorithm was previously constructed from a series of studies where participants played the role of a teacher and chose the difficulty of a subsequent exercise for a learner based on their performance, mental effort and self esteem. In this paper, we present these findings to real teachers to gain insights into whether the algorithm is effective and appropriate for future inclusion in an intelligent tutoring system. Overall, we found that teachers believed that the recommendations from the algorithm were appropriate.",

keywords = "eLearning, Exercise selection, Personality, Personalization, Self-esteem",

author = "Okpo, {Juliet A.} and Judith Masthoff and Matt Dennis",

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doi = "10.1145/3450614.3462240",

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Okpo, JA, Masthoff, J & Dennis, M 2021, Qualitative evaluation of an adaptive exercise selection algorithm. in J Masthoff, E Herder & N Tintarev (eds), UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc, pp. 167-174, 29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021, Virtual, Online, Netherlands, 21/06/20. https://doi.org/10.1145/3450614.3462240

Qualitative evaluation of an adaptive exercise selection algorithm. / Okpo, Juliet A.; Masthoff, Judith; Dennis, Matt.
UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. ed. / Judith Masthoff; Eelco Herder; Nava Tintarev. Association for Computing Machinery, Inc, 2021. p. 167-174.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Y2 - 21 June 2020 through 25 June 2020

ER -

Qualitative evaluation of an adaptive exercise selection algorithm

Abstract

Conference

Keywords

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Cite this