Investigating the effectiveness of an advanced adaptive mechanism for considering learning styles in learning management systems

Sabine Graf; Ting Wen Chang; Anne Kersebaum; Thomas Rath; Jeffrey Kurcz

doi:10.1109/ICALT.2014.41

Investigating the effectiveness of an advanced adaptive mechanism for considering learning styles in learning management systems

Sabine Graf, Ting Wen Chang, Anne Kersebaum, Thomas Rath, Jeffrey Kurcz

School of Computing and Information systems

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

3 Citations (Scopus)

Abstract

Blended and online learning becomes more and more popular and learning management systems (LMSs) are used by many educational institutions to host such blended or online courses. However, such LMS typically do not adapt to students' individual characteristics and provide each student with the same content and presentation. Such one-size-fits-all approach does not fit most students particularly well and can lead to low student performance and satisfaction. In this paper, we present a study to evaluate an advanced adaptive mechanism that extends LMSs with adaptive functionality to automatically provide students with courses that fit their learning styles. The results of this study showed two significant benefits of the adaptive mechanism for students: receiving higher grades on adaptive lessons than on non-adaptive ones while spending a similar amount of time on both, and spending less time on adaptive lessons than on non-adaptive ones while receiving on average the same grades. Based on these results, the proposed adaptive mechanism can be seen as an effective extension to LMSs in order to support students in learning.

Original language	English
Title of host publication	Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014
Editors	Demetrios G. Sampson, Michael J. Spector, Nian-Shing Chen, Ronghuai Huang, Kinshuk
Pages	112-116
Number of pages	5
ISBN (Electronic)	9781479940387
DOIs	https://doi.org/10.1109/ICALT.2014.41
Publication status	Published - 17 Sep. 2014
Event	14th IEEE International Conference on Advanced Learning Technologies, ICALT 2014 - Athens, Greece Duration: 7 Jul. 2014 → 9 Jul. 2014

Publication series

Name	Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014

Conference

Conference	14th IEEE International Conference on Advanced Learning Technologies, ICALT 2014
Country/Territory	Greece
City	Athens
Period	7/07/14 → 9/07/14

Keywords

Learning management systems
adaptivity
learning styles

Access to Document

10.1109/ICALT.2014.41

Cite this

Graf, S., Chang, T. W., Kersebaum, A., Rath, T., & Kurcz, J. (2014). Investigating the effectiveness of an advanced adaptive mechanism for considering learning styles in learning management systems. In D. G. Sampson, M. J. Spector, N.-S. Chen, R. Huang, & Kinshuk (Eds.), Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014 (pp. 112-116). Article 6901412 (Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014). https://doi.org/10.1109/ICALT.2014.41

Graf, Sabine ; Chang, Ting Wen ; Kersebaum, Anne et al. / Investigating the effectiveness of an advanced adaptive mechanism for considering learning styles in learning management systems. Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014. editor / Demetrios G. Sampson ; Michael J. Spector ; Nian-Shing Chen ; Ronghuai Huang ; Kinshuk. 2014. pp. 112-116 (Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014).

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title = "Investigating the effectiveness of an advanced adaptive mechanism for considering learning styles in learning management systems",

abstract = "Blended and online learning becomes more and more popular and learning management systems (LMSs) are used by many educational institutions to host such blended or online courses. However, such LMS typically do not adapt to students' individual characteristics and provide each student with the same content and presentation. Such one-size-fits-all approach does not fit most students particularly well and can lead to low student performance and satisfaction. In this paper, we present a study to evaluate an advanced adaptive mechanism that extends LMSs with adaptive functionality to automatically provide students with courses that fit their learning styles. The results of this study showed two significant benefits of the adaptive mechanism for students: receiving higher grades on adaptive lessons than on non-adaptive ones while spending a similar amount of time on both, and spending less time on adaptive lessons than on non-adaptive ones while receiving on average the same grades. Based on these results, the proposed adaptive mechanism can be seen as an effective extension to LMSs in order to support students in learning.",

keywords = "Learning management systems, adaptivity, learning styles",

author = "Sabine Graf and Chang, {Ting Wen} and Anne Kersebaum and Thomas Rath and Jeffrey Kurcz",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 14th IEEE International Conference on Advanced Learning Technologies, ICALT 2014 ; Conference date: 07-07-2014 Through 09-07-2014",

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doi = "10.1109/ICALT.2014.41",

language = "English",

series = "Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014",

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Graf, S, Chang, TW, Kersebaum, A, Rath, T & Kurcz, J 2014, Investigating the effectiveness of an advanced adaptive mechanism for considering learning styles in learning management systems. in DG Sampson, MJ Spector, N-S Chen, R Huang & Kinshuk (eds), Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014., 6901412, Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014, pp. 112-116, 14th IEEE International Conference on Advanced Learning Technologies, ICALT 2014, Athens, Greece, 7/07/14. https://doi.org/10.1109/ICALT.2014.41

Investigating the effectiveness of an advanced adaptive mechanism for considering learning styles in learning management systems. / Graf, Sabine; Chang, Ting Wen; Kersebaum, Anne et al.
Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014. ed. / Demetrios G. Sampson; Michael J. Spector; Nian-Shing Chen; Ronghuai Huang; Kinshuk. 2014. p. 112-116 6901412 (Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014).

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

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T1 - Investigating the effectiveness of an advanced adaptive mechanism for considering learning styles in learning management systems

AU - Graf, Sabine

AU - Chang, Ting Wen

AU - Kersebaum, Anne

AU - Rath, Thomas

AU - Kurcz, Jeffrey

PY - 2014/9/17

Y1 - 2014/9/17

N2 - Blended and online learning becomes more and more popular and learning management systems (LMSs) are used by many educational institutions to host such blended or online courses. However, such LMS typically do not adapt to students' individual characteristics and provide each student with the same content and presentation. Such one-size-fits-all approach does not fit most students particularly well and can lead to low student performance and satisfaction. In this paper, we present a study to evaluate an advanced adaptive mechanism that extends LMSs with adaptive functionality to automatically provide students with courses that fit their learning styles. The results of this study showed two significant benefits of the adaptive mechanism for students: receiving higher grades on adaptive lessons than on non-adaptive ones while spending a similar amount of time on both, and spending less time on adaptive lessons than on non-adaptive ones while receiving on average the same grades. Based on these results, the proposed adaptive mechanism can be seen as an effective extension to LMSs in order to support students in learning.

AB - Blended and online learning becomes more and more popular and learning management systems (LMSs) are used by many educational institutions to host such blended or online courses. However, such LMS typically do not adapt to students' individual characteristics and provide each student with the same content and presentation. Such one-size-fits-all approach does not fit most students particularly well and can lead to low student performance and satisfaction. In this paper, we present a study to evaluate an advanced adaptive mechanism that extends LMSs with adaptive functionality to automatically provide students with courses that fit their learning styles. The results of this study showed two significant benefits of the adaptive mechanism for students: receiving higher grades on adaptive lessons than on non-adaptive ones while spending a similar amount of time on both, and spending less time on adaptive lessons than on non-adaptive ones while receiving on average the same grades. Based on these results, the proposed adaptive mechanism can be seen as an effective extension to LMSs in order to support students in learning.

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Graf S, Chang TW, Kersebaum A, Rath T, Kurcz J. Investigating the effectiveness of an advanced adaptive mechanism for considering learning styles in learning management systems. In Sampson DG, Spector MJ, Chen NS, Huang R, Kinshuk, editors, Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014. 2014. p. 112-116. 6901412. (Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014). doi: 10.1109/ICALT.2014.41

Investigating the effectiveness of an advanced adaptive mechanism for considering learning styles in learning management systems

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Keywords

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