Adaptive recommendations to students based on working memory capacity

Ting Wen Chang; Jeffrey Kurcz; Moushir M. El-Bishouty; Sabine Graf; Kinshuk

doi:10.1109/ICALT.2014.27

Adaptive recommendations to students based on working memory capacity

Ting Wen Chang, Jeffrey Kurcz, Moushir M. El-Bishouty, Sabine Graf, Kinshuk

School of Computing and Information systems

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

6 Citations (Scopus)

Abstract

An adaptive learning system is able to consider students' cognitive characteristics and then provide them with personalized content, presentation, and navigation supports. Working memory capacity (WMC) is one of the important cognitive characteristics to keep active a limited amount of information for a very brief period of time. Students might forget the important information or the learning guidelines from their limited working memory among all the information available in learning systems. Therefore, this paper proposes a mechanism to provide students with suitable and timely recommendations in learning systems based on individual student's WMC. Six types of adaptive recommendations are used to remind and suggest additional learning activities to students based on their WMC. In this mechanism, we also consider different types of objects in different situations to suit different learning scenarios.

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	57-61
Number of pages	5
ISBN (Electronic)	9781479940387
DOIs	https://doi.org/10.1109/ICALT.2014.27
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

adaptive learning system
recommendation mechanism
working memory capacity

Access to Document

10.1109/ICALT.2014.27

Cite this

Chang, T. W., Kurcz, J., El-Bishouty, M. M., Graf, S., & Kinshuk (2014). Adaptive recommendations to students based on working memory capacity. 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. 57-61). Article 6901398 (Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014). https://doi.org/10.1109/ICALT.2014.27

Chang, Ting Wen ; Kurcz, Jeffrey ; El-Bishouty, Moushir M. et al. / Adaptive recommendations to students based on working memory capacity. 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. 57-61 (Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014).

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title = "Adaptive recommendations to students based on working memory capacity",

abstract = "An adaptive learning system is able to consider students' cognitive characteristics and then provide them with personalized content, presentation, and navigation supports. Working memory capacity (WMC) is one of the important cognitive characteristics to keep active a limited amount of information for a very brief period of time. Students might forget the important information or the learning guidelines from their limited working memory among all the information available in learning systems. Therefore, this paper proposes a mechanism to provide students with suitable and timely recommendations in learning systems based on individual student's WMC. Six types of adaptive recommendations are used to remind and suggest additional learning activities to students based on their WMC. In this mechanism, we also consider different types of objects in different situations to suit different learning scenarios.",

keywords = "adaptive learning system, recommendation mechanism, working memory capacity",

author = "Chang, {Ting Wen} and Jeffrey Kurcz and El-Bishouty, {Moushir M.} and Sabine Graf and Kinshuk",

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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Chang, TW, Kurcz, J, El-Bishouty, MM, Graf, S & Kinshuk 2014, Adaptive recommendations to students based on working memory capacity. in DG Sampson, MJ Spector, N-S Chen, R Huang & Kinshuk (eds), Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014., 6901398, Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014, pp. 57-61, 14th IEEE International Conference on Advanced Learning Technologies, ICALT 2014, Athens, Greece, 7/07/14. https://doi.org/10.1109/ICALT.2014.27

Adaptive recommendations to students based on working memory capacity. / Chang, Ting Wen; Kurcz, Jeffrey; El-Bishouty, Moushir M. 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. 57-61 6901398 (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 - Adaptive recommendations to students based on working memory capacity

AU - Chang, Ting Wen

AU - Kurcz, Jeffrey

AU - El-Bishouty, Moushir M.

AU - Graf, Sabine

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PY - 2014/9/17

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N2 - An adaptive learning system is able to consider students' cognitive characteristics and then provide them with personalized content, presentation, and navigation supports. Working memory capacity (WMC) is one of the important cognitive characteristics to keep active a limited amount of information for a very brief period of time. Students might forget the important information or the learning guidelines from their limited working memory among all the information available in learning systems. Therefore, this paper proposes a mechanism to provide students with suitable and timely recommendations in learning systems based on individual student's WMC. Six types of adaptive recommendations are used to remind and suggest additional learning activities to students based on their WMC. In this mechanism, we also consider different types of objects in different situations to suit different learning scenarios.

AB - An adaptive learning system is able to consider students' cognitive characteristics and then provide them with personalized content, presentation, and navigation supports. Working memory capacity (WMC) is one of the important cognitive characteristics to keep active a limited amount of information for a very brief period of time. Students might forget the important information or the learning guidelines from their limited working memory among all the information available in learning systems. Therefore, this paper proposes a mechanism to provide students with suitable and timely recommendations in learning systems based on individual student's WMC. Six types of adaptive recommendations are used to remind and suggest additional learning activities to students based on their WMC. In this mechanism, we also consider different types of objects in different situations to suit different learning scenarios.

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Chang TW, Kurcz J, El-Bishouty MM, Graf S, Kinshuk. Adaptive recommendations to students based on working memory capacity. In Sampson DG, Spector MJ, Chen NS, Huang R, Kinshuk, editors, Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014. 2014. p. 57-61. 6901398. (Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014). doi: 10.1109/ICALT.2014.27

Adaptive recommendations to students based on working memory capacity

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