Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment

Phat Nguyen Huu; Preecha Tangworakitthaworn; Lester Gilbert

doi:10.1109/TALE52509.2021.9678777

Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment

Phat Nguyen Huu
, Preecha Tangworakitthaworn
, Lester Gilbert

Computer Science Academic Group

Mahidol University
University of Southampton

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

4 Citations (Scopus)

Abstract

Self-regulated individual learning is widely used in academia. Besides the model's advantages, such as flexible learning in time and space, some implementations have limitations, for example fixed learning paths, and unclear relationships between learning activities and intended learning outcomes. This paper introduces an individualized learning model based on Bloom's cognitive taxonomy and Biggs' Principle of Constructive Alignment (PCA). The model provides individual tailored learning paths, adjusted for different background knowledge and ability to learn, based on regularly measured achievement of the intended learning outcomes.

Original language	English
Title of host publication	TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	465-472
Number of pages	8
ISBN (Electronic)	9781665436878
DOIs	https://doi.org/10.1109/TALE52509.2021.9678777
Publication status	Published - 2021
Event	2021 IEEE International Conference on Engineering, Technology and Education, TALE 2021 - Wuhan, China Duration: 5 Dec 2021 → 8 Dec 2021

Publication series

Name	TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings

Conference

Conference	2021 IEEE International Conference on Engineering, Technology and Education, TALE 2021
Country/Territory	China
City	Wuhan
Period	5/12/21 → 8/12/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education

Keywords

cognitive taxonomy
constructive alignment
learning effectiveness
self-regulated learning

Access to Document

10.1109/TALE52509.2021.9678777

Cite this

Huu, P. N., Tangworakitthaworn, P., & Gilbert, L. (2021). Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment. In TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings (pp. 465-472). (TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TALE52509.2021.9678777

Huu, Phat Nguyen ; Tangworakitthaworn, Preecha ; Gilbert, Lester. / Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment. TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 465-472 (TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings).

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abstract = "Self-regulated individual learning is widely used in academia. Besides the model's advantages, such as flexible learning in time and space, some implementations have limitations, for example fixed learning paths, and unclear relationships between learning activities and intended learning outcomes. This paper introduces an individualized learning model based on Bloom's cognitive taxonomy and Biggs' Principle of Constructive Alignment (PCA). The model provides individual tailored learning paths, adjusted for different background knowledge and ability to learn, based on regularly measured achievement of the intended learning outcomes.",

keywords = "cognitive taxonomy, constructive alignment, learning effectiveness, self-regulated learning",

author = "Huu, \{Phat Nguyen\} and Preecha Tangworakitthaworn and Lester Gilbert",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Engineering, Technology and Education, TALE 2021 ; Conference date: 05-12-2021 Through 08-12-2021",

year = "2021",

doi = "10.1109/TALE52509.2021.9678777",

language = "English",

series = "TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings",

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Huu, PN, Tangworakitthaworn, P & Gilbert, L 2021, Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment. in TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings. TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 465-472, 2021 IEEE International Conference on Engineering, Technology and Education, TALE 2021, Wuhan, China, 5/12/21. https://doi.org/10.1109/TALE52509.2021.9678777

Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment. / Huu, Phat Nguyen; Tangworakitthaworn, Preecha; Gilbert, Lester.
TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 465-472 (TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings).

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

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AU - Huu, Phat Nguyen

AU - Tangworakitthaworn, Preecha

AU - Gilbert, Lester

PY - 2021

Y1 - 2021

N2 - Self-regulated individual learning is widely used in academia. Besides the model's advantages, such as flexible learning in time and space, some implementations have limitations, for example fixed learning paths, and unclear relationships between learning activities and intended learning outcomes. This paper introduces an individualized learning model based on Bloom's cognitive taxonomy and Biggs' Principle of Constructive Alignment (PCA). The model provides individual tailored learning paths, adjusted for different background knowledge and ability to learn, based on regularly measured achievement of the intended learning outcomes.

AB - Self-regulated individual learning is widely used in academia. Besides the model's advantages, such as flexible learning in time and space, some implementations have limitations, for example fixed learning paths, and unclear relationships between learning activities and intended learning outcomes. This paper introduces an individualized learning model based on Bloom's cognitive taxonomy and Biggs' Principle of Constructive Alignment (PCA). The model provides individual tailored learning paths, adjusted for different background knowledge and ability to learn, based on regularly measured achievement of the intended learning outcomes.

KW - cognitive taxonomy

KW - constructive alignment

KW - learning effectiveness

KW - self-regulated learning

UR - https://www.scopus.com/pages/publications/85125944045

U2 - 10.1109/TALE52509.2021.9678777

DO - 10.1109/TALE52509.2021.9678777

M3 - Conference contribution

AN - SCOPUS:85125944045

T3 - TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings

SP - 465

EP - 472

BT - TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Engineering, Technology and Education, TALE 2021

Y2 - 5 December 2021 through 8 December 2021

ER -

Huu PN, Tangworakitthaworn P, Gilbert L. Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment. In TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 465-472. (TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings). doi: 10.1109/TALE52509.2021.9678777

Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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