Work-in-progress: model of self-regulated smart learning environment

Yusufu Gambo; Muhammad Zeeshan Shakir

doi:10.1109/EDUNINE51952.2021.9429090

Work-in-progress: model of self-regulated smart learning environment

Yusufu Gambo^*, Muhammad Zeeshan Shakir

^*Corresponding author for this work

School of Computing, Engineering and Physical Sciences

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

2 Citations (Scopus)

57 Downloads (Pure)

Abstract

The use of smart and mobile technologies provides a smart learning environment that can support diverse learning needs. The self-regulated learning process has been identified as one of the strategies that can support students in the online learning environment. Metacognitive skills such as goal setting, task strategy, self-reaction, help-seeking, time management can be developed in a smart learning environment to support the learning process. However, despite the increasing research in the smart learning environment, there is a scarcity of well-documented work on how the metacognitive components in a smart learning environment could be modeled to support the development of a self-regulated smart learning environment. This paper explored the metacognitive components in the smart learning environment to propose the hybrid model, referred to as MSLEM: Metacognitive Smart Learning Environment Model which can be beneficial to researchers for providing a personalized learning environment for supporting the online learning process

Original language	English
Title of host publication	Proceedings of the 2021 IEEE World Conference on Engineering Education (EDUNINE)
Subtitle of host publication	The Future of Engineering Education: Current Challenges and Opportunities
Editors	Claudio da Rocha Brito, Melany M. Ciampi
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	4
ISBN (Electronic)	9781665403023
ISBN (Print)	9781665448321
DOIs	https://doi.org/10.1109/EDUNINE51952.2021.9429090
Publication status	Published - 19 May 2021
Event	IEEE World Conference on Engineering Education - Guatemala, Guatemala Duration: 14 Mar 2021 → 17 Mar 2021 https://edunine.eu/edunine2021/eng/index.html

Conference

Conference	IEEE World Conference on Engineering Education
Abbreviated title	EDUNINE2021
Country/Territory	Guatemala
City	Guatemala
Period	14/03/21 → 17/03/21
Internet address	https://edunine.eu/edunine2021/eng/index.html

Keywords

smart learning environment
self-regulated learning
metacognitive skills
learning model

UN SDGs

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

Access to Document

10.1109/EDUNINE51952.2021.9429090

2020 12 31 Gambo et al WIP acceptedAccepted author manuscript, 449 KB

Cite this

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title = "Work-in-progress: model of self-regulated smart learning environment",

abstract = "The use of smart and mobile technologies provides a smart learning environment that can support diverse learning needs. The self-regulated learning process has been identified as one of the strategies that can support students in the online learning environment. Metacognitive skills such as goal setting, task strategy, self-reaction, help-seeking, time management can be developed in a smart learning environment to support the learning process. However, despite the increasing research in the smart learning environment, there is a scarcity of well-documented work on how the metacognitive components in a smart learning environment could be modeled to support the development of a self-regulated smart learning environment. This paper explored the metacognitive components in the smart learning environment to propose the hybrid model, referred to as MSLEM: Metacognitive Smart Learning Environment Model which can be beneficial to researchers for providing a personalized learning environment for supporting the online learning process",

keywords = "smart learning environment, self-regulated learning, metacognitive skills, learning model",

author = "Yusufu Gambo and Shakir, {Muhammad Zeeshan}",

year = "2021",

month = may,

day = "19",

doi = "10.1109/EDUNINE51952.2021.9429090",

language = "English",

isbn = "9781665448321",

editor = "{da Rocha Brito}, Claudio and Ciampi, {Melany M.}",

booktitle = "Proceedings of the 2021 IEEE World Conference on Engineering Education (EDUNINE)",

publisher = "IEEE",

address = "United States",

note = "IEEE World Conference on Engineering Education, EDUNINE2021 ; Conference date: 14-03-2021 Through 17-03-2021",

url = "https://edunine.eu/edunine2021/eng/index.html",

}

Gambo, Y & Shakir, MZ 2021, Work-in-progress: model of self-regulated smart learning environment. in C da Rocha Brito & MM Ciampi (eds), Proceedings of the 2021 IEEE World Conference on Engineering Education (EDUNINE): The Future of Engineering Education: Current Challenges and Opportunities. IEEE, Piscataway, NJ, IEEE World Conference on Engineering Education, Guatemala, Guatemala, 14/03/21. https://doi.org/10.1109/EDUNINE51952.2021.9429090

Work-in-progress: model of self-regulated smart learning environment. / Gambo, Yusufu; Shakir, Muhammad Zeeshan.
Proceedings of the 2021 IEEE World Conference on Engineering Education (EDUNINE): The Future of Engineering Education: Current Challenges and Opportunities. ed. / Claudio da Rocha Brito; Melany M. Ciampi. Piscataway, NJ: IEEE, 2021.

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

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T2 - IEEE World Conference on Engineering Education

AU - Gambo, Yusufu

AU - Shakir, Muhammad Zeeshan

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N2 - The use of smart and mobile technologies provides a smart learning environment that can support diverse learning needs. The self-regulated learning process has been identified as one of the strategies that can support students in the online learning environment. Metacognitive skills such as goal setting, task strategy, self-reaction, help-seeking, time management can be developed in a smart learning environment to support the learning process. However, despite the increasing research in the smart learning environment, there is a scarcity of well-documented work on how the metacognitive components in a smart learning environment could be modeled to support the development of a self-regulated smart learning environment. This paper explored the metacognitive components in the smart learning environment to propose the hybrid model, referred to as MSLEM: Metacognitive Smart Learning Environment Model which can be beneficial to researchers for providing a personalized learning environment for supporting the online learning process

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Work-in-progress: model of self-regulated smart learning environment

Abstract

Conference

Keywords

UN SDGs

Access to Document

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