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Computers in Human Behavior
Article . 2017 . Peer-reviewed
License: Elsevier TDM
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Article . 2020
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Article . 2017
License: CC BY NC ND
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Learning with mobile technologies – Students’ behavior

Authors: Laura Briz Ponce; Anabela Pereira; Lina Carvalho; Juan Antonio Juanes Méndez; Francisco José García-Peñalvo;

Learning with mobile technologies – Students’ behavior

Abstract

[EN]The increasing growth of mobile technology in our Society has become a reality. This paper was designed to research about the different factors and drivers that could influence students? behaviour into the usage of mobile technologies for learning. The methodology was based on a quantitative survey grounded on the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology. Data were collected from medical students in University of Coimbra. This model pointed to a behaviour pattern based on the experience and application by medical students, correlating with a strong attitude towards using mobile technology for learning (57%) and willingness to recommend it (40.5%). In line with previous studies, Social Influence raised to be an important factor towards the Attitude and Behavioural Intention of using Mobile Learning. In addition, according to the results, the student?s ease of perception seems to be the main factor affecting the Social Influence (31.9%) and the reliability for recommending this technology for learning was the main factor that affected the Behavioural Intention. Findings provide support for Technology Acceptance Model and the implications of these findings are discussed within the context of Innovation in Education.

Countries
Spain, Portugal
Keywords

Medical education, TAM, Mobile learning, Mhealth, Mobile application, Innovation

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    240
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
240
Top 1%
Top 1%
Top 1%
bronze
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