Learning with mobile technologies - Students' behavior

By Laura Briz Ponce, Anabela Pereira, Lina Carvalho, Juan Antonio Juanes Méndez, Francisco José García-Peñalvo. Computers in Human Behavior, 2017

358 citations recorded by the source corpus.

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 students 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. We analyse the factors that affect the students behaviour towards the use of mobile technologies.We used the TAM and UTAUT model to design the survey distributed to the medical students.This research used a component-based SEM technique (Partial Least Square).Medical students have a strong attitude towards using mobile technologies for learning.Medical students have a medium willing to adopt the mobile technologies for learning.

Subfields: Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/9f253a059e20259ec67f3d7dde19179b3ae97be4

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Citations in the corpus

An empirical examination of continuous intention to use m-learning: An integrated model (2020, background): “In the same vein, Briz-Ponce et al. (2017) argued that social influences were significant factors in predicting students ’ attitudes and behavioral intentions toward using mobile technologies.”
Mobile-Based Assessment: Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and Technology Acceptance (2017, background): “Furthermore, many external variables have been added so far to predict and explain behavioral intention to use mobile learning: performance expectancy, effort expectancy, social influence, perceived playfulness (Wang, Wu, & Wang, 2009), facilitating conditions (Iqbal & Qureshi, 2012), quality of ser”
Mobile-based assessment: Investigating the factors that influence behavioral intention to use (2017, background): “The acceptance and adoption of mobile learning is a topic of growing interest in the field of education and it is still evolving (Briz-Ponce, Pereira, Carvalho, Juanes-M endez, & García-Pe~nalvo, 2016; Cheon, Lee, Crooks, & Song, 2012; Liu, Han, & Li, 2010).”