1 UCAM Universidad Católica de Murcia (SPAIN)
2 Universidad de Almería (SPAIN)
About this paper:
Appears in: INTED2020 Proceedings
Publication year: 2020
Pages: 4342-4348
ISBN: 978-84-09-17939-8
ISSN: 2340-1079
doi: 10.21125/inted.2020.1204
Conference name: 14th International Technology, Education and Development Conference
Dates: 2-4 March, 2020
Location: Valencia, Spain
The massive development of information and communication technologies has allowed teachers and professors to improve the teaching-learning process. The access to the Internet from tablets, computers or smartphones has opened new possibilities for learning. Access to information, pictures or interactive activities is today better than any time in the past. This has a positive impact in children, young adults and adults’ education. However, there are also negative consequences of bringing the laptop, tablet or smartphone to class. Some students impulsively, systematically and deliberatively use their own electronic devices in class not to learn but to entertain. For example, if electronic devices usage is allowed at university, some students update their social networks or play video games in class instead of paying attention to the lecture. These types of behaviours are pernicious for students and for professors. Firstly, students miss key information from lectures or discussions. Secondly, professors, in some cases, are driven crazy for the lack of interest in their subjects. We have collected data about smartphone addiction, impulsivity and self-reported academic performance in a sample or university students. We use network analysis techniques and Bayesian networks to explore and describe the relationships between these three variables. We think our results will be helpful to understand the nature of impulsivity involved in smartphone addiction. On the other hand, our data will be useful to suggests actions to minimize the problems related with smartphone usage in class.
Impulsivity, smartphone addiction, university, academic performance, network analysis, Bayesian networks.