Using smart mobile devices in social-network-based health education practice: A learning behavior analysis

Wu, Ting‐Ting

doi:10.1016/j.nedt.2014.01.013

Cited by 46 publications

(44 citation statements)

References 25 publications

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“…Social networks can improve interaction among peers and between educators and students (Wu 2014). Research in the context of education have shown that social networks have a strong impact on trust (Rienties and Kinchin 2014), problem-based learning and collaborative work (Liccardi et al 2007), teacher involvement (Daly et al 2010), and engagement in discussions (Vercellone-Smith et al 2012).…”

Section: Social Network and Social Network Analysis In Educationmentioning

confidence: 99%

Analysing the correlation between social network analysis measures and performance of students in social network-based engineering education

Putnik

Costa

Alves

et al. 2015

Int J Technol Des Educ

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Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment, considering two different dimensions: (1) to organize the education process as a social network-based process; and (2) to analyze the students' interactions in the context of evaluation of the students learning performance. The objective of this paper is to present a new model for students evaluation based on their behavior during the course and its validation in comparison with the traditional model of students' evaluation. The validation of the new evaluation model is made through an analysis of the correlation between social network analysis measures (degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and average tie strength) and the grades obtained by students (grades for quality of work, grades for volume of work, grades for diversity of work, and final grades) in a social network-based engineering education. The main finding is that the obtained correlation results can be used to make the process of the students' performance evaluation based on students interactions (behavior) analysis, to make the evaluation partially automatic, increasing the objectivity and productivity of teachers and allowing a more scalable process of evaluation. The results also contribute to the behavioural theory of learning performance evaluation. More specific findings related to the correlation analysis are: (1) the more different interactions a student had (degree centrality) and the more frequently the student was between the interaction paths of other students (betweenness centrality), the better was the quality of the work; (2) all five social network measures had a positive and strong correlation with the grade for volume of work and with the final grades; and (3) a student with high average tie strength had a higher grade for diversity of work than those with low ties.

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Section: Social Network and Social Network Analysis In Educationmentioning

confidence: 99%

Analysing the correlation between social network analysis measures and performance of students in social network-based engineering education

Putnik

Costa

Alves

et al. 2015

Int J Technol Des Educ

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“…Furthermore, using the highly interactive knowledge sharing features of a virtual community can increase the student's learning motivation (Wasko and Faraj, 2005). According to a study by Wu et al (2014), the diverse resources provided in virtual communities promoted critical thinking, problem analysis, and problem-solving skills of the researched nursing students. With regard to our survey, more than one-third of the students (36.3%) spent two to four hours per day online with their mobile devices.…”

Section: Discussionmentioning

confidence: 99%

The Use of Mobile Devices and Online Services by German Veterinary Students

Kasch

Haimerl

Arlt

et al. 2016

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Objective: The research question of our study was to determine the adoption and use of mobile devices and online services by veterinary students.Background: New technologies support learning, communication and information transfer during the preprofessional years and the professional lifetime. Hence, mobile devices may also be a feasible tool for information access for the practitioner at any place and any time, and consequently may support better informed clinical decision making.

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“…For the purposes of this study, mobile learning in placements was problematic, but students did appreciate being able to load materials on to their devices rather than having to carry large textbooks around. This was also found to be an advantage by Wu (2014). Participants also commented on the usefulness of being able to use the devices to incorporate learning into their daily routines, such as when travelling to placements.…”

Section: Use Of Devices In Clinical Placementsmentioning

confidence: 96%

“…Though much of the literature focusses on mobile technology as an "add on", there is evidence that this is changing (Cochrane and Bateman 2010). Wu (2014) notes the potential for developing diverse learning methods (e.g. sharing information, online discussion) through the use of mobile learning, demonstrating the advantages of interactivity, speed and enjoyment to nursing students learning with mobile devices.…”

Section: Introductionmentioning

confidence: 99%

Leave Them to Their Own Devices: Healthcare Students' Experiences of Using a Range of Mobile Devices for Learning.

Attenborough

Abbott

2018

ij-sotl

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Given recent advances in mobile technologies, there has been a shift from e-learning to mobile learning in UK universities, yet there are few data about how students select and use mobile devices, and whether e-learning systems and materials are fully compatible.Healthcare students have placements in clinical practice where they use mobile devices to access university course information and learning materials.This study investigated student use of devices in the School of Health Sciences, City University London. Eight final-year students from different disciplines participated: they were issued with a device and reported on how they used it via a personal blog, interviews and focus groups.Thematic analysis was applied to the data. Two groups of themes were identified: factors relating to the devices themselves (strengths and weaknesses; learning to use them; enjoying them) and to the course (university work; in clinical placement).Students had similar observations and experiences of the different devices, both at the university and in clinical practice, and this paper reports on their perceptions. The importance of Wi-Fi access, suitable learning spaces and permissions to use devices were important findings, as was evidence of the enjoyment students found in using the devices.

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Using smart mobile devices in social-network-based health education practice: A learning behavior analysis

Cited by 46 publications

References 25 publications

Analysing the correlation between social network analysis measures and performance of students in social network-based engineering education

Analysing the correlation between social network analysis measures and performance of students in social network-based engineering education

The Use of Mobile Devices and Online Services by German Veterinary Students

Leave Them to Their Own Devices: Healthcare Students' Experiences of Using a Range of Mobile Devices for Learning.

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