Date Presented 4/1/2017
This study explores smartphone use in undergraduate students’ self-directed learning. The factors were identified, providing an understanding of how undergraduate students could be more motivated and effective in their short-term, self-directed learning under the influence of smartphone use.
Primary Author and Speaker: Ya-chun Chang
Additional Authors and Speakers: Kuan-Chen Chen, Wen Ting Chen, Ya-syuan Cai
Contributing Authors: Chang-Chih Robin Kuo
PURPOSE: Nowadays, most undergraduate students are surrounded by various gadgets much of the time, and using mobile devices is an important ingredient in their occupational participation. Among the mobile devices, smartphones are used most frequently in daily living. Learning is one of the crucial occupations for undergraduate students. However, self-directed learning is not the same as learning acquired through practical application of the knowledge acquired in class. Self-directed learning makes people acquire additional abilities that are not available from classroom learning. Although the advantages and disadvantages of mobile device, including smartphone, use in learning have been researched, a scarcity of studies on self-directed learning was observed. This study aims to explore the pivotal role smartphone use may play in self-directed learning.
DESIGN AND METHOD: The study entailed qualitative research using a semistructured interview. Each undergraduate student (N = 8) was interviewed multiple times until data saturation was achieved. The interviews took place during the spring semester of 2016. The informants were currently registered in undergraduate programs at two universities located in the southern Taiwanese city of Kaohsiung. Interviews were then transcribed, coded, and analyzed. Strategies to ensure dependability and reduce threats to reliability were executed during data analysis.
RESULTS: The interview results indicated variable facets that explained the convenience of smartphone use in self-directed learning. Eight domains of explanations were identified: (1) changing the mood and facilitating motivation for self-directed learning, (2) establishing the learning plan, (3) providing instant learning resources, (4) storing massive amounts of data, (5) making use of the interstices of time, (6) creating a proper self-directed learning context, (7) using the smartphone in various physical postures, and (8) user-friendly devices and diverse applications. At the same time, this study revealed three drawbacks of using a smartphone: (1) decreased efficiency of learning, (2) overreliance on the smartphone, and (3) nonsustainable and nonsystematic learning.
CONCLUSION: Among the identified domains, several are original findings after comparing them to those of previous studies on the influence of smartphone use in learning. Being able to be used in various physical postures, such as lying in bed or sitting on the toilet, had never been noted as a positive influence. Moreover, while previous studies discovered that the convenience of using mobile devices anytime and anywhere improved learning efficacy, our findings suggest that smartphone use helped undergraduate students actively make use of the interstices of time and create a proper self-directed learning context. The eight domains of convenient inducement and three drawbacks provide an in-depth understanding of how undergraduate students could be more motivated and effective in their short-term self-directed learning under the influence of smartphone use.
References
Candy, P. C. (1991). Self-direction for lifelong learning: A comprehensive guide to theory and practice. San Francisco: Jossey-Bass.
Chan, N. N., Walker, C., & Gleaves, A. (2014). An exploration of students’ lived experiences of using smartphones in diverse learning contexts using a hermeneutic phenomenological approach. Computers and Education, 82, 96–106. https://doi.org/10.1016/j.compedu.2014.11.001
Rashid, T., & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement and academic performance: Examining the interrelations. Computers in Human Behavior, 63, 604–612. https://doi.org/10.1016/j.chb.2016.05.084