Factors Influencing the Online Learning Behaviors of Nursing Students: The Lower Northern Region
Main Article Content
Abstract
The purpose of this predictive correlation study were to: 1) examine the level of online learning behaviors among nursing students, and 2) investigate the predictive factors of the online learning behaviors among nursing students. Sample were 170 nursing students by stratified random sampling from the lower northern region, Thailand. Data were collected by using online questionnaire, the online learning behaviors and the factors influencing online learning behavior questionnaire include seven aspects; Observe learning, Self-efficacy, Self-regulation, Achievement motive, Intention, Environment
and Social support. The questionnaire was reviewed by five experts. The content validity index were .93, .94, .95, .90, .95, .96, .98 and .98 respectively. Cronbach's alpha coefficient reliability were .88, .97, .96, .95, .95, .97, .92, .93 and .84 respectively. Data were analyzed using descriptive statistic such as frequency, percentage, mean, standard deviation, and Stepwise multiple regression analysis was performed as inferential statistic. The results were as follows:
1) The online learning behaviors of nursing students was at the highest level ( = 4.17, SD =.35)
2) The factors influencing the online learning behaviors of nursing students of the lower northern region were self-regulation, observe learning and achievement motive. Those factors can predict 63.60 % of variance of the online learning behaviors with statistical significance (p<.01)
Article Details
Journal of Nursing and Health Science Research attribution-non-commercial 4.0 international (CC BY-NC 4.0). For more detail please visit https://creativecommons.org/licenses/by-nc/4.0/ . The ideas and opinions expressed in the Journal of Nursing and Health Science Research are those of the authors and not necessarily those of the editor .
References
Best, J. W. (1981). Research in education. (4th edition). Englewood Cliffs, New Jersey: Prentice Hall. Inc.
Boocha, P., Intarakamhang, U. & Tansuwannond, J. (2018). The causal relationship model of psychosocial factors affecting e-learning behavior of undergraduate student. Journal of Behavioral Science, 24(1), 83-101. (in Thai).
Boocha, P. (2018). Causal relationship model to develop an effective of psychosocial characteristics promotion program on the learning behavior of undergraduate students. (phd’s thesis), Srinakharinwirot University. (in Thai).
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175-191.
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160.
Iamsupasit, S. (2007). Theory and techniques for behavior modification. (6th edition). Bangkok: Chulalongkorn University Press. (in Thai).
Mansukpol, W., & Jinangsuka, P. (2015). A Study of behavior and demands for using e-learning of graduate students Faculty of Education, Silpakorn University. Veridian E-Journal, Slipakorn University, 8(3), 347 – 358. (in Thai).
Ministry of Education Thailand. (2020). Notification of the Ministry of Education on the closure of Educational Institutions Affiliated with and under the Supervision of the Ministry of Education. Retrieved (2020, Mach 17) from https://www.moe.go.th/ให้สถานศึกษาในสังกัดแล. (in Thai).
Polit, D. F. & Hungler. (1999). Nursing research principles and methods (7th edition). Philadelphia: Lippincott Williams & Wilkins.
Thammametha, T. (2014). E-Learning: from theory to practice. Nonthaburi: Sahamit Pringting and Company Publishing Co., Ltd. (in Thai).
Thongmeekhaun, T., Saetiaw, S., Juntaveemuang, V., Kitrungrote, T., & Paenkaew, J. (2020).
Online social network using behavior among nursing students of Boromarajonani college of Nursing, Songkhla. Journal of Nursing, Siam University, 21(41). 67-77. (in Thai).