A Model for Medical Students' Behavioral Intention to Use Mobile Learning


Kucuk S., Baydas Onlu O., Kapakin S.

JOURNAL OF MEDICAL EDUCATION AND CURRICULAR DEVELOPMENT, cilt.7, 2020 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 7
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1177/2382120520973222
  • Dergi Adı: JOURNAL OF MEDICAL EDUCATION AND CURRICULAR DEVELOPMENT
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI)
  • Anahtar Kelimeler: Mobile learning, medical students, technology acceptance, structural equation modeling, TECHNOLOGY ACCEPTANCE MODEL, EDUCATION
  • Atatürk Üniversitesi Adresli: Evet

Özet

The use of mobile devices that have high technical capabilities has increased in the last years. These devices are appropriate instructional tools reflecting the trends in modern education by providing instant access to information that is used with mobile learning purposes. As is in many areas of education, m-learning has been becoming widespread in medical education. Therefore, medical students' readiness for m-learning is highly important. This study aims to investigate how medical students' beliefs influence their behavioral intention to use mobile devices for learning purposes. The 376 medical students (222 juniors, 154 sophomores; aged between 18 and 24 years; 214 males, 162 females) participated in this study. All participants had mobile devices. Data were collected through a survey. Structural equation modeling was used to analyze the findings. The proposed model, which is created based on the theory of planned behavior, was tested in the study. Based on the findings, the medical students' perceived ease of use, perceived usefulness, learning autonomy, intention to use, perceived self-efficacy toward mobile devices, and m-learning are found to be high level. However, according to medical students, instructors' readiness to apply m-learning has been found to be low level. The findings showed that the proposed model explains medical students' behavioral intention to use m-learning reasonably well. The behavioral intention is explained with a variance of 76% in the model. Subjective norm is the main indicator of behavioral intention, followed by perceived behavioral control and attitude. The proposed model in the study could be useful to design m-learning applications, environments, and implementation plans effectively in medical education.