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Factors Associated with the Use of E-Learning among Medical University Students; An Application of Technology Acceptance Model. 3 https://doi.org/10.58209/hehp.11.4.667
URL: http://daneshafarand.org/article-1-72130-en.html
URL: http://daneshafarand.org/article-1-72130-en.html
Abstract: (2265 Views)
Aims: Determining the effective factors in the use of e-learning among students can be useful in providing appropriate and practical solutions to increase the use of e-learning. Therefore, this study aimed to determine the factors related to the behavior of using e-learning among medical university students using the technology acceptance model (TAM) as a conceptual framework.
Instrument & Methods: This cross-sectional study was conducted among 425 students of Hamadan University of Medical Sciences who were selected by stratified random sampling. The data collection tools included a questionnaire, including demographic data and TAM constructs. The data were analyzed in SPSS 23 software using one-way analysis of variance, independent t-test, Pearson correlation coefficient, and linear regression analysis.
Findings: The age range of participants was between 18 and 54 years with an average age of 24.2±3.96 years. The history of participating in e-learning classes was significantly related to all the constructs of the TAM (p<0.05). Also, undergraduate and graduate students had a significantly more positive attitude, higher perceived usefulness, and more usage intention and behavior concerning e-learning than professional doctorate students (p<0.05). Attitude (β=0.394), perceived usefulness (β=0.313), and external variables (β=0.196) were respectively the strongest predictors of intention to use e-learning (p<0.05). The intention of e-learning usage directly and significantly predicted the behavior of using it (β=0.483, p<0.05).
Conclusion: The TAM constructs predict the behavior of using e-learning among students.
Instrument & Methods: This cross-sectional study was conducted among 425 students of Hamadan University of Medical Sciences who were selected by stratified random sampling. The data collection tools included a questionnaire, including demographic data and TAM constructs. The data were analyzed in SPSS 23 software using one-way analysis of variance, independent t-test, Pearson correlation coefficient, and linear regression analysis.
Findings: The age range of participants was between 18 and 54 years with an average age of 24.2±3.96 years. The history of participating in e-learning classes was significantly related to all the constructs of the TAM (p<0.05). Also, undergraduate and graduate students had a significantly more positive attitude, higher perceived usefulness, and more usage intention and behavior concerning e-learning than professional doctorate students (p<0.05). Attitude (β=0.394), perceived usefulness (β=0.313), and external variables (β=0.196) were respectively the strongest predictors of intention to use e-learning (p<0.05). The intention of e-learning usage directly and significantly predicted the behavior of using it (β=0.483, p<0.05).
Conclusion: The TAM constructs predict the behavior of using e-learning among students.
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