ارائه مدلی برای پذیرش سیستم آموزش الکترونیک در دانشگاه علوم‌پزشکی قزوین
محورهای موضوعی : مديريت دانش
حسن رشیدی
1
(دانشگاه علامه طباطبایی)
مرضیه موحدين
2
(دانشگاه آزاد اسلامی، واحد قزوین، )
چکیده مقاله :
با گسترش اینترنت و شیوع ویروس کرونا، محیط‌های یادگیری الکترونیکی و آموزش مبتنی بروب، رشد و تکامل زیادی در سال‌ها و ماه‌های اخیر تجربه کرده است، لیکن این نوع آموزش با چالش‌های زیادی مواجه است. مهم‌ترین این چالش‌ها به استفاده از الگوهاي یادگیری، مدل‌سازی رفتار دانشجویی، ارزیابی پشتیبانی و بازخورد دانشجو، برنامه‌ریزی درسی، تعیین توالی و پشتیبانی مدرسان، بر می‌گردد. یكی از راه‌های مقابله با این چالش‌ها، یافتن عوامل مؤثر در پذیرش و ارتقاء كیفیت آموزش در سیستم‌های یادگیری، كشف قوانین و الگوهای آموزشی و استفاده از آنها در پیشگویی نتایج آینده است. هدف از این تحقیق شناسایی و معرفی عوامل مؤثر در پذیرش آموزش الکترونیکی بر‌اساس مدل پذیرش فناوری است. بدین منظور با بررسی مطالعات صورت گرفته در این زمینه، متغیرهایی از جمله خودکارآمدی کامپیوتر، کیفیت محتوا، پشتیبانی از سیستم، طراحی رابط کاربری، ابزارهای فناوری و اضطراب کامپیوتر به‌عنوان عوامل مؤثر بر پذیرش سیستم آموزش الکترونیک، استخراج شدند و بر‌اساس آنها، مدل مفهومی تحقیق شکل گرفت. برای سنجش مدل و ارتباط‌های بین متغیرهای مدل، پرسشنامه‌ای در اختیار کاربران سیستم آموزش الکترونیکی دانشگاه علوم پزشکی قزوین قرار داده شد. نتایج تحلیل داده‌ها با استفاده از روش مدل معادلات ساختاری درستی تمام فرضیه‌‌ها، به جز تأثیر ابزارهای فناوری بر پذیرش سیستم آموزش الکترونیک را تأیید کرد. یافته‌های این تحقیق به مدیران آموزشی دانشگاه و همچنین اساتید مرتبط با این سیستم کمک می‌کند با ایجاد زمینه‌های لازم در‌خصوص اعمال فاکتورهای مؤثر، دانشجویان را به استفاده بهینه از سیستم ترغیب نمایند.
With the advent of the Internet and Coronavirus outbreak, e-learning environments and web-based learning, there has been a considerable growth and development in recent months and years, but this type of education faces many challenges. The most important of these challenges are the use of learning patterns, modeling student behavior, evaluating student support and feedback, curriculum planning, sequencing, and teacher support. One way to deal with these challenges is to find effective factors on accepting and improving the quality of education in learning systems, discovering rules and educational patterns, and using them to predict future outcomes. The aim of this study is to identify factors affecting the adoption of e-learning, based on the technology acceptance model. For this purpose, studies in this area were investigated. Variables such as computer self-efficacy, quality of content, system support, user interface design, technology tools and computer anxiety as factors affecting adoption of e-learning system were extracted. According to them, the conceptual model of this research was developed. In order to evaluate the model and relationships between variables, a questionnaire was provided to the users of e-learning system in Qazvin University of Medical Sciences. The results of data analysis by using structural equation modeling have approved authenticity of all hypotheses excluding the impact of technology tools on the acceptance of e-learning system. Findings of this research can help educational managers of university and related instructors with this system to encourage students making optimum use of the system by providing the necessary fields for the imposition of effective factors.
1- B. Ashokka, S. Y. Ong, K. H. Tay, N. H. W. Loh, C. F. Gee, and D. D. Samarasekera, "Coordinated responses of academic medical centres to pandemics: Sustaining medical education during COVID-19," Medical Teacher, 2020, pp. 1-10.
2- M. Mesiono, "Peer Review dan Hasil Turnitin E-Learning Management of State Islamic University of North Sumatera In Pandemic Covid-19", 2020.
3- A. Khorasani, J. Abdolmaleki, and H. Zahedi, "Factors Affecting E-Learning Acceptance among Students of Tehran University of Medical Sciences Based on Technology Acceptance Model (TAM)," Iranian Journal of Medical Education, vol. 11, 2012, pp. 664-673.
4- A. Farahi, M. gholipour, and A. Haghighat, "Adoption of e-learning in continuing education of physicians By using TAM model," presented at the 1st Congress of Information Technology in Health, Mazandaran University of Medical Sciences, Sari (In persian), 2011.
5- C.-S. Ong and J.-Y. Lai, "Gender differences in perceptions and relationships among dominants of e-learning acceptance," Computers in Human Behavior, vol. 22, 2006, pp. 816-829.
6- A. Hassanzadeh, D. Karimzadgan, and H. Motaghian, "Assessing the Factors Influencing University Instructors’ Adoption of Web-Based Learning Systems Using an Integrated Model," Journal of Management researches in Iran, 2013, vol. 17, pp 41-72.
7- A. Bandura, Social foundations of thought and action: A social cognitive theory: Prentice-Hall, Inc, 1986.
8- G. a. Tabarsa and A. H. Nazarpoori, "Considering Effective Factors on Electronic Learning System Acceptance (ELS) According to Technology Acceptance Model (TAM)," Journal of Education Technology(In persian), vol. 9, 2015, pp. 123-130.
9- M. Siadati and F. Taghiyareh, "E-learning: Alternative for traditional education or Its complement," presented at the Electronic learning Conference, Zanjan, 2006.
10- W.-H. Wu, Y.-C. J. Wu, C.-Y. Chen, H.-Y. Kao, C.-H. Lin, and S.-H. Huang, "Review of trends from mobile learning studies: A meta-analysis," Computers & Education, vol. 59, 2012, pp. 817-827.
11- H. M. Selim, "Critical success factors for e-learning acceptance: Confirmatory factor models," Computers & Education, vol. 49, 2007, pp. 396-413.
12- A. Hanif, F. Q. Jamal, and M. Imran, "Extending the Technology Acceptance Model for Use of e-Learning Systems by Digital Learners," IEEE Access, vol. 6, 2018, pp. 73395-73404.
13- M. Al-Emran, V. Mezhuyev, and A. Kamaludin, "Technology Acceptance Model in M-learning context: A systematic review," Computers & Education, vol. 125, 2018, pp. 389-412.
14- Y.-M. Cheng, "Effects of quality antecedents on e-learning acceptance," Internet Research, vol. 22, 2012, pp. 361-390.
15- T. Farahat, "Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities," Procedia-Social and Behavioral Sciences, vol. 64, 2012, pp. 95-104.
16- H.-R. Chen and H.-F. Tseng, "Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan," Evaluation and program planning, vol. 35, 2012, pp. 398-406.
17- R. Cheung and D. Vogel, "Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning," Computers & Education, vol. 63, 2013, pp. 160-175.
18- A. Y. Alsabawy, A. Cater-Steel, and J. Soar, "IT infrastructure services as a requirement for e-learning system success," Computers & Education, vol. 69, 2013, pp. 431-451.
19- J. C. Roca, C.-M. Chiu, and F. J. Martínez, "Understanding e-learning continuance intention: An extension of the Technology Acceptance Model," International Journal of human-computer studies, vol. 64, 2006, pp. 683-696.
20- Y.-M. Cheng, "Roles of interactivity and usage experience in e-learning acceptance: a longitudinal study," International Journal of Web Information Systems, vol. 10, 2014, pp. 2-23.
21- Á. F. Agudo-Peregrina, Á. Hernández-García, and F. J. Pascual-Miguel, "Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning," Computers in Human Behavior, vol. 34, 2014, pp. 301-314.
22- Y. J. Joo, H. W. Lee, and Y. Ham, "Integrating user interface and personal innovativeness into the TAM for mobile learning in Cyber University," Journal of Computing in Higher Education, vol. 26, 2014, pp. 143-158.
23- Y. Kowitlawakul, S. W. C. Chan, J. Pulcini, and W. Wang, "Factors influencing nursing students' acceptance of electronic health records for nursing education (EHRNE) software program," Nurse education today, vol. 35, 2015, pp. 189-194.
24- M. El-Masri and A. Tarhini, "Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)," Educational Technology Research and Development, vol. 65, 2017, pp. 743-763.
25- M. A. de Souza Rodrigues, P. Chimenti, and A. R. R. Nogueira, "An exploration of eLearning adoption in the educational ecosystem," Education and Information Technologies, 2020, pp. 1-31.
26- F. Kanwal, M. Rehman, and M. M. Asif, "E-Learning Adoption and Acceptance in Pakistan: Moderating Effect of Gender and Experience," Mehran University Research Journal of Engineering and Technology, vol. 39, 2020, pp. 324-341.
27- Y.-C. Lee, "An empirical investigation into factors influencing the adoption of an e-learning system," Online Information Review, vol. 30, 2006, pp. 517 - 541.
28- H. Motaghian, A. Hassanzadeh, and D. K. Moghadam, "Factors affecting university instructors' adoption of web-based learning systems: Case study of Iran," Computers & Education, vol. 61, 2013, pp. 158-167.
29- J.-W. Lee, "Online support service quality, online learning acceptance, and student satisfaction," The Internet and Higher Education, vol. 13, 2010, pp. 277-283.
30- V. Cho, T. E. Cheng, and W. J. Lai, "The role of perceived user-interface design in continued usage intention of self-paced e-learning tools," Computers & Education, vol. 53, 2009, pp. 216-227.
31- R. Hussein, U. Aditiawarman, and N. Mohamed, "E-Learning acceptance in a developing country: A case of the Indonesian Open University," in German e-Science conference, 2007.
32- C.-S. Ong, J.-Y. Lai, and Y.-S. Wang, "Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies," Information & management, vol. 41, 2004, pp. 795-804.
33- E. M. Van Raaij and J. J. Schepers, "The acceptance and use of a virtual learning environment in China," Computers & Education, vol. 50, 2008, pp. 838-852.
34- T. Teo, Ö. F. Ursavas, and E. Bahçekapili, "Efficiency of the technology acceptance model to explain pre-service teachers' intention to use technology: A Turkish study," Campus-Wide Information Systems, vol. 28, 2011, pp. 93 - 101.
35- S.-H. Liu, H.-L. Liao, and J. A. Pratt, "Impact of media richness and flow on e-learning technology acceptance," Computers & Education, vol. 52, 2009, pp. 599-607.
36- J. F. Hair Jr, M. Sarstedt, L. Hopkins, and V. G. Kuppelwieser, "Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research," European Business Review, vol. 26, 2014, pp. 106-121.
37- H. Hooman, "Detecting structural equation models with application software LISREL," Publisher side. 2nd ed. Tehran: Samt, 2008.
38- R. H. Hoyle, Handbook of structural equation modeling: Guilford Press, 2012.
39- J. L. Adelson, "Examining Relationships and Effects in Gifted Education Research An Introduction to Structural Equation Modeling," Gifted Child Quarterly, vol. 56, 2012, pp. 47-55.
40- P. A. Dion, "Interpreting structural equation modeling results: a reply to Martin and Cullen," Journal of Business Ethics, vol. 83, 2008, pp. 365-368.
41- W.-T. Wang and C.-C. Wang, "An empirical study of instructor adoption of web-based learning systems," Computers & Education, vol. 53, 2009, pp. 761-774.
42- H. M. S. Ahmed, "Hybrid E‐Learning Acceptance Model: Learner Perceptions," Decision Sciences Journal of Innovative Education, vol. 8, 2010, pp. 313-346.
43- I.-F. Liu, M. C. Chen, Y. S. Sun, D. Wible, and C.-H. Kuo, "Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community," Computers & education, vol. 54, 2010, pp. 600-610.
44- V. Venkatesh and H. Bala, "Technology acceptance model 3 and a research agenda on interventions," Decision sciences, vol. 39, 2008, pp. 273-315.
45- L. J. Cronbach, "Coefficient alpha and the internal structure of tests," Psychometrika, vol. 16, 1951, pp. 297-334.