فراترکیب عوامل موثر بر اثربخشی اجرای‌ سیستم‌ های آموزش الکترونیک در دانشگاه مازندران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس معاونت پژوهش و فنآوری اطلاعات دانشگاه مازندران

2 دکترای برنامه ریزی آموزشی، استاد گروه علوم تربیتی دانشگاه مازندران. بابلسر، ایران.

10.22080/shrm.2024.4833

چکیده

اخیراً توجه زیادی به آموزش الکترونیکی در نظام آموزشی شده است این نظام آموزشی از عواملی تشکیل شده است که تأثیر بسزایی در موفقیت فرایند آموزش الکترونیکی دارند و منجر به ارتقا یا کاهش کیفیت اجرایی‌سازی سیستم آموزش الکترونیکی می‌شود. هدف پژوهش حاضر ارائه دسته‌بندی جامعی از چالش‌های پیاده‌سازی حکمرانی مشارکتی در آموزش عالی بر اساس مطالعات این حوزه و تحلیل آن‌ها با بهره‌گیری از روش فراترکیب است. روش پژوهش حاضر کیفی با رویکرد اکتشافی است. از دیدگاه هدف، پژوهشی تبیینی - کاربردی است. در این روش با بهره‌گیری از روش فراترکیب و انتخاب حدود 150 مقاله و پژوهش مرتبط که اکثراً از سال 2010 تا 2022 است و از پایگاه‌های علمی معتبر مختلفی جمع اوری شده که نهایتاً با استفاده از این روش 100 مقاله منتخب شناسایی و عوامل اثربخش در بهبود اجرایی‌سازی سیستم آموزش الکترونیکی شناسایی شدند.که در نهایت محقق به 188 کد و 28 مقوله فرعی و 5 دسته اصـلی یا همان عوامل دست یافت. در نتیجه عوامل برنامه‌ریزی و تعیین چشم‌انداز کاربردی، عوامل سخت‌افزاری و نرم‌افزاری، عوامل محتوایی و یادگیری، عوامل حمایتی و عوامل تحلیل عملکردی و بازخورد مهم ترین عوامل در بهبود سیستم اموزش الکترونیکی جهت اجرایی سازی معرفی شدند.

کلیدواژه‌ها


عنوان مقاله [English]

The combination of effective factors on the effectiveness of e-learning systems in Mazandaran University

نویسندگان [English]

  • hossein zabetpour kourdi 1
  • Ebrahim Salehi Omran 2
1 employee of university of mazandaran
2 PhD in educational planning, professor of the Department of Educational Sciences, Mazandaran University. Babolsar, Iran.
چکیده [English]

Recently, a lot of attention has been paid to e-learning in the educational system. This educational system consists of factors that have a significant impact on the success of the e-learning process and lead to the improvement or reduction of the quality of the implementation of the e-learning system. The aim of the current research is to provide a comprehensive classification of the challenges of implementing participatory governance in higher education based on studies in this field and their analysis using the meta-composite method. The current research method is qualitative with an exploratory approach. From the objective point of view, it is an explanatory-applied research. In this method, by using the meta-combination method and selecting about 150 articles and related researches, which are mostly from 2010 to 2022 and collected from various reliable scientific databases, finally using this method, 100 selected articles were identified and effective factors in improving the implementation. The electronic education system was identified. Finally, the researcher found 188 codes, 28 subcategories and 5 main categories or the same factors. As a result, the factors of planning and determining the application perspective, hardware and software factors, content and learning factors, support factors and functional analysis factors and feedback were introduced as the most important factors in improving the electronic learning system for implementation.

کلیدواژه‌ها [English]

  • e-learning
  • e-learning implementation improvement
  • e-learning effectiveness
  • hybrid method
Aarabi, S., Boudlaie, H. (2011). Phenomenological Research Strategy. (in persian). Methodology of Social Sciences and Humanities, 17(68), 31-58.
Abdollahi, M. H., gholami torksaluye, S., & abbasian, M. (2022). Developing a model of effective factors in the effectiveness of virtual education in general physical education lessons in corona pandemic conditions. Research on Educational Sport9(25), 89-110. doi:  10.22089/res.2021.10469.2092
Ahmed Elzainy, A. E. (2020). Experience of e-learning and online assessment during the COVID-19 pandemic at the College of Medicine, Qassim University.  Journal of Taibah University Medical Sciences, 15(6), 456-462.
Alizadehsani, R., Khosravi, A., Roshanzamir, M., Abdar, M., Sarrafzadegan, N., Shafie, D., Khozeimeh, F., Shoeibi, A., Nahavandi, S., Panahiazar, M., Bishara, A., Beygui, R. E., Puri, R., Kapadia, S., Tan, R. S., & Acharya, U. R. (2021). Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991–2020. In Computers in Biology and Medicine (Vol. 128).  https://doi.org/10.1016/j.compbiomed.2020.104095
Alkinani, E. A. (2021). Acceptance and Effectiveness of Distance Learning in Public Education in Saudi Arabia During Covid19 Pandemic: Perspectives from Students, Teachers and Parents. IJCSNS International Journal of Computer Science and Network Security, 21(2).
Alqahtani, A. Y., & Rajkhan, A. A. (2020). E-learning critical success factors during the covid-19 pandemic: A comprehensive analysis of e-learning managerial perspectives. Education sciences10(9), 216.
Alqudah, N. M., Jammal, H. M., Saleh, O., Khader, Y., Obeidat, N., & Alqudah, J. (2020). Perception and experience of academic Jordanian ophthalmologists with E-Learning for undergraduate course during the COVID-19 pandemic. Annals of Medicine and Surgery, 59. https://doi.org/10.1016/j.amsu.2020.09.014
artino, F., Varricchio, S., Russo, D., Merolla, F., Ilardi, G., Mascolo, M., Dell’aversana, G. O., Califano, L., Toscano, G., De Pietro, G., Frucci, M., Brancati, N., Fraggetta, F., & Staibano, S. (2020). A machine-learning approach for the assessment of the proliferative compartment of solid tumors on hematoxylin-eosin-stained sections. Cancers, 12(5). https://doi.org/10.3390/cancers12051344
Asvial, M., Mayangsari, J., & Yudistriansyah, A. (2021). Behavioral Intention of e-Learning: A Case Study of Distance Learning at a Junior High School in Indonesia due to the COVID-19 Pandemic. International Journal of Technology, 12(1).
https://doi.org/10.14716/ijtech.v12i1.4281
Bennett, E. E., & McWhorter, R. R. (2021). Virtual HRD’s Role in Crisis and the Post Covid-19 Professional Lifeworld: Accelerating Skills for Digital Transformation. Advances in Developing Human Resources, 23(1). https://doi.org/10.1177/1523422320973288
Blaga, P., & Gabor, M. R. (2016). Evaluation of the e-learning program impact over organizations in the romanian pharmaceutical industry. Indian Journal of Pharmaceutical Education and Research, 50(4).
https://doi.org/10.5530/ijper.50.4.3
Bower, M. (2019). Technology ‐mediated learning theory. British Journal Education Tech- nology, 50, 1035–1048. 10.1111/bjet.12771.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77–101. https://doi.org/10.1191/1478088706qp063oa
Cooper, D. M., Afghani, B., Byington, C. L., Cunningham, C. K., Golub, S., Lu, K. D., Radom-Aizik, S., Ross, L. F., Singh, J., Smoyer, W. E., Lucas, C. T., Tunney, J., Zaldivar, F., & Ulloa, E. R. (2021). SARS-CoV-2 vaccine testing and trials in the pediatric population: biologic, ethical, research, and implementation challenges. Pediatric Research.
 https://doi.org/10.1038/s41390-021-01402-z
Dari, S., Nazeri, N. & Atashi, A. (2017). The Effective Factors on Success of E-learning in Medical Sciences Fields. Journal of Health and Biomedical Informatics.
Daramola, O., Oladipupo, O., Afolabi, I., & Olopade, A. (2017). Heuristic evaluation of an institutional E-learning system: A Nigerian case. International Journal of Emerging Technologies in Learning, 12(3).https://doi.org/10.3991/ijet.v12i03.6083
De Leeuw, R. A., Westerman, M., Walsh, K., & Scheele, F. (2019). Development of an instructional design evaluation survey for postgraduate medical E-learning: Content validation study. Journal of Medical Internet Research, 21(8). https://doi.org/10.2196/13921
Demir, O. (2015). The investigation of e-learning readiness of students and faculty members: Hacettepe unıversity faculty of education example. Master Thesis. Ankara: Hacettepe University.
Demirci, M. D. S., & Adan, A. (2020). Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection. PeerJ, 2020(6). https://doi.org/10.7717/peerj.9369
Denzink, Norman & yvonas Lincoln (1998). Strategies of qualitative inquiry. thousand oaks California, sage.
Ding, J., Yu, G., He, X., Feng, F., Li, Y., & Jin, D. (2021). Sampler Design for Bayesian Personalized Ranking by Leveraging View Data. IEEE Transactions on Knowledge and Data Engineering, 33(2).
https://doi.org/10.1109/TKDE.2019.2931327
Di Pietro, G., & Karpiński, Z. (2021). COVID-19 and online adult learning. Publications Office of the European Union.
Dzyabura, D., & Peres, R. (2021). Visual Elicitation of Brand Perception. Journal of Marketing, 85(4). https://doi.org/10.1177/0022242921996661
Encarnacion, R. F. E., Galang, A. A. D., & Hallar, B. J. A. (2021). The impact and effectiveness of e-learning on teaching and learning. Online Submission5(1), 383-397.
isna.ir/xdJ6h8
Mahmoud, E. A. (2021). The effect of e-Learning practices during the Covid-19 pandemic on enhancing self-regulated learning skills as perceived by university students. Amazonia investiga10(39), 129-135.
Martin, F., Wang, C., & Sadaf, A. (2018). Student perception of helpfulness of facilitation strategies that enhance instructor presence, connectedness, engagement and learning in online courses. The Internet and Higher Education37, 52-65.
Mohd Satar, N. S., Morshidi, A. H., & Dastane, D. O. (2020). Success factors for e-Learning satisfaction during COVID-19 pandemic lockdown. International Journal of Advanced Trends in Computer Science and Engineering, ISSN, 2278-3091.
Naveed, Q. N., Qureshi, M. R. N., Tairan, N., Mohammad, A., Shaikh, A., Alsayed, A. O., ... & Alotaibi, F. M. (2020). Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making. Plos one15(5), e0231465.
Nariman, D. (2021). Impact of the interactive e-learning instructions on effectiveness of a programming course. In Complex, Intelligent and Software Intensive Systems: Proceedings of the 14th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS-2020) (pp. 588-597). Springer International Publishing.
Gao, H., Ou, Y., Zhang, Z., Ni, M., Zhou, X., & Liao, L. (2021). The Relationship Between Family Support and e-Learning Engagement in College Students: The Mediating Role of e-Learning Normative Consciousness and Behaviors and Self-Efficacy. Frontiers in Psychology, 12.
https://doi.org/10.3389/fpsyg.2021.573779
Garad, A., Al-Ansi, A. M., & Qamari, I. N. (2021). The role of e-learning infrastructure and cognitive competence in distance learning effectiveness during the covid-19 pandemic. Cakrawala Pendidikan, 40(1). https://doi.org/10.21831/cp.v40i1.33474
Ghalyan, S., & Zalpour, A. (2019). Identifying Factors of Success in E-Learning Case Study: Physical Education Students at Shahid Chamran University of Ahvaz. Educational Development of Judishapur10(2), 135-143. doi: 10.22118/edc.2019.90842
Gonzalez, T., de la Rubia, M., Hincz, K., Lopez, M.C., .Subirats, L., Fort, S. et al. (2020, 20). Influence of COVID-19 confinement in students’ performance in higher education
https://doi.org/ 10.35542/osf.io/9zu
Gope, P., Gheraibia, Y., Kabir, S., & Sikdar, B. (2021). A Secure IoT-Based Modern Healthcare System with Fault-Tolerant Decision-Making Process. IEEE Journal of Biomedical and Health Informatics, 25(3).https://doi.org/10.1109/JBHI.2020.3007488
Hall, A. N., & Matz, S. C. (2020). Targeting Item-level Nuances Leads to Small but Robust Improvements in Personality Prediction from Digital Footprints. European Journal of Personality, 34(5).
https://doi.org/10.1002/per.2253
Hao, Y., & Borich, G. (2009). A practical guide to evaluate quality of online courses. In Handbook of Research on Human Performance and Instructional Technology.
https://doi.org/10.4018/978-1-60566-782-9.ch020
Aguilera-Hermida, A. P. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International journal of educational research open1, 100011.
Hibbi, F. Z., Abdoun, O., & Khatir, H. El. (2020). Coronavirus pandemic in Morocco: Measuring the impact of containment and improving the learning process in higher education. International Journal of Information and Education Technology, 11(1).https://doi.org/10.18178/ijiet.2021.11.1.1485
Hong, D., Gao, L., Yokoya, N., Yao, J., Chanussot, J., Du, Q., & Zhang, B. (2021). More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification. IEEE Transactions on Geoscience and Remote Sensing, 59(5). https://doi.org/10.1109/TGRS.2020.3016820
Hoq, M. Z. (2020). E-Learning During the Period of Pandemic (COVID-19) in the Kingdom of Saudi Arabia: An Empirical Study. American Journal of Educational Research, 8(7).
Hudaifah, F. (2020). Peran self-regulated learning di era pandemi covid-19. Jurnal Ilmiah Fakultas Keguruan Dan Ilmu Pendidikan, 6(2).
Humeniuk, I., Kuntso, O., Popel, N., & Voloshchuk, Y. (2021). MASTERING LISTENING COMPREHENSION AT ESP CLASSES USING TED TALKS. Advanced Education, 8(18). https://doi.org/10.20535/24108286.226733
Imran, S. M., & Malik, B. A. (2017). Evaluation of e-learning web-portals. DESIDOC Journal of Library and Information Technology, 37(3).  https://doi.org/10.14429/djlit.37.3.10961
Jain, G., Sharma, N., & Shrivastava, A. (2021). Enhancing training effectiveness for organizations through blockchain-enabled training effectiveness measurement (BETEM). Journal of Organizational Change Management, 34(2). https://doi.org/10.1108/JOCM-10-2020-0303
Kagola, O., & Khau, M. (2020). Using collages to change school governing body perceptions of male foundation phase teachers. Educational Research for Social Change, 9(2). https://doi.org/10.17159/2221-4070/2020/v9i2a5
Karatas¸ , S. (2005). Comparisons of internet-based and face-to-face learning systems based on 'equivalency of experiences' according to students' academic achievements and satisfactions. Doctoral Dissertation. Ankara: Ankara University
Kelly, R. F., Mihm-Carmichael, M., & Hammond, J. A. (2021). Students’ engagement in and perceptions of blended learning in a clinical module in a veterinary degree program. Journal of Veterinary Medical Education, 48(2).
https://doi.org/10.3138/jvme.2019-0018
Koh, J. H. L., & Kan, R. Y. P. (2021). Students’ use of learning management systems and desired e-learning experiences: are they ready for next generation digital learning environments? Higher Education Research and Development, 40(5). https://doi.org/10.1080/07294360.2020.1799949
Krefting, L. (1991). Rigor in qualitative research: The assessment of trustworthiness. American Journal of Occupational Therapy,45(3),214-222.
Lee, C. M. (2021). Descriptive Swot Analysis About Online Learning Abstract: Introduction: Literature Review: April, 1–10.
Leelavathy, S., & Nithya, M. (2021). Public opinion mining using natural language processing technique for improvisation towards smart city. International Journal of Speech Technology, 24(3). https://doi.org/10.1007/s10772-020-09766-z
Luu, N. N., Yver, C. M., Douglas, J. E., Tasche, Hoq, M. Z. (2020). K. K., Thakkar, P. G., & Rajasekaran, K. (2021). Assessment of YouTube as an Educational Tool in Teaching Key Indicator Cases in Otolaryngology During the COVID-19 Pandemic and Beyond: Neck Dissection. Journal of Surgical Education, 78(1).
https://doi.org/10.1016/j.jsurg.2020.06.019
Luo, Y., Lin, J., & Yang, Y. (2021). Students’ motivation and continued intention with online self-regulated learning: A self-determination theory perspective. Zeitschrift für Erziehungswissenschaft24(6), 1379-1399.
Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. In Education and Information Technologies. https://doi.org/10.1007/s10639-021-10557-5
Maldonado-Mahauad, J., Pérez-Sanagustín, M., Kizilcec, R. F., Morales, N., & Munoz-Gama, J. (2018). Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses. Computers in Human Behavior, 80. https://doi.org/10.1016/j.chb.2017.11.011
Masterson, S., Heffernan, E., Keegan, D., Clarke, B., Deasy, C., O’Donnell, C., Crowley, P., Breen, R., Kelly, M. E., & Murphy, A. W. (2021). Rapid response and learning for later: establishing high quality information networks and evaluation frameworks for the National Ambulance Service response to COVID-19 – the ENCORE COVID Project Protocol. HRB Open Research, 3.  https://doi.org/10.12688/hrbopenres.13149.2
Moerer, Tammy & John Creswell (2004). “using transcendental henomenology to explore the ripple effect in a leadership mentoring program”. International journal of qualitative methods, 3 (2).
Mohammadi Chemardani, H., & Rahmani, M. (2019). Identifying effective factors in the success of electronic training courses (mixed research). Journal of Educational Scinces26(1), 137-154. doi: 10.22055/edus.2019.27633.2677
Moustakas, C.E. (1994). Phenomenological research methods. Thousand oaks, CA, sage pub
Nazeri N, Dorri S, Atashi A. (2017). The Effective Factors on Success of E-learning in Medical Sciences Fields. jhbmi; 4 (2):98-
Nurhayati, D., Az-zahra, H. M., & Herlambang, A. D. (2019). Evaluasi User Experience Pada Edmodo Dan Google Classroom Menggunakan Technique for User Experience Evaluation in E-Learning (TUXEL) (Studi Pada SMKN 5 Malang). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(4).
Overmyer, K. A., Shishkova, E., Miller, I. J., Balnis, J., Bernstein, M. N., Peters-Clarke, T. M., Meyer, J. G., Quan, Q., Muehlbauer, L. K., Trujillo, E. A., He, Y., Chopra, A., Chieng, H. C., Tiwari, A., Judson, M. A., Paulson, B., Brademan, D. R., Zhu, Y., Serrano, L. R., … Jaitovich, A. (2021). Large-Scale Multi-omic Analysis of COVID-19 Severity. Cell Systems, 12(1). https://doi.org/10.1016/j.cels.2020.10.003
Pennell, N. A., Dillmon, M., Levit, L. A., Allyn Moushey, E., Alva, A. S., Blau, S., Cannon, T. L., Dickson, N. R., Diehn, M., Gonen, M., Gonzalez, M. M., Hensold, J. O., Hinyard, L. J., King, T., Lindsey, S. C., Magnuson, A., Marron, J., McAneny, B. L., McDonnell, T. M., … Burris, H. A. (2021). American society of clinical oncology road to recovery report: Learning from the covid-19 experience to improve clinical research and cancer care. Journal of Clinical Oncology, 39(2).
https://doi.org/10.1200/JCO.20.02953
Pérez-Sanagustín, M., Sapunar-Opazo, D., Pérez-Álvarez, R., Hilliger, I., Bey, A., Maldonado-Mahauad, J., & Baier, J. (2021). A MOOC-based flipped experience: Scaffolding SRL strategies improves learners’ time management and engagement. Computer Applications in Engineering Education, 29(4).  https://doi.org/10.1002/cae.22337
Pinilla, S., Cantisani, A., Klöppel, S., Strik, W., Nissen, C., & Huwendiek, S. (2021). Curriculum development with the implementation of an open-source learning management system for training early clinical students: An educational design research study. Advances in Medical Education and Practice, 12.  https://doi.org/10.2147/AMEP.S284974
Prihastiwi, W. J., Prastuti, E., & Eva, N. (2021). E-Learning Readiness and Learning Engagement during the Covid-19 Pandemic. KnE Social Sciences. https://doi.org/10.18502/kss.v4i15.8212
Regmi, K., & Jones, L. (2020). A systematic review of the factors–enablers and barriers–affecting e-learning in health sciences education. BMC medical education20, 1-18.
Reynisson, B., Alvarez, B., Paul, S., Peters, B., & Nielsen, M. (2021). NetMHCpan-4.1 and NetMHCIIpan-4.0: Improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Research, 48(W1). https://doi.org/10.1093/NAR/GKAA379
Rivers, D. J. (2021). The role of personality traits and online academic self-efficacy in acceptance, actual use and achievement in Moodle. Education and Information Technologies26(4), 4353-4378.
Sarid, M., Peled, Y., & Vaknin-Nusbaum, V. (2021a). The relationship between second language college students’ perceptions of online feedback on draft-writing and academic procrastination. Reading and Writing, 34(5). https://doi.org/10.1007/s11145-020-10111-8
Shi, J., Miskin, N., Dabiri, B. E., DeSimone, A. K., Schaefer, P. M., Matalon, S. A., Uyeda, J. W., Guenette, J. P., & Gaviola, G. C. (2021). Beyond business as usual: Radiology residency educational response to the COVID-2019 pandemic. In Clinical Imaging (Vol. 69). https://doi.org/10.1016/j.clinimag.2020.10.010
Smajic, H., & Duspara, T. (2021). Education 4.0: An Remote Approach for Training of Intelligent Automation and Robotic During COVID19. TH Wildau Engineering and Natural Sciences Proceedings, 1.
https://doi.org/10.52825/thwildauensp.v1i.21
Swords, C., Bergman, L., Wilson-Jeffers, R., Randall, D., Morris, L. L., Brenner, M. J., & Arora, A. (2021). Multidisciplinary Tracheostomy Quality Improvement in the COVID-19 Pandemic: Building a Global Learning Community. Annals of Otology, Rhinology and Laryngology, 130(3). https://doi.org/10.1177/0003489420941542
Tang, K. Y., Chang, C. Y., & Hwang, G. J. (2021). Trends in artificial intelligence-supported e-learning: a systematic review and co-citation network analysis (1998–2019). In Interactive Learning Environments https://doi.org/10.1080/10494820.2021.1875001
Tsekea, S., & Chigwada, J. P. (2021). COVID-19: strategies for positioning the university library in support of e-learning. Digital Library Perspectives, 37(1). https://doi.org/10.1108/DLP-06-2020-0058
Ulrich, F., Helms, N. H., Frandsen, U. P., & Rafn, A. V. (2021). Learning effectiveness of 360° video: experiences from a controlled experiment in healthcare education. Interactive Learning Environments, 29(1).https://doi.org/10.1080/10494820.2019.1579234
Valtonen, T., Kukkonen, J., Dillon, P., & Vaisanen, P. (2009). Finnish high school students' readiness to adopt online learning: Questioning the assumptions. Computers & Education, 53(3), 742e748.
Vahedi M. )2020). The Effect of E-Learning Readiness on Self-Regulated Learning Strategies and Students’ Behavioral Tendency to Web-based Learning: The Mediating Role of Motivational Beliefs. Education Strategy Medical Science, 13(2) :133-142.
Wang, C. H. , Shannon, D. M. , & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34 (3), 302–323.
Wang, Y. H. (2021). Exploring the effectiveness of adopting anchor-based game learning materials to support flipped classroom activities for senior high school students. Interactive Learning Environments, 29(3). https://doi.org/10.1080/10494820.2019.1579238
Watermeyer, R., Crick, T., Knight, C., & Goodall, J. (2021). COVID-19 and digital disruption in UK universities: afflictions and affordances of emergency online migration. Higher Education, 81(3).
https://doi.org/10.1007/s10734-020-00561-y
Wilde, N., & Hsu, A. (2019). The influence of general self-efficacy on the interpretation of vicarious experience information within online learning. International Journal of Educational Technology in Higher Education, 16 (1), 1–20.
Wu, W., & Plakhtii, A. (2021). E-Learning Based on Cloud Computing. International Journal of Emerging Technologies in Learning, 16(10). https://doi.org/10.3991/ijet.v16i10.18579
Yan, C., Gong, B., Wei, Y., & Gao, Y. (2021). Deep Multi-View Enhancement Hashing for Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(4). https://doi.org/10.1109/TPAMI.2020.2975798
Yang, H. S., Hou, Y., Zhang, H., Chadburn, A., Westblade, L. F., Fedeli, R., Steel, P. A. D., Racine-Brzostek, S. E., Velu, P., Sepulveda, J. L., Satlin, M. J., Cushing, M. M., Kaushal, R., Zhao, Z., & Wang, F. (2021). Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile. Health Data Science, 2021. https://doi.org/10.34133/2021/7574903
Yue, Z., Gao, F., Xiong, Q., Wang, J., Huang, T., Yang, E., & Zhou, H. (2021). A Novel Semi-Supervised Convolutional Neural Network Method for Synthetic Aperture Radar Image Recognition. Cognitive Computation, 13(4).
 https://doi.org/10.1007/s12559-019-09639-x
Zaharias, P., & Koutsabasis, P. (2011). Heuristic evaluation of e-learning courses: A comparative analysis of two e-learning heuristic sets. In Campus-Wide Information Systems (Vol. 29, Issue 1). https://doi.org/10.1108/10650741211192046
Zareisaroukolaei, M., Shams, G., Rezaeizadeh, M., & ghahremani, M. (2020). Determinants of e-learning effectiveness: A qualitative study on the instructor. Research in Teaching8(2), 79-55. doi:
https://doi.org/10.34785/J012.2020.124
zhang, W., & Cheng, Y. L. (2012). Quality assurance in e-learning: PDPP evaluation model and its application. International Review of Research in Open and Distance Learning, 13(3). https://doi.org/10.19173/irodl.v13i3.1181
Zhou, Z., Wang, Z., Yu, H., Liao, H., Mumtaz, S., Oliveira, L., & Frascolla, V. (2021). Learning-Based URLLC-Aware Task Offloading for Internet of Health Things. IEEE Journal on Selected Areas in Communications, 39(2). https://doi.org/10.1109/JSAC.2020.3020680
Sandelowski, M., & Barroso, J. (2003). Toward a metasynthesis of qualitative findings on motherhood in HIV-positive women. Research in Nursing & Health, 26(2),    153-170.
Sandelowski, M., & Barroso, J. (2007). Handbook for synthesizing qualitative research. Springer Publishing Company
Sohrabi, B., khalili Jafarabad., Roodi, (2017). Discover the Properties of Emerging Research Areas Using Meta-Synthesis Method. Journal of Science and Technology Policy, 10(4), 15-30.
Stewart, David W; & Kamins, Michael A. (1993). Secondary research: Information sources and methods (Vol. 4). Sage.