Medical Education During the COVID-19 Pandemic: Habits, Computer Skills, Internet Use Disorder, and Success

Year 2023, Volume: 13 Issue: 2, 129 - 138, 25.08.2023

Elif Kervancıoğlu Demirci Gülnaz Kervancıoğlu Salih Çayır Eren Kervancıoğlu Ertan Sarıdoğan

Abstract

Aim: COVID-19 pandemic had a significant impact on the way medical education is delivered. In most countries education was provided remotely. In this study, we aimed to study whether social and online habits, computer readiness, online communication skills, and Internet Use Disorder (IUD) were significant characteristics affecting the preferences of medical students for online or traditional classroom teaching in histology and embryology.
Material and Method: In 2019-2020, the fall semester for Turkish universities was face-to-face. Due to the COVID-19 pandemic, the spring semester of that academic year was online. We asked 1,144 public and private university students in Istanbul about demographic characteristics and their preferences in learning and general living, using two subscales of the Online Learning Readiness Scale (OLRS) and the Young Internet Addiction Scale. We compared online and face-to-face success levels using quiz results.
Results: Students generally preferred traditional lectures. However, those with higher OLRS scores preferred online lectures and learning materials and were more adapted to online living. Internet use disorder did not correlate with online lecture preferences. The results from private and public universities were not considerably different. The success levels after online lectures were better than after face-to-face lectures, which suggests that the preferences and satisfaction of students are different from their learning achievement.
Conclusion: Preferences of students should be the priority in planning medical education. They want online lectures available as complementary to face-to-face lectures. Therefore, instructors should improve their online communication and teaching skills to enhance students’ online lecture satisfaction. The IUD does not possess a risk in this condition. Contrarily, regularly planned complementary online courses will enhance students online learning habits, online readiness and achievement and enables a more effective continuation of education in cases of natural disasters and pandemics.

Keywords

distance education, histology, internet addiction disorder, online education

References

• 1. Henderson M, Selwyn N, Aston R. What works and why? Student perceptions of ‘useful’ digital technology in university teaching and learning. Stud High Educ. 2017;42(8):1567–79.
• 2. Anderson J, Van Weert T, Duchâteau C. Information and communication technology in education: A curriculum for schools and programme of teacher development. 2002.
• 3. Kuo Y-C, Walker AE, Belland BR, Schroder KE. A predictive study of student satisfaction in online education programs. International Review of Research in Open and Distributed Learning. 2013;14(1):16–39.
• 4. Rafi A, Rauf A, Anwar MI. Significance of actually drawing microscopic images and its impact on students’ understanding of histology. Journal of the Dow University of Health Sciences (JDUHS). 2017;11(3):77–81.
• 5. Hamilton PW, Wang Y, McCullough SJ. Virtual microscopy and digital pathology in training and education. APMIS. 2012;120(4):305–15.
• 6. Thompson AR, Lowrie DJ, Jr. An evaluation of outcomes following the replacement of traditional histology laboratories with self-study modules. Anat Sci Educ. 2017;10(3):276–85.
• 7. Chimmalgi M. Off-line virtual microscopy in teaching histology to the undergraduate medical students: do the benefits correlate with the learning style preferences? Journal of the Anatomical Society of India. 2018;67(2):186–92.
• 8. Ashokka B, Ong SY, Tay KH, Loh NHW, Gee CF, Samarasekera DD. Coordinated responses of academic medical centres to pandemics: Sustaining medical education during COVID-19. Med Teach. 2020:1–10.
• 9. Wellman B, Haythornthwaite C. The Internet in everyday life: John Wiley & Sons;2008.
• 10. Romano M, Osborne LA, Truzoli R, Reed P. Differential psychological impact of Internet exposure on Internet addicts. PLoS One. 2013;8(2):e55162.
• 11. Aarseth E, Bean AM, Boonen H, Colder Carras M, Coulson M, Das D, et al. Scholars’ open debate paper on the World Health Organization ICD-11 Gaming Disorder proposal. J Behav Addict. 2017;6(3):267–70.
• 12. Association AP. Diagnostic and statistical manual of mental disorders (DSM-5®): American Psychiatric Pub; 2013.
• 13. Potenza MN. OR-66: Internet addiction: Diagnostic and classification considerations in the time of DSM-5 and ICD-11. J Behav Addict. 2015;4(S1):31–2.
• 14. Young KS. Internet addiction: The emergence of a new clinical disorder. Cyberpsychol Behav. 1998;1(3):237–44.
• 15. Byun S, Ruffini C, Mills JE, Douglas AC, Niang M, Stepchenkova S, et al. Internet addiction: Metasynthesis of 1996–2006 quantitative research. Cyberpsychol Behav. 2009;12(2):203–7.
• 16. Sinan K. Dijital Okuryazarlik Ve Diğer Değişkenlerle İnternet Bağimliliği İlişkisinin İncelenmesi. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Derg. 2020;4(1):28–41.
• 17. Balcı Ş, Gülnar B. Üniversite öğrencileri arasında Internet bağımlılığı ve Internet bağımlılarının profili. Selçuk İletişim. 2009;6(1), 5–22.
• 18. Kuo Y-C, Walker AE, Schroder KE, Belland BR. Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. Internet High Educ. 2014;20:35–50.
• 19. Eom SB, Wen HJ, Ashill N. The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decis Sci. 2006;4(2):215–35.
• 20. Hung M-L, Chou C, Chen C-H, Own Z-Y. Learner readiness for online learning: Scale development and student perceptions. Comput Educ. 2010;55(3):1080–90.
• 21. İlhan M, Çetin B. Çevrimiçi Öğrenmeye Yönelik Hazır Bulunuşluk Ölçeği’nin (ÇÖHBÖ) Türkçe Formunun Geçerlik ve Güvenirlik Çalışması. Eğitim Teknolojisi Kuram ve Uygulama. 2013;3(2):72–101.
• 22. Tang M, Byrne R. Regular versus online versus blended: A qualitative description of the advantages of the electronic modes and a quantitative evaluation. Int J E-Learning. 2007;6(2):25766.
• 23. Veletsianos G. Emerging technologies in distance education: Athabasca University Press;2010.
• 24. Boyce BF. An update on the validation of whole slide imaging systems following FDA approval of a system for a routine pathology diagnostic service in the United States. Biotech Histochem. 2017;92(6):381–9.
• 25. Nauhria S, Hangfu L. Virtual microscopy enhances the reliability and validity in histopathology curriculum: Practical guidelines. Med Ed Publish. 2019;8.
• 26. Senn GJ. Comparison of face-to-face and hybrid delivery of a course that requires technology skills development. Journal of Information Technology Education: Research. 2008;7(1):26783.
• 27. Suryadevara V, Adusumalli C, Adusumilli PK, Chalasani SH, Radhakrishnan R. Mental Health Status among the South Indian Pharmacy Students during Covid-19 Pandemic Quarantine Period: A Cross-Sectional Study. medRxiv. 2020.
• 28. Masters K, Ellaway R. e-Learning in medical education Guide 32 Part 2: Technology, management and design. Med Teach. 2008;30(5):474–89.
• 29. Choi-Lundberg DL, Al-Aubaidy HA, Burgess JR, Clifford CA, Cuellar WA, Errey JA, et al. Minimal effects of reduced teaching hours on undergraduate medical student learning outcomes and course evaluations. Med Teach. 2020;42(1):58–65.
• 30. Shen L. Computer technology and college students’ reading habits. Chia-Nan Annual Bulletin. 2006;32:559–72.
• 31. PrakashYadav G, Rai J. The Generation Z and their social media usage: A review and a research outline. Global Journal of Enterprise Information System. 2017;9(2):110–6.
• 32. Horgan A, Sweeney J. University students’ online habits and their use of the Internet for health information. CIN: Computers, Informatics, Nursing. 2012;30(8):402–8.
• 33. Deonisius RF, Lestari I, Sarkadi S. The effect of digital literacy to internet addiction. Jurnal EDUCATIO. Jurnal Pendidikan Indonesia. 2019;5(2):71–5.
• 34. Leung L, Lee PS. Impact of internet literacy, internet addiction symptoms, and internet activities on academic performance. Soc Sci Comput Rev. 2012;30(4):403–18.
• 35. Langarizadeh M, Naghipour M, Tabatabaei SM, Mirzaei A, Vaghar ME. Prediction of internet addiction based on information literacy among students of Iran University of Medical Sciences. Electronic Physician. 2018;10(2):6333.
• 36. Chak K, Leung L. Shyness and locus of control as predictors of Internet addiction and Internet use. Cyberpsychol Behav. 2004;7(5):559–70.
• 37. Li C, Shi X, Dang J. Online communication and subjective wellbeing in Chinese college students: The mediating role of shyness and social self-efficacy. Comput Human Behav. 2014;34:89–95.
• 38. Panigrahi R, Srivastava PR, Sharma D. Online learning: Adoption, continuance, and learning outcome—A review of literature. Int J Inf Manage. 2018;43:1–14.
• 39. Allen M, Bourhis J, Burrell N, Mabry E. Comparing student satisfaction with distance education to traditional classrooms in higher education: A meta-analysis. Am J Distance Educ. 2002;16(2):83–97.
• 40. Johnson SD, Aragon SR, Shaik N. Comparative analysis of learner satisfaction and learning outcomes in online and faceto-face learning environments. Journal of Interactive Learning Research. 2000;11(1):29–49.
• 41. Croxton RA. The role of interactivity in student satisfaction and persistence in online learning. J Online Learn Teach. 2014;10(2):314.
• 42. Kang M, Im T. Factors of learner-instructor interaction which predict perceived learning outcomes in online learning environment. J Comput Assist Learn. 2013;29(3):292–301.
• 43. Lee SJ, Srinivasan S, Trail T, Lewis D, Lopez S. Examining the relationship among student perception of support, course satisfaction, and learning outcomes in online learning. Internet High Educ. 2011;14(3):158–63.
• 44. Deniz H, Cakir H. Design principles for computer-assisted instruction in histology education: An exploratory study. J Sci Educ Technol. 2006;15(5-6):399–408.