|Year : 2018 | Volume
| Issue : 3 | Page : 293-300
E-learning readiness from perspectives of medical students: A survey in Nigeria
IE Obi, AN Charles-Okoli, CC Agunwa, BI Omotowo, AC Ndu, OR Agwu-Umahi
Department of Community Medicine, Faculty of Medical Sciences, College of Medicine, University of Teaching Hospital, Enugu State, Nigeria
|Date of Acceptance||18-Dec-2017|
|Date of Web Publication||09-Mar-2018|
Dr. I E Obi
Department of Community Medicine, Faculty of Medical Sciences, College of Medicine, University of Nigeria Teaching Hospital, Ituku-Ozalla, Enugu State
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Context: Learning in the medical school of the study university is still by the traditional face-to-face approach with minimal e-communication. Aim: This paper assesses student's perspectives of E-learning readiness, its predictors and presents a model for assessing them. Settings and Design: A descriptive cross-sectional study of medical students. Subjects and Methods: By proportional quota sampling 284 students responded to a semi-structured self-administered questionnaire adapted from literature. Ethical issues were given full consideration. Statistical Analysis Used: Analysis was with SPSS version 20, using descriptive statistics, ANOVA, Spearman's correlation, and multiple regression. Statistical significance was considered at P < 0.05. Results: Medical students are ready for E-learning (Mlr = 3.8 > Melr = 3.4), beyond reliance on the face-to-face approach (69.7%), expecting effective (51.1%), and quality improvement in their learning (73.1%). Having basic information and communications technology skills (68.9%) (Mict = 3.7 > Melr = 3.4), access to laptops (76.1%), ability to use web browsers confidently (91.8%) (Mwb = 4.3 > Melr = 3.4), with only few able to use asynchronous tools (45.5%), they consider content design important to attract users (75.6%), and agree they need training on E-learning content (71.4%). They however do not believe the university has enough information technology infrastructure (62.4%) (Mi = 2.7 < Melr = 3.4) nor sufficient professionals to train them (M = 2.9). Predictors are attitude, content readiness, technological readiness, and culture readiness. The model however only explains 37.1% of readiness in the population. Conclusions: Medical students in this environment are ready to advance to E-learning. Predicted by their attitude, content, technological and cultural readiness. Further study with qualitative methodology will help in preparing for this evolution in learning.
Keywords: Assessment, E-learning, medical students, readiness
|How to cite this article:|
Obi I E, Charles-Okoli A N, Agunwa C C, Omotowo B I, Ndu A C, Agwu-Umahi O R. E-learning readiness from perspectives of medical students: A survey in Nigeria. Niger J Clin Pract 2018;21:293-300
|How to cite this URL:|
Obi I E, Charles-Okoli A N, Agunwa C C, Omotowo B I, Ndu A C, Agwu-Umahi O R. E-learning readiness from perspectives of medical students: A survey in Nigeria. Niger J Clin Pract [serial online] 2018 [cited 2023 Feb 7];21:293-300. Available from: https://www.njcponline.com/text.asp?2018/21/3/293/226956
| Introduction|| |
Conventionally, learning was carried out in classrooms with face-to-face interaction of teachers and students. However, globally, the spreading out of the internet and use of electronic devices over the years, has changed the way in which learning is carried out in various levels of educational institutions. This wave of change initially started in the developed regions of the world, but has as expected, expanded to include the developing regions such as Sub Saharan Africa region, where Nigeria belongs. The concept of E-learning has revolutionized learning. There are several advantages of E-learning such as broadening the scope of learning, allowing learners to learn at different paces, and global reach although it is also not without some disadvantages like being “uncomfortable for some users.” E-learning has for long also been recognized to be an effective method of teaching health and medical education in developed parts of the world.,,,
E-learning was initially thought to be synonymous with distant education where learning is done solely on web-based medium; however, its scope has broadened. E-learning has been defined to comprise all arrangements of electronically-mediated teaching. In other words, it is teaching and learning that is enabled by information and communications technology (ICT), both inside and outside the classroom. Negash and Wilcox described six types of E-learning as (1) e-learning with physical presence and without e-communication (by use of power points, digital versatile discs, etc. in the classroom); (2) E-learning without physical presence and without e-communication (self-learning); (3) E-learning without physical presence and with e-communication (asynchronous learning); (4) E-learning with virtual presence and with e-communication (synchronous learning); (5) E-learning with occasional presence and with e-communication (hybrid-asynchronous learning); and (6) E-learning with physical presence and with e-communication (hybrid-synchronous learning). A systematic review on E-learning for medical education in developing regions of the world, expounded on the E-learning strategies being used in these countries which include: use of ICT tools for accessing outside information; networked telemedicine systems that allow students to interact with educators at a distance; ICT as facilitators for problem or case-based learning sessions; and blended (combining ICT tools and physical presence) teaching platforms, implemented either in addition to or as replacements for face-to-face interaction between teachers and students. In this review as with other studies, it was found that E-learning and blended learning environments, produced complimentary results when compared to the traditional face-to-face instruction.,, It was thus the prediction that, in years to come, E-learning will be a central part of health education in developing countries also and institutions will seek out the best times and ways to utilize it successfully, to the benefit of learners and faculty.
Medical training in Nigeria, lasts for 6 years and ranges from 100 level (1st year) to 600 level (6th year). Teeming population of candidates aspire to gain admission into the tertiary institutions and they outweigh the available resources for the traditional face-to-face learning operational in most institutions in the country. It has become vital that schools adopt and expand their learning strategies to include E-learning. This phenomenon has been recognized in medical education in developing countries, especially in relation to addressing health workforce shortages.,,,,, To achieve this, E-learning readiness assessment is necessary because one of the major components of design of E-learning programs are self-directed learning which implies that readiness of users to engage in E-learning and the provision of enabling E-learning environment are both necessary for the success of E-learning programs. In the medical school of the study university, students are mostly taught by the traditional classroom approach and by physical presence in clinics with minimal e-communication. This is set to change as the university is currently setting up infrastructure and testing E-learning models with other faculties. It has however been found that the deployment of E-learning in higher institutions neither necessarily assure the tolerability nor sustainability.
This study, therefore, set out to determine the E-learning readiness, predictors of E-learning readiness and to develop a model for E-learning readiness among medical students in the study university.
E-learning according to Rosenberg is “the use of Internet technologies to deliver a broad array of solutions that enhance knowledge and performance. It is networked, delivered to end users through standard Internet technology, and focuses on the broadest view of learning.” While Merriam Webster dictionary defined readiness as “the state of being ready or prepared for something.” Therefore, E-learning readiness in the context of this study is the state of readiness of medical students of the study university toward the use of internet technologies to enhance their learning.
Different scholars have proposed various concepts for the assessment of E-learning readiness. Borotis and Poulymenakou proposed a seven component model (business, technology, content, training process, culture, human resources, and financial) based on literature review and their preliminary studies. Aydain and Tasci also proposed a seven component model (human resources, learning management system, learners, content, information technology (IT), finance, and vendor). However, their model was business oriented. Psycharis proposed a 3-broad component model (education, resources and environment) with 2–3 sub-components under each of the main components. Oketch et al. argued that most of the E-learning readiness assessment models were more suited to the developed world and proposed a 4-broad component model for developing countries with the following components: demographic factors, culture readiness, technological readiness, and content readiness.
Recognizing that different population groups differ in the way they respond to and accept learning initiatives, therefore, the conceptual framework of this paper was adapted from literature reviews of similar studies carried out in other developing countries., Focus is on these five factors that have been reported to influence E-learning readiness: sociodemographic factors, attitudes toward E-learning, technological readiness, content readiness, and culture readiness.
| Subjects and Methods|| |
This was a descriptive cross-sectional study of medical students from second to final year in the medical school of the study university. The 100 level (1st year) medical students were excluded because they reside in another campus of the university, several kilometers away. The sample size was estimated with the formula for one proportion sample size determination (N = Z2.p. q/d2) and 384 students were sampled. Ethical clearance was obtained from the institutions ethical review board. Study participants were first stratified by level of study and then by proportionate sampling, the proportion of students for each level to make up the sample was selected using simple random sampling. Four trained research assistants participated in data collection over a duration of 1 month. The study questionnaire was a semi-structured pretested self-administered questionnaire. It was developed by the researchers from adaptation of the E-learning readiness model proposed by Oketch et al. and other literature., Sociodemographic variables, attitude toward E-learning, content readiness, culture readiness, and technological readiness were independent variables while personal E-learning readiness was the outcome variable. There were unequal number of questions in each dimension which ranged from 4 to 8 as follows: personal E-learning readiness-7; attitudes/disposition toward E-learning-4; content readiness-4; technology-13; and culture-8. For each question, respondents chose options from strongly agree, agree; neutral, disagree, and strongly disagree and were scored between 1 for strongly agree and 5 for strongly disagree. Negatively worded questions were reverse-scored before computing the dimension score. The mean level of readiness was taken as 3.4 as proposed by Aydain and Tasci and adopted by Oketch et al. Data were analyzed using SPSS version 20 (IBM Corp. 2011. Armonk, NY). Results were summarized using descriptive statistics and presented using tables. Tests of association were carried out using ANOVA and Spearman's correlation. One step multiple regression was applied to determine the predictors of E-learning readiness. An E-learning assessment model was developed from the result of the multiple regression. Privacy and confidentiality were maintained throughout the duration of the study.
| Results|| |
A total of 284 questionnaires were returned giving a response rate of 74% which was adequate for analysis. Cronbach's alpha was 0.88. The data were approximately normally distributed, using Shapiro–Wilkes test and Q-Q plots.
The results in [Table 1] show the sociodemographic characteristics of the respondents. Majority of them were aged 20–24 years (201; 70.8%) with mean age at first computer use as 11.6 ± 4.23 years. The gender distribution was almost equal although there were more males (156; 54.9%) than females (128; 45.1) in the study.
The results in [Table 2] show that overall mean of attitude toward E-learning among respondents was lower than the mean expected readiness level (Mat= 3.1 < Melr = 3.4). Furthermore, the mean for all the variables on attitude toward E-learning, was lower than the mean expected readiness level except for the mean for readiness to accept E-learning which is above the mean expected readiness level (Mra = 3.7 > Melr = 3.4). Majority of students (56.3%) do not feel that lack of face-to-face interaction in E-learning will be a hindrance while 46.8% agree that E-learning can lead to social isolation and thus, do not favor it.
|Table 2: Attitude toward E-learning and E-learning readiness among medical students|
Click here to view
The results in [Table 2] also show that medical students are ready for E-learning with a mean score, for E-learning readiness higher than that of the mean expected readiness level (Mlr = 3.8 > Melr = 3.4). Majority of them (52.1%) are personally committed to E-learning and are quite ready to collaborate and share information (Mc = 4.0 > Melr = 3.4). However, a very high proportion of respondents (75.6%) agreed that the design of the E-learning content is important in attracting users. Although majority of the respondents (69.7%) are ready to move beyond a predominant reliance on classroom training to E-learning approach (M = 3.8), a higher proportion (71.4%) indicated that they need more training for E-learning content.
The results in [Table 3] show that although the overall mean of content readiness among respondents was lower than the mean expected readiness level (Mcr = 2.9 < Melr = 3.4), the mean for availability of E-learning teaching materials was equal to the mean for expected readiness level (Matm = 3.4 = Melr = 3.4) while the mean for the required basic ICT skills for E-learning was higher than the mean for expected readiness level (Mict = 3.7 > Melr = 3.4). Despite the high proportion of students that indicated that they have the required basic ICT skills for E-learning (68.9%), 62.4% feel that the university does not have reliable IT infrastructure to support E-learning.
Also, results in [Table 3] show that the overall mean of culture readiness among respondents was higher than the mean expected readiness level (Mcr = 3.6 > Melr = 3.4). Furthermore, majority of respondents (68.6%) agreed that they find it easy to use E-learning tools and 73.1% indicated that E-learning can improve the quality of their learning. In addition, over half of the respondents (52%) indicated that adopting E-learning can increase their satisfaction, and almost the same proportion (51.1%) believed that E-learning is more effective than the traditional classroom approach.
The results in [Table 4] show that the overall mean of technological readiness among respondents was higher than the mean expected readiness level (Mtr = 3.6 > Melr = 3.4). Although majority (76.1%) of the respondents agreed to a large extent that they have access to a laptop, they indicated that the university lacks infrastructure to support E-learning with a mean score less than the mean expected readiness level (Mi = 2.7 < Melr = 3.4). In addition, that the university lacks professionals that will carry out E-learning trainings (M = 2.9). On the other hand, 91.8% of the respondents can use web browsers confidently with a mean score greater than the mean expected readiness level (Mwb = 4.3 > Melr = 3.4) but only 45.5% can use asynchronous tools.
The results in [Table 5] show that level of study is significantly associated with E-learning readiness (F = 2.988, P = 0.019). Students in 500 level (5th year) were significantly less ready (M = 3.6 ± 0.50) than students in 200 (2nd year) (M = 3.9 ± 0.69) and 300 (3rd year) (M = 3.9 ± 0.53) levels for E-learning. In addition, it showed that while age at first computer use had inverse relationship with E-learning readiness, other variables did not. The correlations ranged from −0.063 (age at first computer use) to 0.513 (culture readiness).
|Table 5: Association between sociodemographic variables and E-learning readiness|
Click here to view
In the multiple regression analysis [Table 6] attitude toward E-learning, technological readiness, content readiness, culture readiness and level of study, statistically significantly predicted E-learning readiness F (5, 275) = 32.465, P < 0.001, R2 = 0.375. Except for level of study, all other variables, added statistically significantly to the prediction, P < 0.05.
From the results of [Table 6], taking all other independent variables at zero, a unit increase in attitude will lead to 0.152 increase in E-learning readiness; a unit increase in content readiness will lead to 0.132 increase in E-learning readiness; a unit increase in technological readiness will lead to 0.261 increase in E-learning readiness while a unit increase in culture will lead to 0.177 increase in E-learning readiness. Thus, the true predictors of E-learning readiness were attitude, content readiness, technological readiness, and culture readiness although content readiness was the least significant factor in E-learning readiness. The model however only explains 37.1% of readiness in this population.
| Discussion|| |
The findings from this study indicate that majority of medical students in this institution are ready for E-learning. It also shows that they have the necessary ICT experience for E-learning, will be committed to E-learning, are willing to collaborate and are ready to move beyond a predominant reliance on classroom training to the E-learning approach. This is an increasingly common finding in medical education.,,, However, they need more training to benefit from E-learning approaches. This highlights the need for formal training because even though the students are ready, they still recognized the need for formal training by the university to use E-learning as their learning approach. This is in keeping with the findings of various studies in Africa and Nigeria that reported that students and their teachers are ready for E-learning but require training by their institutions to use E-learning system.,,,
The lower level of E-learning readiness reported in this study among students in a higher level of study, is contrary to the proposition by Roger that increased educational status has a positive influence on E-learning readiness. Other studies have findings similar to this study and the explanation for this might be that at higher levels of study in medical school, the students may be more preoccupied with passing examinations using the system they are familiar with which is the traditional classroom approach. The lack of association between other sociodemographic variables and E-learning readiness, contradicts the proposal by Aydin and Tasci.
The report of technological readiness as one of the most important determinants of E-learning readiness means that institutions should be ready to invest in technology to start or sustain E-learning. This finding was not surprising as it has been a well-recognized fact in literature that technological readiness is key to E-learning readiness., As important as technological readiness in predicting E-learning readiness are attitudes of students toward E-learning and culture readiness, followed by content readiness. These are pointers to areas that need to be addressed if the university management is to successfully implement E-learning as is done by many leading institutions worldwide. Attitude toward E-learning is important as differences in culture may predispose to how E-learning is perceived and accepted. The model developed [Figure 1] significantly predicted E-learning readiness among medical students in this university, explaining 37.1% of E-learning readiness of the students meaning that there are other factors that influence E-learning readiness among these medical students that were not elicited in the study. This is consistent with a study that used a similar approach in another country in Africa.
| Conclusions|| |
The medical students are ready for E-learning. Ready to move beyond the traditional face-to-face approach, believing that E-learning is more effective and can improve the quality of their learning. They have basic ICT skills, have access to laptops and can use web browsers confidently, though only few can use asynchronous tools. They need training on E-learning content as it is important to attract users. They do not believe that the university has enough IT infrastructure, nor sufficient professionals to train them for E-learning. Predictors of E-learning readiness here are attitude, content readiness, technological readiness, and culture readiness.
In implementing E-learning, the university should enhance faculty and student training sessions, to improve capacity for E-learning content. Medical student's access to ICT resources should be improved on, while medical curriculum reviews should increasingly include, the use of E-learning interactions to advance content and culture and advance learning. Further studies should incorporate qualitative methodology to elicit other factors that may influence E-learning readiness in this population.
The study involved medical students in one university and thus, the findings may not be generalizable to all Nigerian medical students.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
DeNeui DL, Dodge TL. Asynchronous student networks and student outcomes: The utility of online learning components in hybrid courses. J Instr Psychol 2006;33:256.
Frehywot S, Vovides Y, Talib Z, Mikhail N, Ross H, Wohltjen H, et al.
E-learning in medical education in resource constrained low- and middle-income countries. Hum Resour Health 2013;11:4.
Zhang D, Zhao JL, Zhou L, Nunamaker JF. Can e-learning replace classroom learning? Commun ACM 2004;47:75-9.
Issenberg SB, Petrusa ER, McGaghie WC, Felner JM, Waugh RA, Nash IS, et al.
Effectiveness of a computer-based system to teach bedside cardiology. Acad Med 1999;74:S93-5.
Schor NF, Troen P, Adler S, Williams JG, Kanter SL, Mahling DE, et al.
Integrated case studies and medical decision making: A novel, computer-assisted bridge from the basic sciences to the clinics. Acad Med 1995;70:814-7.
Andrews PV, Schwarz J, Helme RD. Students can learn medicine with computers. Evaluation of an interactive computer learning package in geriatric medicine. Med J Aust 1992;157:693-5.
Ruiz JG, Mintzer MJ, Leipzig RM. The impact of E-learning in medical education. Acad Med 2006;81:207-12.
Negash S, Wilcox MV. E-learning classifications: Differences and similarities. In: Handbook of Distance Learning for Real-Time and Asynchronous Information Technology Education. London: Information Science Reference (an imprint of IGI Global); 2008. p. 1-23.
Chumley-Jones HS, Dobbie A, Alford CL. Web-based learning: Sound educational method or hype? A review of the evaluation literature. Acad Med 2002;77:S86-93.
Leatemia LD, Susilo AP, van Berkel H. Self-directed learning readiness of Asian students: Students perspective on a hybrid problem based learning curriculum. Int J Med Educ 2016;7:385-92.
Kebaetse MB, Nkomazana O, Haverkamp C. Integrating eLearning to support medical education at the new university of Botswana school of medicine. Electron J e Learn 2014;12:43-51.
Adanu R, Adu-Sarkodie Y, Opare-Sem O, Nkyekyer K, Donkor P, Lawson A, et al.
Electronic learning and open educational resources in the health sciences in Ghana. Ghana Med J 2010;44:159-62.
Mars M. Building the capacity to build capacity in e-health in sub-Saharan Africa: The KwaZulu-natal experience. Telemed J E Health 2012;18:32-7.
Borotis S, Poulymenakou A. E-Learning readiness components: Key issues to consider before adopting e-Learning interventions. In: Nall J, Robson R, editors. Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education. Chesapeake, VA: Association for the Advancement of Computing in Education (AACE); 2004. p. 1622-9.
Muiruri C, Kapanda G, Tibyampansha D, Ibrahim G, Kulanga A, Lisasi E, et al.
eLearning at a medical school in sub-Saharan Africa: Use of the technology acceptance model to evaluate implementation effectiveness. Ann Glob Health 2014;80:171-2.
Rosenberg MJ. E-learning: Strategies for Delivering Knowledge in the Digital Age. New York: McGraw-Hill; 2001.
Aydain CH, Tasci D. Measuring readiness for e-learning: Reflections from an emerging country. Educ Technol Soc 2005;8:244-57.
Psycharis S. Presumptions and actions affecting an e-learning adoption by the educational system-Implementation using virtual private networks. Eur J Open Distance E Learn 2005;8:1-10.
Oketch HA, Njihia JM, Wausi AN. E-learning readiness assessment model in Kenyas' higher education institutions: A case study of university of Nairobi. Int J Sci Knowl 2014;5:29-41.
Eggermont S, Bloemendaal PM, van Baalen JM. E-learning any time, any place, anywhere on mobile devices. Perspect Med Educ 2013;2:95-8.
Changiz T, Haghani F, Nowroozi N. Are postgraduate students in distance medical education program ready for e-learning? A survey in Iran. J Educ Health Promot 2013;2:61.
Muganda RA. E-Learning Implementation: A Survey of University of Nairobi Academic Staff Attitudes and Readiness Towards E-Learning. Project Report 2006.
Olatubosun O, Olusoga F, Shemi A. Direct determinants of user acceptance and usage behavior of eLearning system in Nigerian tertiary institution of learning. J Inf Technol Econ Dev 2014;5:95.
Rogers EM. Diffusion of Innovations. Edition. New York: Free Press; 2003.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]