Medicine Students’ Opinions Post-COVID-19 Regarding Online Learning in Association with Their Preferences as Internet Consumers

Abstract

The COVID-19 pandemic highlighted e-learning as a critical component that ensured the continuity of students’ education processes. In this regard, many research groups aim to provide new scientific data about the efficiency and benefits of e-learning for healthcare students. Our study aims to evaluate the attraction of e-learning among medical and dental Romanian students, in association with their preferences as internet and computer consumers. The study enrolled 551 students in medicine from four Romanian Universities of Medicine and Pharmacy, located in major cities (Iași—64.6%, Craiova—19.6%, Timișoara—14.5% and Cluj Napoca—1.3%), mostly females (76.2%), from the first and second years of study (63.7%) or the fourth to sixth years of study (23.3%), aged 18–20 years (53.9%). To investigate their opinions about the efficiency of e-learning, we used an anonymous questionnaire with 31 items regarding advantages (17 items) and possible drawbacks (14 items). The students in medicine had favourable opinions about online learning because these tools are more comfortable (75.2%) and more flexible (60.1%). The main reasons for disagreement were the lack of direct communication and human interaction with teachers (53.2%), limitations due to the particularities of some disciplines that cannot be easily transferred to the online environment (46.4%), and the lack of proper motivation (32.5%). Older students, who liked to use multimedia resources in the learning process and used the internet mainly for information purposes or domestic facilities, had the highest scores for favourable opinions about online learning. The younger students, who did not prefer using multimedia resources in the learning process, also had the highest scores for disagreement regarding online learning. There were no statistically significant differences between genders.

1. Introduction

The concept of e-learning (also known as computer-assisted learning, online learning, or web-based learning) includes distance learning through electronic and digital educational technology (computers, communication technologies, software, electronic platform) and digital data that are used by the trainee to receive teaching content through the computer, tablet, or smartphone [1,2]. E-learning can be carried out synchronously (online lectures, teleconferencing, webinars, or live streaming), as well as asynchronously (pre-recorded sessions, educational materials in electronic format, or educational applications installed on the computer, tablet, or smartphone) [1,3]. E-learning can be used alone or in a hybrid form, combining a physical presence in the classroom with interaction through the internet [1]. E-learning implementation began in the 1990s due to the development of distance learning programs proposed by national and international educational institutions. Over the past decade, e-learning became more and more present in post-secondary education and catalyzed a pedagogical shift from passive students and top-down lecturing to a collaborative approach between teachers and students in controlling the learning process [4,5,6]. In 2013, more than 25% of undergraduates in the United States attended at least one online course, and the number of distance learners increases year by year [7]. The breakout and widespread implementation of e-learning tools and techniques happened in 2020 during the COVID-19 pandemic [8]. This pandemic period accelerated the advancement of medical education, transforming and innovating curricula for many medical disciplines [9]. Moreover, the COVID-19 pandemic added value to e-learning as a critical component that allowed the continuity of students’ education processes, even during a crisis [10]. It has been estimated that, over the next years, online learning will grow from its current 2% to 30% of all education provided throughout the globe [11]. In addition, the World Federation for Medical Education endorses the use of technology in best-practice medical education [12].

There is no doubt nowadays that e-learning technologies proved their utility and will become a strong partner at all levels of education. Therefore, it has become more and more important to be aware of their strengths and to find the best tools for their improvement.

Nowadays, research is oriented towards studying the efficiency of e-learning programs by recording and comparing them with traditional teaching methods, in respects such as the acquisition of new knowledge, the improvement of already acquired knowledge (theoretical concepts, practical abilities), and so on. A drawback already reported in such programs is that, despite high enrollment rates, a significant number of learners do not successfully complete them [5], for various reasons, including a lack of time, insufficient prior knowledge, the inability to understand the course content, and the lack of human assistance [13]. Learning online is challenging for students because they receive increased responsibilities regarding the learning process: they need to cope by themselves with the delivered information, set their own goals, and evaluate their progress [14], all of which can be overwhelming for unprepared learners, especially when the system is not specifically designed to stimulate their engagement [15].

There are many successful strategies to obtain and maintain a student’s engagement, leading directly to online academic success [16]. In this regard, teachers’ task is to design online courses to make them more consistent with the needs of potential students, in order to stimulate engagement and therefore facilitate academic success; previous studies approached this issue and revealed efficient tools to solve it. For example, Campbell (2004) showed that, in order to be efficient, online learning in higher education must stimulate metacognition, as well as reflective and collaborative learning among students [17]. Other required features of online didactic platforms are those meant to facilitate interaction between the instructor and the learner, including an appealing visual aspect, flexibility, ease of navigation, clear and well structured content, and good usability [18,19,20]. A high level of interactivity in the online educational environment increases students’ satisfaction and engagement, since dialogue with the teacher and their colleagues helps them to fill their learning gaps and removes their sense of isolation [21,22]. Maintaining a less demanding atmosphere during lessons leads to a lower degree of learning anxiety, which is also a desired outcome [23]. According to Ryan et al. (2000), satisfaction comprises all the positive emotions triggered by the experience of online learning and the user’s personal assessment of it [24]. A high level of satisfaction motivates students to persist in learning and thus improve their learning engagement and performance [25]; satisfaction is directly related to their level of acceptance and comes from the programs’ attractiveness. A recent study, initiated during the COVID-19 pandemic, suggested that online platforms’ availability has the greatest impact on students’ satisfaction, compared with other subjective factors [26]. There are also studies initiated during the same period that promote other features, such as perceived usefulness and ease of use [27] or stimulation and attractiveness [28,29], as the highest driving factors of satisfaction.

Previous studies also showed that the human factor plays a very significant role in this framework, affecting both learning strategies and outcomes. Learners’ age, gender, and level of giftedness, as well as their level of study and even their country of origin, affect their proficiency, because different schools employ different online learning environments, leading to different experiences—one size obviously does not fit all [29,30,31,32]. Each context requires specific understanding of students’ responses—this is the best way to develop suitable strategies for the enhancement of online learning efficiency.

At last, but not least, e-learning has significant advantages over traditional techniques, as it can disseminate large amounts of information and educational resources to a high number of students at a reduced cost [33], but there are pros and cons to this issue too. A review of the literature data concluded that a control of e-learning cost-effectiveness in the implementation of e-learning technologies can bring cost benefits and significant advantages over traditional teaching [34]. Meinert et al. (2019) suggest that the costs related to the delivery of e-learning courses are underestimated and recommend the management of factors affecting the cost of course production [35].

Nevertheless, we cannot ignore the deficiencies of online learning activities; the most frequent problems are caused by poor internet connection, lack of technical expertise from teachers and learners, inadequate, and sometimes overly expensive hardware and software, and insufficient learner orientation [36]; overreliance on technology is usually meant to facilitate learning, but there are situations when its role is opposite, and a virtual classroom may impede such activities and diminish their quality [37,38]. The most important and pending drawback is students’ inability to undertake practical skills; there are situations when this drawback can be mitigated by designing practical, organized, clear, and motivating virtual classrooms [39], but there are also certain domains where good and accurate practical skills are mandatory for training and professional development, and they can be acquired only through exercises in real time and space. This is the case of students in medicine, who can only become good professionals through direct contact and interaction with patients.

Nonetheless, even in the medical field, online learning can be useful, and it has certain successful applications. Many research groups have provided scientific data regarding the effectiveness of collaborative web-based e-learning tools, which are already available, as well as the benefits of e-learning for healthcare students and professionals in various disciplines or fields: anatomy and physiology [3,35,40], medical imaging and radiation therapy for radiologists [8,41], the management of knee osteoarthritis for physiotherapists [42], pharmacy [43], or nursing [44].

Our study addresses this wide issue, its purpose being to evaluate the level of e-learning attraction among medical and dental students in Romania and the human factors influencing this issue: students’ demographic features (gender and age group), as well as their personal background, expressed through their preferences as internet and computers consumers.

2. Materials and Methods

Participants: Our study enrolled 551 students in medicine, from four Romanian universities. The students were mostly females (76.2%)—because in Romania the number of girls who want to have a career in the medical area is much higher compared with the similar number of boys, from the first and second years of study (63.7%), aged 18–20 years (53.9%). We also succeeded in including in our study a small percentage of students from the higher years of study (fourth to sixth)—23.3%, as well as 28 resident physicians in orthodontics and dental-facial orthopaedics (5.1%). Most students come from the Faculties of Dental Medicine (47.5%) or General Medicine (34.5%), of “Grigore T. Popa” University of Medicine and Pharmacy from Iași, Romania, but we also enrolled in our study 108 students (19.6%) from the University of Medicine and Pharmacy from Craiova and 80 students (14.5%) from “Victor Babeș” University of Medicine and Pharmacy from Timișoara. Therefore, we can claim that the sample analysed reflects well the general opinions of students from medical Romanian universities, at a national level.

Table 1. The sample’s demographic characteristics.

		n (%)
Gender	male	131 (23.8)
	female	420 (76.2)
Age group	18–20 years	297 (53.9)
	21–24 years	158 (28.7)
	over 25 years	96 (17.4)
Year of study	1	228 (41.4)
	2	123 (22.3)
	3	44 (8.0)
	4	2 (0.4)
	5	44 (8.0)
	6	82 (14.9)
	resident physician	28 (5.1)
Specialty	Dental Medicine	262 (47.5)
	General Medicine	190 (34.5)
	Dental Technique	70 (12.7)
	Assistants in Prophylactics	1 (0.2)
	Orthodontics and Dental-Facial Orthopaedics	28 (5.1)
University	Grigore T. Popa” University of Medicine and Pharmacy from Iași, Romania	356 (64.6)
	University of Medicine and Pharmacy from Craiova, Romania	108 (19.6)
	“Victor Babeș” University of Medicine and Pharmacy from Timișoara, Romania	80 (14.5)
	“Iuliu Hațieganu” University of Medicine and Pharmacy from Cluj-Napoca, Romania	7 (1.3)
Previously graduated university studies	yes	55 (10.0)
	no	496 (90.0)
Total		551 (100.0)

shows the general characteristics of the samples.

Data collection: To investigate the students’ opinions about the efficiency of e-learning methods, we used an anonymous questionnaire made of 31 items—17 items regarding possible advantages of online learning and 14 items regarding possible drawbacks of online learning (Appendix A—

Table A1. The questionnaire’s structure.

Favourable Opinions About Online Learning:
Item 1a	I am satisfied with the online didactic activities I attended so far.
Item 2a	I prefer to learn using digital tool and resources.
Item 3a	I prefer to work autonomously.
Item 4a	The online didactic activities are more efficient than classical ones.
Item 5a	The online didactic activities focus on the quality of the transmitted materials.
Item 6a	The online didactic activities are better in communicating the essence of materials than the classical ones.
Item 7a	The online didactic activities make me understand faster and easier the presented concepts.
Item 8a	The online didactic activities make me be more productive as a student.
Item 9a	The online didactic activities are more comfortable because I don’t need to go to the faculty.
Item 10a	The online didactic activities are flexible because I can learn when I want to.
Item 11a	The online lectures are more useful for me than the classical ones.
Item 12a	The practical online activities (seminars, laboratories) are more useful for me than the classical ones.
Item 13a	The teachers work more during online didactic activities than during classical ones.
Item 14a	The students are asked to solve more homework and tasks during online didactic activities than during the classical ones.
Item 15a	The tasks are clearer and easier to solve during online didactic activities than during classical ones.
Item 16a	I have the option to customize the tasks to solve, according to my own learning pace.
Item 17a	The digital competencies acquired through online learning will be useful in my future didactic and professional activities.
Not favourable opinions about online learning: I do not prefer to participate in online didactic activities because of the following reasons:
Item 1b	The poor level of my digital competencies
Item 2b	Technical difficulties (platforms to install, browsers, accounts)
Item 3b	Rigid and not flexible tools
Item 4b	Limited access to the Internet
Item 5b	I don’t have a computer with the required technical features
Item 6b	I don’t have the time necessary to understand and use adequately the digital tools and resources
Item 7b	I don’t find a proper motivation
Item 8b	I don’t have the habit to learn using these technologies
Item 9b	Lack of teachers’ control and constant monitoring of my activities
Item 10b	Lack of direct communication and human interaction with teachers
Item 11b	Lack of an efficient structure of the content taught by the teaching staff
Item 12b	Lack of focused and relevant feedback from teachers
Item 13b	Lack of a well-structured schedule for didactic activities
Item 14b	Limitations due to the particularities of some disciplines (in case of my study objects, the learning activities cannot be easily transferred to the online environment)
General opinion about using multimedia resources in the learning process:
Item 1c	I prefer to learn using multimedia tools and resources.
General preferences as Internet users:
Item 2c	I use the internet mainly for information
Item 3c	I use the internet mainly for communication
Item 4c	I use the internet mainly for entertainment
Item 5c	I use the internet mainly for domestic facilities

). The questionnaire was presented and explained separately to each subject, along with the research goals. The students were asked to specify their level of agreement with each item on a 5-unit Likert scale (1 = total disagreement, 5 = total agreement).

The students were also asked to specify their opinion about using multimedia resources in the learning process (item 1c) and their preferences as internet users (items 2c–5c). The students were asked to answer item 1c on a 5-unit Likert scale regarding their level of agreement and to order the items 2c–5c according to their personal preferences (Appendix A—Table A1).

Variables: The study’s variables were the students’ answers to the questionnaire. We reported them globally and comparatively by gender, age group, opinion about using multimedia resources in the learning process (item 1c), and preferences as internet users (items 2c–5c).

Statistical analysis: The data from the questionnaire were recorded in a data file in SPSS 27.0 (SPSS Inc., Chicago, IL, USA) for Windows. The sample size was calculated using the formula for a finite population, where the margin of error is 5%, the confidence level is 95%, and the population size equals 63,216 Romanian students enrolled in 2020–2021, at the Faculty of Health and Social Assistance. Based on the sample size calculation, the study proposed needed a minimum of 382 participants. The answers to each item were characterized through frequency distributions and contingency tables. The numerical variables were characterized through descriptive statistics (average, standard deviation, range, and median). The comparisons between samples were performed using the Chi-squared test for categorical data and the Mann-Whitney and Kruskal-Wallis tests for quantitative data, according to the results of Shapiro-Wilks tests of normality. We considered the p ≤ 0.05 value as statistically significant (*) and the p ≤ 0.01 value as highly significant (**). In order to investigate the internal connections between the items used to characterize the favourable and not favourable opinions about online learning, we used two-step clustering; the number of clusters was calculated automatically, using the Schwarz’s Bayesian criterion (BIC) and a maximal limit of 15 clusters.

Ethical statement: Participation in our study was voluntary. The subjects were informed about the study and the content of the questionnaire and they agreed with the informed consent. The questionnaires were filled anonymously in order to protect the subjects’ intimacy and to obtain objective answers as much as possible. The study was approved by the Ethical Committee of “Grigore T. Popa” University of Medicine and Pharmacy from Iasi, Romania (decision no. 21/16.11.2020).

3. Results

The students’ global opinions about the issues proposed are presented in detail in Appendix A—

Table A2. The students’ global answers at the investigated issues.

	Recorded Answers
	1—Total Disagreement n (%)	2—Partial Disagreement n (%)	3—Neutral n (%)	4—Partial Agreement n (%)	5—Total Agreement n (%)
Favourable opinions about online learning:
Item 1a	18 (3.3)	78 (14.2)	258 (46.8)	147 (26.7)	50 (9.1)
Item 2a	17 (3.1)	77 (14)	196 (35.6)	165 (29.9)	96 (17.4)
Item 3a	6 (1.1)	59 (10.7)	229 (41.6)	175 (31.8)	82 (14.9)
Item 4a	135 (24.5)	190 (34.5)	156 (28.3)	51 (9.3)	19 (3.4)
Item 5a	26 (4.7)	80 (14.5)	208 (37.7)	165 (29.9)	72 (13.1)
Item 6a	140 (25.4)	178 (32.3)	153 (27.8)	55 (10)	25 (4.5)
Item 7a	138 (25)	175 (31.8)	166 (30.1)	44 (8)	28 (5.1)
Item 8a	135 (24.5)	163 (29.6)	154 (27.9)	61 (11.1)	38 (6.9)
Item 9a	11 (2)	36 (6.5)	90 (16.3)	185 (33.6)	229 (41.6)
Item 10a	16 (2.9)	49 (8.9)	155 (28.1)	196 (35.6)	135 (24.5)
Item 11a	98 (17.8)	120 (21.8)	174 (31.6)	83 (15.1)	76 (13.8)
Item 12a	237 (43)	169 (30.7)	103 (18.7)	19 (3.4)	23 (4.2)
Item 13a	77 (14)	120 (21.8)	230 (41.7)	91 (16.5)	33 (6)
Item 14a	69 (12.5)	182 (33)	220 (39.9)	54 (9.8)	26 (4.7)
Item 15a	75 (13.6)	176 (31.9)	228 (41.4)	48 (8.7)	24 (4.4)
Item 16a	29 (5.3)	52 (9.4)	203 (36.8)	183 (33.2)	84 (15.2)
Item 17a	41 (7.4)	115 (20.9)	184 (33.4)	139 (25.2)	72 (13.1)
Not favourable opinions about online learning:
Item 1b	327 (59.3)	111 (20.1)	80 (14.5)	26 (4.7)	7 (1.3)
Item 2b	189 (34.3)	141 (25.6)	120 (21.8)	75 (13.6)	26 (4.7)
Item 3b	204 (37)	145 (26.3)	122 (22.1)	64 (11.6)	16 (2.9)
Item 4b	315 (57.2)	114 (20.7)	71 (12.9)	40 (7.3)	11 (2)
Item 5b	340 (61.7)	104 (18.9)	55 (10)	37 (6.7)	15 (2.7)
Item 6b	294 (53.4)	120 (21.8)	93 (16.9)	37 (6.7)	7 (1.3)
Item 7b	139 (25.2)	100 (18.1)	133 (24.1)	94 (17.1)	85 (15.4)
Item 8b	180 (32.7)	106 (19.2)	131 (23.8)	90 (16.3)	44 (8)
Item 9b	171 (31)	138 (25)	140 (25.4)	73 (13.2)	29 (5.3)
Item 10b	90 (16.3)	63 (11.4)	105 (19.1)	142 (25.8)	151 (27.4)
Item 11b	118 (21.4)	146 (26.5)	157 (28.5)	78 (14.2)	52 (9.4)
Item 12b	130 (23.6)	120 (21.8)	139 (25.2)	93 (16.9)	69 (12.5)
Item 13b	171 (31)	136 (24.7)	123 (22.3)	58 (10.5)	63 (11.4)
Item 14b	101 (18.3)	69 (12.5)	125 (22.7)	111 (20.1)	145 (26.3)

.

From a general point of view, the medical students have favourable opinions about online learning; more than 50% of them enjoy, partially or entirely, online didactic activities because they are more comfortable (item 9a—75.2%) and more flexible (item 10a—60.1%). In addition to that, more than 40% of the students enjoy partially or entirely online didactic activities because they prefer learning with the use of digital tools and resources (item 2a—47.3%) and working autonomously (item 3a—46.7%). They believe that online didactic activities focus on the quality of the materials transmitted (item 5a—43.0%) and that they can customize the tasks to solve, according to their own learning pace (item 16a—48.4%). 38.3% of students believe that the digital competencies acquired will be useful in their future didactic and professional activities (item 17a). It is also interesting to notice that, while 28.9% of students believe that online lectures are more useful for them than classical ones (item 11a), only 7.6% of students believe that online practical activities (seminars or laboratories) are more useful than the classical ones (item 12a).

Regarding the not favourable opinions, the percentages of students who dislike partially or entirely online learning are rather small. The most frequent reasons for disagreement are the lack of direct communication and human interaction with teachers (item 10b—53.2%), the lack of a strong motivation for online learning (item 7b—32.5%), the lack of focused and relevant feedback from teachers (item 12b—29.4%), and the limitations due to the particularities of some disciplines, which cannot be easily transferred in the online environment (item 14b—46.4%). A positive aspect is that most students have enough IT competencies to be able to attend online didactic activities without major technical problems: only 6% of students admit that their level of digital competencies is poor (item 1b), 8% of them claim that they do not have the necessary time to understand and to use properly the digital tools (item 6b), and 14.5% think that IT tools are rigid and not flexible (item 3b). An amount of 9.4% of students do not have a computer with the required technical features (item 5b), 9.3% of them have limited access to the Internet (item 4b), and 18.3% had technical difficulties (item 2b).

Due to the pandemic context, the students enrolled in our research had an increased level of involvement in online didactic activities. We asked them to specify how they attended online didactic activities; details about this issue are provided in Appendix A—

Table A3. The students’ level of involvement in online didactic activities.

	Recorded Answers
	1—Very Low n (%)	2— Low n (%)	3— Average n (%)	4— High n (%)	5—Very High n (%)
To what extent have you participated in the online lectures /seminars /labs of your study program?	4 (0.7)	12 (2.2)	71 (12.9)	170 (30.9)	294 (53.4)
To what extent have you had active interventions during the online lectures /seminars /labs of your study program?	49 (8.9)	120 (21.8)	216 (39.2)	120 (21.8)	46 (8.3)
To what extent would you prefer to participate in online lectures in the future?	81 (14.7)	58 (10.5)	170 (30.9)	144 (26.1)	98 (17.8)
To what extent would you prefer to participate in online seminars /labs in the future?	174 (31.6)	112 (20.3)	135 (24.5)	67 (12.2)	63 (11.4)

. An amount of 84.3% of students attended online didactic activities to a high or very high extent. Instead, the number of active interventions was a bit smaller: 39.2% of students had an average level of active interventions and only 30.1% chose active involvement often or very often. It is interesting to notice that the students generally agreed to online lectures (43.9% of them would prefer to participate in such lectures in the future too), but not so much to online practical stages or seminars (only 23.6% of students would prefer to participate in such activities in the future, too). These results were well correlated with the answers previously reported at items 11a and 12a and were actually expected from medical students because it is obvious that practice on real patients is mandatory in order to become good professionals.

Most of the students prefer using multimedia resources during the learning process (41.2%) or at least have a neutral opinion about this learning style (40.1%). Asked about their main purposes in using the internet, most of the students declared that they used the internet mainly for communication—e-mail, instant messaging, social networks, or dating (60.6%). The second purpose was entertainment (e-books, music, movies, or games), which was the most important for 33.2% students. Another one was information, reported by 32.7% students. Only a small number of students (6.4%) used the internet mainly for domestic facilities (e-commerce, online payments, providing services, or job offers)—Appendix A—

Table A4. General preferences as Internet and multimedia resources users.

		n (%)
Opinion about using multimedia resources in learning process:
	1—total disagreement	20 (3.6)
	2—partial disagreement	83 (15.1)
	3—neutral	221 (40.1)
	4—partial agreement	116 (21.1)
	5—total agreement	111 (20.1)
Internet used mainly for information (on a scale from 1 to 4):
	1—the most important	180 (32.7)
	2—important	214 (38.8)
	3—less important	138 (25.0)
	4—the least important	19 (3.4)
Internet used mainly for communication (on a scale from 1 to 4):
	1—the most important	334 (60.6)
	2—important	154 (27.9)
	3—less important	58 (10.5)
	4—the least important	5 (0.9)
Internet used mainly for entertainment (on a scale from 1 to 4):
	1—the most important	183 (33.2)
	2—important	190 (34.5)
	3—less important	144 (26.1)
	4—the least important	34 (6.2)
Internet used mainly for domestic facilities (on a scale from 1 to 4):
	1—the most important	35 (6.4)
	2—important	89 (16.2)
	3—less important	143 (26.0)
	4—the least important	284 (51.5)
Total		551 (100.0)

.

We have applied the two-step clustering procedure to the items in our questionnaire (Appendix A—Table A1) in order to detect the significant predictors among them and their inner correlations.

The students’ clustering according to the first 17 items, describing favourable opinions about online learning, revealed three clusters; the largest one comprises 52.5% of students and the smallest one 16.5% of students (Figure 1). The most important predictor is Item 6a: Online didactic activities are better in transmitting the essence of materials than the classical ones (predictor importance PI = 1.00), followed by Item 8a: Online didactic activities make me be more productive as a student (PI = 0.92), Item 7a: Online didactic activities make me understand faster and easier the concepts presented (PI = 0.90), Item 4a: Online didactic activities are more efficient than the classical ones (PI = 0.88) and Item 11a: Online lectures are more useful for me than the classical ones (PI = 0.76); the other items have PI coefficients < 0.70.

Therefore, the most relevant classification of students according to their favourable opinions about online learning is the one taking into consideration their answers to items 6a, 8a, 7a, 4a, and 11a. From the list of 17 investigated items, these are actually the most pragmatic because they refer strictly to the degree in which online learning is able to fulfil its purpose: the capacity to transmit the essence of the materials and to obtain to the audience (i.e., students, who will become more productive and understand concepts faster and easier). The first cluster (31.0% cases) contains students with negative opinions—they disagree, totally, with the idea that online learning is able to transmit the essence of materials and do not think that online learning is useful to them. The second cluster comprises more than half of students (52.5%), who are neutral or in partial disagreement with the idea that online learning is useful for them. The third cluster contains a relatively small percentage of students (16.5%), who are in total or partial agreement with the idea that online learning is useful for them (

Table 2. The description of clusters for favourable opinions about online learning.

	Recorded Answers
	1—Total Disagreement n (%)	2—Partial Disagreement n (%)	3—Neutral n (%)	4—Partial Agreement n (%)	5—Total Agreement n (%)
Cluster 1 (n = 171–31.0% cases):
Item 6a	123 (71.9)	33 (19.3)	12 (7.0)	3 (1.8)	-
Item 8a	112 (65.5)	42 (24.6)	14 (8.2)	3 (1.8)	-
Item 7a	115 (67.3)	51 (29.8)	4 (2.3)	-	1 (0.6)
Item 4a	110 (64.3)	53 (31.0)	8 (4.7)	-	-
Item 11a	89 (52.0)	50 (29.2)	19 (11.1)	12 (7.0)	1 (0.6)
Cluster 2 (n = 289–52.5% cases):
Item 6a	17 (5.9)	144 (49.8)	110 (38.1)	18 (6.2)	-
Item 8a	22 (7.6)	118 (40.8)	122 (42.2)	24 (8.3)	3 (1.0)
Item 7a	23 (8.0)	124 (42.9)	125 (43.3)	16 (5.5)	1 (0.3)
Item 4a	25 (8.7)	134 (46.4)	116 (40.1)	14 (4.8)	-
Item 11a	9 (3.1)	69 (23.9)	143 (49.5)	42 (14.5)	26 (9.0)
Cluster 3 (n = 91–16.5% cases):
Item 6a	-	1 (1.1)	31 (34.1)	34 (37.4)	25 (27.5)
Item 8a	1 (1.1)	3 (3.3)	18 (19.8)	34 (37.4)	35 (38.5)
Item 7a	-	-	37 (40.7)	28 (30.8)	26 (28.6)
Item 4a	-	3 (3.3)	32 (35.2)	37 (40.7)	19 (20.9)
Item 11a	-	1 (1.1)	12 (13.2)	29 (31.9)	49 (53.8)

).

The students’ clustering, according to the last 14 items, describing not favourable opinions about online learning, revealed two clusters with fair quality. The largest one comprises 65.2% of cases and the smallest one includes 34.8% of cases (Figure 2). The most important predictor is Item 8b: I don’t have the habit of learning using these technologies (predictor importance PI = 1.00), followed by Item 9b: Lack of teachers’ control and constant monitoring of my activities (PI = 0.82), Item 10b: Lack of direct communication and human interaction with teachers (PI = 0.72) and Item 11b: Lack of an efficient structuring of the content taught by the teaching staff (PI = 0.70); the other items have PI coefficients < 0.70.

The students’ classification according to their not favourable opinions about online learning takes into consideration mainly their answers to items 8b, 9b, 10b, and 11b, revealing again a pragmatic approach. The most consistent cluster is the second one, with 65.2% cases; these are the students who feel that they do not have the habit of learning using this technology, they miss their teacher’s control and constant monitoring, as well as direct communication and human interaction and think that the online content is poorly structured. The other cluster, the first one, includes only 34.8% of students who do not agree with these opinions (

Table 3. The description of clusters for not favourable opinions about online learning.

	Recorded Answers
	1—Total Disagreement n (%)	2—Partial Disagreement n (%)	3—Neutral n (%)	4—Partial Agreement n (%)	5—Total Agreement n (%)
Cluster 1 (n = 192–34.8% cases):
Item 8b	156 (81.3)	17 (8.9)	10 (5.2)	7 (3.6)	2 (1.0)
Item 9b	142 (74.0)	30 (15.6)	15 (7.8)	5 (2.6)	-
Item 10b	88 (45.8)	29 (15.1)	41 (21.4)	18 (9.4)	16 (8.3)
Item 11b	108 (56.3)	45 (23.4)	24 (12.5)	8 (4.2)	7 (3.6)
Cluster 2 (n = 359–65.2% cases):
Item 8b	24 (6.7)	89 (24.8)	121 (33.7)	83 (23.1)	42 (11.7)
Item 9b	29 (8.1)	108 (30.1)	125 (34.8)	68 (18.9)	29 (8.1)
Item 10b	2 (0.6)	34 (9.5)	64 (17.8)	124 (34.5)	135 (37.6)
Item 11b	10 (2.8)	101 (28.1)	133 (37.0)	70 (19.5)	45 (12.5)

).

As far as we found in the scientific literature, this is a new approach in surveys among students regarding their opinions about online learning. Usually, all surveys investigate and classify the students’ answers according to different criteria, and not the items themselves. Instead, we have tried a deeper analysis of the items through clustering in order to establish their hierarchy and gather the students according to this hierarchy—a more accurate method of study. Our purpose is to better understand the inner reasons for which students prefer or dislike online learning, because, with this sort of knowledge, we can choose better the adequate tools to make this technology more efficient.

We further characterized the clusters identified by comparison according to a few criteria, which can be relevant, as well: gender, age group, opinion about multimedia resources in the learning process, and general preferences as internet users; the results obtained are presented in

Table 4. Comparative study—the clusters extracted from favourable opinions about online learning.

		Generic Concept: Online Learning Transmits the Essence of Materials; Students Are More Productive and Understand the Concepts More Easily and Faster
		Cluster 1—Total Disagreement n (%)	Cluster 2—Neutral or Partial Disagreement n (%)	Cluster 3—Total Agreement n (%)	p-Value
Gender					0.476
	Male	43 (25.1)	63 (21.8	25 (27.5)
	Female	128 (74.9)	226 (78.2)	66 (72.5)
Age group					0.000 **
	18–20 years	118 (69.0)	146 (50.5)	33 (36.3)
	21–24 years	35 (20.5)	87 (30.1)	36 (39.6)
	over 25 years	18 (10.5)	56 (19.4)	22 (24.2)
Opinion about using multimedia resources in the learning process:					0.000 **
	1—total disagreement	17 (9.9)	3 (1.0)
	2—partial disagreement	53 (31.0)	29 (10.0)	1 (1.1)
	3—neutral	67 (39.2)	150 (51.9)	4 (4.4)
	4—partial agreement	19 (11.1)	73 (25.3)	24 (26.4)
	5—total agreement	15 (8.8)	34 (11.8)	62 (68.1)
Internet used mainly for information (on a scale from 1 to 4):					0.000 **
	1—the most important	39 (22.8)	90 (31.1)	51 (56.0)
	2—important	66 (38.6)	128 (44.3)	20 (22.0)
	3—less important	58 (33.9)	64 (22.1)	16 (17.6)
	4—the least important	8 (4.7)	7 (2.4)	4 (4.4)
Internet used mainly for communication (on a scale from 1 to 4):					0.009 **
	1—the most important	112 (65.5)	178 (61.6)	44 (48.4)
	2—important	45 (26.3)	74 (25.6)	35 (38.5)
	3—less important	11 (6.4)	37 (12.8)	10 (11.0)
	4—the least important	3 (1.8)		2 (2.2)
Internet used mainly for entertainment (on a scale from 1 to 4):					0.093
	1—the most important	62 (36.3)	87 (30.1)	34 (37.4)
	2—important	67 (39.2)	97 (33.6)	26 (28.6)
	3—less important	33 (19.3)	83 (28.7)	28 (30.8)
	4—the least important	9 (5.3)	22 (7.6)	3 (3.3)
Internet used mainly for domestic facilities (on a scale from 1 to 4):					0.005 **
	1—the most important	7 (4.1)	14 (4.8)	14 (15.4)
	2—important	27 (15.8)	43 (14.9)	19 (20.9)
	3—less important	47 (27.5)	75 (26.0)	21 (23.1)
	4—the least important	90 (52.6)	157 (54.3)	37 (40.7)
Total		171 (100.0)	289 (100.0)	91 (100.0)

Pearson Chi-squared test; ** p < 0.01 statistically highly significant.

and

Table 5. Comparative study—the clusters extracted from not favourable opinions about online learning.

		General Concept: Online Learning Is Lacking Monitoring and Human Interaction. The Content Is Poorly Structured; The Students Do not Have the Habit to Learn Using It
		Cluster 1–Disagreement n (%)	Cluster 2–Agreement n (%)	p–Value
Gender				0.123
	Male	53 (27.6)	78 (21.7)
	Female	139 (72.4)	281 (78.3)
Age group				0.002 **
	18–20 years	88 (45.8)	209 (58.2)
	21–24 years	57 (29.7)	101 (28.1)
	over 25 years	47 (24.5)	49 (13.6)
Opinion about using multimedia resources in learning process:				0.000 **
	1—total disagreement	5 (2.6)	15 (4.2)
	2—partial disagreement	11 (5.7)	72 (20.1)
	3—neutral	54 (28.1)	167 (46.5)
	4—partial agreement	46 (24.0)	70 (19.5)
	5—total agreement	76 (39.6)	35 (9.7)
Internet used mainly for information (on a scale from 1 to 4):				0.001 **
	1—the most important	82 (42.7)	98 (27.3)
	2—important	72 (37.5)	142 (39.6)
	3—less important	33 (17.2)	105 (29.2)
	4—the least important	5 (2.6)	14 (3.9)
Internet used mainly for communication (on a scale from 1 to 4):				0.191
	1—the most important	109 (56.8)	225 (62.7)
	2—important	55 (28.6)	99 (27.6)
	3—less important	27 (14.1)	31 (8.6)
	4—the least important	1 (0.5)	4 (1.1)
Internet used mainly for entertainment (on a scale from 1 to 4):				0.039 *
	1—the most important	66 (34.4)	117 (32.6)
	2—important	52 (27.1)	138 (38.4)
	3—less important	60 (31.3)	84 (23.4)
	4—the least important	14 (7.3)	20 (5.6)
Internet used mainly for domestic facilities (on a scale from 1 to 4):				0.011 *
	1—the most important	16 (8.3)	19 (5.3)
	2—important	41 (21.4)	48 (13.4)
	3—less important	38 (19.8)	105 (29.2)
	4—the least important	97 (50.5)	187 (52.1)
Total		192 (100.0)	359 (100.0)

Pearson Chi-squared test; * p < 0.05 statistically significant; ** p < 0.01 statistically highly significant.

.

Regarding the three clusters identified among the students according to their favourable opinions about online learning, there are no statistically significant differences between genders, but almost all the other criteria reveal significant differences (Table 4). The students in the first cluster, who do not feel that online learning is useful for them, are mostly young (69.0%, aged 18–20 years), do not prefer generally to use multimedia resources for learning (40.9%), and use the internet mainly for communication (65.5%), followed by entertainment (36.3%), information (22.8%), and domestic facilities (4.1%). The students in the second cluster, who partially disagree with online learning or are neutral, are also mostly young (50.5% aged 18–20 years), but they prefer using multimedia resources for learning (37.1%). They also use the internet mainly for communication (61.6%), followed instead by information (31.1%), entertainment (30.1%), and domestic facilities (4.8%). The students in the third cluster, who feel that online learning is useful for them, are older (63.8% aged over 21 years), prefer using multimedia resources for learning (94.5%), and use the internet mainly for information (56.0%), followed by communication (48.4%), entertainment (37.4%), and domestic facilities (15.4%). These results are consistent: the students’ clustering was made according to very objective criteria regarding the extent to which they feel that online learning is efficient for them. It is natural for mature students to think also that online learning is useful for them and to use IT technologies mainly for professional development and information, and less for entertainment, while younger students are more preoccupied with communication and entertainment and not very interested in learning.

Regarding the two clusters identified among the students according to their not favourable opinions about online learning, there are again no statistically significant differences between genders, and almost all the other criteria reveal significant differences (Table 5). The students in the first cluster, who are generally satisfied by online learning and do not suffer because of lack of constant monitoring and communication with teachers are mature (54.2% aged over 21 years), prefer using multimedia resources for learning (63.6%), and use the internet mainly for communication (56.8%), followed by information (42.7%), entertainment (34.4%), and domestic facilities (8.3%). The students in the second cluster, who do not have the habit of learning online, think that the online content is poorly structured and miss the constant monitoring and human interaction with teachers, are instead mostly younger (58.2% aged 18–20 years), are neutral or in partial disagreement with multimedia resources using for learning (66.6%), and use the internet also mainly for communication (62.7%), followed instead by entertainment (32.6%), information (27.3%), and domestic facilities (5.3%); the results obtained are consistent again.

We quantified further our results by calculating two general average scores, one for the favourable opinions recorded, IO_FAV, with an average value of 2.950 ± 0.697, a range between 1.24 ÷ 5.00 and a median value of 2.882, and the other one for not favourable opinions, IO_NF, with an average value of 2.392 ± 0.794, a range between 1.00 ÷ 4.64, and a median value of 2.429. We studied comparatively these scores against the same criteria as before (genders, age groups, opinion about using multimedia resources in the learning process, and general preferences as internet users) and also between the identified clusters—

Table 6. Comparative study—the score for favourable opinions about online learning IO_FAV.

IO_FAV (Score for Favourable Opinions)		n	Mean ± SD	Min ÷ Max	Median	p-Value
Total		551	2.950 ± 0.697	1.24 ÷ 5.00	2.882
Gender	male	131	2.956 ± 0.743	1.24 ÷ 5.00	2.882	0.910 †
	female	420	2.948 ± 0.683	1.29 ÷ 5.00	2.882
Age group	18–20 years	297	2.817 ± 0.627	1.29 ÷ 4.88	2.765	0.000 ‡**
	21–24 years	158	3.063 ± 0.789	1.24 ÷ 5.00	2.941
	over 25 years	96	3.174 ± 0.657	1.94 ÷ 4.76	3.177
Opinion about using multimedia resources in the learning process:	1—total disagreement	20	2.168 ± 0.486	1.24 ÷ 2.82	2.265	0.000 ‡**
	2—partial disagreement	83	2.404 ± 0.440	1.35 ÷ 3.41	2.412
	3—neutral	221	2.792 ± 0.472	1.35 ÷ 4.12	2.824
	4—partial agreement	116	3.126 ± 0.528	1.76 ÷ 4.47	3.059
	5—total agreement	111	3.629 ± 0.790	2.00 ÷ 5.00	3.647
Internet used mainly for information:	1—the most important	180	3.180 ± 0.783	1.24 ÷ 5.00	3.088	0.000 ‡**
	2—important	214	2.878 ± 0.591	1.29 ÷ 5.00	2.882
	3—less important	138	2.772 ± 0.659	1.35 ÷ 4.59	2.706
	4—the least important	19	2.873 ± 0.684	1.94 ÷ 3.88	2.765
Internet used mainly for communication:	1—the most important	334	2.916 ± 0.685	1.24 ÷ 5.00	2.853	0.224 ‡
	2—important	154	2.989 ± 0.764	1.35 ÷ 5.00	2.941
	3—less important	58	3.045 ± 0.542	1.59 ÷ 4.12	3.059
	4—the least important	5	2.882 ± 0.997	2.00 ÷ 4.18	2.471
Internet used mainly for entertainment:	1—the most important	183	2.987 ± 0.734	1.35 ÷ 4.88	2.941	0.063 ‡
	2—important	190	2.844 ± 0.669	1.24 ÷ 4.76	2.765
	3—less important	144	3.053 ± 0.700	1.29 ÷ 5.00	2.941
	4—the least important	34	2.907 ± 0.567	1.71 ÷ 4.12	2.941
Internet used mainly for domestic facilities:	1—the most important	35	3.335 ± 0.904	1.35 ÷ 4.88	3.235	0.005 ‡**
	2—important	89	3.060 ± 0.731	1.47 ÷ 5.00	3.000
	3—less important	143	2.856 ± 0.687	1.24 ÷ 4.76	2.824
	4—the least important	284	2.915 ± 0.645	1.35 ÷ 5.00	2.882
Two-Step Clustering	Cluster 1	171	2.228 ± 0.336	1.24 ÷ 2.82	2.294	0.000 ‡**
	Cluster 2	289	3.022 ± 0.291	2.41 ÷ 3.76	3.000
	Cluster 3	91	4.076 ± 0.445	3.24 ÷ 5.00	4.000

^† Mann-Whitney test U; ^‡ Kruskal-Wallis test H; ** p < 0.01 statistically highly significant.

and

Table 7. Comparative study—the score for not favourable opinions about online learning IO_NF.

IO_NF (Score for Not Favourable Opinions)		n	Mean ± SD	Min ÷ Max	Median	p-Value
Total		551	2.392 ± 0.794	1.00 ÷ 4.64	2.429
Gender	male	131	2.308 ± 0.803	1.00 ÷ 4.57	2.286	0.111 †
	female	420	2.418 ± 0.790	1.00 ÷ 4.64	2.500
Age group	18–20 years	297	2.521 ± 0.781	1.00 ÷ 4.64	2.571	0.000 ‡**
	21–24 years	158	2.332 ± 0.776	1.00 ÷ 4.21	2.357
	over 25 years	96	2.090 ± 0.776	1.00 ÷ 3.79	2.071
Opinion about using multimedia resources in the learning process:	1—total disagreement	20	2.950 ± 0.852	1.50 ÷ 4.64	3.000	0.000 ‡**
	2—partial disagreement	83	2.806 ± 0.662	1.50 ÷ 4.29	2.857
	3—neutral	221	2.511 ± 0.686	1.00 ÷ 4.43	2.500
	4—partial agreement	116	2.270 ± 0.742	1.00 ÷ 4.14	2.286
	5—total agreement	111	1.871 ± 0.821	1.00 ÷ 4.57	1.714
Internet used mainly for information:	1—the most important	180	2.262 ± 0.873	1.00 ÷ 4.64	2.214	0.019 ‡*
	2—important	214	2.398 ± 0.783	1.00 ÷ 4.43	2.393
	3—less important	138	2.542 ± 0.656	1.00 ÷ 4.14	2.571
	4—the least important	19	2.455 ± 0.876	1.00 ÷ 4.00	2.714
Internet used mainly for communication:	1—the most important	334	2.426 ± 0.790	1.00 ÷ 4.64	2.500	0.195 ‡
	2—important	154	2.356 ± 0.822	1.00 ÷ 4.43	2.429
	3—less important	58	2.255 ± 0.729	1.00 ÷ 4.14	2.286
	4—the least important	5	2.814 ± 0.714	1.71 ÷ 3.57	3.000
Internet used mainly for entertainment:	1—the most important	183	2.329 ± 0.827	1.00 ÷ 4.64	2.357	0.000 ‡**
	2—important	190	2.558 ± 0.760	1.00 ÷ 4.43	2.643
	3—less important	144	2.207 ± 0.748	1.00 ÷ 4.00	2.214
	4—the least important	34	2.580 ± 0.794	1.00 ÷ 4.29	2.429
Internet used mainly for domestic facilities:	1—the most important	35	2.145 ± 0.948	1.00 ÷4.57	2.000	0.002 ‡**
	2—important	89	2.204 ± 0.902	1.00 ÷ 4.64	2.143
	3—less important	143	2.564 ± 0.779	1.00 ÷ 4.29	2.571
	4—the least important	284	2.394 ± 0.724	1.00 ÷ 4.43	2.429
Two-Step Clustering	Cluster 1	192	1.541 ± 0.420	1.00 ÷ 2.79	1.500	0.000 †**
	Cluster 2	359	2.847 ± 0.526	1.86 ÷ 4.64	2.857

^† Mann-Whitney test U; ^‡ Kruskal-Wallis test H; * p < 0.05 statistically significant; ** p < 0.01 statistically highly significant.

.

There are no statistically significant differences between genders concerning the scores for favourable opinions about online learning (p = 0.910) and not favourable opinions (p = 0.111). We noticed instead such differences between age groups: mature students tend to agree more with online learning than younger ones (p = 0.000), while younger students tend to have not favourable opinions about online learning (p = 0.000).

The highest scores for favourable opinions about online learning were recorded in the group of students who like using multimedia resources during the learning process (3.629 ± 0.790). These students also have the lowest scores for not favourable opinions about online learning (1.871 ± 0.821). At the opposite end, there are students who disagree with the use of multimedia resources during the learning process: they have the lowest scores for favourable opinions about online learning (2.168 ± 0.486) and the highest scores for not favourable opinions about online learning (2.950 ± 0.852). The differences recorded are again statistically significant (p = 0.000 for both recorded scores).

Students who use the internet mainly for information also have the highest scores for favourable opinions about online learning (3.180 ± 0.783) and the lowest scores for not favourable opinions (2.262 ± 0.873), the differences identified being again statistically significant (p = 0.000 for IO-FAV and p = 0.019 for IO_NF). There are no statistically significant differences between the scores of opinions about online learning among students who use the internet mainly for communication. Anyway, students who do not use the internet for communication also have the highest scores for not favourable opinions about online learning (2.814 ± 0.714)—they are generally against IT technologies. There are no statistically significant differences between students who use the internet mainly for entertainment regarding their scores of favourable opinions about online learning (p = 0.063). We found instead such differences regarding the scores for not favourable opinions about online learning (p = 0.000): students who use the internet mainly for entertainment tend to have a smaller score for not favourable opinions about online learning (2.329 ± 0.827) compared with the students who do not use the internet systematically for entertainment (2.580 ± 0.794), but the trend is not as clear as in the other situations. Finally, the students who use the internet mainly for domestic facilities, even if they are a few, have high scores for favourable opinions about online learning (3.335 ± 0.904) and low scores for not favourable opinions about online learning (2.145 ± 0.948), the differences recorded being statistically significant in both cases (p = 0.005 for IO_FAV and p = 0.002 for IO_NF). Students who are able to use internet for domestic facilities, such as e-commerce, online payments, providing services or job offers, are anyway advanced users, with good knowledge of computers and therefore are familiar with online learning tools and can use them without difficulties.

The score for favourable opinions about online learning is also significantly different between the three clusters identified through the 17-item two-step clustering. The first cluster gathers students with the lowest scores (2.228 ± 0.336), who dislike online learning, the second cluster has intermediary values (3.022 ± 0.291), and the third cluster gathers students with the highest scores (4.076 ± 0.445), who therefore prefer online learning. The score for not favourable opinions about online learning is significantly different between the two clusters identified through the 14-item two-step clustering; again, the first cluster gathers students with the lowest scores (1.541 ± 0.420), and the second cluster has significantly higher values (2.847 ± 0.526). These results are also consistent with the clusters’ other features, as we already showed before (Table 2, Table 3, Table 4 and Table 5).

4. Discussion

The transition from traditional in-class teaching to online learning (full or blended with traditional learning) is an inevitable step, along with the digitalization of modern society. The breakout of the COVID-19 pandemic was the catalyst in this process, pushing medical schools to find quick emergency ways to replace medical teaching in hospitals and clinics with virtual teaching. Obviously, the pandemic was a very disturbing and traumatizing experience for the whole of mankind, but a small benefit was to reveal that the IT technology was prepared to assist human beings in most daily activities, ensure proper communication and interaction even in lockdown and facilitate working, learning, and entertaining, even in isolation. Online learning proved to be a good and practical solution, able to bring important advantages, which are strong enough to counterbalance the inherent drawbacks, and we can be sure that this technology will not be abandoned in the following years, but it will gain its particular place among the other didactic tools.

The current trend in e-learning for medical students and healthcare professionals is the development of clinical skills using a combination of medical imaging data archives with simulations and virtual reality technology [45]. E-learning can be used effectively for training, maintaining, and updating the skills of healthcare professionals according to their personal needs, the ultimate goal being to decrease significantly the errors during clinical practice [2]. Examples can be given in various medical fields, such as radiology, physiotherapy, nursing, and so on. Medical imagistic teaching can be improved by web-based simulation platforms used to manipulate and process radiological images with reconstruction algorithms, allowing one to fully customize their parameters [8,46]. In physiotherapy, video conference-based telehealth allows to deliver exercise-based interventions (i.e., management of knee osteoarthritis) [42]; the literature reported an increased trend of using online education in this specialty, and 56.3% of the respondents already use e-communication to schedule in-person consultations with patients requiring physiotherapy [47]. There is no doubt that e-learning is satisfactory in acquiring theoretical knowledge, but it is less effective in acquiring clinical and technical skills. For example, despite the availability of numerous anatomy virtual libraries for students, these are not as effective in exploring the complex structures in the human body as the dissection of cadavers or models [3,25,33]. Students prefer face-to-face demonstration for understanding the spatial orientation of the body organ-systems and visualizing the complex relations between anatomical structures in a clinical context, but find online demonstrations helpful for retaining key information, with less stress, better concentration, and easy access to the content at their own pace [48,49,50,51,52]. A teacher has a major role in counteracting the limitations of e-learning by explaining the content and highlighting the concepts during online sessions, aiming to improve the knowledge gained and making students more confident about the usefulness of e-learning [53].

Romanian medical students are generally open to online didactic activities, and they particularly enjoyed online courses. Almost half of the students (43.9%) would prefer to participate in online courses in the future because they find them useful (28.9%), and almost a quarter of students (23.6%) would prefer to participate in online practical stages in the future, which, anyway, are less useful in their opinion. Almost half of the students think that there are medical disciplines that cannot be easily transferred in the online environment due to objective limitations. Our students believe that online learning is more comfortable and more flexible than traditional classes because the didactic activities focus on the quality of the materials transmitted. They prefer to learn using digital tools and resources, to work autonomously, and to customize the tasks to solve according to their own learning pace. They also think that the digital competencies acquired will be useful in their future didactic and professional activity. Amongst the drawbacks of online learning, our students dislike the lack of communication and human interaction with teachers and the lack of relevant feedback from them; they do not always feel properly motivated for online learning, and they are aware of its limitations due to the particularities of some medical disciplines.

A new approach proposed in our study was to investigate deeper students’ inner motivation for certain favourable or not favourable opinions about online learning, which come from their personal preferences and development background. We started from the idea that students with good technical skills, who prefer using gadgets, computers, and the internet, will also be more open to all modern learning technologies and, particularly, to online learning. In order to quantify this behaviour, we asked the students to specify to which extent they use the internet and for which main purposes, divided into four categories: information, communication, entertainment, and domestic facilities. We correlated these answers with the scores for favourable and not favourable opinions about online learning. We found, indeed, significant results and certain associations between these elements, as depicted in our results. In short, students who use the internet mainly for information, which shows their concern for documentation and professional development, also have the highest scores for favourable opinions about online learning and the lowest scores for not favourable opinions. Students who use the internet mainly for domestic facilities, being therefore advanced users, with good knowledge about computers, also have significant higher scores for favourable opinions about online learning and significant lower scores for not favourable opinions about online learning. Nowadays, most students use the internet for communication on social networks; in this regard, the fact that we did not record statistically significant differences between their scores of opinions about online learning is not random. Students who use internet mainly for entertainment have smaller scores for not favourable opinions about online learning, which means that they generally agree with such technologies. Furthermore, students who generally prefer using multimedia resources during the learning process also enjoyed online learning, having the highest scores for favourable opinions and the lowest scores for not favourable opinions, while students who disagree to use multimedia resources during the learning process have the lowest scores for favourable opinions about online learning and the highest scores for not favourable opinions about online learning. All these differences were statistically significant.

Another new approach was to classify students according to their favourable and not favourable opinions about online learning through automated clustering, i.e., the two-step clustering procedure. We chose this procedure because it was able not only to identify relevant clusters among students, but also to identify the most relevant predictors for those clusters among the items analysed; the predictors found are interesting because they are not similar to the most popular reported items.

Regarding the favourable opinions, the most important predictor is Item 6a—online didactic activities transmit better the essence of material than the classical ones, accompanied by equally pragmatic assertions: online activities make students more productive, allow them to understand faster and easier the concepts presented and are more efficient than the traditional classes and, particularly, online lectures are more useful for students than the classical ones. According to these predictors, there are three categories of students: those who dislike online learning—they are young, aged 18–20 years, do not prefer, generally, using multimedia resources for learning and use the internet mainly for communication and entertainment; students in the second cluster partially disagree with online learning or are neutral—they are also young (aged 18–20 years), but they prefer using multimedia resources for learning and use the internet mainly for communication and information. Students in the third cluster feel that online learning is useful for them—they are older (aged over 21 years), prefer using multimedia resources for learning, and use the internet mainly for information, followed by communication.

Regarding the not favourable opinions, the most important predictor is Item 8b—students do not have the habit of learning using these technologies, accompanied by items involving the human factor: students miss their teacher’s control and constant monitoring, as well as direct communication and human interaction and think that the online content is poorly structured. According to these predictors, there are two categories of students: one of them contains students who agree with the enumerated drawbacks of online learning—they are mostly young, aged 18–20 years, neutral or in partial disagreement with multimedia resources using for learning and use the internet mainly for communication and entertainment; the other category contains students who do not believe so much in the enumerated drawbacks—again, these students are older (aged over 21 years), prefer using multimedia resources for learning, and use the internet mainly for communication and information.

The practical implications of these findings are important because we propose a different approach to investigating students’ opinions about modern didactic methods and, particularly, about online learning. It is not enough just to report the results and eventually analyse them comparatively according to different stratification criteria; instead, it is more accurate to investigate students’ inner reasons leading to these results, related to their general preferences, personal background, and even psychological profile. This way, we can understand better why students prefer or do not prefer certain didactic tools, and we can choose among them more wisely.

The research in the area is vivid, especially in the current post-pandemic period, because a large amount of fresh data are available, and all people are eager to understand the changes in human society triggered by this experience and their long-term implications. We would prefer to mention a few examples in this regard, relevant for framing our results in the appropriate context:

Franklin et al. [54] evaluated the medical students’ experiences, satisfaction, and knowledge in e-learning, tele-education, and telehealth during the COVID-19 period. The results show that the top three specialties mostly affected were surgery, internal medicine, and obstetrics-gynaecology. Only 35% of the students were satisfied with their e-learning programs in health care. Students preferred case-based video learning and readings, combined with “clinical immersion periods”. Medical students stated that tele-education and e-learning are not as effective as traditional medical education (in-person didactic classroom training and in-person, face-to-face in hospital clerkships) and prefer more ‘real’ cases to follow, instead of the presentation of ready-made cases. Our study confirms these findings: our students are satisfied with the online learning activities they attended in a percentage of 35.8%, while many students (46.8%) are neutral about this issue. Anyway, even if the general opinion is positive and 43.9% students would prefer to participate in online lectures in the future and find them useful (28.9%), our students are reserved regarding practical training: only 23.6% of them would prefer to participate in such activities in the future, and only 7.6% of them find such activities useful.

Orellano and Carcamo [55] evaluated the efficiency of online recorded lectures in teaching clinical courses. The research was designed in four phases: 1. a pre-post uncontrolled study to evaluate knowledge gained with recorded lectures; 2. a non-randomized crossover study to compare learning with recorded lectures before or after a face-to-face lecture; 3. focus groups to evaluate students’ perceptions about recorded lectures; 4. a randomized controlled trial to verify if the addition of questions to a recorded lecture every 10 min, and a summary webpage that improves knowledge. It was reported that knowledge gained through recorded lectures was similar to face-to-face lectures. Medical students enjoy recorded lectures, but without replacing face-to-face lectures; the study concluded that recorded lectures are efficient only when associated with traditional learning methods.

Hani et al. [56] assessed medical students’ satisfaction and knowledge achieved through online learning during the COVID-19 pandemic in a cross-sectional, questionnaire-based study performed on 1,000 students in medicine. An amount of 65.5% of all students were either satisfied or neutral with online learning; 63.6% of students in the first cycle (basic sciences) and 59.5% of students in the second cycle (clinical sciences) stated that they acquired knowledge at the same level or even better than through traditional learning. The study reported also that students’ levels of satisfaction and knowledge acquired are significantly affected by their level of preparedness, the teacher’s performance, and the website accessibility [56]. These results are partially confirmed by our findings: 43.0% of students think that online learning is focused better on the quality of the materials presented and 28.9% students think that online lectures are useful for them. Our study reveals instead other strengths of online activities: the efficiency, the capacity to transmit the essence better, and the improved clarity (students understand the concepts faster and easier).

Elshami et al. [57] aimed to identify factors affecting medical schools and students’ satisfaction with online learning during the COVID-19 pandemic period. Students’ satisfaction was lower than medical schools’ satisfaction (41.3% vs. 74.3%). The most important reasons for students’ satisfaction were improved communication and flexibility, while the most important factor for dissatisfaction was the presence of technical problems; the academic teaching staff was instead dissatisfied by the higher workload and time required to prepare the didactic materials. The research group suggests that the best methods to adopt for students’ better engagement and increased satisfaction are the combination between synchronous and asynchronous educational techniques and feedback in real-time. Our study shows that the main reasons for satisfaction among students are also flexibility, improved comfort, and the opportunity to work autonomously, but students’ dissatisfaction is caused mainly by human factors: the lack of habit to learn using these technologies, the lack of teachers’ control, and constant monitoring, combined with the lack of direct communication and human interaction and the poorly structured didactic content.

Hamilton et al. [43] reported that 30% of pharmacy schools prefer a blended online education (online and classroom components), while 47% of students prefer live lectures exclusively; 61% of students use smartphones and 37% use tablets “always” or “often” for academic activities, while only 31% of students think that paper textbooks are extremely valuable for academic success. The research group recommends to schools of pharmacy to include online learning methodologies in pharmacy curricula.

There is strong evidence that open-access teaching with medical experts enables students to acquire the latest medical advancements and preserve knowledge despite the suspension of university classes and clinical stages [58].

A particular case is represented by online courses, where most of the enrolled students are employed adults with full-time work and family commitments; in such a case, higher flexibility allows them to fulfil their educational goals and other responsibilities simultaneously [59].

Although most studies highlight the advantages of e-learning (adaptability, diversity, economic benefits), there are still ongoing limitations concerning this topic. First, not all the data provided by the literature are entirely objective. A review performed by Barteit et al. [60] found that 73% of e-learning research is based on pilot studies, and 45% of these studies used questionnaires. Considerable variation was reported concerning the studies’ design, evaluation, and assessment methods, evaluation periods, outcomes, and effectiveness measures. Moreover, most studies used subjective measures and custom-built evaluation frameworks, with low comparability and poor validity [53].

The limitations of e-learning are also related to the difficulty of evaluating the outcome regarding effectiveness, user satisfaction, changes in learners’ behaviour, or patient outcomes [61]. Other weaknesses of e-learning concern technical challenges, confidentiality issues, lower student engagement, lack of assessments, and inequalities of virtual teaching services worldwide [58]. Our study confirms such results: 84.3% of our students participated in a high extent in online activities, but only 30.1% chose to have active interventions during these activities, so their level of engagement is not the best. A positive aspect revealed by our study is that most students have enough IT competencies to be able to attend online didactic activities without major technical problems: only 6% of students admit that their level of digital competencies is poor, and others claim that they do not have the time necessary to understand and to use adequately digital tools and think that IT tools are rigid and not flexible; less than 10% students do not have a computer with the required technical features or have limited access to the internet, and 18.3% had technical difficulties. Challenges of online learning can include, therefore, the need to improve the IT infrastructure, internet access, and network connectivity. Furthermore, an increased number of students in each class decreases the interactivity with each student, and some students can require special training in IT and e-learning [62].

Blended learning (e-learning + traditional learning) seems to be the best-recommended solution among healthcare students and professionals [63]. It is also recommended to maintain the right balance between synchronous and asynchronous teaching methods to improve the learning pace, while the integration of more self-directed learning strategies can motivate medical students to prefer e-learning as teaching practice [3].

The effectiveness of the learning environment depends also on the learning style of the new generation of medical students. The learning style refers to how students attain, interpret, organize, evaluate, and retain information [64]. In online learning, medical students use regularly visual resources (YouTube), search engines (Google Academic), and active learning (participation, discussions with teachers and classmates, and documents access and sharing); the development of clinical skills can be made through clinical observation in virtual or real environments [65]. Shorey et al. [65] highlighted the need to understand the real preferences of medical students, to provide healthcare educators with the necessary knowledge allowing them to adapt their curricula and teaching style to online educational environments and improve students’ familiarization, guidance, and support.

Reed et al. [66] reviewed such initiatives designed to improve teaching effectiveness in medical education (such as experiential learning, focused feedback, and effective relationships with peers) and highlighted the necessity to understand students’ neurobiology of learning. According to their findings, the most effective learning methods are using standardized patients to develop communication skills, online quizzes for knowledge assessment and self-directed learning support, and practical sessions, videoclips and case-based discussions to develop clinical skills.

The education of medical students and healthcare professionals is obviously multifactorial; in this regard, e-learning must be considered as a partner of traditional learning, aiming to improve the effectiveness of the knowledge gained (theoretical and practical skills) in a safe teaching environment. The providers of online learning must answer the following questions: ‘How effective are online platforms in fulfilling the learning needs in various medical fields?’, ‘How are the students dealing with the teaching scenarios on online platforms?’, ‘How are the students dealing with the technical challenges?’ and ‘What changes must be incorporated into the academic curricula to make it more effective?’ [3,63]. This is a whole paradigm change, with many complex issues and undiscovered benefits, and only the future will reveal the best answers. Data provided by research in this area address mainly the decision makers and stakeholders who are responsible to implement effective e-learning programs and to spread this concept in university teaching [67].

5. Conclusions

As we have already showed in this paper, the concept of online education is not new, it did not appear together with the COVID-19 pandemic, and it will definitely not disappear once the pandemic is over. We can claim without a doubt that the pandemic had a beneficial role because it brought this concept to the attention of public opinion, decision-makers, and stakeholders worldwide, by putting it quickly and mandatorily into practice as the only solution to continue a normal life, despite the isolation regime. The educational process at all levels and in all fields was forced to take place in another and completely new way, which until now had only been tested by research groups, through pilot studies and small-scale experiments, without a real stake. It was a real trial by fire for online education, and it was promoted successfully, despite the inherent difficulties of any beginning. Research groups from all over the world had the rare opportunity to access a vast amount of data, on all levels, that would allow the most in-depth understanding of this phenomenon on multiple coordinates, regarding its practical and significant aspects, which are the most reliable tools for its effective implementation, and, most importantly, which are the results of its use both from the perspective of the main users—teachers, as well as of the main beneficiaries—students.

In the scientific literature, plenty of studies have been and are being published, from all regions of the world, about the effectiveness of online education and about how this tool was adopted by teachers and students, highlighting its advantages and disadvantages. Most of them are based on surveys that record the pro and con opinions of students and teachers, relate them to the performances level (eventually compared with the level acquired through traditional methods), and compare them according to the demographic characteristics of the subjects, such as gender, age group, specialization, residence, income level, etc. The main goal is to identify the set of minimum requirements to be fulfilled by online teaching tools to be functional, efficient, accepted without doubts by both teachers and students, and to improve clearly students’ level of knowledge.

Such an approach is obviously right and useful, providing valuable information for understanding the phenomenon, but our study proposes a slight change in perspective, which we also consider to be useful: it is not enough to evaluate the students as a homogenous block, in order to understand their group reactions caused by standard social features, such as gender or age group, but it is much more challenging to treat each student as a separate individual and to understand the personal reasons that make them accept or not online education and be able to adapt to the new requirements.

Our study is addressed to a particular category of students—those in medicine. Medicine is a difficult specialization, where students are usually competitive and interested in obtaining good academic results, and quality training, both theoretically and practically, which are essential requirements to become a good doctor. Therefore, a teacher does not have the right to fail during the didactic process. From this perspective, we proposed a deeper analysis of the features of online education that can make the difference between its acceptance and non-acceptance, as well as of students’ behavioural models that can influence their opinions towards this type of tools. We designed our study around the central idea that a student’s opinion towards IT tools in general, translated by the way they choose to use such tools both for didactic training, as well as in everyday life, will significantly influence the way they will position themselves towards online education and consequently, the results they will be able to obtain in such an environment.

The results we obtained are suggestive and outline a different picture, more complex in our opinion, both of the online didactic tools that can work in higher medical education, as well as of the typology of students who will enjoy using such tools and will be able to make the most of their advantages.

A successful platform for medical online education must be able, first of all, to transmit better the essence of uploaded materials, making students more productive and allowing them to understand faster and easier the presented concepts. This is a very pragmatic point of view and shows that medical students have a mature mindset and are primarily interested in the effectiveness of the tools they use in their training and how such tools can help them to evolve. Regarding the drawbacks of online learning, the most important difficulty in students’ opinion is the lack of habit to learn using such technology, followed by issues involving human factors: the lack of teachers’ control and constant monitoring, the lack of direct communication and human interaction, and the poorly structured online content.

Regarding students’ profiles, we also have some interesting results: students who feel that online learning is useful for them are mature (aged over 21 years), prefer using multimedia resources for learning and use the internet mainly for information and communication. Around a half of medical students partially disagree with online learning or are neutral—they are younger (aged 18–20 years), prefer using multimedia resources for learning, and use the internet mainly for communication and information, and around 30% of medical students dislike online learning—they are also young, aged 18–20 years, do not prefer generally to use multimedia resources for learning, and use the internet mainly for communication and entertainment.

We intend to refine our study in the future by adding new elements regarding students’ behavioural patterns, which can be relevant, such as the style and intensity in using the internet and personal opinions about the general utility of IT and the internet. Such an approach can be also naturally related to aspects regarding students’ psychological typologies. In this regard, we intend to investigate the students from the perspective of their Motivational Persistence Scale—a measuring scale able to quantify an individual’s inner level of motivation and tenacity in reaching their personal goals. These features are mandatory for a medical student, and we think that interesting new correlations can be revealed through a study in this direction. We also think that such an approach can be useful to investigate not only the potential of online learning, but also the potential and the efficiency of all the other tools, which compose the general concept of blended learning, because it is undeniable that the learning style will change radically in the coming years, and blended tools will become a common practice.