Demographic characteristics
A total of 634 students responded and accounted for 81.28% of all the RTME-GXMU students (780 in total). After data cleaning including consistency check, estimation, and casewise deletion, eventually, the sum of validated questionnaires fixed on 612 and valid response rate equaled to 78.46%. Out of 612 RTME-GXMU students, 352 (57.52%) were female and 260 (42.48%) were male; 313 (51.14%) were being in the phase of preclinical medicine education, 134 (21.89%) in clinical medicine education, and 165 (26.97%) in clinical clerkship; 108 (17.65%) achieved GPA in the last semester equivalent to or higher than 3, 365 (59.64%) reported GPA between 2 to 3, and 139 (22.71%) obtained GPA equivalent to or lower than 2. The survey indicated a relatively higher proportion of female than that of male, which corresponded to the national data of medical students (See Table 1).
Table 1
Demographic Characteristics of GXMU-RTME students
| Participants, no (%), n = 612 | National data, % |
Gender | | |
Female | 352 (57.52) | 55.0* |
Male | 260 (42.48) | 45.0* |
Learning Phases | | |
Preclinical medical education | 313 (51.14) | |
Clinical medical education | 134 (21.89) | |
Clinical clerkship | 165 (26.97) | |
GPA in the last semester | | |
≤ 2 | 139 (22.71) | |
2∼3 | 365 (59.64) | |
≥ 3 | 108 (17.65) | |
*Data Source: 2019 China Medical Student Survey (CMSS) authorized by Ministry of Education (MOE) and National Health Commission (NHC).
General Situation Of Online Learning
At the beginning of the COVID-19 outbreak, the RTME-GXMU students received various types of online courses. Out of 612 students, 312 (51.14%) participated in general and preclinical medicine courses, 134 (21.89%) in clinical courses, and 165 (26.97%) in both clinical courses and skilled-related courses. Every responder had online learning experience in two or three patterns combined. MOOC combined with live stream and videotape was the most common permutation as it was picked by as many as 313 (51.14%) students, and the following was live stream plus videotape with responses from 299 (48.86%) students (See Table 2).
Cellphone together with personal computer chosen by 389 (63.56%) students was the typical device combination used for online learning, tailed by cellphone plus tablet computer clicked by 178 (29.08%) and personal computer by 45 (7.36%) (See Table 2).
A majority of responders (300, 49.02%) conducted online learning at home or in dormitory, 237 (38.72%) at home or in dormitory or in classrooms, 56 (9.15%) at home or in dormitory or in the library on campus, and 19 (3.11%) at home or in dormitory or in cybercafé (See Table 2).
Table 2
General Information of online learning
| Participants, no (%), n = 612 |
Courses | |
General courses + Preclinical medicine courses | 313 (51.14) |
Clinical medicine courses | 134 (21.89) |
Clinical medicine courses + Skill-related courses | 165 (26.97) |
Online learning patterns | |
MOOC + live stream + videotape * | 313 (51.14) |
live stream + videotape ** | 299 (48.86) |
Online learning devices | |
Cellphone + personal computer | 389 (63.56) |
Cellphone + tablet computer | 178 (29.08) |
Personal computer | 45 (7.36) |
Location of online learning | |
Home + dormitory | 300 (49.02) |
Home + dormitory + classroom | 237 (38.72) |
Home + dormitory + library | 56 (9.15) |
Home + dormitory + cybercafé | 19 (3.11) |
* The combination of MOOC, live stream, and videotape, the latter two by faculty of GXMU; |
**The combination of live stream and videotape both by faculty of GXMU.
Learning Efficiency Of Online Learning And Classroom Learning In Person
As exhibited in Table 3, online learning efficiency was tagged as equivalent to or lower than 30% by 130 (21.24%) RTME-GXMU students, as between 30–70% by 274 (44.77%), and as equivalent to or higher than 70% by 208 (33.99%), compared to classroom learning efficiency rated as equivalent to or lower than 30% by 93 (15.20%), as between 30–70% by 295 (48.20%), and as equivalent to or higher than 70% by 224 (36.60%). The comparison of distributions between both learning efficiency indicated significant differences in statistic (p < 0.001), which meant that learning efficiency of online learning was obviously lower than that of classroom learning in the RTME-GXMU students’ opinion. The result that online learning efficiency was inferior to classroom learning efficiency resonated among the following multiple comparisons in male students or female students or every given learning phase or GPA level. Consequently, students perceived learning efficiency in online learning lower than in classroom learning despite of gender, learning phases, and GPA level (See Table 3).
Table 3
Comparison of learning efficiency between online learning and classroom learning
| Online learning efficiency, Participants, no (%) | Classroom learning efficiency, Participant, no (%) | Z | P |
≤30% | 30∼70% | ≥70% | ≤30% | 30∼70% | ≥70% |
Overall Sample, n = 612 | 130 (21.24) | 274 (44.77) | 208 (33.99) | 93 (15.20) | 295 (48.20) | 224 (36.60) | 7.28 | < 0.001a |
Gender | | | | | | | | |
Female, n = 352 | 72 (20.45) | 153 (43.47) | 127 (36.08) | 48 (13.64) | 169 (48.01) | 135 (38.35) | 5.66 | < 0.001b |
Male, n = 260 | 58 (22.31) | 121 (46.54) | 81 (31.15) | 45 (17.31) | 126 (48.46) | 89 (34.23) | 4.583 | < 0.001c |
Learning Phases | | | | | | | | |
Preclinical medical education, n = 313 | 89 (28.43) | 156 (49.84) | 68 (21.73) | 68 (21.73) | 171 (54.63) | 74 (23.64) | 5.20 | < 0.001d |
Clinical medical education, n = 134 | 27 (20.15) | 50 (37.31) | 57 (42.54) | 21 (15.67) | 55 (41.04) | 58(43.29) | -2.65 | 0.008e |
Clinical clerkship, n = 165 | 14 (8.48) | 68 (41.21) | 83 (50.31) | 7 (4.24) | 73(44.24) | 85(51.52) | 7.50 | 0.003f |
GPA in the last semester | | | | | | | | |
≤ 2 n = 139 | 69 (49.64) | 63 (45.32) | 7 (5.04) | 52 (37.41) | 78(56.12) | 9 (6.47) | 4.36 | < 0.001g |
2∼3 n = 365 | 61 (16.71) | 200 (54.79) | 104 (28.50) | 41 (11.23) | 213(58.36) | 111(30.41) | 5.20 | < 0.001h |
≥ 3 n = 108 | 0 | 11 (10.19) | 97 (89.81) | 0 | 4 (3.70) | 104(96.30) | 2.65 | 0.008i |
a: The overall median of difference between online learning efficiency and classroom learning efficiency not as zero, P< 0.001; |
b: Among female responders, the overall median of difference between online learning efficiency and classroom learning efficiency not as zero, P< 0.001; |
c: Among male responders, the overall median of difference of between online learning efficiency and classroom learning efficiency not as zero, P< 0.001; |
d: In the phase of preclinical medical education, the overall median of difference between online learning efficiency and classroom learning efficiency not as zero, P< 0.001; |
e: In the phase of clinical medical education, the overall median of difference between online learning efficiency and classroom learning efficiency not as zero, P = 0.008; |
f: In the phase of clinical clerkship, the overall median of difference between online learning efficiency and classroom learning efficiency not as zero, P = 0.003; |
g: As for GPA ≤ 2 in the last semester, the overall median of difference between online learning efficiency and classroom learning efficiency not as zero, P<0.001; |
h: As for GPA between 2 to 3 in the last semester, the overall median of difference between online learning efficiency and classroom learning efficiency not as zero, P<0.001; |
i As for GPA ≥ 3 in the last semester, the overall median of difference between online learning efficiency and classroom learning efficiency not as zero, P = 0.008. |
Correlations Of Online Learning Efficiency With Learning Phases Or Gpa Levels Or Gender
Amid three learning phases, in the preclinical medical education phase showed the largest percentage (28.43%) of students who evaluated online learning efficiency as “≤30%” and the smallest percentage (21.73%) of students who assessed it as “≥70%” (See Table 3). Contrarily, from the clinical clerkship phase emerged the most proportion (50.31%) of students who chose “≥70%” for online learning efficiency and the least proportion (8.48%) of students who selected “≤30%”(See Table 3). As a learning phase moved up or down to the next more advanced or lower phase, the number of students who positively assessed online learning efficiency appeared to increase or decrease accordingly (See Table 3). The comparison of online learning efficiency distributions between any two learning phases presented statistically different (P<0.001).
Perceived online learning efficiency among three GPA levels revealed a trend similar to amid the three learning phases. The higher GPA level students achieved, the more likely they reported good online learning efficiency. Out of 108 RTME-GXMU students with GPA ≥ 3, 97 (89.81%) reported online learning efficiency equal to or more than 70%, and no one chose the rank of “≤30%”(See Table 3). The students with GPA ≤ 2 made quite a different case in which a total of 69 (49.64%) students picked the lowest rank of “≤30%” and only as few as 7 (5.04%) selected the highest rank of “≥ 70%” (See Table 3). Every comparison of learning efficiency distributions between any two levels of GPA indicated statistical difference (P<0.001). However, the learning efficiency distributions of both genders did not differ from each other statistically (P = 0.24).
Kendall’s Tau-b coefficient was 0.78 (P<0.001) in the investigation of any possible correlation between learning phases and online learning efficiency, and 0.81 (P<0.001) between GPA levels and online learning efficiency, which strongly suggested that both learning phases and GPA levels
positively correlated with online learning efficiency (See Table 4). Spearman’s coefficient was as low as 0.04 (P = 0.24) between gender and online learning efficiency, which highly implied no correlation between the both (See Table 4).
Table 4
Correlations between online learning efficiency and learning phases or GPA or Gender online
| Kendall’s Tau-b /rs | T | P |
Learning phases VS Online learning efficiency | 0.78 | 50.29 | <0.001 |
GPA level VS Online learning efficiency | 0.81 | 30.35 | <0.001 |
Gender VS Online learning efficiency | 0.04 | -1.16 | 0.24 |
Learning Phases, Gpa Levels, And Online Learning Efficiency In Logistic Regression
Correlations amid learning phases, GPA levels, and online learning efficiency were further examined through polynomial logistic regression. Online learning efficiency as the dependent variable, and learning phase as well as GPA level as two independent variables were included in a cumulative logit model, and the results were presented in Table 5. The regression coefficient estimate (RCE), Wald, and P value were − 3.52, 14.69, and < 0.001 for from the preclinical education phase to clinical education phase, and − 3.52, 16.17, and < 0.001 for from the clinical education phase to clinical clerkship phase, which suggested a positive correlation between online learning efficiency and learning phases. Moreover, since the above two RCEs presented the same values, the extent to which the shift from the phase of preclinical education to clinical education influenced online learning efficiency was similar to the one from the phase of clinical education to clinical clerkship. As in the case of GPA levels, the RCE (-3.41), Wald(-9.61), and P value (0.002) for from GPA equivalent to or less than 2 to between 2 to 3 supported that online learning efficiency was associated with GPA level. The students with GPA above 2 were more likely to give positive opinion to online learning efficiency than those with GPA equivalent to or below 2. But the RCE (-3.99), Wald(0), and P value (1) for from GPA between 2 to 3 to above 3 implied that impact of GPA between 2 to 3 on online learning efficiency did not statistically differ from that of GPA higher than 3.
Table 5
Parameter estimates of cumulative logits model
| Regression coefficient estimate | S‾x | Wald | df | Significance | 95%CI |
Lower limit | Upper limit |
[online learning efficiency = 1]* | -7.60 | 0.678 | 125.797 | 1 | <0.001 | -8.928 | -6.272 |
[ online learning efficiency = 2]** | -3.738 | 0.628 | 35.485 | 1 | <0.001 | -4.969 | -2.508 |
[learning phase = 1]# | -3.521 | 0.919 | 14.690 | 1 | <0.001 | -5.321 | -1.720 |
[learning phase = 2]## | -3.521 | 0.875 | 16.174 | 1 | <0.001 | -5.236 | -1.805 |
[learning phase = 3]### | 0a | - | - | 0 | - | - | - |
[GPA = 1]$ | -3.412 | 1.101 | 9.606 | 1 | 0.002 | -5.570 | -1.254 |
[GPA = 2]$$ | -3.990 | 1.068 | 0 | 1 | 1.000 | -2.093 | 2.093 |
[GPA = 3]$$$ | 0a | | | 0 | - | - | - |
*Learning efficiency≤30%; **Learning efficiency30-70%; # Preclinical medical education phase; ## Clinical medical education; ### Clinical Clerkship; $ GPA≤ 2; $$ GPA 2–3; $$$ GPA≥3.
Factors Negatively Affecting Online Learning Efficiency
From the RTME-GXMU students’ point of view, the five top factors that most damaged learning efficiency of online learning were low academic motivation, poor course design, inferiority in online teaching ability, limited interactions between faculty and students or among students, and insufficient learner engagement. Other factors mentioned with less frequencies included distracted learning environment, deficiency in supporting learning materials, inefficient learning strategies, lack of goals and plans for online learning, variation in online learning platform that meant lack of compatibility among learning platforms used for various courses, and absence of learning assistance before, during, and after online learning.