Adapting to new challenges in medical education: a three-step digitization approach for blended learning | BMC Medical Education

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In this section, we present the findings from our study comparing the three-step digitization approach to its traditional face-to-face counterpart in medical education. The analysis encompasses descriptive statistics to outline students’ emotional responses, inferential statistics and qualitative analyses, offering a comprehensive view of the impact of our digitization approach versus traditional instruction on both emotional and cognitive outcomes.

Regarding the descriptive statistics of positive emotions—measured on a visual analog scale ranging from minimum 1.00 to maximum 10.00—during the lecture, it can be stated that interest (INT) and motivation (MTV) showed the highest median values at 7.00 and 6.00 respectively. Joy (JOY) and hope (HPE) had median values of 5.00, while pride (PRD) and relaxation (RLX) presented the lowest medians at 4.00 and 3.00. The mean values align closely with the medians, with interest at 7.04 and motivation at 6.13 being the most prominent. Standard deviation values, such as 2.52 for pride (PRD) and 2.35 for hope (HPE), indicate variability in responses. The skewness for interest at -0.49 and kurtosis for pride at -0.99 provide details on the distribution shapes (Table 1).

Table 1 Descriptive Statistics of positive emotions during the lecture

Median scores for boredom (BRD), frustration (FRS), and stress (STR) were noted at 4.00, 5.00, and 6.00 respectively, indicating moderate levels of these emotions. The mean scores closely follow, with curiosity (CUR) displaying a high mean of 6.91, frustration showing a mean of 5.22, and stress at 5.84, reflecting notable occurrences of these emotions. Standard deviation values, such as 2.80 for anxiety and 2.73 for worry, demonstrate variability in students’ experiences of these negative emotions. The interquartile range (IQR) for most emotions spans from 3.00 to 4.00, suggesting a consistent spread of responses across the cohort. Variance, skewness, and kurtosis values offer additional insights into the distribution of these emotions, with skewness for sadness (SAD) at 1.36 indicating a heavier tail towards higher scores. The maximum scores for all emotions reached 10.00, showing that some students experienced high levels of negative emotions during the lecture (Table 2).

Table 2 Descriptive Statistics of negative emotions during the lecture

Regarding positive emotions during the digital course, median scores indicate a strong presence of interest (INT), motivation (MTV), and curiosity (CUR) at 7.00, alongside contentment (CNT) and relaxation (RLX) at 6.00, reflecting positive engagement with the digital format. The mean scores further support this, with interest at 6.87 and motivation at 6.33 showcasing high levels of engagement. Standard deviation values, such as 2.76 for relaxation (RLX) and 2.71 for pride (PRD), suggest variability in emotional experiences among students. The interquartile range (IQR) for most emotions was between 2.00 and 3.00, indicating consistency in responses. Variance, like 5.41 for joy and 5.57 for enthusiasm, along with skewness and kurtosis values, offer insight into the distribution of these positive emotions, with most showing slight deviations from normal distribution. The maximum scores reached 10.00 for all emotions, indicating that some students experienced high levels of positive emotions in the digital learning environment (Table 3).

Table 3 Descriptive Statistics of positive emotions during the digital course

For negative emotions experienced during the digital course, the median values indicate lower levels of negative emotions, with sadness (SAD) and shame (SHM) at a median of 1.00, suggesting infrequent experiences of these emotions. Frustration (FRS) and confusion (CNF) showed slightly higher medians of 2.00 and 3.00, respectively, indicating a moderate presence. The mean values, such as 3.74 for boredom (BRD) and 3.13 for frustration (FRS), reflect a general trend of lower negative emotional responses in the digital learning environment. Standard deviation and interquartile range (IQR) values demonstrate variability among students’ responses, with standard deviation figures like 2.13 for boredom and 2.23 for frustration. Variance, skewness, and kurtosis metrics provide further insight into the distribution, with skewness for sadness at 2.51 indicating a positive skew, and kurtosis for shame at 9.44 suggesting a leptokurtic distribution. The minimum and maximum scores span from 1.00 to 10.00 for all emotions, showing a range of emotional experiences among participants in the digital course (Table 4).

Table 4 Descriptive Statistics of negative emotions during the digital course

Paired samples t-tests comparing positive emotional responses between a traditional lecture and the three-step digitization approach revealed significant findings. Joy (JOY), hope (HPE), pride (PRD), relaxation (RLX), and contentment (CNT) all showed significant increases in the digital setting, with t-values of -3.579 (p < 0.001) for joy, -3.958 (p < 0.001) for hope, -3.209 (p = 0.002) for pride, -9.800 (p < 0.001) for relaxation, and -6.228 (p < 0.001) for contentment. These results suggest a notable enhancement of these emotions through digital learning. Conversely, enthusiasm (ENT), interest (INT), and motivation (MTV) displayed no significant differences between the formats, with p-values of 0.174, 0.283, and 0.287, respectively (Fig. 2).

Fig. 2
figure 2

This figure presents bar plots for each measured emotion, contrasting the average levels of positive emotional responses between traditional face-to-face lectures (Lecture) and our three-step digitization approach (Digital course). Each bar represents the mean value of the respective emotion in the traditional and digital settings, with error bars indicating the standard error of the mean (SEM). Significant differences between the two formats are highlighted as; ** denotes p < 0.01, *** denotes p < 0.001, n.s. denotes not significant

The analysis of negative emotions via paired samples t-tests between the traditional lecture and the digitized lecture showed a significant reduction in negative emotions in the digital format. Specifically, feelings of boredom (BRD) were lower in the digital setting, t(230) = 2.575, p = 0.011. More pronounced reductions were observed for frustration (FRS), t(230) = 10.339, p < 0.001; disappointment (DIS), t(230) = 5.870, p < 0.001; desperation (DSP), t(230) = 9.991, p < 0.001; sadness (SAD), t(230) = 5.695, p < 0.001; stress (STR), t(230) = 12.999, p < 0.001; demotivation (DMT), t(230) = 3.458, p < 0.001; anxiety (ANX), t(230) = 9.992, p < 0.001; worry (WRY), t(230) = 11.127, p < 0.001; shame (SHM), t(230) = 4.041, p < 0.001; and confusion (CNF), t(230) = 9.486, p < 0.001.

These results indicate that participants experienced significantly fewer negative emotions during the digital course compared to the face-to-face lecture (Fig. 3).

Fig. 3
figure 3

This figure presents bar plots for each measured emotion, contrasting the average levels of negative emotional responses between traditional face-to-face lectures (Lecture) and our three-step digitization approach (Digital course). Each bar represents the mean value of the respective emotion in the traditional and digital settings, with error bars indicating the standard error of the mean (SEM). Significant differences between the two formats are highlighted as; * denotes p < 0.05 and *** denotes p < 0.001

The comparison of knowledge gain between traditional face-to-face lectures and digitized lectures was assessed through a paired samples t-test, accounting for prior knowledge in both scenarios. The analysis revealed that knowledge gain in the digitized format was significantly higher than in the traditional lecture setting. Specifically, the t-test showed a statistically significant difference in knowledge acquisition favoring the digitized approach, with a t-value of -2.795 (df = 230, p = 0.006) (Fig. 4). This indicates that students experienced a greater enhancement of their understanding and retention of the material when engaged with the content through the digitized learning format.

Fig. 4
figure 4

The figure shows scatterplots depicting the subjects’ prior knowledge for both the lecture (light yellow) and the digitized lecture (light blue) as well as the perceived knowledge after the lecture (orange) and after the digitized lecture (light purple). The respective difference is shown in the center of the figure, where the increase in knowledge from the lecture (ochre yellow) and from the digitized lecture (dark purple) are depicted. Significant differences between the two formats are highlighted as; ** denotes p < 0.01

Analysis for perceived levels of concentration and distraction offered insightful contrasts between the traditional lecture hall setting and the online learning environment. Notably, students reported significantly higher levels of concentration when participating in online learning, as indicated by a t-value of -5.801 (df = 230, p < 0.001) (Fig. 5A). Substantiating this finding, the analysis regarding perceived distractions revealed that students experienced a higher level of distraction in the face-to-face lecture setting compared to the online environment. The statistical outcome, with a t-value of 2.848 (df = 230, p = 0.005) (Fig. 5B), supports the notion that the traditional classroom setting may present more elements that divert attention away from the learning material.

Fig. 5
figure 5

Chart A illustrates concentration levels, indicating enhanced focus in digital settings. Chart B assesses distractions from external factors, with digital learning showing reduced interference. Charts C and D, using the AEQ, reveal lower anxiety and higher enjoyment in digital formats, respectively. Each chart presents mean values with error bars indicating SEM. Significant differences between the two formats are highlighted as; ** denotes p < 0.01 and *** denotes p < 0.001

Building on the previous findings that online learning environments potentially can enhance concentration while reducing distractions, the Achievement Emotions Questionnaire (AEQ) results further illuminate the emotional benefits of our digitization approach. Specifically, the AEQ results revealed a significant decrease in anxiety levels in the online learning environment, as shown by a t-value of 9.446 (df = 230, p < 0.001) (Fig. 5C). In parallel, enjoyment levels significantly increased, as shown by a t-value of -4.717 (df = 230, p < 0.001), indicating heightened enjoyment in our online setting (Fig. 5D).

In assessing student preferences and perceptions regarding course format, our findings reveal a distinct inclination towards the digital learning environment introduced by our three-step digitization approach. According to the collected data, a significant majority of students (61.0%) expressed a preference for the digital course format over the traditional lecture (26.4%), with a small portion remaining undecided (12.6%) (Fig. 6A). Further analysis aimed to discern whether this preference was merely due to convenience or attributed to perceived educational efficiency. The results unequivocally showed that students regard the digital learning setting as more efficient, with a substantial 71.4% endorsing the digital course for its efficacy, compared to 14.3% favoring the traditional lecture format, and an equal percentage (14.3%) remains undecided (Fig. 6B).

Fig. 6
figure 6

A shows student preferences for traditional lectures, our digital course, or undecided. A significant majority prefer the digital course format. B illustrates perceptions of learning efficiency between traditional lectures and digital courses, with an option for undecided. The majority view the digital format as more efficient, with data presented in percentages. *** denotes p < 0.001

Our qualitative analysis of student feedback on the digitized lecture format revealed insightful perspectives on its advantages and disadvantages. Positive feedback emphasized the digital format’s flexibility, with 104 mentions (45.0%) of flexible time management as a significant benefit, allowing students to tailor their learning schedules to personal needs. The extensive and versatile learning experience was highlighted by 99 (42.9%) participants, appreciating the diversity in learning materials and approaches. Direct feedback on learning progress was noted 43 (18.6%) times as a key advantage, fostering a sense of immediate understanding and improvement. Improved concentration and stress reduction were also mentioned, with 21 (9.1%) and 31 (13.4%) mentions respectively, indicating an environment conducive to focused learning and lower anxiety levels. Conversely, negative feedback centered on aspects unique to traditional lectures. Twenty-eight (12.1%) participants felt a better understanding of material during in-person lectures, pointing to potential limitations in digital content delivery. The absence of a social component in digital settings was a concern for 16 (6.9%) respondents, suggesting a missed opportunity for peer interaction and support. A preference for auditory learning and the benefit of fixed lecture schedules in providing daily structure were mentioned by 11 (4.8%) and 6 (2.6%) participants, respectively, highlighting personal learning preferences and organizational benefits associated with face-to-face lectures. This qualitative feedback underscores the complex balance between the digital format’s convenience and effectiveness versus the perceived depth of understanding and social interaction offered by traditional lectures.

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