| Literature DB >> 35409816 |
Azhar Iqbal1,2, Kiran Kumar Ganji3,4, Osama Khattak1, Deepti Shrivastava3, Kumar Chandan Srivastava5, Bilal Arjumand6, Thani AlSharari7, Ali Mosfer A Alqahtani8, May Othman Hamza9, Ahmed Abu El Gasim AbdelrahmanDafaalla10.
Abstract
E-learning has completely transformed how people teach and learn, particularly in the last three pandemic years. This study evaluated the effectiveness of additional procedure-specific video demonstrations through E-learning in improving the knowledge and practical preclinical skills acquisition of undergraduate dental students in comparison with live demonstration only. A randomized controlled trial was conducted for the second-year dental students in the College of Dentistry, Jouf University, to evaluate the impact of E-learning-assisted videos on preclinical skill competency levels in operative dentistry. After a brief introduction to this study, the second-year male and female students voluntarily participated in the survey through an official college email. Fifty participants were enrolled in the study after obtaining informed consent. The participants were randomly divided into two groups, twenty-five each. The control group (Group A) was taught using traditional methods, and the intervention group (Group B) used E-learning-assisted educational videos and traditional techniques. An objective structured practical examination (OSPE) was used to assess both groups. The faculty members prepared a structured, standardized form to evaluate students. After OSPE, statistical analysis was done to compare the grades of OSPE between Group A and Group B. Logistic regression analysis was done to express the effect of components of the OSPE on gender, cumulative gross point average (CGPA), Group A and Group B. The results showed a significant difference in the experimental groups after the intervention (p < 0.000). The simulator position parameter demonstrated that the participants had a significant competence level after the intervention by procedure-specific videos (p < 0.000) and an exponential value of 6.494. The participants taught by E-learning-assisted procedure-specific videos and traditional teaching strategies demonstrated an enhanced learning and skill competency level than participants who used only traditional teaching strategies.Entities:
Keywords: E-learning; dental education; dental skills; operative dentistry; procedure-specific videos
Mesh:
Year: 2022 PMID: 35409816 PMCID: PMC8999006 DOI: 10.3390/ijerph19074135
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1Summary of research methodology and experimental protocol.
McNemar test analysis for competent and non-competent in the control group.
| 2 × 2 Contingency Table for Control Group (Before & After) | |||||
|---|---|---|---|---|---|
| Control Group After | Total | ||||
| Non-Competent | Competent | ||||
| Control group before | Non-competent | 122 | 0 | 122 | 0.352 |
| Competent | 12 | 41 | 53 | ||
| Total | 134 | 41 | 175 | ||
McNemar test analysis for competent and non-competent in the experimental group.
| 2 × 2 Contingency Table for Experimental Group (Before & After) | |||||
|---|---|---|---|---|---|
| Experimental Group After | Total | ||||
| Non-Competent | Competent | ||||
| Experimental group before | Non-competent | 34 | 0 | 34 | 0.000 |
| Competent | 22 | 119 | 141 | ||
| Total | 56 | 119 | 175 | ||
Expressing the effect of infection control and operator position parameters on gender, CGPA, Group A and Group B using binary logistic regression analysis.
| Parameter | Variables | B | S.E. | Wald | df | Sig. | Exp (B) | 95% C.I. for EXP (B) | |
|---|---|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||||
| Infection control & Operator position | Gender | 3.69 | 1.10 | 11.17 | 1 | 0.001 | 4.38 | 4.62 | 5.097 |
| CGPA | 5.7 | 1.47 | 15.448 | 1 | 0.000 | 5.25 | 18.17 | 5.25 | |
| Group A | 0.66 | 0.82 | 0.644 | 1 | 0.422 | 1.93 | 0.38 | 9.68 | |
| Group B | 1.2 | 0.72 | 2.78 | 1 | 0.095 | 3.32 | 0.81 | 13.65 | |
| Constant | −26.99 | 6.94 | 15.11 | 1 | 0.000 | 0.000 | |||
Expressing the effect of tray organization parameter on gender, CGPA, Group A and Group B using binary logistic regression analysis.
| Parameter | Variables | B | S.E. | Wald | df | Sig. | Exp (B) | 95% C.I. for EXP (B) | |
|---|---|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||||
| Tray Organization | Gender | 0.80 | 1.05 | 0.58 | 1 | 0.445 | 2.23 | 0.28 | 3.68 |
| CGPA | 3.25 | 1.10 | 8.72 | 1 | 0.003 | 25.91 | 2.98 | 3.45 | |
| Group A | −1.33 | 0.84 | 2.47 | 1 | 0.115 | 0.26 | 0.05 | 1.38 | |
| Group B | 1.08 | 0.88 | 1.48 | 1 | 0.223 | 2.94 | 0.51 | 2.76 | |
| Constant | −13.14 | 5.24 | 6.28 | 1 | 0.012 | 0.00 | |||
Expressing the effect of simulator position parameter on gender, CGPA, Group A and Group B using binary logistic regression analysis.
| Parameter | Variables | B | S.E. | Wald | df | Sig. | Exp (B) | 95% C.I. for EXP (B) | |
|---|---|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||||
| Simulator position | Gender | 1.87 | 0.92 | 4.13 | 1 | 0.042 | 6.49 | 1.06 | 6.46 |
| CGPA | 2.41 | 0.89 | 7.20 | 1 | 0.007 | 11.17 | 1.91 | 65.11 | |
| Group A | −1.68 | 0.75 | 5.04 | 1 | 0.025 | 0.18 | 0.04 | 0.80 | |
| Group B | 0.00 | 0.69 | 0.00 | 1 | 1.000 | 1.00 | 0.25 | 3.92 | |
| Constant | −10.15 | 4.24 | 5.71 | 1 | 0.017 | 0.00 | |||
Expressing the effect of cavity outline and extension parameter on gender, CGPA, Group A and Group B using binary logistic regression analysis.
| Parameter | Variables | B | S.E. | Wald | df | Sig. | Exp (B) | 95% C.I. for EXP (B) | |
|---|---|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||||
| Cavity outline and extension | Gender | −1.65 | 1.18 | 1.93 | 1 | 0.164 | 0.19 | 0.01 | 1.96 |
| CGPA | 1.72 | 0.99 | 3.04 | 1 | 0.081 | 5.62 | 0.80 | 3.14 | |
| Group A | −2.15 | 1.02 | 4.40 | 1 | 0.036 | 0.11 | 0.01 | 0.86 | |
| Group B | 0.33 | 0.82 | 0.16 | 1 | 0.684 | 1.39 | 0.28 | 6.97 | |
| Constant | −4.60 | 4.89 | 0.88 | 1 | 0.347 | 0.01 | |||
Expressing the effect of resistance form and retention form parameter on gender, CGPA, Group A and Group B using binary logistic regression analysis.
| Parameter | Variables | B | S.E. | Wald | df | Sig. | Exp (B) | 95% C.I. for EXP (B) | |
|---|---|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||||
| Resistance form & Retention form | Gender | 0.04 | 1.09 | 0.00 | 1 | 0.965 | 1.04 | 0.12 | 8.89 |
| CGPA | 3.93 | 1.31 | 8.98 | 1 | 0.003 | 11.01 | 3.89 | 5.36 | |
| Group A | −0.23 | 0.93 | 0.06 | 1 | 0.804 | 0.79 | 0.126 | 4.99 | |
| Group B | 2.09 | 0.95 | 4.83 | 1 | 0.028 | 8.09 | 1.25 | 3.57 | |
| Constant | −16.28 | 6.20 | 6.88 | 1 | 0.009 | 0.00 | |||