Research Design:
This study centered on the attitude and motivational adoption of the medical professor in the use of virtual simulation technology of radiotherapy in medical education context in Chongqing Medical University. The quantitative method defined by Apuke (2021) was chosen to determine significant relationships and differences established in the statement of the problems.
Moreover, this investigation particularly utilized descriptive-correlational research design. As used by the researcher in the study, a current status of demographic profile of the respondents, medical professors’ attitudes and motivation to the use of virtual simulation technology of radiotherapy, were inquired. A correlation was attempted to be established either accepting and rejecting the hypothesis stated.
Population And Sampling
The respondents of this investigation consisted of 143 medical professors from Chongqing Medical University for the first semestral term of 2022–2023 ,selected through convenience sampling method, in the Peoples Republic of China.
Research Instrument
This study adopted and modified the research instrument of McInerney et. al. (2019), Ghanizadeh et. al. (2019), and Olasoji et. al. (2019) scaling the leverage attitude of medical professors in the use of technology in teaching. And this study adopted and modified the research instrument of Sharma and Srivastava (2019), Paudel (2020), and Mahdum et. al. (2019) scaling the leverage of motivation to adopt technology of the medical professors.
Furthermore, the research instrument was divided into three separate parts, to provide convenience and simplicity of utilization. The first part provides the demographic profile of the profile of the medical professors. The second part provides the attitude of medical professors in the use of virtual simulation technology of radiotherapy and utilizes the following Likert Scale:
Scale | Range | Verbal Description |
4 | 3.51–4.50 | Very Positive |
3 | 2.51–3.50 | Positive |
2 | 1.51–2.50 | Negative |
1 | 1.00-1.50 | Very Negative |
While the third part was for the motivation to adopt virtual simulation technology of radiotherapy of the medical professors. The survey questionnaire utilizes the following Likert Scale:
Scale | Range | Verbal Description |
4 | 3.51–4.50 | Highly Motivated |
3 | 2.51–3.50 | Motivated |
2 | 1.51–2.50 | Unmotivated |
1 | 1.00-1.50 | Highly Unmotivated |
Statistical Treatment Of Data
To assess the demographic profile of the respondents, simple percentage and frequency were utilized.
Weighted mean was employed, to determine the medical professors’ attitudes towards the use of virtual simulation technology of radiotherapy in the clinical teaching environment and compute the level of motivation to adopt it .
T-test and one-way ANOVA were utilized, to establish the significant difference in attitude and the significant difference in the motivations of medical professors to the use of virtual simulation technology of radiotherapy in the clinical teaching context, when grouped according to their profile,
To formulate the significant relationship between the demographic profile and attitudes of the respondents towards the use of technology in the clinical teaching environment, Pearson correlation coefficient (r) and Chi-Squared test of association were employed.
To compute the significant relationship between the medical professors’ attitudes towards the use of technology in the clinical teaching environment and the medical professors’ motivation to adopt technology, Pearson correlation coefficient (r) was employed.
Result:
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Demographic profile
Table No. 1
The profile of the respondents in terms of age, sex, and length of service
Age | Frequency | Percent |
21 to 30 | 11 | 7.69 |
31 to 40 | 50 | 34.97 |
41 to 50 | 39 | 27.27 |
51 and above | 43 | 30.07 |
Total | 143 | 100.00 |
Sex | Frequency | Percentage |
Male | 70 | 48.95 |
Female | 73 | 51.05 |
Total | 143 | 100.00 |
Length of Service | Frequency | Percent |
1 to 10 | 38 | 26.57 |
11 to 20 | 39 | 27.27 |
21 to 30 | 55 | 38.46 |
31 to 40 | 11 | 7.69 |
Total | 143 | 100.00 |
The table shows that the respondents with the age ranging from 31 to 40 years old ranked first while the respondents with age ranging from 21 to 30 years old ranked last. In terms of sex, respondents from the female group dominated the sample while male group is the minority. Moreover, the respondents with 21 to 30 years of service in the institution ranked first while respondents with 31 to 40 years of service ranked last.
This finding means that medical professors in Chongqing Medical University are dominated by female. In addition, medical professors are coming from age 31 to 40 years old group and with 21 to 30 years of service in the institution. It can be inferred that the medical profession highly values the seniority in their field.
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Attitude towards the use of virtual simulation technology of radiotherapy
Table No. 2
The level of medical professors’ attitude towards the use of virtual simulation technology of radiotherapy in the clinical teaching context with respect to pedagogy
PEDAGOGY | Mean | SD | Rank |
1.The use of virtual simulation technology of radiotherapy can facilitate student-centered learning. | 3.70 | 0.52 | 1 |
2.The use of virtual simulation technology of radiotherapy provides an opportunity to improve the quality of my teaching. | 3.67 | 0.49 | 4 |
3.The use of virtual simulation technology of radiotherapy can develop teacher's pedagogical abilities in the art of questioning. | 3.65 | 0.52 | 5 |
4.The use of virtual simulation technology of radiotherapy has more effective role in medical education in class discussions. | 3.69 | 0.49 | 2 |
5.The use of virtual simulation technology of radiotherapy has a complementary role in medical education particularly in classroom dynamics. | 3.68 | 0.54 | 3 |
Weighted Mean | 3.68 | 0.51 | |
It can be deduced from the table that item no. 1 “The use of virtual simulation technology of radiotherapy can facilitate student-centered learning” ranked first while item no. 5 “The use of virtual simulation technology of radiotherapy can develop teacher’s pedagogical abilities in the art of questioning” ranked last. With a weighted mean of 3.68, the level of medical professors’ attitude towards the use of it in the clinical teaching context with respect to pedagogy if at “Very Positive” level.
This means that the medical professors’ attitude towards the use virtual simulation technology of radiotherapy with respect to pedagogy is at very positive level. Medical professors believe that it can facilitate learning inside their class. In addition, respondents also believe that with the use of it, their pedagogical skills improve as well.
Table No. 3
The level of medical professors’ attitude towards the use of virtual simulation technology of radiotherapy in the clinical teaching context with respect to content
CONTENT | Mean | SD | Rank |
1.The use of virtual simulation technology of radiotherapy can prepare students for their lessons. | 3.62 | 0.54 | 5 |
2.The use of virtual simulation technology of radiotherapy can improve students' understanding of the lessons. | 3.68 | 0.51 | 2 |
3.The use of virtual simulation technology of radiotherapy provides an opportunity to follow the latest information. | 3.65 | 0.53 | 4 |
4.The use of virtual simulation technology of radiotherapy can provide opportunities to study new things. | 3.66 | 0.53 | 3 |
5.The use of virtual simulation technology of radiotherapy can make learning more meaningful. | 3.69 | 0.49 | 1 |
Weighted Mean | 3.66 | 0.52 | |
It can be inferred from the table that item no. 5 “The use of virtual simulation technology of radiotherapy can make learning more meaningful” ranked first while item no. 1 “The use of virtual simulation technology of radiotherapy can prepare students for their lessons” ranked last. All in all, the level of medical professors’ attitude towards the use of it in the clinical teaching context with respect to content is 3.66 with verbal interpretation of “Very Positive”.
This means that the level of medical professors’ attitude towards the use of technology with respect to content is at “Very Positive” level. Medical professors believe that the use of virtual simulation technology of radiotherapy in the classroom will be beneficial for both the medical instructors and the students. Using this technology inside the class provides opportunities for the students to learn updated content in the field of medicine thus, improving the understanding of lessons.
Table No. 4
The level of medical professors’ attitude towards the use of virtual simulation technology of radiotherapy in the clinical teaching context with respect to assessment
ASSESSMENT | Mean | SD | Rank |
1.The use of ICT can contribute to making students work more actively and problem-based. | 3.69 | 0.50 | 3 |
2.The use of virtual simulation technology of radiotherapy can inspire and make students able to express themselves. | 3.66 | 0.52 | 4.5 |
3.The use of virtual simulation technology of radiotherapy can improve the quality of student learning and accomplish tasks and assignments. | 3.66 | 0.57 | 4.5 |
4.The use of virtual simulation technology of radiotherapy can increase self confidence of students to answer quizzes and exams. | 3.70 | 0.49 | 2 |
5.The use of technology encourages students to submit their assignments. | 3.71 | 0.50 | 1 |
Weighted Mean | 3.68 | 0.51 | |
It can be deduced from the table from the table that item no. 5 “The use of virtual simulation technology of radiotherapy encourages students to submit their assignments” ranked first while item no. 2 “The use of virtual simulation technology of radiotherapy can inspire and make students able to express themselves” and item no. 3 “The use of virtual simulation technology of radiotherapy can improve the quality of student learning and accomplish tasks and assignments” tied at the bottom of the rank list. All in all, the level of medical professors’ attitude towards the use of virtual simulation technology of radiotherapy in the clinical teaching context with respect to assessment is 3.68 with a verbal interpretation of “Very Positive”.
This means that the level of medical professors’ attitude towards the use of it with respect to assessment is at a very positive level. Medical professors view the use of it in assessment in their class as impactful. Moreover, using it in classroom-based assessment promotes active students’ participation in the feedback mechanisms. It also improves the quality of outputs that the students are submitting, thus, making them confident in their submitted outputs in class.
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Motivation to adopt virtual simulation technology of radiotherapy
Table No. 5
The level of medical professors’ motivation to adopt virtual simulation technology of radiotherapy with respect to value belief
VALUE BELIEF | Mean | SD | Rank |
1. I believe that use of virtual simulation technology of radiotherapy in teaching will help my students. | 3.68 | 0.51 | 3 |
2. I believe that virtual simulation technology of radiotherapy integration will positively affect my students. | 3.73 | 0.53 | 1 |
3. I believe that use of virtual simulation technology of radiotherapy will improve my teaching. | 3.65 | 0.60 | 4 |
4. I believe if I use virtual simulation technology of radiotherapy in my teaching it will help me in my future growth. | 3.62 | 0.60 | 5 |
5. I believe that using virtual simulation technology of radiotherapy improves the quality of my teaching. | 3.71 | 0.58 | 2 |
Weighted Mean | 3.68 | 0.56 | |
It can be inferred from the table that item no. 2 “I believe that virtual simulation technology of radiotherapy integration will positively affect my students” ranked first while item no. 4 “I believe if I use virtual simulation technology of radiotherapy in my teaching, it will help me in my future growth” ranked last. With a weighted mean of 3.68, the level of medical professors’ motivation to adopt this technology with respect to value belief is at “Highly Motivated” level.
This means that the level of medical professors’ motivation to adopt it with respect to value belief is at highly motivated level. Medical professors believe that with respect to value belief systems, they are highly motivated to adopt virtual simulation technology of radiotherapy inside their class. In addition, medical professors also believes that when using virtual simulation technology of radiotherapy inside their respective classes, the quality of their teaching will improve thus, positively affective the quality of learning of the students.
Table No. 6
The level of medical professors’ motivation to adopt virtual simulation technology of radiotherapy with respect to social influence
SOCIAL INFLUENCE | Mean | SD | Rank |
1. I use virtual simulation technology of radiotherapy in teaching under the expectations of my friends and colleagues. | 3.63 | 0.67 | 1 |
2. When I use virtual simulation technology of radiotherapy in teaching, I often consult other people for help to choose the best alternative available. | 3.52 | 0.71 | 5 |
3. I achieve a sense of belonging with my friends and colleagues by using virtual simulation technology of radiotherapy in teaching. | 3.60 | 0.67 | 3 |
4. When I use virtual simulation technology of radiotherapy in teaching, I ask my friends for useful information. | 3.62 | 0.67 | 2 |
5. When I use virtual simulation technology of radiotherapy in teaching, I frequently gather information from friends or colleagues. | 3.59 | 0.71 | 4 |
Weighted Mean | 3.59 | 0.69 | |
It can be deduced from the table that item no. 1 “I use virtual simulation technology of radiotherapy in teaching under the expectations of my friends and colleagues” ranked first while item no. 2 “When I use virtual simulation technology of radiotherapy in teaching, I often consult other people for help to choose the best alternative available” ranked last. All in all, the level of medical professors’ motivation to adopt virtual simulation technology of radiotherapy with respect to social influence is at 3.59 with verbal interpretation of “Highly Motivated” level.
This means that the level of medical professors’ motivation to adopt virtual simulation technology of radiotherapy with respect to social influence is at a highly motivated level. Medical professors believe that when they use virtual simulation technology of radiotherapy inside their class, it is important to ask for assistance from another colleague. In addition, it is assumed that teachers, faculty, and instructors nowadays, are using it inside their class to reach the expectations of the academic community.
Table No. 7
The level of medical professors’ motivation to adopt virtual simulation technology of radiotherapy with respect to behavioral intention
BEHAVIORAL INTENTION | Mean | SD | Rank |
1. I intend to increase the use of virtual simulation technology of radiotherapy in the future. | 3.70 | 0.50 | 2 |
2. I will frequently use virtual simulation technology of radiotherapy in my teaching. | 3.66 | 0.69 | 3.5 |
3. I find virtual simulation technology of radiotherapy useful to me in my teaching career. | 3.71 | 0.54 | 1 |
4. It is easy for me to become skillful at using virtual simulation technology of radiotherapy. | 3.65 | 0.63 | 5 |
5. Overall, I believe that virtual simulation technology of radiotherapy is easy to use. | 3.66 | 0.63 | 3.5 |
Weighted Mean | 3.67 | 0.60 | |
It can be inferred from the table that item no. 3 “I find virtual simulation technology of radiotherapy useful to me in my teaching career” ranked first while item no. 4 “It is easy for me to become skillful at using virtual simulation technology of radiotherapy” ranked last. With a weighted mean of 3.67, the level of medical professors’ motivation to adopt virtual simulation technology of radiotherapy with respect to behavioral intention is at “Highly Motivated” level.
This means that the level of medical professors’ motivation to adopt virtual simulation technology of radiotherapy with respect to behavioral intention is at highly motivated level. Medical professors believe that by using it today, it affects the way they will use it in their class in the future.
Table No. 8
The level of medical professors’ motivation to adopt virtual simulation technology of radiotherapy with respect to personal utilization
PERSONAL UTILIZATION | Mean | SD | Rank |
1. I would feel comfortable using virtual simulation technology of radiotherapy in my class on my own. | 3.55 | 0.74 | 3 |
2. If I wanted to, I could easily operate any of the technological tools in my class on my own. | 3.56 | 0.74 | 2 |
3. I would be able to operate any of the technological tools in my class even if there is no one to show me around. | 3.45 | 0.82 | 5 |
4. For me being able to use virtual simulation technology of radiotherapy on my own is important. | 3.60 | 0.67 | 1 |
5. My interaction with virtual simulation technology of radiotherapy is easy and understandable. | 3.55 | 0.77 | 4 |
Weighted Mean | 3.54 | 0.75 | |
It can be deduced from the table that item no. 4 “For me, being able to use virtual simulation technology of radiotherapy on my own is important” ranked first while item no. 3 “I would be able to operate any of technological tools in my class even if there is no one to show me around” ranked last. All in all, the level of medical professors’ motivation to adopt virtual simulation technology of radiotherapy with respect to personal utilization is at 3.54 with a verbal interpretation of “Highly Motivated” level.
This means that the level of medical professors’ motivation to adopt virtual simulation technology of radiotherapy with respect to personal utilization is at highly motivated level. Medical professors believe that it is important to navigate the technology inside their class on their own. They also believe that when they independently use it in their class, their confidence level rises thus, making their strategies more impactful to the students.
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Significant difference in attitude of medical professors when grouped according to their profile
Table No. 9
Significant difference in attitude of medical professors towards the use of virtual simulation technology of radiotherapy in the clinical teaching context when grouped according to age profile
Age | | Sum of Squares | df | Mean Square | F | p-value | Decision | Int. |
Content | Between Groups | 0.518 | 3 | 0.173 | 1.126 | 0.341 | Failed to Reject Ho | Not Significant |
Within Groups | 21.309 | 139 | 0.153 | | | | |
Total | 21.827 | 142 | | | | | |
Pedagogy | Between Groups | 0.236 | 3 | 0.079 | 0.536 | 0.659 | Failed to Reject Ho | Not Significant |
Within Groups | 20.407 | 139 | 0.147 | | | | |
Total | 20.643 | 142 | | | | | |
Assessment | Between Groups | 0.019 | 3 | 0.006 | 0.042 | 0.989 | Failed to Reject Ho | Not Significant |
Within Groups | 21.527 | 139 | 0.155 | | | | |
Total | 21.546 | 142 | | | | | |
Overall | Between Groups | 0.177 | 3 | 0.059 | 0.451 | 0.717 | Failed to Reject Ho | Not Significant |
Within Groups | 18.173 | 139 | 0.131 | | | | |
Total | 18.349 | 142 | | | | | |
A one-way ANOVA was performed to compare the level of attitude of medical professors towards the use of virtual simulation technology of radiotherapy when grouped according to their age profiles. The results show that for Content (F(3, 139) = 1.126, p = .341), there is no significant differences exist between the group, thus, failing to reject the null hypothesis. Moreso, the results for Pedagogy (F(3, 139) = 0.536, p = .659) poses no significant differences between age groups, thus failing to reject the null hypothesis. When it comes to Assessment (F(3, 139) = 0.042, p = .989), no significant differences were also found between groups, thus, failing to reject the null hypothesis as well. This means that the Overall (F(3, 139) = 0.451, p = .717) level of attitude of medical professors towards the use of technology in the clinical teaching context when grouped according to the age profiles has no significant differences.
This means that regardless of the age group of the respondents, they have a “Very Positive” attitude towards the use of virtual simulation technology of radiotherapy in teaching inside their classes in the context of clinical teaching. Age poses no issue in using it in medical class.
Table No. 10
Significant difference in attitude of medical professors towards the use of virtual simulation technology of radiotherapy in the clinical teaching context when grouped according to sex profile
Sex | Attitude | N | Mean | Std. Deviation | t | df | p-value | Decision | Int. |
Male | Content | 70 | 3.686 | 0.372 | 0.805 | 141 | 0.422 | Failed to Reject Ho | Not Significant |
Female | 73 | 3.633 | 0.411 |
Male | Pedagogy | 70 | 3.689 | 0.374 | 0.314 | 141 | 0.754 | Failed to Reject Ho | Not Significant |
Female | 73 | 3.668 | 0.390 |
Male | Assessment | 70 | 3.694 | 0.404 | 0.353 | 141 | 0.725 | Failed to Reject Ho | Not Significant |
Female | 73 | 3.671 | 0.378 |
Male | Overall | 70 | 3.690 | 0.358 | 0.531 | 141 | 0.597 | Failed to Reject Ho | Not Significant |
Female | 73 | 3.658 | 0.363 |
An independent sample t-test was performed to examine the level of attitude of medical professors towards the use of technology when grouped according to their sex profile. In Content, there was no significant difference found between Male (M = 3.686, SD = 0.372) and Female (M = 3.633, SD = 0.411) groups; t(141) = 0.805, p = .422, thus failing to reject the null hypothesis. In Pedagogy, there was no significant difference found between Male (M = 3.689, SD = 0.374) and Female (M = 3.668, SD = 0.390) groups; t(141) = 0.314, p = .754, thus failing to reject the null hypothesis. In Assessment, there was no significant difference found as well between Male (M = 3.694, SD = 0.404) and Female (M = 3.671, SD = 0.378) groups; t(141) = 0.353, p = .725, thus failing to reject the null hypothesis as well. Overall (t(141) = 0.531, p = .597), there were no significant differences in the level of attitude of medical professors towards the use of technology in the clinical teaching context when grouped according to sex profile.
This means that regardless of the sex of the respondents, they have a “Very Positive” attitude towards the use of virtual simulation technology of radiotherapy in teaching inside their classes in the context of clinical teaching. Sex poses no issue in using technology in medical class.
Table No. 11
Significant difference in attitude of medical professors towards the use of virtual simulation technology of radiotherapy in the clinical teaching context when grouped according to length of service profile
Length of Service | | Sum of Squares | df | Mean Square | F | p-value | Decision | Int. |
Content | Between Groups | 1.574 | 3 | 0.525 | 3.601 | 0.015 | Reject Ho | Significant |
Within Groups | 20.253 | 139 | 0.146 | | | | |
Total | 21.827 | 142 | | | | | |
Pedagogy | Between Groups | 1.406 | 3 | 0.469 | 3.388 | 0.020 | Reject Ho | Significant |
Within Groups | 19.236 | 139 | 0.138 | | | | |
Total | 20.643 | 142 | | | | | |
Assessment | Between Groups | 0.389 | 3 | 0.13 | 0.852 | 0.468 | Failed to Reject Ho | Not Significant |
Within Groups | 21.157 | 139 | 0.152 | | | | |
Total | 21.546 | 142 | | | | | |
Overall | Between Groups | 0.981 | 3 | 0.327 | 2.618 | 0.053 | Failed to Reject Ho | Not Significant |
Within Groups | 17.368 | 139 | 0.125 | | | | |
Total | 18.349 | 142 | | | | | |
A one-way ANOVA was calculated to analyze the level of attitude of medical professors towards the use of virtual simulation technology of radiotherapy when grouped according to their length of service profiles. The results show that for Content (F(3, 139) = 3.601, p = .015), there is a significant difference that exist between the group. The results for Pedagogy (F(3, 139) = 3.388, p = .020) pose a significant difference between groups. In addition, Assessment (F(3, 139) = 0.852, p = .468), no significant differences were found between groups. This means that the Overall (F(3, 139) = 2.618, p = .053) level of attitude of medical professors towards the use of it in the clinical teaching context when grouped according to the age profiles has no significant differences.
Table No. 11.1
Post Hoc Tests (Scheffe) for Table 11
Dependent Variable | Length of Service | Mean Difference | Std. Error | Significance Level | Decision | Int. |
Content | 1 to 10 | 11 to 20 | − .055 | .087 | .939 | FR Ho | NS |
21 to 30 | − .071 | .081 | .857 | FR Ho | NS |
31 to 40 | .333 | .131 | .095 | FR Ho | NS |
11 to 20 | 1 to 10 | .055 | .087 | .939 | FR Ho | NS |
21 to 30 | − .015 | .080 | .998 | FR Ho | NS |
31 to 40 | .388 | .130 | .034 | Reject Ho | S |
21 to 30 | 1 to 10 | .071 | .081 | .857 | FR Ho | NS |
11 to 20 | .015 | .080 | .998 | FR Ho | NS |
31 to 40 | .404 | .126 | .019 | Reject Ho | S |
31 to 40 | 1 to 10 | − .333 | .131 | .095 | FR Ho | NS |
11 to 20 | − .388 | .130 | .034 | Reject Ho | S |
21 to 30 | − .404 | .126 | .019 | Reject Ho | S |
Pedagogy | 1 to 10 | 11 to 20 | − .012 | .085 | .999 | FR Ho | NS |
21 to 30 | .045 | .078 | .955 | FR Ho | NS |
31 to 40 | .376 | .127 | .037 | Reject Ho | S |
11 to 20 | 1 to 10 | .012 | .085 | .999 | FR Ho | NS |
21 to 30 | .057 | .078 | .911 | FR Ho | NS |
31 to 40 | .389 | .127 | .029 | Reject Ho | S |
21 to 30 | 1 to 10 | − .045 | .078 | .955 | FR Ho | NS |
11 to 20 | − .057 | .078 | .911 | FR Ho | NS |
31 to 40 | .331 | .123 | .069 | FR Ho | NS |
31 to 40 | 1 to 10 | − .376 | .127 | .037 | Reject Ho | S |
11 to 20 | − .389 | .127 | .029 | Reject Ho | S |
21 to 30 | − .331 | .123 | .069 | FR Ho | NS |
Legend: FR Ho = Failed to Reject Null Hypothesis; NS = Not Significant; S = Significant |
Table 11.1 presents the Post Hoc Tests for Table 11. As seen in Table 11, the one-way ANOVA for variables Content and Pedagogy reported a significant difference, hence, Post Hoc Test must be done. Post Hoc Test is done to know which among the groups has significant differences. Moreover, Scheffe test analysis was used because the groups have unequal number of respondents. It can be inferred from the table 11.1 that for variable “Content”, there is a significant difference between the means of 11 to 20 and 31 to 40, and 21 to 30 and 31 to 40, thus rejecting the null hypothesis for both groups. Furthermore, for variable “Pedagogy”, there is a significant difference between the means of 1 to 10 and 31 to 40, and 11 to 20 and 31 to 40, thus rejecting the null hypothesis for both groups as well.
This means that the length of service of the respondents have an effect in the attitude or medical professors towards the use of virtual simulation technology of radiotherapy in teaching inside their classes in the context of clinical teaching. Length of service poses an effect in the use of technology inside a medical class.
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Significant relationship between the demographic profile and attitudes
Table No. 12
Significant relationship between the demographic profile of the respondents and medical professors’ attitudes towards the use of virtual simulation technology of radiotherapy in the clinical teaching context
Profile | Attitude towards the use of technology in the clinical teaching context | Statistical Tool | Computed Value | P-value | Decision | Int. |
Age | Content | Pearson's Correlation | 0.107 | 0.205 | Failed to Reject Ho | Not Significant |
Sex | Chi-square Test of Association | 1.043 | 0.594 | Failed to Reject Ho | Not Significant |
Length of Service | Pearson's Correlation | -0.078 | 0.355 | Failed to Reject Ho | Not Significant |
Age | Pedagogy | Pearson's Correlation | 0.032 | 0.703 | Failed to Reject Ho | Not Significant |
Sex | Chi-square Test of Association | 1.220 | 0.543 | Failed to Reject Ho | Not Significant |
Length of Service | Pearson's Correlation | -0.177 | 0.034 | Reject Ho | Significant |
Age | Assessment | Pearson's Correlation | 0.015 | 0.860 | Failed to Reject Ho | Not Significant |
Sex | Chi-square Test of Association | 0.417 | 0.812 | Failed to Reject Ho | Not Significant |
Length of Service | Pearson's Correlation | -0.100 | 0.235 | Failed to Reject Ho | Not Significant |
Age | overall | Pearson's Correlation | 0.056 | 0.510 | Failed to Reject Ho | Not Significant |
Sex | Chi-square Test of Association | 1.220 | 0.543 | Failed to Reject Ho | Not Significant |
Length of Service | Pearson's Correlation | -0.127 | 0.130 | Failed to Reject Ho | Not Significant |
It can be inferred from the table that for variable “Content”, the p-values of age profile (.205), sex profile (.594), and length of service profile (.355) are higher than 0.05 value. This indicates that the variable “Content” is NOT SIGNIFICANTLY RELATED to any of the demographic profiles stated above, thus, the findings failed to reject the null hypothesis.
Furthermore, for variable “Pedagogy”, the p-values of age profile (.703), and sex profile (.543) are higher than 0.05 value. This indicates that the variable “Content” is NOT SIGNIFICANTLY RELATED to age and sex profiles of the respondents, thus, failing to reject the null hypothesis. However, it is found out that the weak negative relationship between Pedagogy and Length of Service is SIGNIFICANT, thus, rejecting the null hypothesis. This finding can be attributed to the faculty members who prefer to use traditional instructional materials. This means that the more that the medical professor stays in the institution, the more they become traditional in their teaching strategies.
Moreover, for variable “Assessment”, the p-values of age profile (.860), sex profile (.812), and length of service profile (.235) are higher than 0.05 value. This indicates that the variable “Assessment” is NOT SIGNIFICANTLY RELATED to any of the demographic profiles stated above, thus, the findings failed to reject the null hypothesis.
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Significant difference in motivations of medical professors when grouped according to their profile
Table No. 13
Significant difference in motivations of medical professors to adopt virtual simulation technology of radiotherapy when grouped according to age profile
Age | | Sum of Squares | df | Mean Square | F | p-value | Decision | Int. |
Value Belief | Between Groups | 0.144 | 3 | 0.048 | 0.241 | 0.867 | Failed to Reject Ho | Not Significant |
Within Groups | 27.539 | 139 | 0.198 | | | |
Total | 27.683 | 142 | | | | |
Social Influence | Between Groups | 0.211 | 3 | 0.07 | 0.218 | 0.884 | Failed to Reject Ho | Not Significant |
Within Groups | 44.979 | 139 | 0.324 | | | | |
Total | 45.19 | 142 | | | | | |
Behavioral Intention | Between Groups | 0.132 | 3 | 0.044 | 0.172 | 0.915 | Failed to Reject Ho | Not Significant |
Within Groups | 35.602 | 139 | 0.256 | | | | |
Total | 35.734 | 142 | | | | | |
Personal Utilization | Between Groups | 0.329 | 3 | 0.11 | 0.290 | 0.833 | Failed to Reject Ho | Not Significant |
Within Groups | 52.617 | 139 | 0.379 | | | | |
Total | 52.947 | 142 | | | | | |
Overall | Between Groups | 0.073 | 3 | 0.024 | 0.103 | 0.958 | Failed to Reject Ho | Not Significant |
Within Groups | 33.034 | 139 | 0.238 | | | | |
Total | 33.107 | 142 | | | | | |
A one-way ANOVA was performed to compare the level motivations of medical professors to adopt virtual simulation technology of radiotherapy when grouped according to age profiles. The results show that for Value Belief (F(3, 139) = 0.241, p = .867), there is no significant differences exist between the group, thus, failing to reject the null hypothesis. For Social Influence (F(3, 139) = 0.218, p = .884) poses no significant differences between age groups, thus failing to reject the null hypothesis. For Behavioral Intention (F(3, 139) = 0.172, p = .915) poses no significant differences between age groups, thus failing to reject the null hypothesis. When it comes to Personal Utilization (F(3, 139) = 0.290, p = .833), no significant differences were also found between groups, thus, failing to reject the null hypothesis. This means that the Overall (F(3, 139) = 0.103, p = .958) level of motivations of medical professors to adopt technology when grouped according to the age profiles has no significant differences.
This means that regardless of the age group of the respondents, they are “Highly Motivated” to adopt technology in their classes in the context of clinical teaching. Again, age poses no issue in using technology in medical class.
Table No. 14
Significant difference in motivations of medical professors to adopt technology when grouped according to sex profile
Sex | Motivation | N | Mean | SD | t | df | p-value | Decision | Int. |
Male | Value Belief | 70 | 3.731 | 0.401 | 1.413 | 141 | 0.160 | Failed to Reject Ho | Not Significant |
Female | 73 | 3.627 | 0.474 |
Male | Social Influence | 70 | 3.643 | 0.522 | 1.064 | 141 | 0.289 | Failed to Reject Ho | Not Significant |
Female | 73 | 3.542 | 0.601 |
Male | Behavioral Intention | 70 | 3.720 | 0.450 | 1.071 | 141 | 0.286 | Failed to Reject Ho | Not Significant |
Female | 73 | 3.630 | 0.546 |
Male | Personal Utilization | 70 | 3.580 | 0.559 | 0.742 | 141 | 0.459 | Failed to Reject Ho | Not Significant |
Female | 73 | 3.504 | 0.658 |
Male | Overall | 70 | 3.669 | 0.430 | 1.147 | 141 | 0.253 | Failed to Reject Ho | Not Significant |
Female | 73 | 3.576 | 0.528 |
An independent sample t-test was performed to examine the level of motivations of medical professors to adopt technology when grouped according to their sex profile. In Value Belief, there was no significant difference found between Male (M = 3.731, SD = 0.401) and Female (M = 3.627, SD = 0.474) groups; t(141) = 1.413, p = .160, thus failing to reject the null hypothesis. In Social Influence, there was no significant difference found between Male (M = 3.643, SD = 0.522) and Female (M = 3.542, SD = 0.601) groups; t(141) = 1.064, p = .289, thus failing to reject the null hypothesis. In Behavioral Intention, there was no significant difference found between Male (M = 3.720, SD = 0.450) and Female (M = 3.630, SD = 0.546) groups; t(141) = 1.071, p = .286, thus failing to reject the null hypothesis. In Personal Utilization, there was no significant difference found as well between Male (M = 3.580, SD = 0.559) and Female (M = 3.504, SD = 0.658) groups; t(141) = 0.742, p = .459, thus failing to reject the null hypothesis as well. Overall (t(141) = 1.147, p = .253), there were no significant differences in the level of motivations of medical professors to adopt technology when grouped according to their sex profile.
This means that regardless of the sex of the respondents, they are “Highly Motivated” to adopt virtual simulation technology of radiotherapy in their classes in the context of clinical teaching. Again, sex poses no issue in using technology in medical class.
Table No. 15
Significant difference in motivations of medical professors to adopt virtual simulation technology of radiotherapy when grouped according to length of service profile
Length of Service | | Sum of Squares | df | Mean Square | F | p-value | Decision | Int. |
Value Belief | Between Groups | 1.196 | 3 | 0.399 | 2.092 | 0.104 | Failed to Reject Ho | Not Significant |
Within Groups | 26.487 | 139 | 0.191 | | | | |
Total | 27.683 | 142 | | | | | |
Social Influence | Between Groups | 1.283 | 3 | 0.428 | 1.354 | 0.260 | Failed to Reject Ho | Not Significant |
Within Groups | 43.907 | 139 | 0.316 | | | | |
Total | 45.19 | 142 | | | | | |
Behavioral Intention | Between Groups | 1.241 | 3 | 0.414 | 1.668 | 0.177 | Failed to Reject Ho | Not Significant |
Within Groups | 34.493 | 139 | 0.248 | | | | |
Total | 35.734 | 142 | | | | | |
Personal Utilization | Between Groups | 2.672 | 3 | 0.891 | 2.463 | 0.065 | Failed to Reject Ho | Not Significant |
Within Groups | 50.274 | 139 | 0.362 | | | | |
Total | 52.947 | 142 | | | | | |
Overall | Between Groups | 1.348 | 3 | 0.449 | 1.966 | 0.122 | Failed to Reject Ho | Not Significant |
Within Groups | 31.76 | 139 | 0.228 | | | | |
Total | 33.107 | 142 | | | | | |
A one-way ANOVA was performed to compare the level motivations of medical professors to adopt technology when grouped according to length of service profiles. The results show that for Value Belief (F(3, 139) = 2.092, p = .104), there is no significant differences exist between the group, thus, failing to reject the null hypothesis. For Social Influence (F(3, 139) = 1.354, p = .260) poses no significant differences between length of service groups, thus failing to reject the null hypothesis. For Behavioral Intention (F(3, 139) = 1.668, p = .177) poses no significant differences between length of service groups, thus failing to reject the null hypothesis. When it comes to Personal Utilization (F(3, 139) = 2.463, p = .065), no significant differences were also found between groups, thus, failing to reject the null hypothesis. This means that the Overall (F(3, 139) = 1.966, p = .122) level of motivations of medical professors to adopt technology when grouped according to the length of service profiles has no significant differences.
This means that regardless of the length of service of the respondents, they are “Highly Motivated” to adopt virtual simulation technology of radiotherapy in their classes in the context of clinical teaching.
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Significant relationship between attitudes and motivation
Table No. 16
Significant relationship between medical professors’ attitudes towards the use of virtual simulation technology of radiotherapy in the clinical teaching context and the medical professors’ motivation to adopt technology
Variables | Statistical Tool | Computed Value | P-value | Decision | Interpretation |
Attitudes Towards the Use of Virtual Simulation Technology of Radiotherapy in the Clinical Teaching Context | Pearson's Correlation | 0.851 | 0.001 | Reject Ho | Significant |
Motivation to Adopt Virtual Simulation Technology of Radiotherapy |
It can be deduced from the table that the relationship between the attitude towards the use of virtual simulation technology of radiotherapy in the clinical teaching context and motivation to adopt technology is at .851. This means that the relationship between the two variables is a strong positive relationship. This relationship is found to be SIGNIFICANT thus, rejecting the null hypothesis.
Moreover, the more that the medical professor has positive attitude towards the use of it in their class, the more they will become motivated to use it in their class. On the contrary, if they have negative attitude towards the use of technology in their class, they are less likely to be motivated in using them.
8.Based on the findings of the study what output may be crafted?
This proposed blended learning webinar provides instructional support to the faculty in using virtual simulation technology of radiotherapy in their medical classes. It is a way to upskill medical professors in the current technologies used in the field of medicine and medical education. In addition, technology support for the students is also included in the proposed webinar.