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Predicting Factors Influencing the Actual Use of E-Learning Platform among Medical Students in the Philippines: Unified Theory of Acceptance and Use of Technology Approach

Published: 22 October 2024 Publication History

Abstract

eLearning has been utilized as a mode of instruction among medical students amidst the COVID-19 pandemic. The present study aimed to investigate the factors influencing the acceptance of eLearning platforms in medical education during the COVID-19 pandemic in the Philippines by employing the latent variables of UTAUT2, along with two additional latent variables gathered from various related literature. A sum of 360 medical students participated and answered an online survey that consists of 40 questions. A stepwise multiple linear regression was utilized and found that performance expectancy (PE), habit (HB), and instructor characteristics (IC), can successfully predict the actual use (AU) of medical students with a precision of 54.22%. A higher performance expectancy (PE), habit (HB), and instructor characteristics (IC) can enhance and promote the acceptance of medical eLearning platforms. This is the first study that has examined the acceptance of eLearning platforms among medical students in the Philippines during the COVID-19 pandemic. The outcomes of this research can serve as guidelines for the Commission on Higher Education of the Philippines for the enhancement of eLearning platforms. Although this study only utilized the latent variables of UTAUT2 along with two additional variables: Learning Value and Instructor Characteristics; the findings of this study would still hold significant value for promoting open innovation in eLearning platforms in medical fields worldwide.

1 Introduction

The implementation of distance learning in the education sector, specifically in response to the COVID-19 pandemic, has had a substantial impact worldwide [1, 2, 3, 4]. Millions of students in several countries across the globe, such as the Philippines, widely utilized distance learning through the integration of radio broadcasts, television, or modular learning, and even through learning management systems [5].
Learning management systems (LMS) are eLearning platforms that allow a cost-effective and flexible approach to education delivery [6, 7]. These platforms boost the learning and teaching experience in higher education through the integration of asynchronous and synchronous communication channels, provisioned online content, and interactive assessment tools [8]. Implementing an LMS and its effectiveness varies on understanding the factors that impact the intention of students and its usage [8].
Numerous studies were conducted worldwide to examine LMS. In Malaysia, the researchers utilized a Unified Theory of Acceptance and Use of Technology (UTAUT2) to assess the LMS and found that factors such as performance expectancy, social influence, and learning value had notable impacts on the behavioral intentions of students toward it [8]. In Hong Kong, UTAUT2 was also employed to examine consumer acceptance and usage of information technology, including LMS [9]. Meanwhile, other studies examined eLearning acceptance by employing the Technology Acceptance Model (TAM) [10, 11]. These studies demonstrated that computer self-efficacy substantially influenced the perceived ease of use of eLearning systems. Moreover, perceived ease of use was also found to have a substantial impact on the intention to use the e-learning platform [10].
Despite the Philippines experiencing one of the world's strictest and longest lockdowns, there has been a lack of studies focusing on eLearning for medical students in the country. Furthermore, medical schools had limited time to prepare and adapt to the sudden implementation of quarantine measures by the government [12]. To address the issue, Higher Educational Institutions (HEIs) implemented eLearning platforms to continue training and educating students to become professionals. This study aims to investigate the factors affecting the acceptance of medical education eLearning platforms during the COVID-19 pandemic by utilizing the latent variables of UTAUT2 along with two additional variables for multiple linear regression analysis.
This is the first study that has examined the acceptance of eLearning platforms among medical students in the Philippines during the COVID-19 pandemic. The outcomes of this research can serve as guidelines for the Commission on Higher Education of the Philippines for the enhancement of eLearning platforms. Finally, the findings of this study would hold significant value for promoting open innovation in eLearning platforms in medical fields worldwide.

2 Accessibility

The present study utilized the latent variables of UTAUT2 along with two additional factors to evaluate the acceptance of medical students toward medical education eLearning. The latent variables of the original UTAUT2 model presented in Figure 1 [13], except for age, gender, and experience, were considered the independent variables. Whereas the actual use of eLearning platforms served as the dependent variable for the stepwise multiple linear regression analysis.
Figure 1:
A diagram of a model Description automatically generated
Figure 1: Original UTAUT2 Model [13]

2.1 Participants

A sum of 360 medical students, enrolled in accredited medical educational institutions in the Philippines and were pursuing a Doctor of Medicine degree, participated in the present study. As the main focus of this study was on the acceptance of medical education eLearning rather than human performance, the institutional review board waived the requirement for approval. In addition, the data for the study was collected through a survey questionnaire created using Google Forms because of the COVID-19 pandemic and it was made accessible online through various social media platforms. Table 1 represents the descriptive statistics of the respondents.
Table 1:
MeasureValueN%
GenderMale9626.67%
Female26473.33%
Age18–24 years old20456.67%
25–34 years old15041.67%
35–44 years old30.83%
Above 5430.83%
Year Level1st Year339.17%
2nd Year3610.00%
3rd Year9025%
4th Year (Junior Internship)18651.67%
5th Year (Senior Internship)154.17%
Table 1: Descriptive Statistics of the Respondents (N=360)

2.2 Questionnaire

The questionnaire developed for this study combined indicators based on the extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) from various related literature [8, 10, 11, 14] to assess the acceptance of medical education eLearning. It consists of 40 indicator questions for the 10 latent variables, such as (1) performance expectancy (PE), (2) Effort Expectancy (EE), (3) social influence (SI), (4) learning value (LV), (5) facilitating conditions (FC), (6) habit (HB), (7) hedonic motivation (HM), (8) instructor characteristics (IC), (9) behavioral intention (BI), and (10) actual use (AU). To measure the responses to each indicator question, participants were provided with a 7-point Likert scale ranging from one (1) representing "strongly disagree" to five (7) representing "strongly agree.”

3 Results

3.1 Multiple Linear Regression

The gathered data from the survey were evaluated by utilizing the stepwise multiple regression analysis in Minitab version 18. The indicator questions that the respondents answered for each latent variable were averaged by using Microsoft Excel. These were then used as the data for the independent and dependent variables. Multiple linear regression was applied to the collected data to measure the effect of the independent variables on the actual use. Table 1 provides the analysis of variance from the data gathered.
As depicted in Table 1, the leftmost column displays the independent variables along with the regression, error, and total. The present study utilized a confidence interval or alpha value of 0.05. It is observed that the regression and all of the independent variables, such as PE, HB, and IC attained a p-value lower than the 0.05 alpha value. This explains that the null hypothesis for the overall regression is rejected, suggesting that the model effectively explains variation in the response or dependent variable. Whereas the independent variables, such as PE, HB, and IC, were considered statistically significant [15].
Table 2:
SourceDFAdj SSAdj MSF-ValueP-Value
Regression3142.75147.583553.350.001
PE13.6543.65454.10.045
HB18.5628.56259.60.002
IC116.09616.096418.050.001
Error116103.4560.8919  
Total119246.206   
Table 2: Analysis of Variance
Table 3 displays the coefficients of the constant and the independent variables. The variance inflation factor (VIF) is used to evaluate the extent to which a coefficient's variance is inflated due to correlations among the independent variables in the model. The VIF of the independent variables attained a value between 2 and 3, which suggests that the independent variables are moderately correlated with each other and multi-collinearity does not exist [16]. Additionally, the standard error of the coefficient provides an indication if its estimate is precise or not. A lower standard error value implies a more precise estimate [16]. On the other hand, the t-value measures the ratio between the coefficient and its standard error, whereas the p-value represents the probability that measures the strength of evidence against the null hypothesis [15, 16].
Table 3:
TermCoefSE CoefT-ValueP-ValueVIF
Constant0.4680.371.260.209 
PE0.17930.08862.020.0452.2
HB0.30520.09853.10.0022.58
IC0.41480.09764.2502.12
Table 3: Coefficients
Table 4 presents the model summary of the study's findings. The R-square attained a value of 0.5798 and implies that 57.98% of the variation in AU can be explained by the independent variables considered in the analysis [17]. The generated multiple linear regression equation is shown in equation (1) and indicates that the Y-intercept is 0.468 and the coefficients of the independent variables PE, HB, and IC are 0.1793, 0.3052, and 0.4148, respectively [18]. This equation will determine the value of the dependent variable AU with a precision of 54.22% (predicted R-square) [17].
Table 4:
SR-sqR-sq(adj)R-sq(pred)
0.9443830.57980.56890.5422
Table 4: Model Summary
\begin{equation} AU = 0.468 + 0.1793\,PE + 0.3052\,HB + 0.4148\,IC\end{equation}
(1)
Figures 1, 2, 3, and 4 present the residual plots from the collected data, including the normal probability plot, versus fits plot, histogram, and versus order plot, respectively. These residual plots are employed to assess the goodness-of-fit in regression and ANOVA analysis [19]. Assessing the residual plots help in determining if the ordinary least squares assumptions are met or not [19]. It is crucial for the assumptions to meet the requirements in order to achieve unbiased coefficient estimates with minimum variance through ordinary least squares regression [19].
Figure 2:
Figure 2: Normal Probability Plot
The normal probability plot indicates a nearly straight diagonal line. This suggests that it has verified the assumptions that the data or residuals are normally distributed [20]. For the versus fits plot, it does not indicate a pattern because the points are randomly distributed. This also implies that the assumptions of the model have been verified and that the residuals have a constant variance [20]. Moreover, the histogram plot demonstrates an approximate normal distribution due to the data or residuals not being skewed to the left or right [20]. Similarly, the versus order plot displays randomly scattered residuals around the centerline, implying that there is no pattern or correlation among the residuals. This confirms that the residuals are not dependent or correlated with each other [20].
Figure 3:
Figure 3: Versus Fits
Figure 4:
Figure 4: Histogram
Figure 5:
Figure 5: Versus Order

4 Discussion

The findings of the study showed that the independent variables, such as PE, HB, and IC, were deemed statistically significant since they achieved a p-value of less than 0.05. The VIF of the independent variables indicated that it is moderately correlated with each other and multi-collinearity does not exist among the independent variables since it achieved a value between 2 and 3. Moreover, the predicted R-square attained a value of 0.5422, whereas the R-square value attained a value of 0.5798. The model fits the data better as the R-square value is higher. The outcome of the data shows that the achieved R-square value implies that the model moderately fits the data gathered. Whereas the achieved value for the predicted R-square implies that it holds moderate predictive ability to explain the dependent variable for new responses, despite the insignificance of most of the independent variables.
The multiple linear regression equation (1) explains that the dependent variable increases as the independent variables PE, HB, and IC increase. The dependent variable decreases as the independent variables PE, HB, and IC decrease. Furthermore, the multiple linear regression equation (1) can predict people's AU or acceptance of medical education eLearning platforms according to their PE, HB, and IC. Although, the precision of predicting their AU is only 54.22%.
The independent variables such as PE, HB, and IC were found to possess predictive capability on the dependent variable AU. Given that eLearning has become a medium for medical education institutions to address the shortage of medical professionals, it becomes a must to develop a system for examining the quality of eLearning materials through feedback from participants [21].
In this study, the latent variables of the Unified Theory of Acceptance and Use of Technology (UTAUT2) along with two additional variables were employed in a stepwise multiple regression analysis to investigate the factors influencing the acceptance of eLearning platforms for medical education in the Philippines during the COVID-19 pandemic. It is important for the government, administrators, and faculty members to collaborate in order to improve the performance and utilization of eLearning platforms among medical students. This study provides a significant and timely contribution by addressing the technological reputation of Higher Education Institutions (HEIs), the perspectives of the students, and the effectiveness of the eLearning platform.
The researchers suggest utilizing alternative latent variables from other advanced methods or models, such as combining UTAUT2 with several theories. In addition, it is recommended for future studies to compare premedical, medical, and resident students as a future research topic since the current study had a sample size that mainly consists of medical students. Furthermore, the study did not consider the individuals’ characteristics and culture as independent variables. Future research focusing on these two independent variables would be a promising topic.

5 conclusion

eLearning has been utilized as a mode of instruction among medical students amidst the COVID-19 pandemic. The present study aimed to investigate the factors influencing the acceptance of eLearning platforms in medical education during the COVID-19 pandemic in the Philippines by employing the latent variables of UTAUT2, along with two additional latent variables gathered from various related literature. A sum of 360 medical students participated and answered an online survey that consists of 40 questions.
A stepwise multiple linear regression was utilized and found that performance expectancy (PE), habit (HB), and instructor characteristics (IC), can successfully predict the actual use (AU) of medical students with a precision of 54.22%. A higher performance expectancy (PE), habit (HB), and instructor characteristics (IC) can enhance and promote the acceptance of medical eLearning platforms.
This is the first study that has examined the acceptance of eLearning platforms among medical students in the Philippines during the COVID-19 pandemic. The outcomes of this research can serve as guidelines for the Commission on Higher Education of the Philippines for the enhancement of eLearning platforms. Although this study only utilized the latent variables of UTAUT2 along with two additional variables: Learning Value and Instructor Characteristics; the findings of this study would still hold significant value for promoting open innovation in eLearning platforms in medical fields worldwide.

Acknowledgments

The present study would not have been possible without the assistance and continuous moral support of the researchers’ close friends and loved ones. The researchers would also like to extend their acknowledgment to Professor Yogi Tri Prasetyo for his continuous guidance throughout the writing process of this paper.

References

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Said A. Salloum, Ahmad Q. M. Alhamad, Mostafa Al-Emran, Azza A. Monem, and Khaled Shaalan. Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE access 7, 128445-128462.
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Andreea Molnar, Vishanth Weerakkody, Ramzi El-Haddadeh, Habin Lee, and Zahir Irani. 2013. A framework of reference for evaluating user experience when using high definition video to video to facilitate public services in Grand Successes and Failures in IT. Public and Private Sectors: IFIP WG 8.6 International Working Conference on Transfer and Diffusion of IT. Bangalore, Springer, 436-450.
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  1. Predicting Factors Influencing the Actual Use of E-Learning Platform among Medical Students in the Philippines: Unified Theory of Acceptance and Use of Technology Approach

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        ICIEB '24: Proceedings of the 2024 4th International Conference on Internet and E-Business
        July 2024
        110 pages
        ISBN:9798400709739
        DOI:10.1145/3690001
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        Published: 22 October 2024

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        Author Tags

        1. Medical education
        2. Stepwise multiple linear regression
        3. UTAUT2
        4. eLearning platform

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