Examining the Impact of E-Learning on Students’ Knowledge Enhancement in the Sultanate of Oman
Examining the Impact of E-Learning on Students’ Knowledge Enhancement in the Sultanate of Oman
- Subrahmanian Muthuraman
- Amani Said Al Hosni
- Aziza Al Balushi
- 668-680
- Jan 16, 2024
- Education
Examining the Impact of E-Learning on Students’ Knowledge Enhancement in the Sultanate of Oman
Subrahmanian Muthuraman, Amani Said Al Hosni & Aziza Al Balushi
Faculty of Business Studies, Arab Open University, Muscat, Sultanate of Oman
DOI: https://doi.org/10.51244/IJRSI.2023.1012051
Received: 13 December 2023; Revised: 21 December 2023; Accepted: 25 December 2023; Published: 15 January 2024
ABSTRACT
The present study attempted to examine the impact of e-learning system education on students’ knowledge enhancement focusing on the teacher, content, technology, and student and analyzing the e-learning factors on students’ learning. A structured questionnaire was used to collect data from 249 undergraduate students from Arab Open University, Oman campus. The data were analyzed with various statistical tools which can be used to find the relationship between the variables. However, it is revealed that there exists a high effect of e-learning on students’ learning. Further, it is noticed that there exists a moderate effect of e-learning on knowledge enhancement. Multiple regression was run to predict the e-learning system from all four independent variables such as student, teacher, technology, and content and it was proved statistically significant prediction on the dependent variable thereby accepting the study hypotheses. The study also revealed that there are many benefits and challenges associated with the e-learning system. Students were exposed to the e-learning system and felt more confident and comfortable while working on it. It is therefore recommended that e-learning will become the most preferred way of education throughout the Globe. E-learning’s effectiveness also depends on the level of individual and social support available when it is being adopted. Major efforts must be made by universities to continue to improve e-learning that fosters dynamic learning opportunities for students. It is essential to improve technological skills to achieve the best goal of knowledge enhancement. Further, this study can be conducted widely in all the higher education institutions across the country.
Keywords: e-learning, Technology, Teacher, Student, Knowledge
INTRODUCTION
e-Learning will address the needs of the learners and provide quality programs which enable a basic understanding of the modern world. This system emphasizes the independence of the learner and places the responsibility for learning on the learner. e-learning is the common term used to describe the various uses of information and communications technologies to enhance learning and teaching using new strategies (Aldowah, et al., 2015). E-learning is also a uniting term used to define the fields of online learning and teaching, web-based training and management, and technology delivered instruction (Pirani, 2004). In education and training, e-learning is a technique that is developed from online learning which authorizes sharing information and learning at any time and place (Aldowah, et al., 2015). E-learning stimulates ability to discover new ideas and it promotes construction of new knowledge (Dragomir, et al., 2013). In The Middle East scenario, many Arab universities are taking gigantic steps in their use of e-learning to enhance higher education (Abouchedid & Eid, 2004 and Matar, et al., 2011). e-learning is becoming part of the mainstream of educational programs. Digital technologies have also dramatically changed academic research, thanks to the rapid acceleration of computer and network performance, which has allowed researchers to access and manipulate massive data sets, to simulate, model and visualize more complex systems, and to strengthen international communication and collaboration in research (Muthurmana, et al., 2020 and Chiţiba, 2011).
Rationale of the Study
e-learning is becoming part of the mainstream of educational programs. E-learning’s effectiveness depends on the level of individual and social support available when it is being adopted (Cho et al., 2009, Liu et al., 2010). Several arguments are associated with e-learning. Accessibility, affordability, flexibility, learning pedagogy, life-long learning, and policy are some of the arguments related to online pedagogy. Flexibility is another interesting aspect of online learning; a learner can schedule or plan their time for completion of courses available online (Dhawan, 2020). According to the Commonwealth of Learning (2020), online learning is a process of learning and teaching based on the separation of the instructor and the learner in time and place under the mediation of technology delivery with the possibility of face-to-face interaction. Combining face-to-face lectures with technology gives rise to blended learning and flipped classrooms; this type of learning environment can increase the learning potential of the students (Dhawan, 2020). Understanding the challenges that affect individual use of e-learning facilitates the creation of appropriate e-learning environments for teaching and learning. In addition, other aspects related to the acceptance of new technology can be also influenced by several social and organizational factors within a specific culture (Mohammadyari & Singh, 2015; Khan & Nawaz, 2013). Sultanate of Oman, Arab Open University is the pioneer in blended learning system. Transitioning from traditional face-to-face learning to online learning can be an entirely different experience for the learners and the educators, which they must adapt to with little or no other alternatives available (Pokhrel & Chhetri, 2021). E-learning tools have played a crucial role in helping schools and universities facilitate student learning (Subedi et al., 2020). The government also recognizes the increasing importance of online learning in this dynamic world.
Purpose of the Study
The purpose of this study is to analyze the e-learning factors on student’s learning and to understand e-learning in relation to the teacher, content, technology, student. The study will also examine the influence of e-learning on knowledge enhancement.
LITERATURE REVIEW
E-learning refers to the use of Internet technologies to deliver a broad array of solutions that enhance knowledge and performance (Rosenberg, 2001). The use of suitable and relevant pedagogy for online education may depend on the expertise and exposure to information and communications technology (ICT) for both educators and the learners. Some of the online platforms used so far include unified communication and collaboration platforms such as Microsoft Teams, Google Classroom, zoom etc., which allow the teachers to create educational courses, training, and skill development programs (Petrie, 2020). Students can learn anytime and anywhere, thereby developing new skills in the process leading to life-long learning (Dhawan, 2020). Effective online instructions facilitate feedback from learners, make learners ask questions, and broaden the learner horizon for the course content (Keeton, 2004). It is highly important that students focus on the content rather than the delivery method. The system of e-learning refers to the tools by which students can gain access to content.
MATERIALS AND METHODS
The researchers conducted a cross sectional web-based survey of bachelor students during the month of May 2023. The survey population of this study consists of students who are studying in Arab Open University, Sultanate of Oman. Convenience sampling method was used to draw 249 students who were considered for the study. The investigation was approved by the ethical committee in the university. The link of the questionnaire was sent to all the potential participants who are studying bachelor program in Arab Open University. The link was shared in every class through Microsoft Teams and WhatsApp. All the participants for this study were provided with the purpose of this study. The questionnaire was distributed to few sample size for the pilot study and the reliability of the questionnaire was calculated with the help of Cronbach alpha and it was found to be 0.918 and the total numbers of questions were 35. The values were found to be in the range of 0.60 and 0.90, hence it might be suggested that all the scales met the reliability condition (Hair et al., 1998, p.118). The use of statistical distributions such as tables showing frequencies and percentages were adopted in the study. The hypotheses were analyzed with the help of step wise multiple regression, and MANOVA.
Participants
Table 1. Demographical Data
Description | Frequency | Percentage | |
Gender | Male | 91 | 37 |
Female | 158 | 63 | |
Mode of Study | Full Time | 136 | 55 |
Part Time | 113 | 45 | |
Program of Study | Business | 144 | 58 |
Information Technology | 56 | 22 | |
Law | 45 | 18 | |
Foundation | 4 | 2 | |
Level of Study | Fourth Year | 59 | 24 |
Third Year | 77 | 31 | |
Second Year | 74 | 30 | |
First Year | 39 | 15 |
The sample (Table 1) consist of 249 students who are studying different (Business, Information Technology & Law) program in Arab Open University. The gender distribution was 37% male and 63% female students. The sample students were pursing 58 % in business program, 22% in Information Technology program and 18% in Law program. In terms of mode of study, 55% of the sample students were full time students and remaining 45% of them are pursuing part time program in the university. The student’s level of study 24% of them are in fourth year of their study, 31% of them are in third year, 30% of students are in Second year and the remaining 15% of them are doing their first year.
RESULTS
The researchers conducted four different step wise multiple regression analysis to satisfy the objectives of the study (1) to examine the influence of the e-learning on student’s learning (Y) (Table 2), (2) to examine the influence of the Teacher, content Technology and Student on E-learning system (Y) (Table 3), (3) to examine the influence of the e-learning on knowledge enhancement (Y) (Table 4) and (4) to examine the influence of benefits and challenges on e-learning (Y) (Table 5) respectively. The tables display the unstandardized regression co-efficient (B), the unstandardized standard error of regression coefficients (SE B), the standardized regression coefficient (β), R^{2}, and F for changes in R^{2}.
Table 2 Ho: There is no significant impact of e-learning education on student’s learning.
Variables | Model 1 | ||
B | SE B | β | |
Constant | .639 | .487 | |
E-learn | 1.224 | .033 | .917 |
R^{2} | 0.841 | ||
Adjusted R^{2} | 0.840 | ||
F | 1347.65 | ||
df | (1, 255) | ||
Sig (P) | 0.001 |
Unstandardized regression coefficient (B), the Unstandardized standard error of regression coefficients (SE B), the standardized regression coefficient (β)
The table reveals that E-learning variable is entered at Step 1 and predicts only 84% of Student’s learning (R^{2} = 0.841, F (1, 255) = 1347.65, p = 0.001). The R^{2} for the overall study on the above factor suggests that there is a high effect (84%) e-learning on student’s learning. Model Equation: Y = 0.639 +1.224 (E-learn). This would suggest that e-learning plays a significant role on Student’s learning.
Table 3 Ho: There is no significant impact of teacher, content, technology, student on E-learning system.
Variables | Model 1 | Model 2 | Model 3 | Model 4 | ||||||||
B | SE B | β | B | SE B | β | B | SE B | β | B | SE B | β | |
(Constant) | 1.848 | .359 | -.787 | .389 | -1.197 | .360 | -1 .300 | .348 | ||||
Students | .682 | .019 | .915 | .518 | .022 | .694 | .363 | .030 | .487 | .298 | .032 | .399 |
Teacher | .305 | .029 | .316 | .261 | .027 | .270 | .205 | .029 | .213 | |||
Technology | .228 | .033 | .277 | .190 | .033 | .230 | ||||||
Content | .162 | .036 | .196 | |||||||||
R^{2} | 0.837 | 0.888 | 0.907 | 0.914 | ||||||||
AdjustedR^{2} | 0.836 | 0.887 | 0.906 | 0.912 | ||||||||
F | 1256.35 | 967.15 | 787 | 642.15 | ||||||||
df | (1, 245) | (2,244) | (3,243) | (4,242) | ||||||||
Sig (P) | 0.001 | 0.001 | 0.001 | 0.001 |
Unstandardized regression coefficient (B), the Unstandardized standard error of regression coefficients (SE B), the standardized regression coefficient (β)
The table reveals that Student is entered at Step 1 and predicts only 83.6% of E-learning system (R^{2} = 0.837, F (1,245) = 1256.35, p = 0.001). When Teacher is entered at Step 2, there is 5% increase in predictive capacity (R^{2} = 0.887, F (2,244) = 967.15, p = 0.001). Then Technology is entered at step 3, there is 2% increase in predictive capacity (R^{2} = 0.906, F (3,243) = 787, p=0.001). Finally, Content is entered at Step 4 there is an improvement in the model with 91.2% in predictability (R^{2} = 0.914, F (4,242) = 642.15, p = 0.001). The R^{2} for the overall study on the four factors suggest that there is a high effect (91%) on e-learning system. Model Equation: Y = -1.300+0.298(Students) + 0.205 (Teacher) + 0.190 (Technology) + 0.162(Content). This would suggest that e-learning variables like student, teacher, technology, and content play a significant role on e-learning system.
Table 4 Ho: There is no significant impact of e-learning education on knowledge enhancement.
Variables | Model 1 | ||
B | SE B | β | |
Constant | 1.556 | .797 | |
E-learn | 1.104 | .054 | .785 |
R^{2} | 0.616 | ||
Adjusted R^{2} | 0.615 | ||
F | 410.78 | ||
df | (1, 256) | ||
Sig (P) | 0.001 |
Unstandardized regression coefficient (B), the Unstandardized standard error of regression coefficients (SE B), the standardized regression coefficient (β)
The table reveals that E-learning variable is entered at Step 1 and predicts only 62% of knowledge enhancement (R^{2} = 0.616, F (1, 256) = 410.78, p = 0.001). The R^{2} for the overall study suggest that there is a moderate effect (62%) of e-learning on knowledge enhancement. Model Equation: Y = 1.556 +1.104 (E-learn). This would suggest that e-learning plays a significant role on knowledge enhancement.
Table 5 Ho: There is no significant impact of benefits and challenges on e-learning.
Variables | Model 1 | |||
B | SE B | β | Sig (P) | |
(Constant) | 2.715 | 1.230 | .028 | |
benefits | .870 | .044 | .787 | .000 |
challenges | -.091 | 0.045 | -0.080 | .043 |
R^{2} | 0.679 | |||
Adjusted R^{2} | 0.676 | |||
F | 265.44 | |||
df | (2, 251) |
Unstandardized regression coefficient (B), the Unstandardized standard error of regression coefficients (SE B), the standardized regression coefficient (β)
The table reveals that E-learning variable is entered at Step 1 and predicts only 62% of knowledge enhancement (R^{2} = 0.616, F (1, 256) = 410.78, p=0.001). Model Equation: Y = 2.715 +0.870 (Benefits) – 0.091 (Challenges). This would suggest that e-learning system has more benefits than challenges.
MANOVA Tests on Gender and E-learning factors
MANOVA is used to explore taking Gender as independent variable and E-learning factors like content, teacher, and technology as dependent variables to find the interactions among the dependent variable and also among independent variables. Ho: There is no significant effect across the Gender and E-learning factors
Table 6: Multivariate Tests^{a }on Gender and E-learning factors
Effect | Value | F | Hypothesis df | Error df | Sig. | Partial Eta Squared | |
Gender | Wilks’ Lambda | .824 | 17.419^{b} | 3.000 | 245.000 | .000 | 0.176 |
a. Design: Intercept + mode
b. Exact statistic
Table 7: Tests of Between-Subjects Effects on Gender and E-learning factors
Source | Dependent Variable | Type III Sum of Squares | df | Mean Square | F | Sig. | Partial Eta Squared |
Gender | Teacher | 880.302^{a} | 1 | 880.302 | 46.555 | .000 | 0.159 |
Content | 973.500^{b} | 1 | 973.500 | 36.500 | .000 | 0.129 | |
Technology | 1021.131^{c} | 1 | 1021.131 | 38.595 | .000 | 0.135 | |
Error | Teacher | 4670.445 | 247 | 18.909 | |||
Content | 6587.857 | 247 | 26.671 | ||||
Technology | 6534.997 | 247 | 26.457 |
a. R Squared = .159 (Adjusted R Squared = .155): b. R Squared = .129 (Adjusted R Squared = .125)
c. R Squared = .135 (Adjusted R Squared = .132)
Table 8: Estimated marginal means of Gender.
Dependent factors | Gender | Mean | Std. Deviation | N |
Teacher | Male | 20.6703 | 3.65165 | 91 |
Female | 16.7658 | 4.70149 | 158 | |
Total | 18.1928 | 4.73097 | 249 | |
Content | Male | 20.8022 | 3.97274 | 91 |
Female | 16.6962 | 5.73703 | 158 | |
Total | 18.1968 | 5.52172 | 249 | |
Technology | Male | 19.8571 | 4.45364 | 91 |
Female | 15.6519 | 5.50035 | 158 | |
Total | 17.1888 | 5.51981 | 249 |
It is inferred from the table (6, 7 & 8) there is a significant difference between males and females when considered jointly on the E-learning variables, Wilk’s A = 0.824, F (3,245) = 17.419, p = 0.001, partial n^{2} = 0.176. A separate ANOVA was conducted for each dependent variable with each ANOVA evaluated at an alpha level of 0.05. It is also observed from the table that there is a significant difference between males and females on Teacher F (1,247) = 46.55, p = 0.001, partial n^{2 }= 0.157; Content F (1,247) = 36.50 p = 0.001, partial n^{2 }= 0.129; and Technology F (1,247) = 38.595 p = 0.001, partial n^{2 }= 0.135. Further it is concluded from the table that estimated mean scores of Teachers, Content and Technology show males are scoring higher than females. Hence Ho is rejected. It shows that there is a significant effect across the Gender and E-learning factors.
MANOVA Tests on Mode of Study and E-learning factors
MANOVA is used to explore taking mode of study as independent variable and E-learning factors like content, teacher, and technology as dependent variables to find the interactions among the dependent variable and among independent variables.
Ho: There is no significant effect across the mode of study and E-learning factors
Table 9: Multivariate Tests^{a }on Mode of Study and E-learning factors
Effect | Value | F | Hypothesis df | Error df | Sig. | Partial Eta Squared | |
Mode | Wilks’ Lambda | .931 | 6.055b | 3.000 | 245.000 | .001 | 0.069 |
a. Design: Intercept + mode
b. Exact statistic
Table 10: Tests of Between-Subjects Effects on Mode of study and E-learning factors
Source | Dependent Variable | Type III Sum of Squares | df | Mean Square | F | Sig. | Partial Eta Squared |
Mode of Study | Teacher | 355.940^{a} | 1 | 355.940 | 16.924 | .000 | 0.064 |
Content | 434.524^{b} | 1 | 434.524 | 15.060 | .000 | 0.059 | |
Technology | 316.077^{c} | 1 | 316.077 | 10.783 | .001 | 0.042 | |
Error | Teacher | 5194.807 | 247 | 21.032 | |||
Content | 7126.833 | 247 | 28.854 | ||||
Technology | 7240.052 | 247 | 29.312 |
a. R Squared = .159 (Adjusted R Squared = .155)
b. R Squared = .129 (Adjusted R Squared = .125)
c. R Squared = .135 (Adjusted R Squared = .132)
Table 11: Estimated marginal means of Mode of Study.
Dependent factors | Mode | Mean | Std. Deviation | N |
Teacher | Full Time | 17.1029 | 4.59191 | 136 |
Part Time | 19.5044 | 4.57892 | 113 | |
Total | 18.1928 | 4.73097 | 249 | |
Content | Full Time | 16.9926 | 5.80102 | 136 |
Part Time | 19.6460 | 4.80312 | 113 | |
Total | 18.1968 | 5.52172 | 249 | |
Technology | Full Time | 16.1618 | 5.74678 | 136 |
Part Time | 18.4248 | 4.98355 | 113 | |
Total | 17.1888 | 5.51981 | 249 |
It is inferred from the table (9, 10 & 11) there is a significant difference between full time and part time when considered jointly on the E-learning variables, Wilk’s A = 0.931, F (3,245) = 6.055, p = 0.001, partial n2 = 0.069. A separate ANOVA was conducted for each dependent variable with each ANOVA evaluated at an alpha level of 0.05. It is also observed from the table that there is a significant difference between fulltime and part time on Teacher F(1,247) = 16.924, p=0.001, partial n2 = 0.064; Content F(1,247) = 15.060 p = 0.001, partial n2 = 0.059; and Technology F(1,247) = 10.783 p = 0.001, partial n2 = 0.042. Further it is concluded from the table that estimated mean scores of Teachers, Content and Technology show part time mode are scoring higher than full time mode. Hence Ho is rejected. It shows that there is a significant effect across the Mode of Study and E-learning factors.
MANOVA Tests on Program of Study and E-learning factors
MANOVA is used to explore taking program in which students are studying as independent variable and E-learning factors like content, teacher, and technology as dependent variables to find the interactions among the dependent variable and among independent variables.
Ho: There is no significant effect across the program of study and E-learning factors
Table 12: Multivariate Tests^{a }on Program of Study and E-learning factors
Effect | Value | F | Hypothesis df | Error df | Sig. | Partial Eta Squared | |
Program | Wilks’ Lambda | .856 | 4.346 | 9.000 | 591.549 | .000 | 0.051 |
a. Design: Intercept + program
b. Exact statistic
Table 13: Tests of Between-Subjects Effects on Program of study and E-learning factors
Source | Dependent Variable | Type III Sum of Squares | df | Mean Square | F | Sig. | Partial Eta Squared |
Program of Study | Teacher | 432.840^{a} | 3 | 144.280 | 6.907 | .000 | 0.078 |
Content | 271.740^{b} | 3 | 90.580 | 3.044 | .029 | 0.036 | |
Technology | 252.316^{c} | 3 | 84.105 | 2.821 | .040 | 0.033 | |
Error | Teacher | 5117.907 | 245 | 20.889 | |||
Content | 7289.617 | 245 | 29.754 | ||||
Technology | 7303.813 | 245 | 29.811 |
a. R Squared = .078 (Adjusted R Squared = .067)
b. R Squared = .036 (Adjusted R Squared = .024)
c. R Squared = .033 (Adjusted R Squared = .022)
Table 14: Estimated marginal means of Program of Study.
Dependent factors | Program | Mean | Std. Deviation | N |
Teacher | Business | 17.3472 | 5.14723 | 144 |
IT | 18.1071 | 3.42546 | 56 | |
Law | 20.8444 | 3.93097 | 45 | |
Foundation | 20.0000 | 1.15470 | 4 | |
Total | 18.1928 | 4.73097 | 249 | |
Content | Business | 17.6528 | 5.88743 | 144 |
IT | 17.7857 | 4.22854 | 56 | |
Law | 20.4000 | 5.49959 | 45 | |
Foundation | 18.7500 | 2.50000 | 4 | |
Total | 18.1968 | 5.52172 | 249 | |
Technology | Business | 17.2986 | 5.58730 | 144 |
IT | 15.6250 | 5.00386 | 56 | |
Law | 18.7778 | 5.75203 | 45 | |
Foundation | 17.2500 | 1.50000 | 4 | |
Total | 17.1888 | 5.51981 | 249 |
It is inferred from the table (12, 13 & 14) there is a significant difference between various program when considered jointly on the E-learning variables, Wilk’s A = 0.856, F (9, 591) = 4.346, p = 0.001, partial n2 = 0.051. A separate ANOVA was conducted for each dependent variable with each ANOVA evaluated at an alpha level of 0.05. It is also observed from the table that there is a significant difference between various program on Teacher F (3, 245) = 6.907, p = 0.001, partial n2 = 0.078; Content F (3,245) = 3.044 p=0.029, partial n2 = 0.036; and Technology F (3,245) = 2.821 p = 0.040, partial n2 = 0.033. Further it is concluded from the table that estimated mean scores of Teachers, Content and Technology show law program are scoring higher. Hence Ho is rejected. It shows that there is a significant effect across the Program of Study and E-learning factors.
MANOVA Tests on Level of Study and E-learning factors
MANOVA is used to explore taking level in which students are studying as independent variable and E-learning factors like content, teacher, and technology as dependent variables to find the interactions among the dependent variable and among independent variables.
Ho: There is no significant effect across the level of study and E-learning factors
Table 15: Multivariate Tests^{a }on Level of Study and E-learning factors
Effect | Value | F | Hypothesis df | Error df | Sig. | Partial Eta Squared | |
Level | Wilks’ Lambda | .970 | .842 | 9.000 | 591.549 | .578 | .010 |
a. Design: Intercept + level
b. Exact statistic
Table 16: Tests of Between-Subjects Effects on Level of study and E-learning factors
Source | Dependent Variable | Type III Sum of Squares | df | Mean Square | F | Sig. | Partial Eta Squared |
Level of Study | Teacher | 90.368^{a} | 3 | 30.123 | 1.352 | .258 | .016 |
Content | 15.192^{b} | 3 | 5.064 | .164 | .920 | .002 | |
Technology | 61.648^{c} | 3 | 20.549 | .672 | .570 | .008 | |
Error | Teacher | 5460.379 | 245 | 22.287 | |||
Content | 7546.166 | 245 | 30.801 | ||||
Technology | 7494.480 | 245 | 30.590 |
a. R Squared = .016 (Adjusted R Squared = .004)
b. R Squared = .002 (Adjusted R Squared = -.010)
c. R Squared = .008 (Adjusted R Squared = -.004)
Table 17: Estimated marginal means of Level of Study.
Dependent factors | Level | Mean | Std. Deviation | N |
Teacher | First year | 19.1282 | 3.85377 | 39 |
Second year | 17.8514 | 4.79350 | 74 | |
Third year | 17.5974 | 5.04261 | 77 | |
Fourth year | 18.7797 | 4.70901 | 59 | |
Total | 18.1928 | 4.73097 | 249 | |
Content | First year | 18.5385 | 4.87662 | 39 |
Second year | 18.0405 | 5.10825 | 74 | |
Third year | 17.9610 | 6.30464 | 77 | |
Fourth year | 18.4746 | 5.44045 | 59 | |
Total | 18.1968 | 5.52172 | 249 | |
Technology | First year | 17.7692 | 4.88532 | 39 |
Second year | 17.0811 | 4.97874 | 74 | |
Third year | 16.5714 | 6.21422 | 77 | |
Fourth year | 17.7458 | 5.63729 | 59 | |
Total | 17.1888 | 5.51981 | 249 |
It is inferred from the table (15, 16 & 17) there is no significant difference between various levels when considered jointly on the E-learning variables, Wilk’s A = 0.970, F (9, 591) = 0.842 p = 0.598, partial n2 = .010. Hence Ho is accepted. It shows that there is no significant effect across the Level of Study and E-learning factors.
DISCUSSION
E-learning usage and adoption among users is a challenging issue for many universities, both in developed and developing countries, but it is likely to be less of a concern in developed countries over the willingness of their students to accept and use the e-learning system, as significant progressive steps have already been taken, according to literatures (Almaiah et al., 2016). Eltahir (2019) indicated that the challenges of adopting e-learning system in developing countries, however, remain a reality due to the digital divide with the developing countries. E-learning tools are playing a crucial role during this pandemic, it aims to help instructors, schools, and universities facilitate student learning (Almaiah, et al, 2020). There are n number of technologies available for online education but sometimes they create a lot of difficulties. These difficulties and problems associated with modern technology range from downloading errors, issues with installation, login problems, problems with audio and video, and so on (Dhawan, 2020).
The synchronous learning environment is structured in the sense that students attend live lectures, there are real-time interactions between educators and learners, and there is a possibility of instant feedback, whereas asynchronous learning environments are not properly structured. In such a learning environment, learning content is not available in the form of live lectures or classes; it is available at different learning systems and forums. Instant feedback and immediate response are not possible under such an environment (Littlefield, 2018). The learners with a fixed mindset find it difficult to adapt and adjust, whereas the learners with a growth mindset quickly adapt to a new learning environment (Pokhrel & Chhetri, 2021). Findings from both the qualitative and quantitative data suggested that when learners were provided with adequate and appropriate communication tools in e-learning environments it enhanced interaction and collaboration with their peers and tutors and thereby enhance their development of knowledge and skills in the course (Veerasamy, et al., 2020)
Teachers should set time limits and reminders for students to make them alert and attentive. Efforts should be made to humanize the learning process to the best extent possible. Personal attention should be provided to students so that they can easily adapt to this learning environment (Dhawan, 2020). Educators must spend a lot of time making effective strategies for giving online instructions. Educators or teachers in the form of facilitators face a lot of trouble while working on these technologies in the form of how to start using it when to use it, how to reduce distractions for students, how to hone students’ skills via e-learning technologies (Dhawan, 2020). The use of e-learning environments to support teaching and learning has had a great impact on the way content is developed and managed. In most cases, both teachers and students have had to re-adapt the way they prepare, access, and engage with educational matters (Mwanza & Engeström, 2005). E-learning should be designed in such a way that they are creative, interactive, relevant, student-centered, and group based (Partlow & Gibbs, 2003). E-Learning is rapidly becoming an essential component of Oman’s educational process in all the universities and colleges and brings with it the most significant changes. With its rapidly growing workforce of adaptable and well-educated graduates, Oman could have a unique role to play with e-learning in the region (Muthuraman et al., 2020). Another implication is that if the instructors at AOU are the persons to be responsible for improving methods of delivery of the instructional materials, they must be trained and motivated to improve their skills and potentials in this regard (Muthuraman, 2018)
CONCLUSION
Student assessments are also moving online, with a lot of trial and error and uncertainty for everyone. Students should be motivated and satisfied with the instructor’s support and course policies tend to perceive their learning outcomes higher (Veerasamy et al, 2020). The survey conducted was very revealing of the attitude of the students for e-learning skills. There is a general positive attitude towards e-learning among the student group. E-learning is a good solution during this pandemic situation. Even though there are few challenges in adopting e-learning technologies, the educational institutions are supporting in all possible ways and provide an uninterpreted education to all the student community. Further, this study can be conducted widely be carried out in all educational institutions across the country.
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