University Students’ Self-efficacy in Online Learning due to COVID-19

the pandemic COVID 19 shocked the world; it changes everybody's life, especially in education and online learning becomes a new culture. Hence, this study will identify the correlation between learners' motivation, computer anxiety, and social support with self-efficacy on online learning technology due to COVID 19 pandemic. Besides, to determine if there are any gender and age differences in their perception of online learning technology. 166 students in a university were using online learning for the first time participated in this study. The correlation analysis and chi-square are used to answer the objective of the study. The result indicated that online learning technology experience, learners' attitudes, learners' motivation, computer anxiety, and social support correlate with self-efficacy on online learning technology. Furthermore, the finding revealed that male and female respondents and different ages have similar opinions on the factors that contribute to online learning technology.


Learners Attitude
Guyer, Joshua, and Fabrigar, Leandre [22] define learners' attitude as a relatively general and permanent assessment of an object, person, or concept in a positive to negative dimension. Meanwhile, Dursun, Donmez, and Akbulut [23] said it is an individual attitude towards the learning situation. It represents both positive or negative conduct and reflection of feelings and information of a specific idea or subject matter [24].
Attitude plays a vital role in online learning [25] as it is crucial and necessary to accept and adopt online learning [21]. Meanwhile, Rhema & Miliszewska [26] added that age, gender, confidence level contribute to the learners' motivation. Besides, technical skills such as frequency of computer use, ICT work experience, own technical possession, ICT access, and ICT training history [27]. Finally, Peytcheva-Forsyth, Yovkova, and Aleksieva [27] conclude that online learning's critical factor depends on the learner's attitude and perception towards online learning itself [27].

Learners' Motivation
Brown [28] describes motivation as an inner force, impulsive, emotional, or desire which moves one to the action. It is crucial to learn and influence what, when, and how we learn and is a significant performance factor [29]. Motivation use of tactics to help achieve objectives [30] and affect what learner learns how they learn, and when they decide to learn [29].
In learning by using technology, motivation plays as self-regulated learning for learners [31]. The main characteristics of motivations are specific, motivation, goals of success, confidence, self-efficacy, and confidence in power [32]. It is an intrinsic motivation in online learning [29] and influences the learners' ability to perform their tasks [6]. Lastly, Ullah and Obaid [21] conclude that the learners' motivation in online learning can enhance the productivity of students.

Computer Anxiety
Computer anxiety refers to a fear of computers' emotional responses, including disturbance, fear, apprehension, and agitation. it causes unnecessary fear of physiological effects [35]. Salamah, Ganiardi, and Kusumanto [36] claimed that computer anxiety is a negative stress was associated with one specific form of stress computer beliefs, problem, a problem with the use of computers, and machine rejection. In the times, V. Celik and E. Yesilyurt [37] found out that computer anxiety, attitude to technology, perceived computer self-efficacy are important predictors of teacher candidates' attitude toward using computer supported education. Saadé, Kira, Mak, and Nebebe [38], found out that essential students have experienced anxiety with online learning classes. They also indicated that female students are more anxious about taking online courses than males. However, Thinakaran, Ali, and Husin [39] said that the level of anxiety about using computers amongst undergraduate students is low.

Social Support
Sahin-Baltaci and Karatas [40] describe social support as the knowledge that allows the person to believe they are cherished, respected, cared for, and a member of the social network. It is as an appreciation through the exchange of physical or psychosocial means [41]. The social support received reflects significant others' existing resources, such as family members, friends, and partners [42]. The social support for online learning in tertiary institutions is crucial, especially for support from the community, family, and community, said Andi Wahyu Irawan, Dwisona [43]. A study by Lai et al. [44] found similar results: the parents play a significant role in supporting their children. Finally, Sawahel [45] recommended that higher education policy-makers collaborate with the telecommunication industry to provide internet facilities for faculty members and students to facilitate online learning.

III. METHODOLOGY
The collected data in this study is using primary data by constructing a questionnaire. 166 respondents from the university in Shah Alam joined in this study. The variables developed from previous related studies and designed purposely to answer the study's objective. Finally, the data analyzed by using descriptive statistics, correlation, and the Chi-Square test.

Reliability Test
The pilot test was conducted to identify the questionnaires' reliability; the pilot test is conducted to 30 respondents. The Mallery (2003) used to determine the internal consistency of items in the scale.

Correlation Analysis
The analysis would like to identify the correlation between factors that contribute to self-efficacy (online learning technology experience, learners' attitude, learners' motivation, computer anxiety, and social support) and self-efficacy for using online learning technology in universities at Shah Alam. The correlation analysis will be based on the following hypotheses: Ho: There is no significant correlation between factors that contribute to self-efficacy (online learning technology experience, learners' attitude, learners' motivation, computer anxiety, and social support) and self-efficacy for using online learning technology in universities at Shah Alam. H1: There is a significant correlation between factors that contribute to self-efficacy (online learning technology experience, learners' attitude, learners' motivation, computer anxiety, and social support) and self-efficacy for using online learning technology in universities at Shah Alam. In the meantime, Pearson's correlation coefficient is used to measure the strength of a linear relationship between paired data. It is based on Evans (1996) suggests for the absolute value of r: · .00-.19 "very weak." .20-.39 "weak" .40-.59 "moderate" .60-.79 "strong" · .80-1.0 "very strong"  (2-tailed) .000 N 166 166 **. Correlation is significant at the 0.01 level (2-tailed).
Overall, as shown in table 3, there is a positive correlation between all the factors contributing to self-efficacy. They are online learning technology experience, learners' attitude, learners' motivation, computer anxiety, and social support) Furthermore, selfefficacy for using online learning technology, all the p-value<0.05. Hence the H1 accepted, and Ho rejected. It can conclude that online learning technology experience, learners' attitudes, learners' motivation, computer anxiety, and social support significantly influence the respondents' self-efficacy for using online learning technology.
However, the strength differs from weak to strong. Based on above table there was a strong, positive correlation between social support and self-efficacy (r = .695, N=166, p < .001). However, the correlation between learners' motivation and self-efficacy is moderate (r = .578, N=166, p < .001). It is similar to correlation between online learning experience and self-efficacy (r = .495, N=166, p < .001). Result indicated that learners' attitude has weak correlation with self-efficacy (r = .313, N=166, p < .001). Finally, the result revealed that computer anxiety has very weak correlation with self-efficacy(r = .158, N=166, p < .001).
The respondents in this study agreed that the main factors that correlate with their self-efficacy on online learning technology are social support. It means that support from their parents, lecturers, and friends plays a vital role in influencing their self-efficacy using online learning technology. It is exciting to show that their motivation and attitude only moderately influence their self-efficacy on online learning technology. Finally, even though computer anxiety has significant with self-efficacy, however, it is very weak. It is a good sign that mainly respondents have experience in online technology; this is the first time online learning has become mandatory.

Chi-Square Test
In this study, researchers are running the Chi-Square test to identify the relationship between the respondent's genders, the respondent's age, and the dependent variable (self-efficacy) to identify the relationship.
The analysis of the chi-square based on the following hypothesis: Ho: There is no significant relationship between genders and self-efficacy in online learning technology universities at Shah Alam. H1: There is a significant relationship between genders and self-efficacy in online learning technology universities at Shah Alam.  As shown in table 4, shows that there is no significant relationship between genders and self-efficacy. Since the p-value is greater than .005 (Pearson Chi-Square = .120 > .005), H0 is accepted and H1 is rejected. It shows that there is not enough evidence to suggest a significant relationship between genders and self-efficacy. Therefore, genders were found no relationship between self-efficacy. The analysis of the chi-square result is based on the hypothesis below: Ho: There is no significant difference between age and self-efficacy on online learning technology in Shah Alam universities. H1: There is a significant difference between age and self-efficacy on online learning technology in Shah Alam universities. As shown in table 4, there is no significant relationship between age and self-efficacy. Since the p-value is greater than .005 (Pearson Chi-Square = .160 > .005), H0 is accepted and H1 is rejected. It shows that there is not enough evidence to suggest a significant relationship between age and self-efficacy. Therefore, age was found no relationship between self-efficacy.

V. Conclusion and Recommendation
This study's most significant finding reveals that students have self-efficacy on online learning technology, even though this is the first time they use it as mandatory learning. The study also highlighted that social support in online learning. Interestingly, the study's findings also indicated that females and males and different ages have similar opinions on the factors that contribute to online learning technologies.
Overall, the study is beneficial to higher learning institutions, academicians, students, and society. Besides, it contributes to the world of knowledge in the area of technology for teaching and learning. Each of the stakeholders can know their role and contribution to the ease of online learning.
However, this study has some limitations, such as the key factors limiting to online learning technology experience, learners' attitudes, learners' motivation, computer anxiety, and social support. Besides, the sample's number only limit to universities in a city, which may not generalize the large population. Hence, future study is needed with other different factors and with the large population. Finally, the comparison study among universities and different states or different countries, or different methodology such as qualitative, can understand more on factors that contribute to self-efficacy on online learning among university students.