Relationship between Self-Efficacy, Emotional Intelligence, and Parental Social Support with Learning Motivation of University Students in Distance Learning during the Covid-19 Pandemic

Distance learning has an impact on decreasing university students’ motivation to learn. Meanwhile, learning motivation is fundamental in the distance learning process. Learning motivation correlates with internal factors such as self-efficacy and emotional intelligence and external factors such as parental social support. This study aimed to find alternative factors that support learning motivation and examine the relationship between self-efficacy, emotional intelligence, and parental social support with learning motivation in university students carrying out distance learning during the COVID-9 pandemic. The study used quantitative research with the correlational research type involving undergraduate students at the Muhammadiyah University of Surakarta with a total of 408 students. The Academic Motivation Scale-Short version (AMS) was used to measure learning motivation, the General Self-Efficacy Scale (GSES) was used to measure self-efficacy, the Trait Emotional Intelligence Questionnaire-Short Form (TEIQUE-SF) was used to measure emotional intelligence, and the parental social support scale compiled by Fitrotin was used to measure parental social support. Data were analyzed using multiple linear regression tests. The results of the study found that self-efficacy, emotional intelligence, and parental social support had a relationship with learning motivation. This was indicated by the positive value of Beta=27.284, p-value of 0.000 (p <0.005), the regression (F value) of 36.569, the correlation (R)= 0.467, and the effective contribution to learning motivation of 21.8%. Programs to increase self-efficacy, emotional intelligence, and parental social support are important to increase motivation.


INTRODUCTION
Massive changes in every line of life, including the education aspect, have occurred since the COVID-19 pandemic in Indonesia. The transition from face-to-face learning methods to distance learning is also carried out by all universities and colleges. However, this transition raises various problems for educators and students in its implementation. Psychologically, students experience pressure to participate in distance learning (Pohan, 2020). Research conducted by Desriva et al (2020) illustrates that the online learning system and all existing pressures significantly impact motivation in learning for university students.
To see the impact of online learning on learning motivation, researchers collected preliminary data in the form of an open questionnaire about what students felt during participating in distance learning to 108 students. And it was found that as many as 21 (19.6%) students felt sad and disliked distance learning, 22 (20.56%) students felt fed up and bored with distance learning, 20 (18.6%) students felt tired and stressed, and 7 (6.5%) students felt less excited and experienced a decrease in motivation. Setriani and Puspitasari, (2020) mention these characteristics as the impact of the decrease in learning motivation. Preliminary data collection also measured the level of students' learning motivation. It was found that 12.1% of students had low learning motivation, 67.3% had motivation in the medium category, and 20.6% had motivation in the high category. Based on the preliminary data, it can be concluded that only 20.6% of university students had an optimal level of motivation, while most of them were not optimal. This is necessary to pay attention to because the role of motivation in learning is crucial because it is related to student learning achievement results. On the other hand, in the context of online learning, motivation is the main key factor and a significant source of problems as a predictor of the success of the learning process (Nakayama et al., 2014).
An encouragement realized by individuals and assistance from the social environment to engage in a behavior to meet basic psychological needs, namely the need for autonomy, competence and relatedness is called motivation (Ryan & Deci, 2017). Ryan and Deci (2017) mention that there are three main dimensions of motivation, namely: (a) intrinsic motivation, which is defined as spontaneous activities carried out by individuals where individuals produce satisfaction in doing so; (b) extrinsic motivation, which is defined as activities carried out by individuals, not because of their interests or desires, but for some separable consequences such as obtaining social approval and rewards from external, avoiding rewards, or achieving rewarded results; (c) amotivation, is defined as the extent to which individuals are passive and do not have aims to related to a particular set of potential actions.
From the perspective of social cognitive theory, motivation, is seen as a behavior that individuals conduct in a directed manner and comes from the expectation of a goal and the value of that goal (Schunk, 2012;Woolfolk, 2017). The expectations and values of the goals are sourced from individuals and self-regulated by individuals because humans are independent agents who proactively have the capacity to organize and direct themselves (Bandura, 1977), including the capacity to regulate motivation. Motivated behavior is contained in a system called reciprocal determinism between personal, behavioral, and environmental determinants (Bandura, 1977). Reciprocal determinism of individuals' behavior includes learning motivation influenced by personal and environmental factors. Bandura (1999) states that personal factors come from cognitive, affective, and biological factors. In the cognitive process, individuals form beliefs about what can be done to set the goals they want to achieve (Bandura, 1997). This belief is called selfefficacy. Self-efficacy is divided into three dimensions (Bandura, 1997), namely: (a) magnitude, which reflects how complicated individuals are to be able to get certain behaviors; (b) strength, which reflects an individual's belief in being able to perform a task or job; (c) generality, which reflects how self-efficacy relate in some aspects including behavioral, cross-domain behavioral, or cross-time aspects. Self-efficacy of individuals can direct how individuals act and conduct behavior to achieve what is intended (Bandura, 1977). If the individual's self-efficacy has the power to perform a task or purpose, then he will be motivated and direct all his actions to perform the task (Bandura, 1977). The findings of previous studies by Setriani and Puspitasari (2020) and Maraghi et al (2018) found a positive correlation between these two variables with values of 0.717 and 0.88, respectively.
In the same context, another personal factor related to motivation is emotional intelligence. Emotions have an important role in motivation because, in essence, emotions are the driving force to act (Goleman, 2017). Self-regulation related to emotions through cognitive and affective processes is called emotional intelligence (Martinez-Pons, 2000). Petrides (2009) defines emotional intelligence as intelligence in recognizing emotions through cognitive processes and social abilities that exist in individuals. Petrides (2009) suggests four dimensions of emotional intelligence, namely: (a) emotionality, which is the ability related to the feelings of individuals and others so that individuals can feel and express emotions and use them to build relationships with others; (b) self-control, which is the ability to control impulses, desires, and external pressures healthily; (c) social ability, which is the ability to have social interactions, social relationships, and social influences broadly; (d) well-being, which is the ability to feel positive, happy, and fulfilled and become empowered and accomplished.
Emotional intelligence will make students understand the volatile feelings in themselves and others, then able to carefully respond to every piece of information, emotion and energy that exists and then channel it into daily activities such as tasks and work (Listiana & Suharyat, 2016). A study conducted by Listiana and Suharyat (2016) and Basri and Aldina (2019) found a positive relationship between these two variables, with correlation values of 0.977 and 0.555, respectively.
In different contexts, there are environmental factors related to learning motivation, namely parental social support. Support and assistance obtained by individuals from family, friends, teachers, or professionals in the form of emotional, instrumental, informational, and appraisal support are called social support (Tardy, 1985). House (Tardy, 1985) expresses four aspects of social support, namely: (a) emotional support, a type of social support that refers to the provision of love, trust, empathy, and care; (b) appraisal support, which is social support that refers to the provision of feedback with evaluative and relevant communication to individual evaluation; (c) informational support, which is social support in the form of providing information, advice to help overcome individual's personal problems; (d) instrumental support, which is social support that refers to direct or real assistance based on individual's circumstances.
Acceptance of parental support makes university students feel given attention and appreciation, which will have an impact on increasing students' motivation to learn (Malwa, 2018). The findings of previous studies by Tezci et al (2015) and Emeralda and Kristiana (2017) showed a positive correlation between these two variables, with correlation values of 0.44 and 0.694, respectively.
In distance learning, students are required to have special skills in motivating themselves to learn without having face to face classroom with a teacher. Therefore, during the COVID-19 pandemic, learning motivation needs to be studied more deeply. Previous research has found correlations between self-efficacy with learning motivation, emotional intelligence with learning motivation, and parental social support with learning motivation in the face-to-face learning setting. However, similar research in the context of distance learning was rarely done. In addition, the correlation of variables of selfefficacy, emotional intelligence, and parental social support with learning motivation has never been examined simultaneously. Thus, the formulation of this research problem is "Do self-efficacy, emotional intelligence, and parental social support have a relationship with learning motivation in university students carrying out distance learning? Besides, this study aimed to examine the relationship between self-efficacy, emotional intelligence, and parental social support with learning motivation in university students carrying out distance learning during the COVID-19 pandemic.

METHOD
The learning motivation variable was the dependent variable, while the variables of self-efficacy, emotional intelligence, and parental social support were the independent variables. This study used quantitative research, especially correlational research. Active undergraduate students at Muhammadiyah University Surakarta were chosen as the research population. Based on the consideration of population distribution, the sampling technique chosen was the technique of non-random sampling in the form of purposive sampling. Based on these considerations, the calculation of the number of samples used a sample size calculation with a significant level of 5%. Thus, the total sample was 408 university students.
The instrument used to measure learning motivation was the Academic Motivation Scale (AMS) created by Vallerand et al (1992). AMS was compiled based on Deci and Ryan's (1985) motivational dimension and has been adapted and modified to the Indonesian short version by Natalya, (2018) so that it consisted of 15 items. In this study, the results of the content validity test using Aiken's V showed a validity value between 0.75-0.92. Besides, the measurement of the reliability test using Cronbach alpha showed a reliability value of 0.773. General Self-Efficacy Scale (GSES) compiled by Schwarzer and Jerusalem (1995) was used to measure self-efficacy. GSES was compiled based on the self-efficacy dimension (Bandura, 1977) and adapted into an Indonesian version by Novrianto et al (2019) so that it consisted of 10 items. In this study, the results of the content validity test using Aiken's V showed a validity value between 0.78-0.92. Moreover, the measurement of the reliability test using Cronbach alpha showed a reliability value of 0.878. Emotional intelligence was measured using Trait Emotional Intelligence Questionnaire-Short Form (TEIQUE -SF) compiled by Petrides (2009) and has been adapted and modified in the Indonesian version by Tresnawaty (2018). TEIQUE-SF consisted of 15 items. In this study, the results of the content validity test using Aiken's V showed a validity value between 0.75-0.90. Subsequently, the measurement of the reliability test using Cronbach alpha showed a reliability value of 0.780. The scale used to measure the parental social support variable was the parental social support scale compiled by Fitrotin (2017) (Macdonald, 1998) based on the dimension of social support by House (Tardy, 1985). This scale consisted of 40 items which were then modified to the short version by the researchers to 12 items. In this study, the results of the content validity test using Aiken's V showed a validity value between 0.82-0.92. Also, the measurement of the reliability test using Cronbach alpha showed a reliability value of 0.883.
The data collection procedure in the study was begun with the preparation stage, namely adapting the measuring instrument and making modifications according to the needs of the study. Modifications were made by adjusting the scale items to the research context, namely distance learning and the number of answer options. The scale of parental social support was modified by adjusting the research subjects from student to university student. Also, regarding the scales of learning motivation and emotional intelligence, the modification of the item was carried out by adjusting to the research context, namely distance learning or pandemic conditions. Then, the researchers also conducted the validity test using Aiken's V coefficient validity test procedure with the seven expert panels with a scale range of 1-5. Then, the researchers also conducted the reliability test using the Cronbach alpha technique.
The next step was the data collection done by using the non-random sampling technique, namely purposive sampling. A pamphlet was distributed with the criteria of being an active undergraduate student of UMS and carrying out distance learning. Qualified respondents registered by filling in the form. Then, they were collected in one WhatsApp group containing information about the webinar. The measuring instruments were loaded in 2 google form links and shared on the subject in the webinars. The webinars were organized to contribute from researchers to research subjects who were willing to become respondents. The first google form link was shared before the webinar event on July 23, 2021, and the second google form link was distributed after the webinar event on July 24, 2021. Webinars were organized by researchers and teams on the theme of Self-empowerment: Loving yourself with mindfulness. Furthermore, the accumulated data were analyzed using the multiple regression test through the application of SPSS For Window Release, version 23.

RESULTS
Based on the distribution of questionnaires conducted online, the researchers managed to collect 408 online questionnaire respondents. The distribution of data for the range of learning motivation, self-efficacy, emotional intelligence, and parental social support was presented in Tables 1, 2, 3, and 4 below:  The basic assumption test was used for prerequisites before the hypothesis test was carried out. Through normality, linearity, multicollinearity, and heteroscedasticity tests, it was found that the data had met the prerequisite tests and were able to be analyzed using parametric tests. The parametric test used was the multiple linear regression test. Multiple linear regression test results on self-efficacy, emotional intelligence, and parental social support with learning motivation showed a positive value of Beta= 27.284, a p-value of 0.000 (p<0.005), F regression of 36.569, R of 0.467 and R square of 0.218 (21.8%). It can be concluded that self-efficacy, emotional intelligence, and parental social support correlate with learning motivation, with a correlation value of 0.467 and an effective contribution of 0.218 (21.8%). Partial hypothesis test results showed a significance value of 0.000 (p<005) on each independent variable against the dependent variable. It can be concluded that self-efficacy, emotional intelligence, and parental social support partially correlate with learning motivation. Self-efficacy with learning motivation has a correlation value of 0.426, emotional intelligence with learning motivation has a correlation of 0.230, and parental social support with learning motivation has a correlation of 0.318. Hypothesis test results are presented in Table 5: The effective contribution of each independent variable can be obtained through calculations between the coefficient value, cross-product, and regression value (Muhid, 2019). The calculation of the formula is presented below:

Notes:
= Effective contribution of variable = Variable Coefficient (B) CP = Cross-product variable Regression = Regression Value = Total effective contribution In Table 6, the calculation of the effective contribution of independent variables to learning motivation showed that the self-efficacy variable has an effective contribution of 15.3%, the emotional intelligence variable has an effective contribution of 0.0015%, and the parental social support variable has an effective contribution of 6.4%. The results of the calculation of effective contribution per variable are presented in Table 6:

DISCUSSION
This study showed that self-efficacy, emotional intelligence, and parental social support correlate with learning motivation. Self-efficacy, emotional intelligence, and parental social support simultaneously showed a correlation of 0.467 and had an effective contribution to learning motivation of 21.8%. This can be interpreted that 21.8% of the variance in learning motivation can be explained by self-efficacy, emotional intelligence, and parental social support. Meanwhile, another 78.2% can be explained by other variables. The study also revealed that emotional intelligence had a correlation value of 0.230 with an effective contribution of 0.0015%. In contrast, self-efficacy had a correlation value of 0.426, with an effective contribution of 15.3%. Besides, parental social support with learning motivation had a correlation of 0.318, with an effective contribution of 6.4%. It can be concluded that in learning motivation, emotional intelligence is the factor that has the lowest effective contribution, while the factor that has the most effective contribution is self-efficacy.
The findings showed that self-efficacy has a positive relationship with learning motivation. This result is consistent with previous studies, namely the finding of a positive relationship between self-efficacy and learning motivation in university students (Fikriyani et al., 2020;Kheirkhah et al., 2017;Taheri-Kharameh et al., 2018;Zhang et al., 2015), Elementary School (SD) students (Purningsih et al., 2016), Junior High School (SMP) students (Zega, 2020), and Senior High School (SMA) students (Aydın, 2015). On the other hand, the findings of this study are also consistent with studies conducted in Turkey (Aydın, 2015;Malkoç & Mutlu, 2018), Iran (Kheirkhah et al., 2017;Maraghi et al., 2018), Algeria (Mouloud et al., 2017), India (Bhatt & Bahadur, 2018), and China (Zhang et al., 2015). Similar research results with previous research results confirm that efficacy is related to learning motivation; it also plays an important role in students. These relationships and roles apply at all levels of education, age, country, and learning methods, both in the context of face-to-face and distance learning. Self-efficacy is a good measuring tool for predicting behavioral outcomes compared to other motivational constructs (McGeown et al., 2014).
Regarding this research in the context of distance learning, self-efficacy has proven to be an essential variable in the online learning environment (Peechapol et al., 2018). This is because distance learning is a challenging environment that demands students' independence to complete academic tasks. Self-efficacy will give confidence in their abilities, so they can easily solve problems in the learning process (Purningsih et al., 2016). In this study, the effectiveness of the role of self-efficacy can be seen as the variable with the greatest correlation strength value and greater effective contribution than other variables, as many as 15.3%.
Self-efficacy gives individuals trust and confidence in what can be done, directing how to act, and setting and achieving goals (Bandura, 1977). This makes an individual who has confidence that he or she has the strength or ability to be able to do certain tasks, then he or she will tend to have the motivation to do these tasks, and vice versa (Bandura, 1977). According to Bandura (Tsai et al., 2011) self-efficacy can be related to learning motivation because self-efficacy tends to foster interest or motivation intrinsically (internally) in students. Intrinsically motivated students develop a feeling of being able to succeed academically, so they tend to be responsible for their own achievements and failures (Rhew et al., 2018). Through self-efficacy, individuals form independent learning, are motivated to learn, guide the learning process, invest more effort to achieve goals and complete tasks, determine how much effort is made for a task, the length of time that will be taken to survive when difficulties arise, and individuals' resistance in surviving in difficult situations (Prior et al., 2016;Taheri-Kharameh et al., 2018;Van Dinther et al., 2011).
Another factor that has the second largest correlation and effective contribution to learning motivation is parental social support. The findings of this study indicate that parental social support has a positive relationship with learning motivation. These results are consistent with previous studies, namely the finding of a positive relationship between parental social support and learning motivation in university students (Salamor et al., 2021;Sani et al., 2020;Tezci et al., 2015), Elementary School (SD) students (Tan et al., 2013), Junior High School (SMP) students (Malwa, 2018), and Senior High School (SMA) students (Y. Shukla et al., 2015). On the other hand, the results of this study are also in line with the study conducted in India (Y. Shukla et al., 2015) and Turkey (Tezci et al., 2015). Similar results with previous research confirm that efficacy is related and plays an important role in learning motivation in students. These relationships and roles apply at all levels of education, age, country and learning methods, both face-to-face and distance learning.
The novelty of this research is in the context of distance learning, in which parental social support is also an essential variable in the online learning environment. This is because parents act as directors and motivators for children during distance learning (Pieters & Agustina, 2021). On the other hand, the most influential role of parents is to provide social support that can reduce stress because children can solve problems related to material or facilities during distance learning (Pajarianto et al., 2020).
In this study, the effectiveness of social support roles with learning motivation in distance learning can be seen from the correlation value between these two variables of 0.318 and an effective contribution of 6.4%. Compared to self-efficacy, parental social support has a lower effective contribution. The low effective contribution can be due to the age characteristics of respondents. The subjects in the study were undergraduate students (97.3%) who were still in their final adolescence (Santrock, 2003). During this development, there is a shift in social support where the adolescent's perception of parental social support decrease while the adolescent's perception of social support from peers increase (Hombrados-Mendieta et al., 2012). In this case, adolescents only perceive and need instrumental and emotional support of the four types of social support provided by parents (Hombrados-Mendieta et al., 2012). Adolescents perceive emotional support, such as attention, listening and viewing parents as a source of information, material fulfillment, facilities, and responding to cues that indicate that children have difficulties at school (Malecki & Demaray, 2003;Pajarianto et al., 2020;Tucker-Drob, 2017).
As part of the social system that is important in a child's development, parental behavior, such as social support, can have a meaningful psychological impact on children's learning activities and children's personal well-being (Katleyana & Wulanyani, 2019;Malecki & Demaray, 2003;Usman et al., 2021). If the child has good psychological well-being, the child will have an orientation on personal mastery, such as being motivated to learn and complete academic tasks (Bempechat & Shernoff, 2012;Malecki & Demaray, 2003). Previous studies conducted by Z. Wang et al., (2019) found that parental social support in the form of emotional support was associated with more optimal academic results. This is because children will feel that they will get attention, appreciation, and love so that they will be more excited and motivated to learn because children perceive that not only themselves have the desire to develop and progress but also their parents (Tumanggor et al., 2017;Usman et al., 2021).
In the same context, the findings of this study also show that there is a positive relationship between emotional intelligence and learning motivation. This result in conclusions that is in line with previous studies, namely the finding of a positive relationship between emotional intelligence and learning motivation in university students (Chandra, 2017;Chinyere & Afeez, 2019). The results of the research findings are also consistent with previous studies conducted at different levels of education, such as Elementary School (SD) students (Sidik et al., 2020), Junior High School (SMP) students (Roy et al., 2013), and Senior High School (SMA) students (Amador-Licona et al., 2020). On the other hand, the research findings are also consistent with studies conducted in India (Dubey, 2012), Nigeria (Chinyere & Afeez, 2019), Iran and Mexico (Amador-Licona et al., 2020). The alignment of research results with previous studies confirms that emotional intelligence is directly correlated with learning motivation in students. These relationships and roles apply at all levels of education, age, country, and learning methods, both in the context of face-to-face and distance learning. Emotional intelligence plays an important role in all aspects of life, personally, educationally, and professionally (Kant, 2019).
In addition, regarding the novelty of this research, in distance learning, emotional intelligence is also a variable that can be considered in the online learning environment (Parker & Saklofske, 2008). This is because the online learning environment demands strength and resistance to tasks and stress loads. Emotional intelligence will help students to motivate themselves, cope with frustration, regulate their mood, control the burden of stress so as not to paralyze the power of thinking, and overcome problems that make stress in learning to be adaptive behavior (Goleman, 2017;Illeana-Loredana et al., 2017). However, in this study, emotional intelligence had the lowest correlation and effective contribution among other independent variables. Emotional intelligence has an effective contribution value of 0.0015% towards learning motivation. According to Rangkuti (2017), an effective contribution close to zero indicates that the predictor does not provide an important meaning in the prediction, so it can be ignored. This can be interpreted that although it has a positive correlation, emotional intelligence does not significantly affect learning motivation.
The low correlation and effective contribution can be caused by respondents' low emotional intelligence characteristics. In addition, the condition of the research context, namely distance learning, is also considered. This is because students' behaviors and attitudes towards distance learning (online learning) tend to vary compared to face-toface learning (Prior et al., 2016). Distance learning has an impact on the lack of time to socialize among students, at least time for recreation, because they are required to be at home, limited interaction only with family, limited activities (boredom) and a lot of task load that eventually causes stress, decrease excitement, and generates emotional instability (Setyawati & Chelsea, 2021). These impacts and limitations ultimately affect the emotional intelligence possessed. This is because students' emotional intelligence, condition, situation and emotional side develop according to the state of the environment and the surrounding response (Aswat et al., 2021). Meanwhile, while carrying out distance learning, emotions are expressed through technology; and emotional intelligence can only be the main predictor of academic success in online learning when combined with other personality traits such as self-regulatory function, self-management, selfassessment, perseverance, persistence or resilience, good mental health, and psychological well-being (Illeana-Loredana et al., 2017).
In general, the overall findings of this study follow the self-regulatory mechanism mentioned by (Bandura, 1997), namely reciprocal determinism or reciprocal interactions between behavioral, personal, and environmental determinants that are interconnected. In the reciprocal determinism of individual behavior, learning motivation correlates with personal and environmental factors. The personal context consists of self-efficacy and emotional intelligence, while the context of the individual environment is in the form of parental social support.
The practical implication of this study is that students realize that learning motivation is the key to success in distance learning and needs to be optimized. Good and optimal learning motivation will impact students' achievement levels and learning outcomes. Considering the greatest correlation and effective contribution of self-efficacy to learning motivation, university students can utilize self-efficacy to manage motivation optimally while carrying out distance learning. In addition, considering the importance of emotional intelligence in distance learning and highlighting the level of emotional intelligence of university students that is not optimal, they need to improve their emotional intelligence to achieve the optimal stage.

CONCLUSION
Self-efficacy, emotional intelligence, and parental social support simultaneously correlate with learning motivation in university students carrying out distance learning during the COVID-19 pandemic. The regression (F value) is 36.569, and the correlation (R) is 0.467, with an effective contribution of 21.8%. It can be concluded that selfefficacy, emotional intelligence, and parental social support can be alternatives to factors determining improvement related to learning motivation in university students carrying out distance learning during the COVID-19 pandemic.
Partially, self-efficacy has a significant relationship with learning motivation, with a correlation value of R of 0.426 and an effective contribution of 15.3%. Emotional intelligence also has a significant relationship with learning motivation, with a correlation value of R of 0.230 and an effective contribution of 0.0015%. Then, parental social support has a significant relationship with learning motivation, with a correlation value of R of 0.318 and an effective contribution of 6.4%. It can be concluded that in distance learning, self-efficacy is the variable that has the greatest relationship and effective contribution to learning motivation in university students carrying out distance learning. Subsequently, emotional intelligence is the variable with the weakest relationship and effective contribution to learning motivation in university students participating in distance learning. Thus, university students need to optimize self-efficacy to improve learning motivation and succeed in carrying out distance learning.