Keywords

Achievement Motivation, Emotional Intelligence, Self-Regulation, Academic Resilience.

Introduction

More than 107 countries are affected by the COVID-19 pandemic, that has been forced to change the entire order of human life. All affected areas, including the world of education, are forced to make learning innovations, which are still unfamiliar with learning media for some people. Distance learning through online media, which has only been used as a supplement, has become the primary and first choice as a learning method during this pandemic. This is done to prevent the emergence of new COVID-19 spread clusters in schools, since analysis indicates that 2-4% of the global transmission of COVID spread leads to interactions in educational institutions(Viner et al., 2020).

Even though online distance learning is the answer to education problems in the pandemic era, this does not mean that this method is not without problems. Technical to nontechnical constraints often appear simultaneously and continue to affect the effectiveness of education services as a whole. Technically for the case in Indonesia, it is clear that the problem of internet access speed, the readiness of learning facilities to the availability of internet infrastructure continues to be a problem that is always voiced as the main problem of distance learning (Aji, 2020; Sadikin & Hamidah, 2020). Non-technical problems found in research include the emotional dynamics of each component, both educators and students, in implementing online education, which is strongly influenced by each party's level of emotional intelligence (Baba, 2020). Besides, student involvement in learning is also often doubted because of the lack of direct control from the teacher on the learning process even though learning is carried out synchronously through virtual face to face with media conferences. However, it is doubtful that their self-regulated learning does play a role in shaping student involvement actively in the learning process. Research shows that students in Indonesia still really need assistance from teachers in independent learning patterns of distance learning (Sulisworo et al., 2020), which means that student involvement in the process of finding and exploring subject matter is carried out independently by students.

The involvement of students in the learning process that has been in the classroom is forced to interact indirectly through the internet. It is often found that intensive interactions influence student`s self-regulation in learning through small groups that inspire student involvement to participate in learning (Terry & Leary, 2011; Timmons et al., 2015). Research shows that self-regulation in learning contributes to children's emotional dynamics in learning (Salisch et al., 2015). It is therefore important that each individual student 's degree of selfregulation be established, so that there is no emotional dynamic that damages the environment and productivity. Further research shows that the construction of self-regulation participates in constructing skills that support the achievement of learning objectives (Black & Allen, 2017; Suchodoletz et al., 2015).

The achievement of educational goals is uncertain only because of the intellectual capital possessed by students. Motivation is one of the driving forces for students to be serious in achieving the best achievements in the educational process. Motivation is even considered to be one of the factors that dominate the contribution of learning success (Inoue, 2013). This motivation must be built from the right culture so that it is honed and continues to develop accordingly and can support the achievement of learning achievement (Maehr, 2008). Achievement motivation refers to the urge to think of humans for how to be able to do and achieve something they want (Wigfield & Cambria, 2009). Achievement motivation is an energy that every student must have in achieving learning success, which is developed together with emotional energy so that non-technical learning factors can support the achievement of educational goals (Wigfield et al., 2015). Achievement motivation also contributes to the ability to manage cognitive and emotional activities when facing learning difficulties so that boredom and learning stagnation can be resolved (Fervaha et al., 2017).

In some cases, achievement motivation contributes to the emotional condition of a child. Even though in a more specific context, it is emotional intelligence that determines a person's ability to manage his emotions (Hourani et al., 2020; Issah, 2018). Research shows that emotional intelligence is always linear with the ability to manage emotions in dealing with problems that hinder the learning process, which tends to lead to the unstable emotional condition of children (Zysberg & Tell, 2013). Besides, emotional intelligence also affects cognitive activity because the emotional stability that is created will facilitate human cognitive activity to be more stable and fluent in thinking (Christopher, 2006). This reality requires all parties involved in education to pay attention to the level of emotional intelligence of children, the goal is to support the achievement of educational goals even in challenging conditions.

In pandemic conditions like today, students are faced with various problems in the learning process.The problems referred to range from technical problems of supporting facilities and infrastructure, family socio-economic conditions to emotional instability due to being bored for too long at home, or sudden changes. Adequate academic resilience is needed so that students do not get caught up in problems that interfere with the educational process. Students with good academic resilience tend to quickly get used to various obstacles and return emotional situations that had experienced problems (Hawkins & Mulkey, 2005) due to unusual learning patterns. Recent research shows self-resilience as a form of academic resilience has an essential role in the current conditions due to COVID-19, especially those related to the squeeze of socio-economic problems (Liu & Platow, 2020) that are currently hitting various countries.

The phenomenon as previously described is quite interesting to study, especially how the effect of each variable to other variables. How the influence of emotional intelligence and achievement motivation on self-regulation until the impact on student academic resilience. This is important because it is known that their existence can be easily shaken by the social and economic dynamics that occur around them (Zamroni et al., 2020). This article will briefly review how each variable is related to other variables, which can later be used as the basis for improving the learning system during the COVID-19 pandemic.

Methods

Research Design and Subject

The research was conducted with a quantitative approach to determine the causal relationship between independent and dependent variables(Creswell & Creswell, 2018). Path analysis was chosen to test the causal relationship between variables with structural equation models. Variable X1 is student achievement motivation, X2 is emotional intelligence, Y is selfregulation, and variable Z is student academic resilience(Creswel, 2009). The total population is 196 new students for the 2019-2020 academic year from the guidance and counseling study program, Indonesian language and literature education, Indonesian language tadris, and early childhood education at Muria Kudus University, IAIN Takengon, IAI AL Qolam, and Muhammadiyah Sidenreng Rappang University. The students are in the age range of 18-20 years. The sampling technique used is proportional random sampling with the Slavin method and a margin of error of 5%. The result was that the number of samples selected was 60 students. The sample is selected to represent the entire population. In detail, the distribution of the sample in this study is described in Table 1.

Table 1 Population and Sample
No	Institution	Population	Sample
1	Universitas Muria Kudus	48	15
2	IAIN Takengon	46	15
3	Universitas Muhammadiyan Sidenreng Rappang	49	15
4	IAI Al Qolam	51	15
Total		194	60

Institutions, as described in the Table 1 spread from the provinces of Aceh, Central Java, East Java to South Sulawesi. This is sufficient to represent the diversity of cultures in each region in Indonesia.

Instrument

This study uses four instruments, namely Achievement Motivation Scale, Emotional Intelligence Scale, Self-Regulation Scale, and Academic Resilience Scale. All instruments were tested on 25 new students of the English Education Program, Muhammadiyah University of Sidenreng Rappang. The validity test was carried out through expert judgment on three psychologists. At the same time, the reliability test is as in Table 2.

Table 2 Reliability Test
No	Instrument	Cronbach's Alpha	N of Items	Result
1	Achievement Motivation Scale	0,767	46	Reliable
2	Emotional Intelligence Scale	0,826	41	Reliable
3	Self-Regulation Scale	0,796	36	Reliable
4	Academic Resilience Scale	0,694	37	Reliable

Based on Table 2, it is known that all instruments have been tested for reliability so that they can be consistently used in various institutions. As is known, this research was conducted in four institutions where the possibility of cultural bias is substantial, so that the reliability of the instrument must be thoroughly tested in order to obtain consistent data accuracy.

Data Analysis

Descriptive analysis techniques are used to describe the data obtained from the research process. The statistical measures that are often used in decision making are (1) looking for a central tendency, such as the mean, median, and mode; (2) looking for dispersions, such as deviation and variance (Sugiyono, 2015). The requirements test used in path analysis followed the classic assumption test of regression analysis, namely (1) normality test, (2) linearity test, (3) multicollinearity test, (4) autocorrelation test; (5) heteroscedasticity test. Hypothesis testing to determine the significance of hypothesis testing in path analysis in simultaneous testing (overall) on each sub-structure can be seen by looking at the comparing between the probability value of 0.05 and the probability value of Sig. If the sig value <0.05, the decision that can be taken is that H0 is rejected and Ha is accepted (Sudjana, 2002; Sugiyono, 2013).

Results

The SPSS test results show the Variance Inflation Factor for each variable is around the value 1, while the tolerance for each variable is close to the value 1.This indicates that there is no multicollinearity problem between the research variables. The relationship between variables is a one-way flow system. Each variable has been measured using valid instruments and variables. The model has been adequately analyzed and identified based on relevant theories and concepts.

Model Test 1

Model 1 test was conducted to determine the causal relationship between X1, X2, and Y, namely the effect of achievement motivation (X1), emotional intelligence (X2) on student selfregulation (Y). The analysis results using SPPS are shown in Table 3.

Table 3 Coefficientsa X1, X2 and Y
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	70.602	7.625		9.259	000
	Achievement Motivation (X1)	-0.333	0.090	-0.433	-3.689	001
	Emotional Intelligence (X2)	0.062	0.043	0.171	1.454	011
a. Dependent Variable: Self-Regulation (Y)

Regression analysis model 1 refers to the results of data analysis in Table 3. Based on Table 3, it is known that the significance of the achievement motivation variable is 0.001 <0.05, while the emotional intelligence variable is 0.011 <0.05. This means that achievement motivation and emotional intelligence affect students' self-regulation. The R Square value is 6.214, which means that the contribution of achievement motivation and emotional intelligence is 62.14% to self-regulation, while the remaining 37.86% is influenced by other variables outside of research.

Model Test 2

Model 2 test was conducted to determine the causal relationship between achievement motivation (X1), emotional intelligence (X2), and self-regulation (Y) as well as its impact on student academic resilience during the pandemic. The results of data analysis using SPSS can be seen in Table 4, 5 & 6.

Table 4 Model Summary X1, X2, Y, and Z
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	4,173a	6,030	6,022	10,63222
a. Predictors: (Constant), Self-Regulation (Y), Emotional Intelligence (X2), Achievement Motivation (X1)

Table 5 ANOVAa X1, X2, Y and Z
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	195,183	3	65,061	576	633b
	Residual	6330,467	56	113,044
	Total	6525,650	59
a. Dependent Variable: Academic Resilience (Z)
b. Predictors: (Constant), Self-Regulation (Y), Emotional Intelligence (X2), Achievement Motivation (X1)

Table 6 Coefficientsa X1, X2, Y and Z
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	95,181	29,290		3,250	002
	Achievement Motivation (X1)	005	244	039	268	019
	Emotional Intelligence (X2)	015	105	020	147	018
	Self-Regulation (Y)	003	322	181	1,222	012
a. Dependent Variable: Academic Resilience (Z)

Regression analysis model 2 refers to the results of data analysis Table 4, Table 5 and Table 6. Based on Table 6 it is known that the significance of the achievement motivation variable is 0.019 <0.05, the emotional intelligence variable is 0.018 <0.05, while the selfregulation variable is 0.012 <0.05, which means achievement motivation, emotional intelligence, and self-regulation affect the academic resilience of students during the C0VID-19 pandemic. Referring to Table 4, the value of R Square is 6,030, which means that the contribution of achievement motivation, self-regulation, and emotional intelligence is 60.3% to the academic resilience of students during the COVID-19 pandemic, other variables outside of research influence the remaining 39.7%.

Discussion

The results of this study indicate that there is a linear and causal relationship between achievement motivation, emotional intelligence, self-regulation, which impacts on the academic resilience of students during the pandemic. This shows that each variable needs to be considered as a supporting variable as well as reinforcing during distance learning during this pandemic. What needs to be understood is that self-regulation that encourages student activity in the learning process always has two sides, namely the natural environment and the cultural environment, which will affect students' emotional learning (Black & Allen, 2017; Geldhof & Lerner, 2012; Viñuales, 2020). The visualization of the relationship between variables can be seen in Figure 1.

Figure 1 explains that each variable affects each other, starting from the influence of achievement motivation and emotional intelligence on self-regulation and having an impact on student academic resilience.This study also supports other studies that reveal that emotional intelligence and achievement motivation will tend to encourage involvement in the process of achieving success, which means that there will be increased self-regulation in students (Elliot et al., 2016; Fletcher & Neumeister, 2012; Hubert et al., 2015). In another context, the urge to always think activities carried out by achievement motivation can prevent students from becoming bored with advanced learning as in existing research (Bintoro, 2015; Colin, 2017; Hsu et al., 2010; James et al., 2015). So that in the end, it will contribute to academic resilience from any problems that threaten students as academic resilience will return to its initial condition before being in a tense or threatening situation (Awlawi et al., 2020; Perez et al., 2009; Sen, 2012; Thiruchelvi & Supriya, 2009; Troth et al., 2012). This result also confirms the research conducted by Martin (2002), who found that between achievement motivation and academic resilience, there will always be a linear relationship and influence each other. However, other factors were found to support the development of academic resilience; such as self-perception, the necessary abilities, understanding of education, strong aspirations about education, empathy, flexibility of attention, children's memorial abilities, and impulsive responses that often arise when facing problems(Gizir & Aydin, 2002; Kangas et al., 2015; Masturi et al., 2015). Therefore it is essential to understand more deeply the level of academic resilience of each student in learning so that learning models can be applied that are more suitable for students during the COVID-19 pandemic (Lin et al., 2019; Liu & Platow, 2020; Mcclelland & Wanless, 2012).

Learning innovation will not only be an online process but combine face-to-face and virtual face-to-face collaboratively in a blended approach to ensure students can study thoroughly (Murtono et al., 2020; Usman et al., 2020; Zamroni et al., 2020). Material for discussion can choose themes or materials with socio-psychological values such as poetry (Nurzin et al., 2020), which is packaged in a discussion model with modern and religious nuances such as the pesantren pattern in bahtsul masa’il activities (Kholik et al., 2020).All of these efforts were made in order to strengthen achievement motivation, hone emotional intelligence, improve self-regulation and form student academic resilience during a pandemic.In addition, this study will provide guidelines for implementing online and offline learning so that students can be seriously involved and avoid learning burnout.

Conclusion

Based on the results of the study, it is known that achievement motivation and emotional intelligence have an influence on self-regulation and have an impact on the academic resilience of students during the COVID-19 pandemic. For this reason, the learning process must touch on these socio-psychological aspects so that students do not easily experience boredom and canattract student involvement in learning even though technical problems still hamper them in the implementation of online learning. This research is still very limited to a population that is not too large. For this reason, the next research is expected to be able to develop the population and sample so that the representation of students from various backgrounds can be accommodated. Of course, it would be better if learning models were developed to anticipate the symptoms of learning barriers that emerged in the implementation of online learning during the COVID-19 pandemic.

Limitations and Further Studies

Although he has represented parts of Indonesia from Eastern Indonesia, Central Indonesia and Western Indonesia, the author is still aware of the possibility of adding subjects from other provinces in order to obtain a more diverse diversity of subjects. For this reason, further research is expected to develop research with broader subjects, more complex variables and a wider variety of subjects. It could also be added to cultural variables and study habits that could influence students' self-regulation.

Acknowledgement

Thank you to Universitas Muria Kudus Research Institute, LP3M IAI Al Qolam, Muhammadiyah University Sidenreng Rappang Research Institute and IAIN Takengon PIAUD Study Program who are willing to cooperate and finance this research.

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Received: 11-June-2021, Manuscript No. JLERI-21-4724; Editor assigned: 14-June-2021, PreQC No. JLERI-21-4724(PQ); Reviewed: 05-July-2021, QC No. JLERI-21-4724; Revised: 28-Dec-2021, Manuscript No. JLERI-21-4724(R); Published: 04-Jan-2022