INTERNATIONAL JOURNAL OF SOCIAL SERVICE AND RESEARCH

THE EFFECT OF PARENTAL SUPPORT AND SELF REGULATED LEARNING ON LEARNING MOTIVATION ON STUDENTS IN SDIT INSAN UTAMA

Tri Widayati*, Akif Khilmiyah, Aris Fauzan

Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia

Abstract

This study aims to determine how far the influence of parental support and self-regulated learning on students' learning motivation. This research is a research model of hypothesis testing (hypothesis testing study) with a quantitative approach. Data were collected using a questionnaire/questionnaire in the form of a Likert scale on parental support, self-regulated learning and learning motivation. The population was taken from all students of SDIT Insan Utama Yogyakarta with a simple random sampling technique with a sample of 152 students. Data analysis consisted of validity and instrument reliability tests, normality tests, linearity tests, hypothesis tests and correlation tests. The results of this study indicate that 1) Parental support has a significant effect on student learning motivation by 7.7% and has a moderate correlation with the Pearson Correlation value of 0.278; 2) Learning motivation has an effect on student achievement by 31.8% and is perfectly correlated with the Pearson Correlation score of 0.564; 3) Parental support and self-regulated learning together have an effect on student learning motivation by 32.9% with a significance value of 0.000. 4) The indicators of parental support variable are the provision of accommodation, motivation, appreciation, regulatory support, comfort, opportunities for activities, discussions and joint activities. Indicators of self-regulated learning variable are learning process control, setting learning goals, setting time and school assignments, motivation, planning, evaluating, implementing plans and asking for help if needed. The indicators of learning motivation variable include having an effort to learn, being able to maintain perseverance in learning and focusing on achieving learning goals.

Keywords: parental support; learning motivation; self-regulated learning

Received 28 October 2022, Revised 11 November 2022, Accepted 26 November 2022

INTRODUCTION

Learning is a process and effort made by a person to obtain a change in behavior for the better (Ahdar & Wardana, 2019). Changes in behavior from not being able to being able to, from not knowing to knowing. Through learning activities, students will have better knowledge and have better skills.

Students are students. This means that as a student, students must have a learning spirit or motivation to learn. However, at this time students' learning motivation has decreased due to the impact of the pandemic. The impact of the Corona virus disease 2019 (Covid-19) pandemic has penetrated the world of education. It is hoped that all educational institutions will refrain from carrying out their normal activities, which will help limit the spread of Covid-19. This is done to prevent the spread of Covid-19. Various countries exposed to this disease have implemented a lockdown or quarantine policy in an effort to reduce the interaction of many people who can provide access to the spread of Covid-19 (Tabatabai, 2020).

The COVID-19 pandemic has indeed become a tough test for all nations, testing their ability to take lessons by continuing to try and endeavor to find solutions to all existing problems. As a big country, Indonesia must be able to overcome all existing problems. This is indicated by Indonesia's readiness to embrace all possibilities, as evidenced by the birth of technology developed by the nation's children to provide online education services (Abidah et al., 2020).

The pandemic has hampered learning for all students (With & Series, 2021) and encouraged digital development at all levels of education forcing the closure of face-to-face classes at both university and school levels (Basilaia & Kvavadze, 2020). Face-to-face learning activities in schools that have shifted to online resulted in learning loss for students (Khan & Ahmed, 2021). Rapid changes in online can pose challenges for every student who needs to adapt to this learning model, as well as have an impact on student motivation in dealing with new conditions in the teaching and learning process (Prananda & Ricky, 2021).

The emergence of the COVID-19 virus has had a profound impact. significant for education. COVID-19 has had an impact on several parties, including teachers, principals, students, and parents (Santaria, 2020). During the COVID-19 pandemic, learning is carried out in a blended between settings and offline As a result, in the absence of face-to-face instruction from teachers, parents must assume greater responsibility for the academic, emotional, and technical support of online their children's. Parents, for example, should provide tutoring, motivate and monitor their children's progress, and assist in the development of skills to manage study time and study persistence (Liu et al., 2022).

Motivation to learn is very important during the COVID-19 pandemic because it can provide enthusiasm for learning and can direct learning activities for the better. In addition, with the motivation of a person will get better consideration in learning activities. The existence of learning motivation can also provide encouragement to make changes in pursuing goals (Dharma et al., 2021).

This decline in learning motivation also occurs in SDIT Insan Utama. This is evidenced by the data obtained through interviews with the parents of SDIT Insan Utama which shows that during this pandemic, children have less enthusiasm for learning. Children often sleep at night and wake up during the day. Research from Usher et al (2021) said that during this pandemic, there was an increase in the use of social media, playing games and time to sleep and a decrease in learning motivation and self-regulated learning (Usher et al., 2021). The decrease in learning motivation will have an impact on student achievement.

Therefore, students must have the ability to set their own learning schedule. It is also often called self-regulated learning which is an intrinsic motivation and is considered an important factor for successful learning (Dent & Koenka, 2016). Self-regulated learning is "an active constructive process in which learners set" goals for their learning and then strive to monitor, regulate and control their cognition, motivation, and behavior, guided and constrained by their goals and contextual features in the environment (Pintrich, 2000).

In addition to self-regulated learning, parental support factors also influence student learning motivation (Grolnick, 2016). Because the family is the first environment faced by children during the socialization process, assistance is needed to increase or maintain student motivation (Rohmahwati et al., 2021).

Relevant research shows that there is a relationship between parental support and learning motivation in adolescents during distance learning during the pandemic at SMPN 2 Pule Trenggalek (Rohmahwati et al., 2021). Furthermore, in online learning activities, self-regulated learning significantly affects learning motivation (Yuruk, 2021). self-regulated learning can affect learning performance (Chou & Zou, 2020).

Based on this phenomenon, this research focuses on parental support, self-regulated learning and learning motivation. Students' motivation, which is thought to be high or low, is influenced by self-regulated learning and parental support. The purpose of this study was to examine how the influence of parental support on student motivation at SDIT Insan Utama, to identify how the effect of self-regulated learning on learning motivation in students at SDIT Insan Utama, and to analyze which variables most influenced SDIT Insan Utama.

METHOD

The type of research used is descriptive correlational research. Correlational descriptive research is a type of non-experimental research that describes the quantitative data obtained in relation to the state of the subject of a population. This study aims to describe, describe and describe the effect of parental support and self-regulated learning on learning motivation at SDIT Insan Utama Yogyakarta.

The data collection technique in this study used a questionnaire method. Questionnaire is a technique or method for collecting data indirectly. The instrument or data collection tool is also called a questionnaire which contains a number of questions that must be answered or responded to by the respondent. The questionnaire instrument or questionnaire in this study uses an ordinal scale.

Data analysis techniques used are organizing data, grouping by category, theme and pattern of answers, testing assumptions or problems with data, writing research results.

a. Classical assumption test

1) Normality Test

Normality test is used to determine whether the sample comes from a population that is normally distributed or not. The normality test in this study used the Kolmogorov-Smirnov test with a significance level of 5%. The data can be said to be normally distributed if the significance is > 0.05. And vice versa, if the significance <0.05, it can be said that the data is not normally distributed.

2) Linearity Test

Linearity test is used to determine the relationship of each independent variable and the dependent variable is linear or not. The linearity test was carried out with a significance level of 5%, the data can be said to be linear if the Deviation from Linearity Sig > 0.05, then there is a significant linear relationship between the independent and dependent variable. Vice versa, if the value of Deviation from Linearity Sig <0.05, then there is no significant linear relationship between the independent and dependent variable.

3) Multicollinearity Analysis

Multicollinearity testing is used to determine whether the independent variables have a strong correlation or not. Regression analysis requires that there is no multicollinearity between the independent variables. The multicollinearity test in this study used the VIF (Variance Inflation Factor) value test. If the VIF resulting tolerance>0.1, then there is no multicollinearity.

4) Heteroscedasticity Test

Heteroscedasticity test is used to determine whether or not there is a difference in variance from the residuals for all observations. In regression analysis, it is required that there is no heteroscedasticity. Heteroscedasticity test in this study used the Glejser test. If the p-value > 0.05 then there is no heteroscedasticity.

b. Multiple regression analysis

Multiple regression analysis was used to determine parental support (X1) and self regulated learning (X2) on students' learning motivation (Y).

1) T test

The hypothesis testing of this research was conducted on statistical hypotheses using t test. The t-test is used to determine the partial effect of each explanatory/independent variable on the dependent variable. (Sugiyono, 2017) Hypothesis testing can be done by paying attention to the level of significance and the beta coefficient. The significance level is used to see whether or not the influence of the independent variable is significant with the dependent variable, while the beta coefficient is used to see the direction of the relationship between the influence of the independent variable on the dependent variable. Decision making whether or not the hypothesis is accepted is based on the direction of the relationship and the significance of the model in question. The criterion for accepting the hypothesis is using the t test, by seeing whether the values obtained by the coefficients are significantly or not between t arithmetic and t table at a 5% confidence level (α = 0.05).

2) F test

F test is used to determine whether simultaneously (together) the regression coefficient of the independent variable has a significant effect or not on the dependent variable. The F or ANOVA test is carried out by comparing the level of significance determined for the study with the probability value of the research results (Sugiyono, 2017).

3) Determination Coefficient (R²)

The coefficient of determination (R_Squared) essentially measures how far the ability to explain variations in the dependent variable. The value of the coefficient of determination is between zero and one. If the value is close to 1, it means that the independent variable provides almost all the information needed to predict the dependent variable.

RESULTS AND DISCUSSION

A. Description of Parental Support Variables, Self-Regulated Learning and Learning Motivation

1. Description of research variables

a) Parental support

Parental support in this research variable contains indicators of providing accommodation, motivation, appreciation, shared opportunities, regulatory support, comfort, opportunities for activities and discussions. This indicator can be described in the following diagram.

Table 2

Test for linearity of variable X₁against variable Y

ANOVA Table

			Sum of squares	df	Mean square	F	Sig.
Learning motivation* Parental support	Between groups	(Combined)	1158.542	32	36.204	1.638	.030
		Linearity	293.403	1	293.403	13.271	.000
		Deviation from Linearity	865.139	31	27.908	1.262	.187
	Within groups		2630.932	119	22.109
	Total		3789.474	151

ANOVA Table

			Sum of squares	df	Mean square	F	Sig.
Learning motivation* Self regulated learning	Between groups	(Combined)	1689.084	29	58.244	3.383	.000
		Linearity	1203.392	1	1203.392	69.898	.000
		Deviation from Linearity	485.692	28	17.346	1.008	.465
	Within groups		2100.390	122	17.216
	Total		3789.474	151

Table 4

Multicollinearity Test

Coefficients^a
model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
model		B	Std. Error	Beta	t	Sig.	Tolerance	VIF
1	(Constants)	24.173	3.728		6.484	.000
	Parental support	.084	.053	.112	1.590	.114	.901	1.109
	Self regulated learning	.445	.059	.528	7.473	.000	.901	1.109
a. Dependant variable: Learning motivation

Table 5

Heteroscedasticity Test

Coefficients^a
model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
model		B	Std. Error	Beta	t	Sig.	Tolerance	VIF
1	(Constants)	6.156	2.249		2.737	.007
	Parental support	-.053	.032	-.142	-1.667	.098	.901	1.109
	Self regulated learning	.002	.036	.005	.055	.957	.901	1.109
a. Dependant variable: Abs_RES

Table 13

Correlation Test

Correlations
		Parental support	Self regulated learning	Learning motivation
Parental support	Pearson correlation	1	.314**	.278**
	Sig. (2-tailed)		.000	.001
	N	152	152	152
Self regulated learning	Pearson correlation	.314**	1	.564**
	Sig. (2-tailed)	.000		.000
	N	152	152	152
Learning motivation	Pearson correlation	.278**	.564**	1
	Sig. (2-tailed)	.001	.000
	N	152	152	152
**. Correlation is significant at the 0.01 level (2-tailed)

Based on the correlation test table, a summary can be made as follow

Table 14

Correlation and regression analysis

Coefficients^a
model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
model		B	Std. Error	Beta	t	Sig.
1	(Constants)	24.173	3.728		6.484	.000
	Parental support	.084	.053	.112	1.590	.114
	Self regulated learning	.445	.059	.528	7.473	.000
a. Dependant variable: Learning motivation

Based on the table above, it can be obtained the calculation results for the effective contribution and the relative contribution between the variables X₁, X₂ and Y.

1. Effective Contribution (SE) variable X₁ to variable Y

Effective contribution variable parental support to learning motivation.

SE (X₁)% = Beta X₁ x rxy x 100

= 0.112 x 0.278 x 100

= 3.11%

Based on the results of the above calculation, it can be seen that the Effective Contribution (SE) of the parental support variable (X₁) on learning motivation (Y) is 3.1%.

2. Relative Contribution of variable X₁ to variable Y

Relative Contribution of parental support to learning motivation

SR (X₁) % = SE X₁%/R _square

= 3.1% / 32.9%

= 9.4%

Based on the results of the above calculations, it can be concluded that the relative contribution (SR) of the learning support variable to learning motivation is 9.4%

3. Effective contribution of variable X₂ to variable Y

SE (X₂)% = Beta X₂ x rxy x 100

= 0.528 x 0.564 x 100

= 29.77%

Based on the results of the above calculations, it can be concluded that the Effective Contribution (SE) of the self-regulated learning (X₂) to learning motivation (Y) is 29.8%.

4. Relative Contribution of variable X₂ to variable Y

Relative Contribution self regulated learning to learning motivation

SR (X₂) % = SE X₂%/R_square

= 29.8% / 32.9%

= 90.6%

Based on the above calculation results, it can be concluded that the relative contribution (SR) of the learning support variable to learning motivation is 9.4%.

5. Effective contribution of variables X₁ and X₂ to variable Y

Effective contribution of parental support and self-regulated learning to learning motivation.

SE Total = SE (X₁) + SE (X₂)

= 3.1% + 29.8%

= 32.9%

Based on the calculation results above, it can be concluded that the self-regulated learning (X₂) has a more dominant influence on the learning motivation variable (Y). For the total SE is 32.9% or the same as the coefficient of determination (R_square) regression analysis is 32.9%.

6. Relative Contribution of variables X₁ and X₂ to variable Y

Relative Contribution of variable parental support and self-regulated learning on learning motivation:

SR Total = SR (X₁) + SR (X₂)

= 9.4% + 90.6%

= 100%

Based on the results of the above calculations, it can be concluded that the relative contribution (SR) of the parental support variable and self regulated learning on learning motivation is 100%. For the total SR is 100 or equal to 1.

CONCLUSION

Parental support has an effect on student learning motivation at SDIT Insan Utama Yogyakarta by 7.7% and has a moderate correlation with the Pearson Correlation 0.278.

Self-regulated learning has an effect on student motivation at SDIT Insan Utama Yogyakarta by 31.8% and is perfectly correlated with the Pearson Correlation 0.564.

Parental support and self-regulated learning have a simultaneous effect on students' learning motivation at SDIT Insan Utama Yogyakarta by 32.9% with a significance value of 0.000.

The variable indicators of parental support are the provision of accommodation, motivation, appreciation, regulatory support, comfort, opportunities for activities, discussions and joint activities. Indicators self-regulated learning are learning process control, setting learning goals, setting time and school assignments, motivation, planning, evaluating, implementing plans and asking for help if needed. The indicators for learning motivation variables include having an effort to learn, being able to maintain perseverance in learning and focusing on achieving learning goals

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Model summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.278^a	.077	.071	6.454
a. Predictors: (Constant) Learning motivation

Model summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.564^a	.318	.313	4.933
a. Predictors: (Constant) Learning motivation

Model summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.574^a	.329	.320	4.131
a. Predictors: (Constant), Self regulated learning, parental support