Application of Deep Learning in Meaningful Learning, Through

Fun Learning Practices in Kindergarden

Dadan Suryana

and Ayu Mustika Sari

Universitas Negeri Padang, Indonesia

STITNU Sakinah Dharmasraya, Indonesia

Keywords: Deep Learning, Meaningful Learning, Through Fun Learning, Kindergarden.

Abstract: This research is conducted to see the effectiveness of deep and enjoyable learning for early childhood, which

can involve all of the child's senses and maximally stimulate the child's growth and development based on the

principles of Mindful, Meaningful, and Joyful learning. Research conducted at TK Islam Terpadu Yadiaksa

Dharmasya, Learning theme of Vegetable Plants. This study uses a quantitative research method with a type

of Quasi Experimental Design and a form of Nonequivalent Control Group Design. The instruments used in

this research employ an observation sheet in the form of a checklist (√). The data collection techniques in this

research use observation techniques and documentation techniques. The data analysis techniques in this

research test validity, descriptive statistics, normality, independent sample t-test, homogeneity, and paired

sample t-test using statistics with the help of SPSS. Based on the calculated t value using the t-test with Sig.

(2-tailed) = 2.563 < 2.048, this means that Ha is accepted and Ho is rejected. Based on the results, it can be

concluded that there is an influence of Deep Learning Implementation on the kognif development of children

in Kindergarten. The statistical test using the t-test with a significance level of 0.05% resulted in a t-value

obtained (2,568) > t-table value (2.048), thus H0 is rejected. Based on the research results, it can be concluded

that deep learning influences children's motor development. Deep learning is more effective in stimulating

children's motor development. This research has found that deep learning has a significant impact on the

motor development of children in kindergarten.

1 INTRODUCTION

Kindergarten as the foundational stage of education

faces challenges in creating interactive, enjoyable

learning methods that are suitable for children's

characteristics. In this context, deep learning can play

a role in enhancing the effectiveness of learning.

Deep learning refers to a learning approach that

encourages deep understanding, active engagement,

and the ability to apply knowledge in various

contexts. Deep learning is a process involving strong

contextual understanding that goes beyond mere

memorization of information (Mehta 2019). Deep

learning includes the acquisition of 6 global

competencies: citizenship character, collaboration,

communication, creativity, and critical thinking

(Fullan, M., Quinn, J., & McEachen, J. 2017). This

approach is taken to produce learning that not only

https://orcid.org/0000-0002-0953-3124

https://orcid.org/0009-0008-3158-6442

masters the content but is also able to apply

knowledge to solve problems in the real world. In

applying deep learning, there are at least three main

pillars that are used as a foundation in its application

or implications. The three main pillars are mindful

learning, meaningful learning, and joyful learning

(Mehta, J., & Fine, S. 2019). Deep learning as the

acquisition of 6 global competencies: citizenship

character, collaboration, communication, creativity,

and critical thinking (Fullan, M., Quinn, J., &

McEachen, J. 2017). This approach is implemented to

produce learning that not only masters the content but

also applies knowledge to solve real-world problems.

In implementing deep learning, there are at least three

main pillars used as a foundation for its application or

implications. The three main pillars are mindful

learning, meaningful learning, and joyful learning

(Mehta, J., & Fine, S. 2019).

258

Suryana, D. and Sari, A. M.

Application of Deep Learning in Meaningful Learning, Through Fun Learning Practices in Kindergarden.

DOI: 10.5220/0014070400004935

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 7th International Conference on Early Childhood Education (ICECE 2025) - Meaningful, Mindful, and Joyful Learning in Early Childhood Education, pages 258-263

ISBN: 978-989-758-788-7; ISSN: 3051-7702

Mindful Learning is an approach that encourages

children to learn with full awareness of what they are

doing, feeling, and learning. It helps children build

focus and emotional regulation (Fullan, M., Quinn, J.,

& McEachen, J 2017). Meaningful Learning means

learning that is connected to the child's experiences

and the real world. It helps children form meaning

and build long-term understanding. Joyful Learning

emphasizes that the learning process should be

enjoyable and involve positive feelings, as good

emotions support engagement and memory (Taguma,

M., & Barrera, M. 2019).

Due to the extensive scope of this research, the

researcher focuses more on how deep learning with

Mindful Learning, Meaningful Learning, and Joyful

Learning can stimulate children's cognitive

development. Cognitive development in early

childhood can be defined as knowing or the process

of knowing, organizing, and using knowledge

(Hasibuan & Suryana, 2021). Cognition is the ability

to learn, think about learning new concepts,

understand what is happening in the surrounding

environment, and the skill of using memory

(Draganoudi et al., 2021). Cognitive can also be

defined as the ability to understand something. The

ability to comprehend something, with a child’s

cognitive ability, they will be able to act and choose

between right and wrong, and solve problems in their

lives. Cognitive development for children aged 4-6

years includes (1) learning through problem-solving

(2) logical thinking, (3) symbolic thinking.

(Ministerial Regulation No. 4 of 2022). A child's

cognitive development is also influenced by their

inner child (Suriana, 2023) therefore the environment

needs to stimulate the child's development. The

importance of implementing learning is to help

children solve problems that exist in their

environment (Junita et al., 2021). facilitating children

to understand the theme (Rofieq et al., 2019),

enhancing problem-solving development, logical

thinking (Ummah et al., 2019). The implementation

of deep learning is effective to apply because it can

enhance children's development in Kindergarten

(Valen & Satria, 2021; Natty et al., 2019), deep

learning is implemented in Kindergarten to improve

children's development (Faridah et al., 2022; Abidin

et al., 2020). In the implementation of deep learning,

we will also need technological media, in this case,

the researcher uses educational game media.

Educational games have been effective in enhancing

child development (Suryana, 2024; Rakimahwati &

Hanifah, 2022), educational games can improve

concentration (Rakimahwati & Hanifah, 2022;

Borman & Erma, 2018), problem-solving skills

(Harris & Isyanti, 2021), stimulate children to

develop, and can generate ideas for determining the

learning to be conducted (Suryana, 2025).

Development can enhance cognitive development in

children (Langer, E. J. 2020, Fullan, M., 2017,

Ramakrishnan, K, 2024).

2 METHOD

This research is a quantitative experimental study,

using two classes, namely a control class and an

experimental class. The implementation of the

research was conducted by comparing the two

classes, where one class was treated using a deep

learning model and the control class was treated using

conventional teaching methods, group learning in the

classroom, or teacher-centered learning. In this study,

the population consists of 30 children in kindergarten,

with two different classes, 15 children in the

experimental class, and 15 children in the control

class. This research was conducted at TK Islam

Terpadu Yadiaksa in West Sumatra.

The instrument used in this study is an

observation sheet in the form of a checklist (√). The

data collection techniques in this study use

observation techniques and documentation

techniques. The data analysis techniques in this study

test validity, descriptive statistics, normality,

independent sample t-test, homogeneity, and paired

sample t-test using statistics with the help of SPSS.

Based on the calculated t value using the t-test with

Sig. (2-tailed) = 0.001 < 0.05, this means that Ha is

accepted and Ho is rejected.

Evaluation Criteria for tools to measure motor

skills in kindergarten children using tools to assess

motor development, with a numerical scale ranging

from 1 to 4, corresponding to different degrees of

development: (1) Not Developed; (2) Starting to

develop; (3) Developing as expected; (4) Developing

Very well. The assessment tool used is a daily

checklist for children's motor development. This

instrument was first tested for validity (authenticity)

and reliability (dependability). Data obtained from

the daily checklist were analyzed using homogeneity

and normality tests. The post-test homogeneity test

aims to determine the follow-up for the difference test

(t-test) that will be used. The normality test of the data

aims to determine whether the final test data is

normally distributed or not.

Application of Deep Learning in Meaningful Learning, Through Fun Learning Practices in Kindergarden

259

3 RESULT AND DISCUSSION

3.1 Result

The result of the implementation of deep learning in

the Integrated Islamic Kindergarten Yadiaksa aimed

at providing experiences and stimulating children's

cognitive development, as preparation for their daily

lives. The effectiveness of the learning was tested

using hypothesis testing with a t-test. Before

conducting the t-test, normality and homogeneity

tests were performed on the research results. After

conducting normality tests on the experimental and

control classes, Ho and Ha were obtained at a

significance level of 0.05, and for N15, it can be seen

in table 1 below.

Table 1: Results of the Liliefors test calculations for the

Experimental Class and Control Class.

No Class N Lo Lt note

1. Ex

eriment 15 0.1783 0.22 Normal

2. control 15 0.1641 0.22 Normal

In Table 1. Above, the experimental class has a

calculated L value of 0.1783, which is less (<) than

the table L value of 0.22 for α 0.05. The experimental

class value is derived from normally distributed data.

In the control class, a calculated L value of 0.1641 is

obtained, which is also less (<) than the table L value

of 0.22 for α 0.05. This means that the control class

data also comes from normally distributed data. The

homogeneity test was carried out using the Bartlett

test. This test is conducted to see whether the data

comes from homogeneous classes, between the

experimental class and the control class. If the

calculated chi-square is < the table chi-square, it

means the data comes from a homogeneous class.

Effectiveness Percentage = Ideal Score / Maximum

Score x 100%.

Table 2: Results of Homogeneity Test Calculation.

Class A X2hitung X2tabel Conclusion

eriment 0.05 0.1512 3.841 Homo

Control 0.05 N

Table 2 above shows the results of the X2

calculation in the experimental class and the control

class, where it is larger than the X2 table (X2

calculated < X2 table), indicating that the

experimental class and the control class have

homogeneous variance. After conducting normality

and homogeneity tests, it was found that both sample

classes are normally distributed and have

homogeneous variance. Therefore, hypothesis testing

can proceed using the t-test technique.

If t calculated > t table, then the null hypothesis is

rejected and the alternative hypothesis is accepted.

If t calculated < t table, then the null hypothesis is

accepted and the alternative hypothesis is rejected.

The following will illustrate the data processing

with the t-test.

Based on the results of the child development

observation sheet conducted by the teachers, the

average development score of the experimental class

is 83.02, which is greater than the control class score

of 75.07. Thus, the deep learning in the integrated

Islamic kindergarten Yadiasa can be clearly seen in

the table below.

Table 3: Effectiveness of Deep Learning to Improve

Children's Cognitive Development.

The following will describe data processing using the

t-test:

In the table above, the df table for a significance

level of α=0.05 (5%) shows a critical t value of 2.048,

thus the calculated t value is greater than the critical t

value (2.563 < 2.048). Therefore, the hypothesis H a

is accepted and H o is rejected. It is concluded that

there is a significant effectiveness on the stimulation

of children's cognitive development when using deep

learning. The average score obtained by learning

using deep learning is higher than that of the class

using other methods. This research acknowledges that

the use of deep learning is very effective in

kindergarten education. The hypothesis test

conducted to see the difference in scores between the

experimental class and the control class is suspected.

There are two hypotheses in this study, namely the

first hypothesis (H0), which is a preliminary

assumption of the research, and the second hypothesis

(H1), which can be seen from the results of the

research conducted. From the T-test, we can observe

the average in each control group and experimental

group. Thus, the results of the hypothesis testing

show the difference in the effectiveness of deep

learning and conventional learning models. The deep

learning model can enhance children’s development

in kindergarten. The success of the research is proven

by the increase in children's cognitive development.

The data analysis results obtained show that deep

learning has a significantly greater impact on

children's cognitive development than the

conventional learning model.

ICECE 2025 - The International Conference on Early Childhood Education

260

Education in Kindergarten (TK) plays a crucial

role in shaping the foundation of a child's overall

development. During this time, children are in a phase

of exploration, play, and learning through concrete

experiences. In the context of the Independent

Curriculum which emphasizes differentiated learning

and the psychological well-being of children, the

application of deep learning technology can be one of

the important innovations. On the other hand, the

approaches of Mindful Learning, Meaningful

Learning, and Joyful Learning are increasingly

recognized as essential elements in early childhood

education. With deep learning technology, learning

can be adaptively tailored to children's needs in real-

time.An example of how Mindful Learning can be

implemented is by introducing the topic of plants,

which will be learned through educational games and

instructional videos. By using educational videos, the

benefits of vegetables, various types of vegetables,

and how to plant vegetables will be explained.

Figure 1: Mindful Learning activities.

After the child understands the concept, it is

continued with the form of Meaningful Learning.

Meaningful Learning means learning that is

connected with the child's experiences and the real

world. It helps the child to form meanings and build

long-term understanding. In this activity, children

will be taken to a vegetable garden, where they will

be taught how to plant vegetables and pick

vegetables; in this case, the children will be directly

involved.

Figure 2: Meaningful Learning Activities.

The Joyful Learning approach emphasizes that the

learning process should be enjoyable and involve

positive feelings, as good emotions support

engagement and memory retention. In support of this,

educators will harvest vegetables together with the

children and cook the vegetables at school.

Figure 3: The form of Joyful Learning.

Based on the results of implementing deep

learning at the Integrated Islamic Kindergarten

Yadiaksa, focusing on vegetables. It was found that

the implementation of deep learning makes children

enthusiastic during activities, helps them understand

that vegetables are healthy food, fosters a love for

plants, and a liking for vegetables. Most importantly,

it stimulates children's cognitive abilities, enhances

problem-solving skills through games, encourages

critical thinking, symbolic thinking can be stimulated,

and through the practice of planting vegetables,

harvesting them, and cooking vegetables, children

can think logically, solve problems, and become more

independent. Overall, the deep learning model can

enhance six aspects of children's development, in

addition to providing benefits for the children,

Among other things, it can strengthen children's

character in developing active potential, children can

also design their own learning, so that they can be

skilled, have a resilient attitude, and possess

knowledge in carrying out projects, children can

manage the allotted time. (Sulistyani Puteri

Ramadhani, Zulela MS, 2021) Deep learning can also

train the ability to solve problems, be responsible and

care for the surrounding environment, as well as take

pride in the results achieved. Meanwhile, the

weakness of deep learning implementation is that

educators are not yet able to design themes to the

fullest because deep learning has not been commonly

integrated into the teaching and learning process

(PBM) so far. Educators are still struggling and need

habituation to link with theme and sub-theme

materials. In addition, the activities conducted have

the potential to consume a lot of time because

children enthusiastically dig for information, making

them lose focus on the results of their practice.

Moreover, educators are also unable to provide

examples of products that can serve as input sources

because deep learning is relatively new for preschool-

aged children. Teachers do not feel they have

maximized in creating modules and lesson plans

Application of Deep Learning in Meaningful Learning, Through Fun Learning Practices in Kindergarden

261

(RPP) due to the absence of a comprehensive

guidebook for teachers on deep learning, especially in

Kindergarten. It is also challenging for teachers to

observe student progress because there is no clear

assessment guidebook for deep learning. That deep

learning affects children's thinking creativity, because

in deep learning they are stimulated to be able to

generate ideas, work in groups, and produce creative

projects. Abidin et al. (2020) also conducted research

showing that deep learning is effective in enhancing

child development. The deep learning model

influences child development because in both the

experimental and control classes, descriptively, the

development of children using deep learning is higher

compared to the control class.

4 CONCLUSIONS

The effectiveness of deep learning in the Integrated

Islamic Kindergarten Yadiaksa is effective in

stimulating children's cognitive development,

evidenced by the average differences between the

experimental class and the control class. The

assessment was carried out through observational

activities when children were engaged in play,

observational assessments from the results of deep

learning activities, as well as assessments using daily

checklist sheets. The evaluation results show that the

standard level of child development achievement can

develop as expected, with an average of 83% of all

development aspects and indicators being at the

expected developmental stage (BSH) and very good

development stage (BSB) after using deep learning.

The application of deep learning can realize active

group learning, which can enhance children's

development. The activities of deep learning

undertaken by children develop their talents and

creativity, as well as conceptual understanding, which

can be achieved through problem-solving alongside

deep learning activities within a specified timeframe.

Deep learning conducted by children in group

activities can increase children's skills and

responsibility towards assigned tasks, thus allowing

them to develop and be optimally stimulated. Deep

learning in kindergarten has great potential to support

the Mindful, Meaningful, and Joyful Learning

approach, creating adaptive, personalized, and

enjoyable learning experiences. However, the success

of this integration heavily relies on teachers'

readiness, technological infrastructure, and the

selection of child-friendly applications. Therefore,

teacher training and inclusivity need to be a primary

focus.

ACKNOWLEDGEMENTS

This research is supported by Padang State

University through the 2025 Research program. We

are very grateful for the financial support provided.

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