STA404 (STATISTICS FOR BUSINESS AND SOCIAL SCIENCES)

GROUP PROJECT

INVESTIGATION ON RATING OF EDUCATIONAL GAMING APPS FOR KIDS

FACULTY : FACULTY OF ACCOUNTANCY

PROGRAMME : BACHELOR OF ACCOUNTANCY (HONS.)

(AC220)

GROUP : AC2205G1

GROUP MEMBERS : 1) MARTIN JUNIOR (2021485568)

2) JOEY RANIEL JOEJY KOMUJI
(2022831154)
3) NURIN SYAFIQAH BINTI MD HADZAID
(2022958591)
4) ANDI NUR FARLEENA BINTI RIDWAN
(2022831336)
5) NOR NABILAH BINTI MOHD SHUKRI
(2022844446)

LECTURER : MADAM ROSELINE MICHAEL

DATE OF SUBMISSION : 25 January 2024

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ACKNOWLEDGEMENT

First and foremost, we would like to give thanks to the grace and blessings of the
Almighty, without which this assignment would not have been possible. We express our
gratitude for the strength and resilience that enabled us to navigate the challenges and
intricacies of this endeavor. It is with a sense of profound appreciation that we recognize the
divine guidance that has accompanied us throughout this journey.

Secondly, we would also like to present our gratitude to Madam Roseline, our lecturer
for Statistics for Business and Social Sciences (STA404), for her invaluable mentorship and
continuous support. Her insightful guidance, constructive feedback, and unwavering
encouragement have been pivotal in shaping the development of this assignment. The
knowledge and expertise she shared with us significantly enriched our understanding of the
subject matter and enhanced the quality of our work.

Finally, we wish to convey our sincere appreciation to our peers, classmates, and team
members who have contributed to this assignment through their constructive comments,
suggestions, and inspiration. Their collaborative spirit and willingness to offer assistance have
played a crucial role in refining and improving the content and structure of this assignment. We
are grateful for their collective input, which has been instrumental in the successful completion
of this project.

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ABSTRACT

This study aims to investigate the rating of educational gaming apps for kids and explore
potential factors influencing gaming app ratings. Due to its potential impact on children's
learning, the need for quality assessment, the importance of guiding parents and support for
educators, the necessity of consumer protection, and the ongoing technological advancements
in the digital landscape.

Using quantitative and qualitative methodology, the study examines a sizable dataset
that includes application reviews and ratings across several educational gaming apps for kids.
User reviews, app ratings, and other specifics are all included in the data. By examining the
patterns and variables in-app ratings, the study can assist parents in making decisions and
assist educators in incorporating technology-based learning tools.

The study also looks into other possible factors that could affect the ratings for gaming
apps. We'll take into account factors like the price of apps if fully purchased, download number,
and statistics related to user interaction. The study attempts to find any meaningful relationships
between these variables and the rating of gaming apps for kids through statistical analyses and
correlation tests.

In conclusion, this study aims to investigate the ratings of educational gaming apps for
kids, considering factors that influence these ratings and the potential impact on children's
learning. By analyzing a substantial dataset comprising user reviews, app ratings, and specific
app details, the research seeks to provide valuable insights for parents and educators, aiding in
informed decision-making and the integration of technology-based learning tools.

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TABLE OF CONTENT

CONTENTS PAGE
NUMBERS
ACKNOWLEDGEMENT ii
ABSTRACT iii
TABLE OF CONTENT iv – v
LIST OF TABLES vi
LIST OF FIGURES vii
1.0 INTRODUCTION
1.1 Background of study 1–4
1.2 The objective of the study 5
1.2.1 Description
1.2.2 Comparison
1.2.3 Relationship
2.0 METHODOLOGY
1.1. Description of Data 6–7
1.2. Method of Analysis 8–9
3.0 DATA ANALYSIS AND RESULT
3.1 Descriptive Analysis
3.1.1 Frequency Table and Graphical Methods for Quantitative Data
3.1.1.1 Frequency Table and Graph for Rating 10 – 11
3.1.1.2 Frequency Table and Graph for Minimum Age of Maturity 12 – 13
3.1.1.3 Frequency Table and Graph for Minimum Number of Downloads 14 – 15
3.1.1.4 Frequency Table and Graph for Download Size (MB) 16 – 17
3.1.1.5 Frequency Table and Graph for Number of Ads in 10 Minutes 18 – 19
3.1.1.6 Frequency Table and Graph for Number of Pictures 20 – 21
3.1.1.7 Frequency Table and Graph for Minimum Price if Fully Purchased 22 – 23

3.1.2 Frequency Table and Graphical Methods for Qualitative Data

3.1.2.1 Frequency Table and Graph for Name of the App 24 – 26
3.1.2.2 Frequency Table and Graph for Marketplace of the App 27
3.1.2.3 Frequency Table and Graph for Minimum Required OS for the App 28 – 29
3.1.2.4 Frequency Table and Graph for Availability of In-app Purchase 30

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 3.1.2.5 Frequency Table and Graph for Availability of Ads in the App 31
3.1.2.6 Frequency Table and Graph for Level of Game in the App 32
3.1.2.7 Frequency Table and Graph for Interest Towards the Game 33
3.1.2.8 Frequency Table and Graph for Complete Playability of the App 34
3.1.2.9 Frequency Table and Graph for Developer of the App 35 – 36

3.1.3 Summary Measure

3.1.3.1 Measure of Centrality 37
3.1.3.2 Measure of Dispersion 38
3.1.3.3 Measure of Position 39
3.1.3.4 Measure of Skewness 40

3.2 Inferential Analysis

3.2.1 Hypothesis Testing for Mean – A One-Way Analysis of Variance 41 – 42
3.2.2 Regression and Correlation 43 – 45

4.0 DISCUSSION
4.1 Explanation of Descriptive Analysis Results 46 – 48
4.2 Explanation of Inferential Analysis Results 49
5.0 CONCLUSION 50
6.0 APPENDICES 51

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LIST OF TABLES

TABLE CONTENT PAGE

NO
1 Description of Data 6
2 Description of Analysis 6-7
3 Method of Analysis 8-9
4 Frequency Table of Rating 10
5 Frequency Table for Minimum Age Maturity 12
6 Frequency Table for Number of Download 14
7 Frequency Table for Download Size 16
8 Frequency Table of Number Ads in 10 Minutes 18
9 Frequency Table of Number of Pictures 20
10 Frequency Table of Minimum Price if the App Fully Purchased 22
11 Frequency Table for Name of Educational Gaming Apps for Kids 24-25
12 Frequency Table for Marketplace 27
13 Frequency Table for Minimum Operating System Requirements 28
14 Frequency Table for Availability of in-Apps Purchase 30
15 Frequency Table for Availability of Advertisement 31
16 Frequency for Level of Game 32
17 Frequency Table for Interest Towards the Games 33
18 Frequency Table for Complete Playability 34
19 Frequency Table for Developers Apps 35
20 Statistic table for Measure Centrality 37
21 Statistic Table for Measure of Dispersion 38
22 Statistic Table for Measure Position 39
23 Statistic Table for Measure Skewness 40

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LIST OF FIGURES

FIGURE CONTENT PAGE

NO
1 Histogram for Rating 11
2 Horizontal Bar Graph for Minimum Age Maturity 12
3 Stem and Leaf Plot for Number of Download 14
4 Histogram for Download Size 17
5 Histogram for Number Ads in 10 Minutes 19
6 Stem and Leaf Plot for Number of Pictures 21
7 Histogram for Minimum Price if the App Fully Purchased 23
8 Pie Chart for Name of Educational Gaming Apps for Kids 26
9 Histogram Table for Marketplace 27
10 Horizontal Bar Graph for Minimum Operating System 28
Requirements
11 Histogram for Availability of in-Apps Purchase 30
12 Histogram Table for Availability of Advertisement 31
13 Pie Chart for Level of Game 32
14 Histogram for Interest Towards the Games 33
15 Histogram Table for Complete Playability 34
16 Pie Chart Table for Developers Apps 36

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1.0 INTRODUCTION

1.1 BACKGROUND OF THE STUDY

Based on the article shared by SpringerLink, in recent years, the use of digital game-
based learning (DGBL) in the early childhood learning process has been growing. Recent
research shows that DGBL has the potential to create new forms of childhood learning, but it is
not yet clear how applications can affect young students' learning. There are also not sufficient
studies to analyze the significant features of DGBL in this area in early childhood. Therefore, to
better understand the impact on childhood DGBL education, this article reviews the systematic
literature. This review analyzes 37 articles in this area. In this paper, PRISMA's principles
analysis of studies on the characteristics of DGBL technology has been used. The classification
focuses on four areas: the objectives of current studies, the impact of technology use on
children's learning, learning theories, and assessment methods in DGBL applications. The
results of this study show that the use of DGBL can have an active effect on strengthening
thinking skills and learning in childhood. This article examines the evolution of this evolving
technology, the challenges, and issues posed by DGBL, and discusses how it can be useful for
younger students to learn and do more research. This study provides insight for researchers,
game designers, and developers in the field of DGBL.

A systematic literature review was conducted by Calderón and Ruiz (2015) who found
that 53 educational games research literature had adopted different methods to assess the
effectiveness of diverse educational games in the period between November 2013 and April
2015, compared to 18 and 20 games used in health and wellness, and the professional learning
and training domain, respectively. They also reported that 60% of these 53 studies examined
the effectiveness of using educational games in higher education settings, compared to only
40% in primary or secondary school settings, indicating that teachers in higher education are
more likely to combine educational games with traditional teaching methods into students’
learning experiences, a sign of creativity of embracing the new strategy to enrich students’
learning experience.

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 The increasing prevalence of educational gaming apps for kids has prompted a need for
comprehensive research into the various factors that contribute to their overall quality and
impact on children's learning experiences. This study aims to explore and analyze a range of
variables associated with educational gaming apps, including the name of the apps,
marketplace, developer name, minimum required operating system (OS), app ratings, minimum
age maturity, minimum number of downloads, download size, availability of in-app purchases,
purchase price, presence of ads, frequency of ads, complete playability, game level, interest
level, and number of pictures. Each of these variables plays a crucial role in determining the
suitability and effectiveness of educational gaming apps for children.

The first variable, the names of educational gaming apps are essential for identification
and categorization. Understanding the names of the apps allows for the recognition and
differentiation of each app, enabling parents and educators to make informed decisions about
which apps are most relevant to their children's educational needs. Going to the second
variable, marketplace, apps are distributed through different marketplaces such as Google Play
Store, V-Appstore, and App Store which also influence accessibility and availability. Other than
that, different marketplaces may also have varying standards for app approval, which can
impact the overall quality and educational value of the apps.

Next, the developer’s name provides insights into the creators of the educational gaming
apps. Understanding the developer can shed light on their reputation, expertise in educational
app development, and commitment to child-friendly and educational content. Each app has a
minimum required OS for the apps to function properly as this can lead to issues such as slow
performance, system crashes, or incompatibility with certain features. The minimum required
OS for the apps determines the compatibility of the apps with different devices. This variable is
crucial for ensuring that educational gaming apps can be accessed and utilized by a wide range
of users without technical limitations.

Followed by the minimum required OS, app ratings serve as an indicator of user
satisfaction and overall quality. Analyzing app ratings can help identify apps that have received
positive feedback from users, highlighting their potential as effective educational tools for
children. As this is the study of educational gaming for kids, we also research the minimum age
maturity to play the game app. Setting an age maturity requirement for the apps ensures that
they are appropriate for specific age groups. Understanding the minimum age maturity can

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assist parents and educators in selecting apps that align with children's developmental stages
and educational needs.

On another note, the minimum number of downloads reflects the popularity and potential
impact of the apps. Apps with a higher number of downloads may have garnered greater
attention and usage, indicating their relevance and appeal to a wider audience. On top of that,
the download size of the apps influences accessibility, especially for users with limited storage
capacity on their devices. This variable is important for ensuring that the apps can be easily
downloaded and utilized without significant technological barriers.

Some of the apps are not fully accessible such as the games that can be played are
restricted and can only be accessed through an in-app purchase meaning the complete
playability of the game apps. The availability of in-app purchases can influence the overall cost
and accessibility of educational gaming apps. Understanding whether in-app purchases are
present allows parents and educators to assess the potential additional costs associated with
the apps. Related to this, we collect the data for the price of the apps if fully purchased. This
provides insights into the financial investment required to access the full content and features.
This variable is essential for evaluating the affordability and value proposition of educational
gaming apps.

One way or another, ads have been a way for developers to gain income, so we
research the availability of ads in gaming apps. The presence of ads and their frequency can
impact the overall user experience and engagement with the apps. Analyzing the availability
and frequency of ads helps assess potential interruptions to children's gameplay and learning
experiences. We also research the level of the game apps whether it's easy, medium, or hard,
and the interest towards the game. The level of the game and interest in the game collectively
contribute to the overall engagement and educational value of the apps. Understanding these
variables allows for the assessment of the apps' ability to sustain children's interest and provide
meaningful learning experiences. We also include the number of pictures as one of our
variables as the inclusion of pictures in educational gaming apps can contribute to visual
engagement and interactive learning experiences. Analyzing the number of pictures provides
insights into the visual content and its potential impact on children's comprehension and
engagement.

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 Researching the rating of educational gaming apps for kids is crucial due to its potential
impact on children's learning, the need for quality assessment, the importance of guiding
parents and support for educators, the necessity of consumer protection, and the ongoing
technological advancements in the digital landscape. To provide kids with positive, helpful, and
enriching experiences in the digital world, this research seeks to understand how well these
apps foster critical thinking and cognitive development, assist parents in making decisions, and
assist educators in incorporating technology-based learning tools.

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1.2 OBJECTIVE OF THE STUDY
1.2.1 Description

This study's first objective is to give a thorough explanation of all the variables connected
to the rating of educational gaming apps for kids. This involves taking a look at the rating given
for the app, minimum age maturity, minimum number of downloads, download size (MB),
number of ads in 10 minutes, number of pictures, minimum price of the app if purchased fully,
marketplace of the app, name of the app, developer of the app, minimum required OS for the
app, availability of advertisement in the app, availability of in-app purchase, complete playability
of the app, interest towards the game and level of game in the app. We hope to provide a
baseline understanding of the app rating landscape by providing a detailed description of these
variables.

1.2.2 Comparison

This study’s second objective is to investigate whether there is a significant difference in

the number of downloads in different marketplaces. Comparing the number of downloads of
educational gaming apps for kids across different marketplaces would provide insights into the
varying levels of app popularity and user engagement across different marketplaces, offering
valuable information for app developers, marketers, and platform strategists. To optimize reach
and impact, decisions about app distribution, marketing tactics, and platform-specific
optimizations can be made with knowledge of the variations in download counts across
marketplaces.

1.2.3 Relationship

This study’s third objective is to investigate the relationship between the minimum price
of fully purchased educational gaming apps for kids and the number of downloads. This study
aims to analyze how variations in-app pricing impact download rates, providing insights into
consumer behavior and preferences in the context of educational gaming apps. Through this
objective, the research seeks to identify potential correlations or patterns between app pricing
and download numbers, offering valuable implications for pricing strategies and user
engagement in the educational gaming app market.

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2.0 METHODOLOGY

2.1 DESCRIPTION OF DATA

Population All educational gaming apps for kids

Sample 100 educational gaming apps for kids

Type of study Sample survey

Sampling unit The sampling unit in this study is the individual apps

Sampling frame The sampling frame is the list of the names of all of the
educational gaming apps for kids available in the
marketplace.

Source of data Primary data

Type of Statistics Descriptive statistics and Inferential statistics

Data collection Direct observation

method

Sampling technique Technique under non-probability, Snowball Sampling

Table 1: Description of Data

No. Variable Type of variable The scale of Graphical

measurement method

1 Name Qualitative variable Nominal scale Pie Chart

2 Marketplace Qualitative variable Nominal scale Bar Chart

3 Developer Qualitative variable Nominal scale Pie Chart

4 Minimum required Qualitative variable Nominal scale Horizontal Bar

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 OS Chart

5 Rating given Quantitative Interval Histogram

continuous variable

6 Minimum age Quantitative Ratio scale Horizontal

maturity continuous variable Histogram

7 Minimum no. of Quantitative discrete Ratio scale Stem-and-Leaf

downloads variable Plot

8 Download size Quantitative Ratio scale Histogram

(MB) continuous variable

9 Availability of in- Qualitative variable Nominal scale Bar Chart

app purchase

10 Price if purchased Quantitative Ratio scale Histogram

fully (RM) continuous variable

11 Availability of ads Qualitative variable Nominal scale Bar Chart

12 No. of ads in 10 Quantitative discrete Ratio scale Histogram

mins variable

13 Complete Qualitative variable Nominal scale Bar Chart

playability

14 Level of game Qualitative variable Ordinal scale Pie Chart

15 Interest towards Qualitative variable Nominal scale Bar Chart

the game

16 No. of pictures Quantitative discrete Ratio scale Box-and-Whisker

variable Plot

Table 2: Description of Variables

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2.2 METHOD OF ANALYSIS

No Objective Variable Statistical method

1 To describe the ● Name ● Pie Chart

quantitative of the rating ● Marketplace ● Bar Chart
given for the app(1), ● Developer ● Pie Chart
minimum age ● Minimum ● Horizontal Bar
maturity(2), minimum required OS Chart
number of downloads(3), ● Rating given ● Histogram
download size (MB)(4), ● Minimum age ● Horizontal
number of ads in 10 maturity Histogram
minutes(5), number of ● Minimum no.of ● Stem-and-Leaf
pictures(6), minimum download Plot
price of the app if ● Download ● Histogram
purchased fully(7), size(MB) ● Bar Chart
marketplace of the ● Availability of in- ● Histogram
app(8), name of the app purchase ● Bar Chart
app(9), developer of the ● Price if ● Pie Chart
app(10), minimum purchased fully ● Bar Chart
required OS for the (RM) ● Box-and-Whisker
app(11), availability of ● Availability of ads Plot
advertisement in the ● No. of ads in 10
app(12), availability of in- mins
app purchase(13), ● Complete
complete playability of playability
the app(14), interest ● Level of game
towards the game(15) ● Interest towards
and level of game in the the game
app(16). ● No. of pictures

2 To see whether there is ● Number of ● Analysis of

a significant difference in downloads variance

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 the number of downloads ● Marketplaces.
in different marketplaces.

3 To observe the ● Minimum price of ● Regression and

relationship between the the app if fully Correlation
minimum price of the app purchased.
if fully purchased and ● Number of
number of downloads. downloads

Table 3: Method of analysis

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3.0 DATA ANALYSIS AND RESULT

3.1 DESCRIPTIVE ANALYSIS

3.1.1 FREQUENCY TABLE AND GRAPHICAL METHODS FOR QUANTITATIVE DATA

3.1.1.1 Frequency Table and Graph for Rating

Table 4: Frequency Table of Rating

We can see the result from frequency table 4 shows app ratings ranging from 3.0
to 5.0. From the frequency table, we can conclude that the frequency value in the range
of 4.60 is the highest at 11. It means that most apps that we gathered have a rating of
4.60. We can see that the app rating of 4.50 has the second highest frequency value of

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10, which means there is a total of 10 apps fall into this range. The rating of 4.4 also
becomes the top three highest with a frequency value of 9, which means 9 of the apps
fall within this range of ratings of apps. Few apps are falling into the frequency of 8 which
is the rating of 4.2, 4.3, and 4.7. Not only that, 7 of the apps have fallen into the rating of
3.9, 4.0, and 4.1. The remaining ratings get a frequency of 1, 2, and 3 only.

Figure 1: Histogram for Rating

The histogram in figure 1 illustrates the frequency of ratings on apps. The x-axis
displays the rating given, while the y-axis represents the frequency or count of each
rating. The height of each bar corresponds to the number of users who rated the app
within a specific category. The histogram shows the majority (46%) of ratings of 100
apps are rated 4.60, 33% are rated 4.50, 9% are from the rating of 4.40, 6% are from the
rating of 4.2, 4.3, and 4.7, 4% from the rating of 3.9 – 4.1, and others are not stated.

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3.1.1.2 Frequency Table and Graph for Minimum Age Maturity

Table 5: Frequency Table of Minimum Age Maturity

We can see the result from frequency table 5 illustrated age maturity ranging
from 1+ to 6+. From the frequency table, it shows that most apps (57 apps) have an age
maturity of 3+, 12 apps have a maturity of 4+, 10 apps have a maturity of 5+, and 18
apps have a maturity of 1+ and 2+.

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 Figure 2: Horizontal Bar Graphs for Age Maturity
The horizontal bar chart in figure 2 represents the frequency of different age
maturity levels. The y-axis represents the maturity levels, which could include categories
like 2+, 3+, 4+, and 5+. The x-axis represents the frequency or count of apps falling
within each maturity level. Each bar in the chart corresponds to a specific maturity level
and its respective frequency. It is shown that the majority (57) of the 100 apps have a
minimum age maturity of 3+, 12 of the apps have an age maturity of 4+, 10 apps have
an age maturity of 5+, and 9 of the apps have an age maturity for 1+ and 2+. The
remaining is 6+ and only gets a frequency of 3.

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 3.1.1.3 Frequency Table and Graph for Number of Downloads

Table 6: Frequency
Table of Number of Downloads

The result from frequency table 6 shows the number of downloads on apps.
From the frequency table, 1 million downloads have the highest frequency value, which
is 32. The second highest frequency is for the unmentioned number of downloads of the
apps which is 30. 100,000 downloads have 13 apps, 5 million downloads have 12 apps,
while the remaining 1 billion, 500,000, and 5 thousand each have frequency of 8, 4, and
1.

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 Figure 3: Stem-and-Leaf Plot for Number of Downloads

The stem-and-leaf plot provides insights into the distribution of the number of
downloads for the apps. From the plot, most apps have a number of downloads falling
into the range of 1 million. This range has the highest frequency, indicated by a value of
32. Additionally, for the second highest frequency which is unmentioned in the apps, we
convert the N/A to number 1. The frequency of 30 consists of the unmentioned number
of downloads. The third and fourth highest frequencies are 13 and 12 for the number of
downloads 100,00 and 5 million.

We can observe that most data points are clustered towards the lower end of the
distribution, specifically within the range of 1 million to 100 million downloads. There is a
noticeable decrease in the frequency as we move towards higher download counts. This
pattern suggests that the stem-and-leaf plot is skewed to the right or positively skewed.
In a positively skewed distribution, the tail of the distribution extends towards the right
side, while most of the data is concentrated towards the left side. This indicates that
there are fewer apps with higher download counts compared to the number of apps with
lower download counts.

Therefore, based on the stem-and-leaf plot's distribution of the number of

downloads, we can conclude that it is skewed to the right or positively skewed.

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3.1.1.4 Frequency Table and Graph for Download Size

Table 7: Frequency Table of

Download Size

We can see the

result from frequency table 7 illustrated download size
ranging from 7.27 - 1300 MB. From the frequency table, it
can be seen that the size (MB) of 34, 90, and 107 has the
highest frequency value of 2, which means that 6 apps fall within this size range. The
remaining size of downloads only get 1 frequency each.

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 Figure 4: Histogram for Download Size

The histogram in figure 4 shows the frequency of download size (MB) of the
apps. The x-axis displays the download size, while the y-axis represents the frequency
or count of each size. The histogram shows the majority (50%) of number of sizes of 100
apps are from 7.27 - 150 MB. 25% are size between 150 - 250 MB, 7% are from size
250 - 300 MB and 400 – 500 MB. Furthermore, from the histogram, we can see there
are certain download sizes fall into 2% and 1%. This shows there are not many apps
that have bigger download sizes in the app marketplace.

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3.1.1.5 Frequency Table and Graph for Number of Ads in 10 Minutes

Table 8: Frequency Table of Number of Ads in 10 Minutes

The result from frequency table 8 shows the number of ads in 10 minutes for
each app. From the frequency table, most of the apps provided for kids do not contain
ads. The frequency shows that 77 out of 100 apps do not contain ads. The second
highest frequency is apps that contain ads within 5 minutes and 7 minutes, which is 4.
The highest minute of ads is 25 minutes but only with 1 frequency.

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 Figure 5: Histogram for Number of Ads in 10 Minutes

The histogram in figure 5 shows the frequency of the number of ads in 10

minutes for each app. The x-axis displays the number of ads in 10 minutes, while the y-
axis represents the frequency or count of each minute. The histogram shows the
majority (77%) of the number of ads in 10 minutes come from apps that do not contain
any ads at all. Children can enjoy playing all this educational gaming without any
interruption from the ads. The second highest in the histogram is 4% for the apps that
contain 5 minutes and 7 minutes of ads. Not only that, for apps that contain 2 minutes
and 6 minutes of ads they get the top three in the histogram with 3%. The remaining
minutes of 1, 4, 8, 10, 14, and 25 minutes consist of only 2% and 1% of the percentage.

3.1.1.6 Frequency Table and Graph for Number of Pictures

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 Table 9: Frequency Table of Number of Pictures

The result from frequency table 9 shows the number of pictures provided by the apps at the
app marketplace. From the frequency table, most of the apps provided 16 pictures with a
frequency of 24. The highest number of pictures gets the highest amount of frequency. The
second highest frequency is 21 with a number of 8 pictures provided by the developer of the
apps. The developer who provided 10 pictures also made it to the top three with a frequency of
11. The frequency table also shows the frequency for number of pictures of 4, 5, 6, 7, 9, 12, 13,
14, and 15 which is 2, 6, 6, 3, 7, 7, 4, 8, and 1.

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 Figure 6: Stem and Plot for Number of Pictures

The frequency table and box-plot above shows the number of pictures provided by each
developer on their apps. As we can see from the data above, 24 apps provide 16 pictures at the
marketplaces and 21 apps provide 8 pictures. Next, about 11 apps provide a total of 10 pictures.
Other than that, about 8 apps provide 14 pictures and for 9 and 7 pictures each has 7 apps. The
developer also provides a number of pictures of 5 and 6 pictures with a frequency of 6. Lastly,
for the number of pictures of 4, 7, 13, and 15 each has a frequency of 2, 3, 4, and 1.

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3.1.1.7 Frequency Table and Graph for Minimum Price of The App if Fully Purchased

Table 10: Frequency Table of Minimum Price of The App if Fully Purchased

The result from frequency table 10 shows the minimum price of the app if fully
purchased. From the frequency table, we can conclude that the price ranges from
RM299.90 get the highest frequency which is 4. Furthermore, the minimum price of
RM19.90 and RM329.90 get the second highest frequency which is 3. Following with
apps that get frequency 2 consist of minimum prices of RM9.49, RM11.99, RM17.99,
RM19.99, RM29.99, RM139.99, RM229.90, RM264.90, RM 339.90, and RM499.90.
While the remaining price on the frequency table only manages to have 1 frequency.

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 Figure 7: Histogram for Minimum Price of The App if Fully Purchased

The histogram in figure 6 shows the frequency of the minimum price of the app if
fully purchased. The x-axis displays the price if fully purchased, while the y-axis
represents the frequency of each price. The histogram shows the majority (72%) of the
minimum price if fully purchased comes from apps that do not have to pay until the
range of RM100.In this case, certain developers just give children to enjoy playing and
learning educational gaming without any payment, but certain developers put prices on
their games for the children to have full access to the game. The second highest in the
histogram is 23% and the price is in the range between RM200 – RM500. The most
expensive games only get 1% of the histogram graph.

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3.1.2 FREQUENCY TABLE AND GRAPHICAL METHODS FOR QUALITATIVE DATA

3.1.2.1 Frequency Table and Graph for Name of Apps.

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Table 11: Frequency Table for Name of Educational Gaming Apps for Kids

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 Figure 8: Pie Chart for Name of Apps

Figure 8 displays a Pie Chart illustrating the names of educational kids' game apps. The chart
represents 100 educational kids' game applications that have been collected. The 1.0% value is
represented by one application in the chart.

3.1.2.2 Frequency Table and Graph for Marketplace

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 Table 12: Frequency Table for Marketplace

Figure 9: Histogram for Marketplace

Figure 9 depicts a histogram illustrating the frequency of various marketplaces. The x-axis
categorizes the types of marketplaces as Google Play Store, V-App Store, and App Store. The
y-axis represents the frequency or count of marketplaces falling within each category. The
histogram reveals that the Google Play Store has the highest percentage at 57%, while the V-
App Store has the lowest at 13%, and the App Store represents 30%.

3.1.2.3 Frequency Table and Graph for Minimum Operating System Requirement

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Table 13: Frequency Table for Minimum Operating system Requirements

Figure 10: Horizontal Bar Graph for Minimum Operating System Required.

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Figure 10 illustrates a horizontal bar graph depicting the minimum operating system
requirements for each app. The y-axis represents the minimum operating system requirements,
including the Android Operating System and iOS Operating System, while the x-axis represents
the frequency and count of apps in percentage terms. According to the horizontal bar graph,
21% of educational kids' games require the use of the Android 5.0 Operating System, and only
1% of the apps necessitate Android 4.0.3. For iPhone users, 11% of educational games apps
require iOS 11, and 1% require iOS 14.5, iOS 15.0, iOS 9.0, and iOS 10.0 each.

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 3.1.2.4 Frequency Table and Graph for Availability of in-App Purchase

Table 14: Frequency table for Availability of in-Apps Purchase

Figure 11: Histogram for Availability of in-App Purchase

Figure 11 illustrates a histogram depicting the availability of in-app purchases. The x-axis
represents the availability of in-app purchases, and the y-axis denotes the frequency or count of
apps in each category, presented as a percentage. This indicates whether they are available for
purchase or not. Based on the histogram, the chart shows that 87% of educational games apps
represent 87 apps that are in-app purchase apps, while 13% represent 13 apps of educational
games apps do not have in-app purchases.

3.1.2.5 Frequency Table and Graph for Availability of Advertisement

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 Table 15: Frequency table for Availability of Advertisement

Figure 12: Histogram of Availability of Advertisement.

Figure 12 presents a histogram illustrating the availability of advertisements in educational

games apps for kids. The x-axis represents the availability of advertisements, while the y-axis,
measured in percentages, indicates the frequency, or count of apps. According to the
histogram, 77% (comprising 77 apps) of educational games apps for kids are not available for
advertisements, whereas 23% (representing 23 apps) of these apps allow for advertising.

3.1.2.6 Frequency Table and Graph for Level of Games

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 Table 16: Table of Frequency for Level of Game

Figure 13: Pie Chart for Level of Game

Figure 13 presents a Pie Chart representing the levels of difficulty in educational games for kids.
The chart illustrates the distribution of game levels, including easy, medium, and hard. Based on
the chart, it indicates that 51% (comprising 51 apps) of Educational Games for Kids are easy to
play, 36% (consisting of 36 apps) are at a medium level, and 13% (comprising 13 apps) are
considered hard to play.

. 3.1.2.7 Frequency Table and Graph for Interest Towards the Games

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 Table 17: Frequency table for Interest towards the Games

Figure 14: Histogram of Interest toward the Games

Figure 14 displays a Histogram illustrating interest in educational games for kids. The x-axis
reflects the interest level towards the games, distinguishing whether they are interesting to play
or not. The y-axis, measured in percentages, indicates the frequency or count of apps.
According to the histogram, 67%, representing 67 educational games for kids, are deemed
interesting to play, while 33%, consisting of 33 educational games for kids, are not considered
interesting to play.

.3.1.2.8 Frequency Table and Graph for Complete Playability

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 Table 18: Frequency table for Complete Playability

Figure 15: Histogram for Complete Playability

Figure 15 presents a Histogram illustrating the complete playability of educational gaming apps
for kids. The x-axis represents the degree of complete playability, while the y-axis, measured in
percentages, indicates the frequency, or count of apps. The histogram reveals that 74%,
representing 74 apps of educational gaming apps for kids are incomplete, while 26%,
representing 26 apps, of these apps are complete and ready to play.

3.1.2.9 Frequency Table and Graph for Developer of Apps

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Table 19: Frequency table for Developer of Apps

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 Figure 16: Pie Chart for Developer of Apps

Figure 16 displays a Pie Chart representing the developers of educational gaming apps for kids.
Each color corresponds to a different developer of apps. According to the pie chart, 5%
(consisting of 5 apps) of educational games for kids have the same developer, 2% (representing
2 apps) share another developer, and 1% corresponds to a single developer name responsible
for developing 1 app.

3.1.3 SUMMARY MEASURE

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 3.1.3.1 Measure of Centrality

Table 20: Statistic table for Measure Centrality

Mean
The mean rating for apps is 4.139, indicating that the average rating for educational gaming
apps for kids is 4.14. The mean minimum number of downloads given is 1,753,500.30,
signifying that, on average, these educational gaming apps for kids have a minimum of
1,753,500 downloads.

Median
The median for rating apps given is 4.30, indicating that 50% of the rating apps for educational
gaming apps for kids fall below 4.30, while the other 50% rise above 4.30. Similarly, for the
median of the minimum number of downloads for educational gaming apps, which is 1,000,000,
it indicates that 50% of the minimum download numbers are below 1,000,000, and the
remaining 50% are above that threshold.

Mode
The mode for app ratings is 4.6, signifying that this particular rating has the highest frequency
among all app ratings. On the other hand, the mode for the minimum number of downloads is
1,000,000, indicating that 1,000,000 is the most frequently occurring value in the distribution of
minimum download counts.

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 3.1.3.2 Measure of Dispersion

Table 21: Statistic Table for Measure of Dispersion

Standard Error for Mean

The standard error for the mean given for the rating of educational gaming apps for kids is 0.729
while the standard error for the mean given for the minimum number of downloads is
288,789.912.

Variance
The variance given for the rating of educational gaming apps for kids is 0.531 while the variance
for the minimum number of downloads is 8.24E+12.

Standard Deviation
Standard deviation for the rating given for educational gaming apps for kids is 0.7290, while the
standard deviation for the minimum number of downloads is 2,887,899.117.

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 3.1.3.3 Measure of Position

Table 22: Statistic Table for Measure Position

Based on Table 22, the percentiles for ratings in educational games apps are as follows: 15% of
ratings are at or below 3.715, 30% of ratings are at or below 4.00, 45% of ratings are at or
below 4.2, 60% of ratings are at or below 4.4, 75% of ratings are at or below 4.575, and 90% of
ratings are at 4.70 or below.

For the percentile of the minimum number of downloads, the distribution is as follows: 15% of
downloads are at or below 1, 30% are at or below 15,000, 45% are at or below 500,000, 60%
and 75% are at or below 1,000,000, and 90% are at or below 5,000,000.

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 3.1.3.4 Measure of Skewness

Table 23: Statistic Tables for Measure of Skewness

According to Table 23, the skewness for ratings given to Educational Gaming Apps for kids is -
3.759, indicating a negative skew. A negative skewness implies a leftward shift in the
distribution, suggesting that the ratings are concentrated towards the higher end, with a longer
tail on the left.

Conversely, the skewness for the minimum number of downloads is 1.990, revealing a positive
skew. This positive skewness indicates a rightward shift in the distribution, suggesting that the
minimum download numbers are concentrated towards the lower end, with a longer tail on the
right.

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3.2 INFERENTIAL ANALYSIS
3.2.1 HYPOTHESIS TESTING FOR A MEAN

The second objective of our study is to see whether there is a significant difference in
the number of downloads of apps in different types of categories.

Table 23: SPSS output for Hypothesis Testing for Mean

Step 1: Hypothesis Statement

● H0 : µ1 = µ2 = µ3
● H1 : Not all three population means are equal

Step 2: Select the distribution

● Testing more than 2 means
● K=3
Use formula for ANOVA
F distribution

Step 3: Significance level, α = 0.05

Step 4: Determine the p-value = 0.001

Step 5: Decision
Decision Rule: Reject the Null Hypothesis if p-value less than or equal to the significance
level. Otherwise, do not reject the Null Hypothesis
Decision: Reject the Null Hypothesis

Step 6: Conclusion
There is a significant difference in the number of downloads in different marketplaces
(Google Play Store, App Store, and V-Appstore) at a 5% significance level.

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Alternative Analysis

Step 1: Hypothesis Statement

● H0 : µ1 = µ2 = µ3
● H1 : Not all three population means are equal

Step 2: Select the distribution

● We are testing for the equality of three means for three normally distributed
categories.
● We use the F Distribution

Step 3: Critical Value and Rejection Region

● Significance level, α = 0.05
● ANOVA is always right-tailed
● df numerator = 2, df denominator = 97
● Critical value F for df = (2, 97) and α = 0.05
● Critical value = 2.21

a=
0.01

2.2
1

Step 4: Test Statistics

From SPSS Output, F calc = 9.411

Step 5: Decision
Decision Rule: Reject Ho if F calc > F 0.05, 2, 97
Decision: Reject the null hypothesis

Step 6: Conclusion
There is a significant difference in the number of downloads in different marketplaces
(Google Play Store, App Store, and V-Appstore) at a 5% significance level.

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3.2.2 REGRESSION AND CORRELATION

The third objective of this study is to observe the relationship between the ratings of the
apps and the number of downloads.

Scatter Diagram

Figure 17: Scatter Diagram from SPSS Output

The Scatter diagram illustrates the data points representing the minimum number
of downloads on the Y-axis and the rating given on the X-axis. In this case, after
examining the scatter plot, it can be concluded that there is no apparent relationship or
correlation between the app ratings and the number of downloads.

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 Table 24: Model Summary

Pearson Coefficient of Correlation, r = 0.039

This indicates that there is a very weak negative linear and almost no relationship
between the ratings of the app and the number of downloads.

In this case, with a correlation coefficient of 0.039, the relationship between the ratings
and the number of downloads is very weak. It suggests that there is a minimal negative
association between these two variables and almost no relationship at all. The number
of downloads has very little impact on the ratings of the apps.

From SPSS Output, R square = 0.002

Coefficient of determination, r2 = 0.002

0.2% of the total variation in the number of downloads is explained by the ratings of
apps. The other 99.8% is explained by other factors.

Table 25: Coefficients of Ratings of Apps and Number of Downloads

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 From the SPSS Output

The regression lines.

a = 1,111,909.41

b = 155,011.09

Y = a + bx

Y = 1,111,909.41 + 155,011.09x

Y = 1,111,909.41 + 155,011.09x

Interpretation of the regression coefficient

Regression coefficients: Y-intercept, a and Slope b

Y-intercept, a 1,111,909.41

Interpretation: if the ratings of the app increase by 1, the number of downloads of the
apps will increase by 155,011.09 of downloads.

4.0 DISCUSSION
4.1 EXPLANATION OF RESULT - DESCRIPTIVE ANALYSIS

The analysis of the provided charts offers valuable insights into various aspects of useful
information about educational gaming apps, that give benefits for both kids and app creators. By
looking at the app ratings, kids and parents can understand how good, reliable, and enjoyable
the apps are in different categories. The information helps families choose apps that match their
kid’s interests and learning. High-rating apps indicate that the app is of good quality and brings
satisfaction to users, making it a reliable choice for educational fun.

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The stem-and-leaf plot provides information on insights into the distribution of the number of
downloads for the apps. This data helps users identify which apps that famous and widely
people prefer to use. From the plot, most apps have a number of downloads falling into the
range of 1 million. This means lots of kids and parents are using them, which probably means
these apps are really good and helpful. For the developers, checking out the download count
distribution can reveal trends and best practices where they can try to improve their apps by
adding cool features, making things easy to use, and so on. It is a handy way for the developers
to learn what is popular and make their educational games even more awesome.

The histogram represents the frequency of app sizes into the range that users, which are the
parents, can expect when downloading the educational gaming app for kids. Most of them are
between 2 and 42 megabytes (MB) in size, showing that they are designed well to balance
having lots of features without taking up too much storage space. This helps parents choose
apps that would not use all their device storage and also shows that the people who make these
apps are working to keep them small and storage-friendly.

Next histogram displays the frequency of the number of ads in 10 minutes for each app. It
shows the majority of the number of ads in 10 minutes come from apps that do not contain any
ads at all. Children can enjoy playing all this educational gaming without any interruption from
the ads. For parents or guardians, this information result is quite positive as it implies that
children can engage with these educational games without being bothered by ads every few
minutes. The absence of ads contributes to a more seamless and uninterrupted gaming
experience that allows kids to focus on the educational content and activities without
distractions.

Examining the histogram illustrating the number of pictures provided by the apps at the app
marketplace, most of the apps provided 16 pictures with a frequency of 24. The highest number
of pictures gets the highest amount of frequency. This information is helpful for parents or
guardians because it gives them an idea of what to expect when they download these apps. In
addition, the histogram represents the frequency of the minimum price of the app if fully
purchased. The majority of the minimum price if fully purchased comes from apps that do not
have to pay until the range of RM100. In other words, many developers are offering their
educational games without charging anything up to this price point. This suggests that a

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significant fraction of developers is making their educational gaming apps available for kids to
enjoy without requiring any payment.

The histogram illustrates the frequency of various marketplaces where these apps
predominantly available. It reveals that Google Play Store has the highest percentage at 57%,
while V-App Store has the least at 13%, and App Store represents 30%. This indicates that
majority of educational gaming apps for kids are accessible through the Google Play Store.
Google Play Store is a popular platform for Android devices, and the high percentage suggests
that a significant part of these apps is designed for Android users. It reflects the diverse
availability across different app marketplaces, which allow users to choose based on their
device usage.

The histogram represents the availability of in-app purchases. Based on the histogram, the
chart shows that the majority of the educational game apps are in-app purchase apps. In other
words, users have the option to buy additional content, features, or items within these apps.
This information is important for parents who may have preferences regarding in-app
purchases. App with in-app purchases may provide additional opportunities for customization or
extended content which involve extra cost. It is the user's decision based on their preferences
and budget considerations when selecting the apps.

The horizontal bar chart depicts the frequency of different age maturity levels. It is shown that
the majority (57) of the 100 apps have a minimum age maturity of 3+, meaning that they are
designed for children aged 3 and older. This indicates a significant portion of the educational
gaming apps in the dataset are tailored for preschool-aged children. This distribution of age
maturity levels provides insights into the intended user for these educational gaming apps. This
information can be valuable for parents, guardians, and educators seeking age-appropriate
educational gaming apps for kids.
The horizontal bar chart shows the minimum operating system requirements for each app. The
graph shows that 21% of educational kids’ games require Android 5.0 as the minimum operating
system. This indicates that a significant portion of these apps are optimized for devices running
Android 5.0 or newer. For the iOS operating system, which is iPhone users, 11% of educational
gaming apps require iOS 11. In terms of educational gaming apps for kids, understanding the
minimum operating system requirements is important for parents or guardians who want to
ensure compatibility with their devices.

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Pie Chart illustrating the names of educational kids' game apps. The chart represents 100
educational kids' game applications that have been collected. The 1.0% value is represented by
one application in the chart. Next, the pie chart also the levels of difficulty in educational games
for kids which are easy, medium, and hard. Based on the chart, the majority of educational
gaming apps are easy to play. These games likely have straightforward challenges and are
designed to be accessible to a wide range of young players. This information about the level of
difficulty is crucial so that parents or guardians can choose games that align with the skill level
and preferences of the child. Those who are looking for a bit more challenge can explore games
in the medium difficulty range while those who are seeking more advanced gaming experience
for their older kids may try the hard-difficulty educational gaming app.

Lastly, the pie chart represents the developers of educational gaming apps for kids. Each color
corresponds to a different developer of apps. According to the pie chart, 5% (consisting of 5
apps) of educational games for kids have the same developer, 2% (representing 2 apps) share
another developer, and 1% corresponds to a single developer name responsible for developing
1 app. This is valuable for users to allow them to explore apps from different developers and
potentially find a range of educational content with varying styles, approaches, and focuses.
Developers with higher percentages may have a more significant impact on the educational
gaming app as compared with the lower percentage developers but still contribute to the overall
diversity of available apps for kids.

4.2 EXPLANATION OF RESULT - INFERENTIAL ANALYSIS

This study aims to investigate the number of downloads of the apps in different types of
categories which are Google Play Store, App Store, and V-Appstore. In addition, it is important
to understand how they are connected through statistical analysis. It is important for both app
users and developers as users, parents, and guardians on behalf of their kids can make a smart
choice when picking the apps that match the platforms that they use depending on their mobile

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phone. As developers, they can learn how to make their apps better by each platform that may
have its unique user base, app offerings, and downloading behavior.

The goal of the Hypothesis Testing for Mean analysis is to figure out if there is a big difference
in the number of downloads for different types of categories. The test result is a p-value of 0.001
when using a significance level of 0.05 of the ANOVA tests. Following the decision rule, since
the p-value is lower than the significance level of 0.05, we reject the null hypothesis. This means
that, according to the analysis, there is a significant difference in the number of downloads in
different marketplaces (Google Play Store, App Store, and V-Appstore).

The objective of Regression and Correlation analysis is to check the connection between the
ratings of apps and the number of downloads. To do this, we use a scatter plot to see the data
points. However, when we look at the scatter plot, it doesn’t seem like there is a clear link or
correlation between app ratings and the number of downloads. By calculating the Pearson
correlation coefficient (r), we get the value of 0.039, suggesting a very weak linear relationship.
The coefficient of determination (r2) is 0.002 meaning that there is 0.2% of the total variation in
the number of downloads is explained by the ratings of apps. The other 99.8% is explained by
other factors. The regression equation coefficients, a, and b, tell us that if the ratings of the app
increase by 1, the number of downloads of the apps will increase by 155,011.09 of downloads.

The results give us useful information about how ratings and downloads work for educational
gaming apps. It turns out that there is not a big difference in how many times these apps get
downloaded based on their categories. Also, when we look at how ratings and downloads are
connected, the high or low ratings do not change how many times the apps are downloaded.

5.0 CONCLUSION

In conclusion, this study aimed to give a thorough understanding of how ratings work in different
categories for educational gaming apps. It looked at various factors like ratings, downloads, app
sizes, age maturity, developers, marketplace categories, and required operating systems. The
results provide useful insights for both the kids who use these apps and the people who make
them, helping them make smart decisions in the world of educational gaming apps.

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The study checked if the type of app category makes a big difference in how many times the
apps get downloaded. Surprisingly, it turns out that the category alone does not affect how
many downloads an app gets. This means that things like how the app is advertised and what
features it has are more important in attracting a lot of users. For the providers who make and
market these apps, this is something to keep in mind when trying to reach more kids effectively.

Additionally, the study looked at how ratings and the number of downloads is connected. The
results show that there's only a small connection between high ratings and lots of downloads.
Things like what other users recommend, how the app is advertised, and the features it offers
seem to be more important in making an app popular with kids. These findings help app
developers and marketers understand the complex relationship between ratings and what kids
like.

Hence, this study provides valuable information about how ratings work for educational gaming
apps. It helps the people who make and use these apps make better choices, improve the
quality of the apps, and understand what kids enjoy in the competitive world of educational
gaming apps.

6.0 APPENDICES

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 Goo gle
Play Store App Store

App information in Google Play Store V-Appstore

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