2D Animation Based DSA Learning Platform With Audio

Narration

PROJECT REPORT - PHASE I

Submitted by

MENILA M (310622205082)
MONISHA S (310622205089)

In partial fulfillment for the award of the degree of

BACHELOR OF TECHNOLOGY
in

INFORMATION TECHNOLOGY

EASWARI ENGINEERING COLLEGE, RAMAPURAM

(Autonomous Institution)
affiliated to
ANNA UNIVERSITY: CHENNAI 600025

September 2025
 EASWARI ENGINEERING COLLEGE, CHENNAI
(AUTONOMOUS INSTITUTION)

AFFILIATED TO ANNA UNIVERSITY, CHENNAI 600025

BONAFIDE CERTIFICATE

Certified that this project report titled “2D ANIMATION BASED DSA LEARNING

PLATFORM WITH AUDIO NARRATION” is the bonafide work of “MENILA M

(310622205082), MONISHA S (310622205089) who carried out the project work under

my supervision during the academic year 2025-2026.

SIGNATURE SIGNATURE

Dr. S. GNANAPRIYA, M.E., Ph.D., Dr. N. ANANTHI, M.E., Ph.D.,

SUPERVISOR HEAD OF THE DEPARTMENT

Information Technology Information Technology

Easwari Engineering College Easwari Engineering College

Ramapuram, Chennai-600089 Ramapuram, Chennai-600089

ii
 PHASE-I EXAMINATION REPORT

The Phase –I examination report submitted by the below students in partial fulfilment for
the award of Bachelor of Technology degree in Department of Information Technology
under Anna University.

S. No Name of the Students Title of the Project

Name of the Supervisor
with designation

MENILA M
1. 2D Animation Based
(310622205082) DSA Learning Dr. S. GNANAPRIYA, M.E.,
Platform With Audio Ph.D., Assistant Professor
MONISHA S Narration Department of IT
2.
(310622205089)

Project Coordinator Examiner Signature

iii
 ACKNOWLEDGEMENT

We express our sincere thanks and deep sense of gratitude to our Founder Chairman,
Dr. T. R. Parivendhar, and our beloved Chairman, Dr. R. Shivakumar, M.D.,
Ph.D., for their constant support, encouragement, and inspiration which motivated us
to successfully complete this project.

We would like to record our respectful thanks to our Principal, Dr. P. Deiva Sundari,
M.E., Ph.D., and our Head of the Department, Dr. N. Ananthi, M.E., Ph.D., for their
valuable guidance, encouragement, and continuous support throughout the project.

We owe our profound gratitude to our project supervisor, Dr.S.Gnanapriya, M.E.,

Ph.D., Assistant Professor, Department of Information Technology, Easwari
Engineering College, for her valuable suggestions, constant encouragement, and keen
interest in our work. Her guidance at every stage helped us to complete this project in
its present form.

We also sincerely thank our project coordinator, Dr. K. Johny Elma, M.Tech.,
Ph.D., for her continuous assistance and support during the course of this work.

Finally, we would like to extend our heartfelt thanks to all the teaching and non-
teaching staff members of the Department of Information Technology for their
cooperation and encouragement which greatly facilitated the smooth completion of
this project.

IV
 ABSTRACT

Data Structures and Algorithms form the backbone of computer science education,

yet students often struggle to grasp these abstract concepts through traditional text-

based learning methods. This paper proposes a 2D Animation based DSA Learning

Platform with Audio Narration, an interactive educational tool designed to simplify

complex algorithmic processes through visual storytelling and auditory guidance.

The system integrates animated visualizations to demonstrate real-time execution

of sorting, searching, and graph algorithms, while synchronized audio narration

enhances comprehension and retention. By combining pedagogy with multimedia

learning principles, the platform provides an engaging, step-by-step explanation of

data flows, memory allocation, and computational efficiency. A modular design

supports incremental learning, offering difficulty based categorization of topics and

interactive quizzes to evaluate understanding. The platform is accessible across

devices, ensuring inclusivity for diverse learners. Additionally, features such as

multilingual narration, speed adjustment, and replay options cater to personalized

learning preferences. By unifying 2D animation with explanatory audio, the project

bridges the gap between theoretical knowledge and practical understanding,

contributing to improved problem-solving skills and fostering deeper interest in

computer science education.

KEYWORDS: 2D animation, Interactive learning, audio narration, visualization,

algorithm simulation, educational technology, multimedia learning, personalized
learning, quizzes and assessment, multilingual support, step-by-step explanation,
cognitive engagement, computer science education, e-learning platform
v
 TABLE OF CONTENTS

CHAPTER TITLE PAGE NO.

ABSTRACT v

LIST OF TABLES ix

x
LIST OF FIGURES
xi
LIST OF ABBREVIATIONS

1. INTRODUCTION

1.1. GENERAL 1

1.2. PROBLEM DESCRIPTION 1

1.3. OBJECTIVES OF THE PROJECT 2

1.4. SCOPE OF THE PROJECT 3

1.5. ORGANISATION OF THE PROJECT REPORT 3

1.6. SUMMARY 4

2. LITERATURE SURVEY

2.1. INTRODUCTION 5

2.2. EXISTING SYSTEM 5

2.3. LIMITATIONS OF EXISTING SYSTEM 6

2.4. RELATED WORKS AND OBSERVATIONS 7

2.5. PROPOSED SYSTEM OVERVIEW 9

vi
 2.6. SUMMARY 10

3. PROPOSED SYSTEM DESIGN

3.1. INTRODUCTION 11

3.2. PROPOSED SYSTEM 11

3.3. FEASIBILITY STUDY 12

3.4. SYSTEM ARCHITECTURE 13

3.4.1. CONTENT ACQUISITION 13

3.4.2. PROCESSING AND DELIVERY LAYER 14

3.4.3. USER INTERACTION AND REPORTING LAYER 16

3.5. SUMMARY 16

4. METHODOLOGY

4.1. INTRODUCTION 18

4.2. SOFTWARE SPECIFICATIONS 19

4.3. MODULE IDENTIFICATION 22

4.4. MODULE EXPLANATION 22

4.5. ALGORITHM / PSEUDOCODE 24

4.6. TOOLS USED 25

4.7. DATASET AND IMPLEMENTATION DETAILS 26

4.8. SNAPSHOTS 26

4.9. SUMMARY 27

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5. TESTING AND RESULTS

5.1. TESTING STRATEGIES 29

5.2. TEST CASES AND EXECUTION 29

5.3. RESULTS 30

5.4. SUMMARY 30

6. CONCLUSION

6.1. INTRODUCTION 31

6.2. CONCLUSION 31

6.3. SUMMARY 31

SAMPLE SOURCE CODE 33

REFERENCES 38

viii
 LIST OF FIGURES

FIGURE NO TITLE PAGE NO

3.1 System Architecture of 2D Animation Based DSA 13

Learning Platform

3.2 Flow of Content Management System 15

4.1 Browser Extension Framework 19

4.2 PyTorch (AI Model Training and Inference) 20

4.3 Google Gemini API 20

4.4 Cyberbullying Detection Module at Fog Layer 23

4.5 Google Cloud Reasoning Engine 23

4.6 Reporting Manager – LearnTrak LMS 24

4.7 Dataset Creation Process 26

4.8 Output1 27

4.9 Output2 27

5.1 System Testing 30

x
 LIST OF ABBREVIATIONS

ABBREVIATION FULL FORM

API Application Programming Interface

AI Artificial Intelligence

DSA Data Structures and Algorithms

GPI Graphical User Interface

IDE Integrated Development Environment

TTS Text-to-Speech

JSON JavaScript Object Notation

OOP Object-Oriented Programming

NLP Natural Language Processing

UI User Interface

UX User Experience

CPU Central Processing Unit

DBMS Database Management System

XML Extensible Markup Language

PYGAME Python Game Development Library (used for 2D

animation)

WAV Waveform Audio File Format

MP3 MPEG Audio Layer III

HTML HyperText Markup Language

xi
 LIST OF TABLES

TABLE NO. TITLE PAGE NO.

5.1 Test cases and Execution 29

ix
 CHAPTER 1
INTRODUCTION

1.1 GENERAL

In the current digital landscape, education in computer science is fundamental to

nurturing future innovators. Central to this field are Data Structures and Algorithms
(DSA), which serve as the backbone for developing efficient problem-solving and
programming capabilities. Nevertheless, many learners find DSA difficult to grasp
because the concepts are often abstract and theoretical when taught through standard
textbooks or static learning materials. Algorithms like sorting, searching, recursion,
and graph traversal are complex and hard to visualize, leading to confusion and
reduced student motivation. Conventional teaching methods, though informative,
often lack interactive and dynamic elements that connect theory with practical
understanding. To tackle this challenge, this project proposes a 2D Animation-based
DSA Learning Platform enhanced with Audio Narration. This platform converts
abstract algorithms into vivid animated sequences paired with explanatory audio,
creating an engaging and intuitive learning experience that strengthens comprehension
and retention through visualization, narration, and interactive practice.

1.2 PROBLEM DESCRIPTION

⚫ Students studying Data Structures and Algorithms face considerable hurdles

because current teaching approaches mainly involve:
⚫ Theoretical textbook content without interactive visuals,
⚫ Static illustrations or code examples that do not demonstrate algorithms
dynamically,
⚫ Lectures that often do not adapt to different learning styles.
⚫ Such methods present several issues:

1
⚫ They fail to show live execution of algorithms, making it hard for students to see
how the concepts operate in practice.
⚫ They struggle to hold learners’ attention, especially for complex ideas like
recursion or tree traversal.
⚫ They do not offer personalized learning experiences, such as language options or
flexible playback.
⚫ They lack integrated assessments that provide timely feedback on understanding.
⚫ These drawbacks result in weak foundations, decreased motivation, and
difficulties in applying DSA knowledge. The proposed solution addresses these
gaps by combining animated visualizations with synchronized audio narration and
interactive quizzes, thereby making the learning process more engaging and
effective.

1.3 OBJECTIVES OF THE PROJECT

The project is developed with the following objectives:

1. Create an interactive platform that uses 2D animations to clarify difficult

DSA topics.
2. Pair the animations with audio narration to explain each step clearly.
3. Offer multilingual narration and playback controls like pause, replay, and
speed adjustment to accommodate diverse learning needs.
4. Build modular animations for key algorithms, showcasing their real-time
operation.
5. Integrate quizzes and assessments to test learners’ grasp of concepts.
6. Enable personalized, self-paced study that bridges theory with real-world
problem-solving skills.

2
1.4 SCOPE OF THE PROJECT

• The platform is designed with various applications in mind:

• Student Support: Helping learners understand challenging DSA topics
through engaging animations and narration.
• Educator Resource: Assisting teachers with a dynamic tool to visually
demonstrate algorithm behavior.
• Flexible Learning: Allowing users to control their learning pace with
replay and speed options, supporting different preferences.
• Skill Enhancement: Reinforcing critical thinking and problem-solving
through interactive practice.
• Future Growth: Providing a foundation that can expand to cover other
computer science subjects such as networking, databases, and operating
systems.

1.5 ORGANISATION OF THE PROJECT REPORT

The report is structured into six chapters, each addressing a key aspect of the project:

Chapter 1 – Introduction: This chapter sets the context by outlining the motivation
behind creating a 2D animation-based learning platform for Data Structures and
Algorithms (DSA). It covers the problem statement, project goals, and the scope of
the work.

Chapter 2 – Literature Review: This section examines current educational tools and
platforms, identifying their shortcomings in effectively conveying abstract concepts.
It establishes the need for a more interactive and engaging solution, positioning the
proposed platform as a novel approach.

3
Chapter 3 – System Design: Here, the focus is on the system’s feasibility,
architectural layout, and the design of key components such as animated visuals, audio
narration, and assessment features.

Chapter 4 – Methodology: This chapter details the step-by-step development

process, including the software environments, animation tools, and implementation
techniques employed in building the platform.

Chapter 5 – Testing and Evaluation: This section describes the testing strategy,
outlines specific test scenarios, and discusses the results. Visual evidence such as
screenshots is provided to showcase the system’s functionality and performance.

Chapter 6 – Conclusion: The final chapter summarizes the project achievements,

highlights its impact on computer science learning, and explores opportunities for
future enhancements.

1.6 SUMMARY

This chapter presented the background and motivation for developing a 2D

Animation-based DSA Learning Platform with Audio Narration, along with the
problem description and limitations of existing learning methods. It also outlined the
objectives, scope, and structure of the report. The proposed solution aims to simplify
complex Data Structures and Algorithms through engaging 2D visualizations
combined with explanatory audio, enabling learners to grasp abstract concepts more
effectively. By integrating interactive quizzes, multilingual narration, and self-paced
learning features, the system not only enhances comprehension and retention but also
bridges the gap between theoretical knowledge and practical application in computer
science education.

4
 CHAPTER 2
LITERATURE SURVEY

2.1 INTRODUCTION

A literature survey helps analyze existing research, tools, and methodologies related
to a project. For an educational platform focused on DSA, relevant areas include
algorithm visualization software, interactive learning systems, multimedia
instructional design, and animation-enhanced pedagogy. Although several tools
attempt to simplify DSA, most rely on static images or text and lack real-time
interaction. Furthermore, many do not include audio explanations or adaptive learning
features, which limits their effectiveness across diverse learner groups. This chapter
reviews the strengths and weaknesses of current tools and explains how the proposed
system fills these gaps by integrating 2D animation with synchronized narration and
interactivity.

2.2 EXISTING SYSTEM

Various resources are currently available for learning Data Structures and
Algorithms:

Textbook Learning: Traditional textbooks and lecture notes explain algorithms with
static images and pseudocode. Though informative, they don’t show the dynamic
behavior of algorithms.

Coding Practice Sites (Hacker Rank, Leet Code, Geeks for Geeks): Provide
problem-solving exercises but rarely offer visual step-by-step explanations.

5
Visualization Websites (e.g., Visual go): Display graphical demonstrations of
algorithm steps but often lack audio narration and multilingual options.

Video Courses (Coursera, Udemy, YouTube): Offer video lectures on DSA topics,
but these tend to be passive and lack interactive elements or replay control.

While these tools support DSA learning to some extent, none provide a fully
integrated, interactive, and personalized experience that effectively simplifies
algorithmic concepts.

2.3 LIMITATIONS OF EXISTING SYSTEM

Existing tools for learning Data Structures and Algorithms (DSA) present several
significant limitations that hinder effective comprehension:

• Static and Non-Interactive Visuals: Many platforms rely on fixed diagrams

or animations that do not allow learners to manipulate or observe algorithmic
processes dynamically, reducing the ability to grasp step-by-step changes.
• Low User Engagement: The absence of interactive elements like real-time
feedback, control over animations, or hands-on activities leads to passive
learning, which can negatively impact motivation and retention.
• Lack of Audio Support: Most tools fail to include synchronized audio
explanations, limiting accessibility for learners who benefit from auditory input
and making the learning experience less immersive.
• Limited Customization Options: Features such as adjustable playback speed,
language selection, or the ability to pause and replay specific parts are rarely
available, restricting personalization and accommodation of diverse learner
needs.

6
 • Insufficient Assessment Integration: Many platforms do not incorporate
formative assessments like quizzes or instant feedback mechanisms, making it
difficult to track progress or reinforce learning effectively.
• Narrow Content Coverage and Scalability Issues: Often, these tools focus on
a limited set of algorithms or data structures and lack the flexibility to expand
content or adapt to different curriculum requirements.

Collectively, these challenges highlight the need for a more versatile and engaging
learning solution that integrates dynamic animations, clear audio narration,
interactivity, and tailored learning pathways to better support diverse educational
needs.

2.4 RELATED WORKS AND OBSERVATIONS

Several studies and educational platforms have laid the groundwork for improving
algorithm learning through visualization and interactivity:

• Naps et al. (2002): Their research demonstrated that interactive algorithm

visualizations significantly enhance students’ conceptual understanding and
motivation by allowing hands-on exploration.
• Shaffer et al. (2010): Found that combining graphical representations with
active learning tasks such as quizzes or problem-solving exercises greatly
improves knowledge retention and learner engagement.
• Visualgo (2015): A widely-used web-based tool offering comprehensive
algorithm visualizations. However, it lacks synchronized audio explanations
and adaptive learning features, limiting accessibility for some learners.
• Kraemer et al. (2004): Provided evidence that presenting information through
both visual and auditory channels reduces cognitive load, leading to more
effective learning experiences.
• Massive Online Learning Platforms: Websites like Coursera and Udemy offer
structured video-based courses on algorithms, but these often lack real-time

7
 interactivity, such as live animations or immediate feedback mechanisms,
which are crucial for active learning.

Despite these valuable contributions, no existing system fully integrates detailed 2D

animations with synchronized audio narration and interactive assessments to create a
cohesive, learner-focused environment. This gap underscores the potential impact of
a platform combining these elements for enhanced DSA education.

Observations:

Existing research and educational tools consistently emphasize the significant role that
visualization and multimedia elements play in enhancing the learning of Data
Structures and Algorithms (DSA). Visual aids help demystify complex concepts by
making abstract processes more concrete, while multimedia components can cater to
diverse learning preferences. However, many current platforms fall short in several
key areas.

Primarily, most tools rely heavily on static images or simple, non-interactive

animations that do not fully capture the dynamic nature of algorithms. Others offer
video tutorials that are largely passive, providing one-way communication without
opportunities for learners to engage or interact with the material in real time. This
limits active learning and reduces opportunities for students to experiment, explore,
and reinforce their understanding.

Furthermore, very few systems integrate comprehensive 2D animated visualizations

with synchronized audio narration. The combination of visual and auditory channels
is essential for reducing cognitive load and improving knowledge retention, especially
when dealing with abstract algorithmic concepts. Additionally, support for multiple
languages is often overlooked, which restricts accessibility for non-native English
speakers or learners from diverse linguistic backgrounds.

Another critical limitation is the lack of embedded interactive assessments. Quizzes,

immediate feedback, and adaptive learning paths are vital for reinforcing learning
8
outcomes and maintaining learner motivation, but these features are rarely
incorporated in current solutions.

Recognizing these shortcomings reveals a clear opportunity to create a more holistic

and learner-focused platform. The proposed system aims to fill this gap by blending
animated storytelling with clear, synchronized audio explanations, offering
multilingual options, and embedding interactive quizzes throughout the learning
journey. This integrated approach not only promotes deeper engagement and better
comprehension but also allows the platform to be scalable and adaptable across
different educational contexts.

By addressing the limitations of existing tools, the proposed platform aspires to deliver
a more effective, inclusive, and motivating educational experience for students
studying Data Structures and Algorithms.

2.5 PROPOSED SYSTEM OVERVIEW

To overcome the identified gaps, the proposed platform introduces several innovative
features designed to elevate the learning experience:

• Step-by-Step 2D Animations: Each algorithm and data structure is broken

down into clear, animated sequences that visually demonstrate operations such
as insertion, deletion, traversal, and sorting, helping learners visualize abstract
concepts.
• Synchronized Audio Narration: Clear, concise explanations accompany the
animations, allowing auditory learners to better understand the logic behind
each step and reinforcing key ideas.
• Multilingual and Playback Controls: Learners can customize their experience
with options like changing the narration language, adjusting playback speed,
pausing, or replaying sections, making the platform more inclusive and
adaptable to individual preferences.

9
 • Interactive Quizzes: After each lesson segment, quizzes assess understanding,
providing immediate feedback and reinforcing learning outcomes. This fosters
active participation and helps track progress.
• Modular Architecture: The platform is designed for scalability, enabling easy
addition of new topics and content updates, ensuring it remains relevant across
various computer science curricula.
• Cross-Device Compatibility: Designed to function seamlessly on desktops,
tablets, and mobile devices, allowing learners to access the content anytime,
anywhere.

By combining visual, auditory, and interactive components, the platform aims to

create a rich, engaging, and learner-centered environment that not only enhances
comprehension but also builds practical skills essential for mastering Data Structures
and Algorithms.

2.6 SUMMARY

The review of existing literature reveals that many current tools designed for learning
Data Structures and Algorithms (DSA) often rely on static presentations or passive
teaching methods, which can limit learner engagement and understanding.
Furthermore, these tools frequently lack features that allow for interactivity or
personalized learning paths, which are essential for addressing the diverse needs of
students.

Studies indicate that combining visual elements with auditory explanations enhances
cognitive processing and retention, especially when paired with active learning
techniques such as quizzes and hands-on exercises. The proposed learning platform
stands out by seamlessly integrating animated visuals with synchronized narration,
interactive controls, and assessments. This combination not only makes complex DSA
concepts easier to grasp but also fosters a more immersive and participatory
educational experience.
10
 CHAPTER 3
PROPOSED SYSTEM DESIGN

3.1 INTRODUCTION
System design plays a vital role in transforming project requirements into a practical
solution, especially for educational software. The objective of this project is to
develop an interactive learning platform that uses 2D animations paired with audio
narration to simplify learning of Data Structures and Algorithms (DSA). This design
focuses on three fundamental features:

Visual Interaction: Demonstrating complicated DSA concepts through stepwise 2D

animations that make algorithmic processes easier to follow.

Audio Guidance: Complementing animations with synchronized voice narration to

enhance understanding and cater to auditory learners.

Active Learning and Evaluation: Including quizzes and exercises to engage users
and assess their progress continuously.

This chapter provides an overview of the proposed system, including its feasibility,
architecture, and main components.ules.

3.2 PROPOSED SYSTEM

The platform is intended as a web-based application that integrates multimedia
capabilities with backend support to deliver a compelling learning experience.

Input Handling: Users select specific DSA topics like arrays, stacks, queues, trees,
or sorting techniques.

Content Library: Each topic is associated with pre-created 2D animation sequences

and synchronized narration scripts.

Preprocessing Steps: Algorithm logic is converted into visual animations showing

stepwise changes in data structures. Corresponding narration is recorded and aligned
11
to animation timing. The learning material is structured into concise segments such as
introductions, stepwise procedures, and example applications.

Learning Delivery: The system presents animations with synchronized narration.

Learners can control playback (pause, rewind, adjust speed) to tailor the pace of
learning.

3.3 FEASIBILITY STUDY

The system’s feasibility was assessed from multiple perspectives:

Technical Feasibility:

Utilizes modern web technologies like HTML, CSS, JavaScript, and frameworks such
as React or Angular. Animations are created using open-source or freely available
animation tools and paired with audio narration.

Operational Feasibility:

The platform features a simple interface with playback controls, making it accessible
for students of varying skill levels. The narrated animations offer a more immersive
experience similar to classroom teaching.

Financial Feasibility:

By leveraging open-source software and hosting on economical cloud servers, the

platform remains affordable for educational institutions and individuals.

Ethical and Legal Considerations:

Ensures all educational materials are original or properly licensed, adhering to

intellectual property laws and academic guidelines.

12
3.4 SYSTEM ARCHITECTURE

The architecture of the 2D Animation-Based DSA Learning Platform (fig 3.1)is

organized into three main layers:

Fig 3.1 System Architecture of 2D Animation Based DSA Learning Platform

3.4.1 Content Acquisition Layer

• Learning Modules: Essential topics in Data Structures and Algorithms,

including arrays, stacks, queues, linked lists, trees, graphs, and algorithms, are
developed as detailed storyboards outlining the teaching flow.
• 2D Animation Engine: These storyboards are transformed into dynamic 2D
animations using specialized animation software to visually represent the
concepts.
• Audio Narration: A carefully scripted voiceover is recorded to explain the
logic behind each step, ensuring that the narration aligns perfectly with the
animated sequences.
• Synchronization: The system integrates the animations with the audio
narration to provide smooth, coordinated playback for an immersive learning
experience.

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3.4.2 Processing and Delivery Layer

Content Management System (CMS): Functions as the core repository for all
learning materials, including animations, voice recordings, quizzes, and exercises. The
CMS streamlines content organization by categorizing resources based on topics and
difficulty levels, ensuring quick retrieval and efficient content updates. It also supports
version control to manage revisions and maintain consistency across the platform.

Playback Engine: Ensures smooth and coordinated playback of animations paired

with audio narration, providing a cohesive learning experience. This engine supports
interactive playback features such as pausing, rewinding, fast-forwarding, and
adjusting playback speed. These controls empower learners to study at their own pace
and revisit challenging sections as needed, thereby improving comprehension.

Quiz and Assessment Engine: Embeds interactive evaluation tools throughout the
learning modules to measure understanding in real time. It offers a variety of question
formats, such as multiple-choice, fill-in-the-blanks, and drag-and-drop activities.
Immediate feedback and hints help learners correct mistakes and deepen their grasp of
concepts. Additionally, the engine tracks performance over time, helping both learners
and instructors monitor progress and identify areas requiring further focus.

User Interaction Layer: Facilitates active learner engagement by providing intuitive

navigation options, bookmarking features, and customizable learning pathways. This
layer supports personalization by allowing users to set learning goals, track their
achievements, and tailor content sequences based on their preferences or proficiency
levels, thereby enhancing motivation and retention.

Analytics and Reporting Module: Gathers detailed data on user interactions, quiz
results, time spent on each module, and overall engagement metrics. This data is
presented through comprehensive dashboards and reports, enabling educators and
administrators to evaluate the effectiveness of the learning materials. Insights gained

14
can guide content improvements, identify struggling learners, and optimize teaching
strategies.

Multilingual Support: Enables the platform to reach a wider audience by offering

content in several languages. Users can select their preferred language for narration,
subtitles, and interface elements. This inclusivity fosters better understanding and
accessibility, catering to learners from diverse linguistic backgrounds.

Additional Features:

• Adaptive Learning Mechanisms: Incorporates algorithms that adjust content

difficulty and presentation style based on learner performance, ensuring an
individualized learning curve.
• Offline Accessibility: Allows users to download modules for offline study,
supporting learners with limited or intermittent internet connectivity.
• Collaborative Tools: Includes forums, discussion boards, or chat features that
encourage peer interaction and collaborative problem-solving, fostering a
community learning environment.
• Accessibility Compliance: Designed to meet standards for users with
disabilities, providing features like screen reader compatibility, adjustable font
sizes, and color contrast options.

Fig 3.2 Flow of Content Management System

15
3.4.3 User Interaction and Reporting Layer

• Student Dashboard: Offers learners a centralized interface to easily access

their courses and learning modules. It tracks their progress through lessons,
displays completed and pending topics, and allows users to bookmark important
sections for quick reference or later review.

• Teacher and Parent Dashboard: Provides educators and guardians with

comprehensive insights into learner activity. This includes monitoring
participation levels, quiz performance, time spent on each module, and
identifying areas where additional support may be needed, facilitating more
effective guidance and intervention.

• Narration Control Features: Enables students to manage audio playback

according to their individual preferences. Controls include options to start,
pause, replay, and adjust the speed of narration, allowing users to follow
explanations at a comfortable pace and revisit difficult concepts as required.

• Cloud-Based Storage and Activity Logging: Securely saves user data such as
progress markers, quiz outcomes, and feedback responses in the cloud. This
ensures that information is preserved across sessions, accessible from multiple
devices, and protected against data loss, supporting a seamless and reliable
learning experience.

3.5 SUMMARY

The designed platform provides a versatile and modular framework for teaching Data
Structures and Algorithms, leveraging the power of animated graphics paired with
synchronized audio narration. This dual-modality presentation transforms abstract
concepts into tangible, easily understandable lessons, catering to different learning
preferences. By integrating interactive elements such as quizzes, exercises, and real-
time feedback, the platform actively involves learners in the educational process,

16
promoting deeper comprehension and retention compared to conventional lecture-
based or textbook-driven approaches.

Additionally, the system incorporates features for monitoring learner progress,

enabling personalized learning paths and allowing educators to identify areas where
students may require additional support. The platform's architecture prioritizes
reliability and user-friendliness, ensuring a smooth experience across various devices
without demanding extensive technical knowledge. Furthermore, its cost-efficient
design makes it accessible for diverse educational settings, from individual learners
and small study groups to larger classrooms and institutions.

Overall, this platform aims to enhance the quality and accessibility of computer
science education by combining innovative teaching methodologies with practical
usability, thereby empowering learners to master complex algorithmic principles more
effectively.

17
 CHAPTER 4

METHODOLOGY

4.1 INTRODUCTION

This chapter describes the organized process followed to develop the 2D Animation-
Based Learning Platform for Data Structures and Algorithms (DSA). It provides a
detailed overview of the development lifecycle, covering planning, design, and
implementation stages. The focus is on building an intuitive and engaging educational
tool that transforms complex DSA topics into clear, animated visuals synchronized
with audio narration to facilitate better understanding.

Additionally, this chapter outlines the required software environments and hardware
specifications necessary to support the platform. It explains the design of individual
system components, the algorithms powering animations and interactivity, as well as
the datasets used to illustrate concepts. By integrating multimedia with an interactive
interface, the platform aims to create a rich learning environment that encourages
active participation and improves knowledge retention.

The methodology serves as a guide for blending technical design principles with
educational best practices, ensuring the platform is both efficient and adaptable to the
diverse needs of computer science learners.

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4.2 SOFTWARE SPECIFICATIONS

Fig 4.1.Browser Extension Framework

•

The platform development leverages the following software tools and technologies:

Operating Systems: Designed for compatibility with Windows 10/11, Ubuntu, and
macOS to support cross-platform deployment.

Programming Languages:

Backend: Python 3.x, chosen for its extensive libraries and API capabilities.

Frontend: JavaScript using frameworks like React or Angular to create a

responsive and dynamic user interface.

Frameworks: Lightweight web frameworks such as FastAPI facilitate backend API

development, while standard web technologies (HTML, CSS, JavaScript) handle
frontend rendering.

19
Animation and Audio Tools: Software like Blender, Synfig, or Adobe Animate is
used for crafting 2D animations, alongside audio recording programs for clear
narration.

Libraries: Open-source libraries assist with animation rendering, audio

synchronization, and interactive quizzes.

This combination provides real-time interactivity, modular architecture, and

scalability, while keeping development costs low.

Fig 4.2 PyTorch (AI Model Training and Inference)

Fig 4.3 Google Gemini API

4.3 OVERALL WORKFLOW

The Toxic Filter for Kids system operates through a well-organized workflow
designed to deliver real-time, precise, and transparent child safety monitoring. The
detailed stepwise process includes:

20
1. Input Collection: The system gathers diverse types of data including textual
content from sources such as YouTube transcripts, user comments, and live
chats, alongside visual inputs like video frames and image snapshots or
thumbnails.
2. Data Preprocessing: Collected data undergoes cleaning to remove noise and
irrelevant elements. Multilingual texts are translated to a common language for
uniform analysis. Videos are processed to extract individual frames for
subsequent visual evaluation.
3. Model Initialization: Advanced natural language processing models based on
RoBERTa are loaded to identify toxic language patterns. Additionally, the
system integrates the Gemini API to provide reasoning capabilities and make
the detection results more interpretable.
4. Detection of Toxicity and Cyberbullying: The textual data is segmented and
analyzed by the RoBERTa model, which assigns probability scores indicating
the presence and severity of toxicity or cyberbullying.
5. Deep Reasoning via Gemini: Text samples flagged as high-risk or ambiguous
by RoBERTa are further processed by the Gemini model. This step enables
nuanced understanding of complex language features like sarcasm, implicit
negativity, and psychological undertones, enhancing the system’s
interpretability.
6. Visual Emotion Analysis: Extracted video frames are examined for emotional
signals, such as facial expressions and mood indicators, to detect potentially
harmful or disturbing visuals that could affect child viewers.
7. Risk Evaluation and Categorization: Results from the text analysis
(RoBERTa), interpretive reasoning (Gemini), and visual emotion recognition
are synthesized into a composite risk score. Based on weighted factors, content
is classified into LOW, MEDIUM, or HIGH risk categories.
8. Responsive System Actions: Depending on the risk classification, the system
initiates appropriate interventions. These may include recording safe content
interactions, issuing warnings for questionable content, blocking access to

21
 harmful material, notifying parents or guardians, and securely storing incident
data for future audits.

This comprehensive process not only identifies unsafe content but also provides
explainable insights behind each decision, fostering greater confidence among
parents, educators, and platform administrators. By combining multimodal analysis
and interpretability, the system ensures timely and effective protection for children in
digital environments.

4.4 MODULE IDENTIFICATION

The proposed Toxic Filter for Kids system is organized into multiple functional
modules, each responsible for a specific task in the workflow. This modular design
ensures scalability, maintainability, and efficient execution. The identified modules
are as follows:

1. Input Module
Collects raw data from diverse sources including text (transcripts,
comments, live chats) and images (video frames, thumbnails). Uses
OpenCV to extract frames from YouTube video streams for further
analysis.
2. Preprocessing Module o Cleans raw text by removing unwanted
symbols, URLs, and noise.
o Translates multilingual text into English using NLP translation
pipelines.
o Splits long transcripts into smaller chunks suitable for model input.
3. Toxicity & Cyberbullying Detection Module Employs RoBERTa-
based NLP models to analyze text chunks.
o Generates toxicity and bullying probability scores. Implements early
stopping logic to save computational resources if high toxicity is
detected.
22
 Fig 4.7 Cyberbullying Detection Module at Fog Layer

4. Gemini Reasoning Module: For borderline or high-risk cases, forwards

text and image inputs to the Gemini API. Provides explainable insights,
including sarcasm detection, implicit harm, and psychological impact.

Fig 4.8 Google Cloud Reasoning Engine

5. Visual Emotion Detection Module Extracts frames from video streams
for visual analysis. Uses emotion recognition techniques to identify
harmful or disturbing scenes (fear, aggression, sadness, etc.).
6. Risk Scoring Module Integrates probability outputs from RoBERTa and
reasoning results from Gemini.
o Assigns a final risk classification into LOW, MEDIUM, or HIGH
categories.
23
 7. Action & Reporting Module : Displays real-time safety analysis results
in the browser extension popup.
o Triggers content blocking, warnings, or parental alerts based on risk
levels.

Fig 4.9 Reporting Manager – LearnTrak LMS

4.5 ALGORITHM

Step 1: Input an unsorted array.

Step 2: Execution tracer captures line-by-line operations.

Step 3: Visualization module highlights compared elements.

Step 4: If swap occurs, animation shows elements switching positions.

Step 5: Narration explains the comparison and swap.

Step 6: Process repeats until array is sorted.

Step 7: Final sorted array displayed with narration confirming completion.

24
Pseudocode: Bubble Sort Execution Tracer
STARTInput array A of size nFOR i from 0 to n-1:
FOR j from 0 to n-i-2:
Highlight elements A[j] and A[j+1]
Narrate "Comparing A[j] and A[j+1]"
IF A[j] > A[j+1]:
Swap A[j], A[j+1]
Animate swap
Narrate "Swapped A[j] and A[j+1]"
ENDIF
ENDFOR
ENDFOR
Narrate "Array is now sorted"
Display final arrayEND

4.6 TOOLS USED

Backend: Python 3.12, FastAPI

Frontend: React.js, TailwindCSS

Visualization: D3.js, Three.js

AI Tutor: OpenAI/GPT API, TTS for narration

Version Control: GitHub for collaboration

Deployment: Docker + Cloud hosting (AWS/Heroku)

25
4.7 DATASET AND IMPLEMENTATION DETAILS

Unlike traditional ML projects, this platform does not rely on large datasets. Instead,
it uses:

Predefined Code Examples: Standard DSA algorithms (sorting, searching,

recursion, graphs).

User-Generated Input: Students can enter custom code snippets.

AI Explanation Training Data: LLMs pretrained on programming

knowledge to generate step-by-step reasoning.

Implementation followed Agile methodology, with iterative sprints covering tracer

development, visualization, and AI integration.

4.8 SNAPSHOTS

Fig 4.10 Dataset Creation Process

26
4.8 SNAPSHOTS

Fig 4.11. Output1

Fig 4.12 Output2

4.9 SUMMARY

This chapter presented the methodology for developing the DSA visualization
platform. The approach combined execution tracing, visualization, AI tutoring, and

27
audio narration into a single interactive tool. By leveraging Python for backend
tracing, React + D3.js for frontend visualization, and AI APIs for narration, the system
bridges the gap between abstract algorithm theory and practical comprehension.

The methodology ensures the platform is modular, scalable, and user-centric, making
it a valuable resource for students learning programming and DSA.

28
 CHAPTER 5
TESTING AND RESULTS

5.1 TESTING STRATEGIES

For validating the 2D Animation-Based DSA Learning Platform with Audio

Narration, the following strategies were adopted:

⚫ Unit Testing: Verified individual modules (animation playback, audio

synchronization, quiz engine).
⚫ Integration Testing: Checked interaction between frontend, backend, and
database.
⚫ System Testing: End-to-end testing of the entire platform across devices (PC,
mobile, tablet).
⚫ Performance Testing: Measured loading time of animations, audio clarity, and
quiz response speed.
⚫ User Acceptance Testing (UAT): Conducted trials with students to ensure
usability, engagement, and comprehension.

5.2 TEST CASES AND EXECUTION

Test
Expected Actual
Case Module Input Result
Output Output
ID
Play Bubble Animation
Animation Worked
TC01 Sort runs Pass
Playback correctly
animation smoothly
Start audio Narration
TC02 Audio Sync with matches Matched Pass
animation steps
Attempt quiz Correct score Accurate
TC03 Quiz Module Pass
after topic calculation results
Switch Audio
TC04 Multilingual narration to changes Successful Pass
Tamil language
29
 Test
Expected Actual
Case Module Input Result
Output Output
ID
Load Binary
Load within Loaded in Pass
TC05 Performance Tree
3 seconds 2.5 seconds
animation

Table 5.1. Test Cases and Execution

5.1. System Testing

5.3 RESULTS

⚫ Platform worked smoothly across Chrome, Firefox, and Edge.

⚫ Animations and narration synchronized without lag.

⚫ Quizzes generated instant feedback.

⚫ Multilingual narration (English & Tamil demo) worked as expected.

⚫ Students reported higher engagement compared to text-based learning.

5.4 SUMMARY

Testing validated the platform’s usability, functionality, and effectiveness for teaching
Data Structures and Algorithms. The results confirm that the integration of animations,
audio narration, and quizzes enhances comprehension and student engagement.

30
 CHAPTER 6
CONCLUSION

6.1 INTRODUCTION

This chapter concludes the project by reflecting on objectives, achievements, and

contributions of the 2D Animation-Based DSA Learning Platform with Audio
Narration.

6.2 CONCLUSION

The project successfully designed and implemented a platform that:

⚫ Visualizes Data Structures and Algorithms through engaging 2D animations.

⚫ Provides synchronized audio narration to enhance comprehension.
⚫ Implements interactive quizzes for active learning and assessment.
⚫ Supports multilingual narration for inclusivity and accessibility.
⚫ Performs smoothly across devices and browsers.

Impact: The platform bridges the gap between abstract theory and practical
understanding, improving motivation and problem-solving skills among students.

6.3 SUMMARY

The project demonstrates that combining visualization, narration, and interactivity

significantly improves student learning outcomes in Data Structures and Algorithms.
Future scope includes:

⚫ Expanding to other subjects (Operating Systems, DBMS, Computer Networks).

⚫ Adding 3D animations for advanced algorithm visualization.
⚫ Integrating AI-powered personalized explanations.
⚫ Including gamification features to boost learner engagement.
31
In conclusion, the project successfully achieved its primary objective of making
Data Structures and Algorithms (DSA) more intuitive, accessible, and engaging
for learners through the innovative use of 2D animation and audio narration. By
combining visual storytelling with auditory guidance, the platform addresses
different learning styles and ensures that complex concepts are simplified for
better comprehension. The interactive animations reduce the abstractness of
traditional DSA teaching, while the narration provides step-by-step clarity, making
the learning process smoother and less intimidating for beginners.

Furthermore, the project demonstrates how technology-driven educational tools

can bridge the gap between theoretical knowledge and practical understanding.
The platform has the potential to enhance self-paced learning, improve retention
of core concepts, and encourage students to explore problem-solving with greater
confidence. It also highlights the effectiveness of multimedia-based learning
environments in promoting active participation and long-term knowledge
retention.

Overall, the project lays a foundation for future enhancements, such as

incorporating quizzes, adaptive learning pathways, or gamification elements,
which could further increase learner engagement. By providing an innovative
approach to teaching DSA, this platform contributes meaningfully to the field of
computer science education and sets the stage for scalable and inclusive digital
learning solutions.

32
SAMPLE SOURCE CODE

from flask import Flask, render_template, request, jsonify

import sys, traceback

app = Flask(__name__)

class OutputCatcher:

"""Simple sys.stdout replacement that buffers writes."""

def __init__(self):

self.buf = []

def write(self, data):

self.buf.append(str(data))

def flush(self):

pass

def get_and_clear(self):

s = ''.join(self.buf)

self.buf = []

return s

def trace_code(code_str, max_steps=500):

"""

Trace code line-by-line, including calls/returns.

Each step: {

'event': 'call' | 'line' | 'return',

33
 'func': function name,

'line_no': int,

'line': str,

'locals': {var: value},

'output': str,

'stack': [ {func, line_no}... ] # call stack snapshot

"""

code_lines = code_str.splitlines()

steps = []

output = OutputCatcher()

old_stdout = sys.stdout

step_counter = {'n': 0}

stack = []

def safe_repr(v):

try:

r = repr(v)

except Exception:

r = '<unrepresentable>'

if len(r) > 200:

r = r[:200] + '...'

return r

34
def tracer(frame, event, arg):

if event in ('call', 'line', 'return'):

step_counter['n'] += 1

if step_counter['n'] > max_steps:

raise RuntimeError(f"Maximum step count exceeded ({max_steps})")

lineno = frame.f_lineno

line_text = code_lines[lineno - 1] if 1 <= lineno <= len(code_lines) else ''

func_name = frame.f_code.co_name

if event == 'call':

stack.append({'func': func_name, 'line_no': lineno})

elif event == 'return':

if stack:

stack.pop()

local_snapshot = {k: safe_repr(v) for k, v in frame.f_locals.items()}

new_out = output.get_and_clear()

steps.append({

'event': event,

'func': func_name,

'line_no': lineno,

'line': line_text,

35
 'locals': local_snapshot,

'output': new_out,

'stack': list(stack) # copy

})

return tracer

error = None

try:

sys.stdout = output

sys.settrace(tracer)

globals_dict = {'__name__': '__main__'}

exec(compile(code_str, '<usercode>', 'exec'), globals_dict, globals_dict)

rem = output.get_and_clear()

if rem:

steps.append({'event': 'end', 'func': '', 'line_no': None, 'line': '<end>',

'locals': {}, 'output': rem, 'stack': list(stack)})

except Exception:

error = traceback.format_exc()

finally:

sys.settrace(None)

sys.stdout = old_stdout

return {'steps': steps, 'error': error}

@app.route('/')
36
def index():

return render_template('index.html')

@app.route('/trace', methods=['POST'])

def trace():

code = request.form.get('code', '')

try:

max_steps = int(request.form.get('max_steps', 500))

except Exception:

max_steps = 500

result = trace_code(code, max_steps=max_steps)

return jsonify(result)

if __name__ == '__main__':

app.run(debug=True)

37
REFERENCES

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Soljačić, “Wireless Power Transfer via Strongly Coupled Magnetic
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2. B. L. Cannon, J. F. Hoburg, D. D. Stancil, and S. C. Goldstein, “Magnetic

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3. M. Pinuela, P. D. Mitcheson, and S. Lucyszyn, “Ambient RF Energy

Harvesting in Urban and Semi-Urban Environments,” IEEE Transactions on
Microwave Theory and Techniques, vol. 61, no. 7, pp. 2715–2726, 2013.

4. N. Shinohara, Wireless Power Transfer via Radiowaves, Wiley, 2014.

5. P. Kamalinejad, C. Mahapatra, Z. Sheng, S. Mirabbasi, V. Leung, and Y. L.

Guan, “Wireless Energy Harvesting for the Internet of Things,” IEEE
Communications Magazine, vol. 53, no. 6, pp. 102–108, 2015.

6. S. Li and C. C. Mi, “Wireless Power Transfer for Electric Vehicle

Applications,” IEEE Journal of Emerging and Selected Topics in Power
Electronics, vol. 3, no. 1, pp. 4–17, 2015.

7. S. Y. Hui and W. X. Zhong, “A Critical Review of Recent Progress in Mid-

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38