This document summarizes a design project that studied the application of augmented reality (AR) in e-learning courses for chemistry. The project developed an AR android application using images from chemistry textbooks as markers to augment 3D content about solid state chemistry. An experiment compared the AR app to web-based learning and found that the AR app helped students' content learning and 3D spatial visualization more than web-based systems, and users had more positive behavioral intentions toward the AR system. The project validated AR's potential to improve learning of chemistry concepts requiring spatial skills over current e-learning methods.

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Department of Design, IIT Guwahati
Indian Institute of Technology Guwahati
Studies in application of augmented reality in E Learning Courses
Himanshu Bansal (516) & Mannu Amrit (523)
Final Year Design Project (2013 – 2014)
Project Guide: Prof. (Dr). Pradeep Yammiyavar
Head, Center for Educational Technology,
IIT Guwahati
Department of Design, IIT Guwahati
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Department of Design, IIT Guwahati 3
Acronyms Used
AR- Augmented Reality
NCERT- National Council of Educational
Research and Training
GUI - Graphical User Interface
3D - 3 Dimensional
CCP- Cubic Closed Packing
HCP- Hexagonal Closed Packing
FCC- Face Centered Cubic
OV – Octahedral Void
TV – Tetrahedral Void
VARK – Visual Auditory Reading &
Kinesthetic
PSVT:R – Purdue Spatial Visualization
Test: Rotation
PEOU – Perceived ease of use
PU – Perceived Usefulness
AT – Attitude
BI – Behavioral Intention
SA – Self Efficacy
PE – Perceived Enjoyment

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Department of Design, IIT Guwahati 4
Figures & Images Used
Figure 1: Chemistry + Augmented Reality
+ E Learning
Figure 2: Homepage, www.coursera.org
Figure 3: The Johnstone triangle
Figure 4: Connecting Design Project 3
and Design Project 4
Figure 5: The Johnstone triangle
Figure 6: 3D structure, tetragonal voids,
Page 17, Standard XII NCERT
Figure 7: Taxonomy of mixed reality
including real to virtual environments
Figure 8: An AR system and the physical
model [6]
Figure 9: NCERT Chemistry Textbook,
Standard XII
Figure 10: Dependent Variables
Figure 11: Independent Variables
Figure 12: Interview at Oriental Tutorials,
Guwahati
Figure 13: Interview at Kendriya
Vidyalaya, IIT Guwahati
Figure 14, 15: D Fusion Studio
Figure 16: Vuforia by Qualcomm
Figure 17: Unity software
Figure 18: Sketchup software
Figure 19: Virtual Buttons (in blue) and
GUI buttons (in black)
Figure 20: Task Flow Diagram, Module 1
Figure 21: App Screenshots, Module 1
Figure 22: App Screenshots, Module 1
Figure 23: AppTest Screenshot, Module 1
Figure 24: Task Flow Diagram, Module 2
Figure 25: App Screenshots, Module 2
Figure 26: 3D Models, Module 1
Figure 27: 3D Models, Module 1 and 2
Figure 28: 3D Models, Module 2
Figure 29: Students using the prototype

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Department of Design, IIT Guwahati 5
Figure 30: Classification of A.R
publications by evaluation method /
approach
Figure 31: Technology Acceptance Model
Figure 32: Sample PSVT Question
Figure 33: Octahedral void as seen in new
prototype
Figure 34: Removal of virtual buttons and
changes in GUI
Figure 35: Addition of interactivity by
touch
Figure 36: Zoomed in view of prototype
Figure 37: PSVT A.R Prototype
Figure 38: Web Interface (Voids)
Figure 39: Web Interface (PSVT)
Figure 40: Participants filling Pre
Questionnaire
Figure 41: Experiment with A.R (up) &
Web based system (below)
Figure 42: Experiment with A.R (up) &
Web based system (below)
Figure 43: Pre Questionnaire mean &
standard deviation
Figure 44: Pre Questionnaire VARK mean
& standard deviation
Figure 45: TAM Mean & Standard
Deviation
Figure 46: Spearman Rho Corelation
value table for Augmented Reality users
Figure 47: Spearman Rho Corelation
value table for Web users

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Department of Design, IIT Guwahati 6
Contents
Acknowledgment 1
IPR Declaration 2
Acronyms 3
Figures & Images used 4
Chapter 1 – Introduction
1.1 Abstract 8
1.2 Motivation 10
1.3 Objectives 11
Chapter 2 - Literature Review
2.1 Why Chemistry? 12
2.2 Augmented Reality 13
2.3 Existing Work 15
Chapter 3 - Project Timeline 18
Chapter 4 - Design Methodology
4.1 Case Study Topic 19
4.2 Research Design 20
4.3 Design Guidelines 21
4.4 User Requirement 22
Analysis
4.4.1 Interview 23
Questionnaire
4.4.2 Summary of 24
Responses
4.4.3 Insights from 24
Interviews
Chapter 5 - Development
5.1 D Fusion 27
5.2 Vuforia & Unity basics 28
5.3 Virtual Button & GUI 29
5.4 Application 30
5.5 App Flow 30
5.5.1 Module 1 30
5.5.2 Module 2 33
5.6 Audio Components 35
Chapter 6 – Initial Feedback 38
Chapter 7 – Literature Review (Phase II)
33
7.1 Evaluation techniques 39
7.2 Spatial Ability 40
7.3 Technology Acceptance 41
Model
7.4 PSVT 42
7.5 VARK 43
Chapter 8 Improvisations in A.R
prototype
8.1 Introduction 44
8.2 Changes 45

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8.3 Final GUI Walkthrough 47
8.4 Additional Application 48
Chapter 9 – Web Interface
9.1 Introduction 49
9.2 Design 50
9.3 Development 51
Chapter 10 – Research Methodology
10.1 Aims & Rationale 52
10.2 Experiment Design 53
10.3 Research Questions 54
10.4 Participants 55
10.5 Setup & materials 56
10.6 Procedure 57
Chapter 11 – Results
11.1 Quantitative 59
11.2 Qualitative 65
Chapter 12 – Discussion 73
Chapter 13 – Proposed design 86
guidelines
Chapter 14 – Conclusion 88
Chapter 15 - References 90
Appendix
Summary of Responses
Image Tracker
The VARK Questionnaire
Solid States Questionnaire- Pre
Questionnaire
Solid States Questionnaire- Main
Study
The Purdue Visualization of
Rotations Test
Technology Acceptance Model
Questionnaire
Web Quantitative Data (Part 1)
Web Quantitative Data (Part 2)
AR Quantitative Data (Part 1)
AR Quantitative Data (Part 2)

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Figure1: Chemistry + Augmented Reality +
E Learning
Chapter 1: Introduction
1.1 Abstract
Previous Studies have indicated that
specific concepts in chemistry education
require visuospatial skills by students.
Researchers have explored augmented
reality (AR) in aiding the spatial
visualization needs of the students in
subjects like Astronomy & Geometry.
Augmented reality is a popular
technology which has come into the
limelight in the recent years. In layman
terms, it is a technology which combines
real and virtual imagery at the same time.
It is a live, direct or indirect, view of a
physical, real-world environment whose
elements are augmented (or
supplemented) by computer-generated
sensory input such as sound, video and
graphics. Being very interactive in real
time, its implications and use cases have
evolved into different domains: health,
education, entertainment etc. The
domain for application of this technology
of particular to interest for us in this
project is E Learning. E Learning refers to
training initiatives which provide learning
material, course communications, and the
delivery of course content electronically
through technology mediation. In this
project, both the domains of AR reality
and E Learning have been explored in the
context of Chemistry for high school
students.
The project was planned out such that
the first phase (Design Project III) began
with a qualitative study conducted with
five high school chemistry teachers in
India. This study was conducted with the
aim to identify existing pedagogical
patterns and issues related to Solid State
Chemistry taught in senior high schools in
India. The results of this study were
analyzed and were found to be validating
the existing literature in chemistry
education. Based on inferences from this
study combined with principles proposed
in previous research, we then
conceptualized and developed an AR
based android application for mobile and
tablet devices. This application uses

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Department of Design, IIT Guwahati 9
standard XII NCERT textbook images as
markers/reference to augment dynamic 3
dimensional content. The content of the
application, decided on the basis of
inputs from study, is interactive and
supported with animation and audio
based feedback.
The next phase (Design Project IV)
focused on testing this application
through a comparative analysis with
existing e-learning modalities such as
web based. The aim of this experiment
was twofold. The first was to understand
and establish if an A.R based e-learning
tool would actually be helpful to
students in content learning, 3D spatial
visualization and behavioral intention of
users towards the system. The second
aim was to identify its strengths when
compared to current e learning
modalities and finally identify its
shortcomings and weaknesses. Based on
the quantitative analysis of results of our
experiment as well as qualitative
feedback received from participants
during the experiment, we establish how
A.R based tools have immense potential
as self-sufficient learning modules and
propose design inferences to be
considered while designing AR based
solutions.

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Figure 2: Homepage, www.coursera.org
1.2 Motivation
Solid State Chemistry which is taught as
the first topic in standard XII in high
school chemistry in India involves several
concepts with 3 dimensional visualization
of atoms and molecules. Having faced
difficulties ourselves in this domain in our
school days, we explored it further as our
topic for addressing an augmented
reality based solution. Also, in parallel,
with websites such as Coursera, EdX and
Udacity gaining immense popularity
amongst students in the recent few
years, we believe that E Learning is an
area wherein lies immense potential for
innovation. The current model of
teaching in E Learning lies heavily on
video lectures, which is a passive means
of interaction.
Thus, we worked towards the
development of an AR based tool and an
experiment to test it versus conventional
teaching practices which could
potentially throw insights on its
feasibility, interactivity, user engagement
and effectiveness in learning in the
future.

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Figure 4: Connecting Design Project 3 and
Design Project 4
1.3 Objectives
The key objectives for the project were
identified as:
 Identify scope of Augmented Reality in
E Learning and in our subject of interest
- Solid State Chemistry.
 Conduct user study for qualitative
feedback about teaching
methodologies for Chemistry concepts
as well as the existing E Learning
model.
 Develop an AR based E Learning
solution for a specific section in Solid
State Chemistry.
 Conduct a comparative study of the
developed solution with a conventional
e learning solution available as of
today.
 Identify strengths and weaknesses of
A.R based E Learning tools and propose
design guidelines for such systems.

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Figure 5: The Johnstone triangle
Figure 6: 3D structure, tetragonal voids,
Page 17, Standard XII NCERT
Chapter 2: Literature Review
2.1 Why Chemistry?
One of the challenges of chemistry
education is that it must address multiple
levels of representation, from the macro
level (tangible and observable) to the
sub-micro explanatory level (atoms,
molecules, ions) [Johnstone,2010]. For
novices, understanding these multiple
levels and the relationships among them
can be challenging. Digital technology,
which offers numerous ways to represent
information, has come to play an
important role in chemistry education,
but there are key aspects of interaction
and interoperability (i.e. differing
operating systems) that still present
problems.
Modern chemistry is characterized by
interdependent, networked thinking in
different representational domains. This
consideration is in the core of
Johnstone’s (1991) famous contribution:
‘Why is science difficult to learn?
Johnstone explained that learning and
thinking in modern chemistry always take
place in a constant shift between three
different representational domains: the
macroscopic, sub-microscopic, and
symbolic domain. If these three domains
(including the accompanying levels
between the macroscopic and sub-
microscopic domains) and their
interactions are misinterpreted,
scientifically unreliable interpretations
will necessarily emerge as a result
[Johnstone, 1991].

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Figure 7: Taxonomy of mixed reality
including real to virtual environments
2.2 Augmented Reality
Augmented Reality (AR) is a technology
that allows virtual images to be
seamlessly mixed with the real world
[Bauer et.al. 2001, Hampshire et.al. 2006,
Steed et.al. 1996]. AR stands between
virtual reality and the real environment.
In contrast, Augmented Virtuality is a
technology that enhances the users’
reality by inserting a real object into a
virtual environment.
AR and a virtual environment can be
divided depending on whether the
environment or object in the real world
appears or not. Hence, an AR application
requires a video input device, e.g. a video
camera, to receive an input from the real
world, and it should also be made
meticulously so that the user cannot
distinguish the virtual world from the
real world. In addition, AR has real-time
properties, since the user should be able
to watch the screen. As the screen with
the AR is displayed to the user, the user
experiences a higher level of immersion
with AR as compared to other
technologies.
Augmented reality technology has been
used in several fields [Azuma, 1997] as
varied as medicine, robotics,
manufacturing, machine repair, aircraft
simulations, entertainment and gaming
[Oda et.al. 2008]. This research presented
concentrates on the use of augmented
reality in education, more specifically E-
Learning.
Several authors [Pantelidis 1995, Winn,
1993] suggested that virtual reality
increases motivation, contributes to
better learning, and enhances the
educational experience for students.
Although AR applications for education
have been in place, its impact on learning
has only now begun to be explored.
AR is a medium which overlays virtual
objects on the real world. What features
does AR have to help conceptual
learning? As a new technology, firstly, AR
naturally draws people’s attention.

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Drawing students’ attention is an
important factor in instruction [Gagne
et.al. 1992]. Second, it is a trend to use
technology to create a constructivist
environment to enhance learning [Dede.
1995]. AR offers an alternative way to
see the chemistry world and allows
students to interact with the system and
discover knowledge by themselves.
Thirdly, AR not only creates visual
images, but also conveys the spatial cues
directly to users [Shelton et.al. 2004]. In
other words, by using AR users can obtain
a sense of spatial feeling. AR has great
potential to be applied to the knowledge
domain of spatial concepts. Another
feature of AR that enhances learning is
that AR allows users to interact with the
system by using their body, especially the
hands, and provides “sensorimotor
feedback” [Shelton et.al. 2004]. The
direct manipulation of AR can
supplement the deficiency of mouse-
based computer-generated visualization
since mouse manipulation is an indirect
physical manipulation [Shelton et.al.
2004]. Lastly, AR can be a tool which
requires users to interact and think
carefully [Schank et.al. 2002]. Since users
have to concentrate on the AR system
and focus on the virtual objects, they may
pay more attention to think about what
happens next, and thus make them think
more deliberately. Overall, AR as an
educational medium provides a great
alternative environment for students to
learn abstract concepts.

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Figure 8: An AR system and the physical
model [6]
2.3 Existing Work
A lot of research has been done towards
application of Augmented Reality in
education. Studierstube was one of the
initial projects in this direction. In
[Szalavári et. al., 1998; Schmalstieg et. al.,
2002], researchers have presented
collaborative, multi-user augmented
reality system Studierstube in which
users wear lightweight see-through head
mounted displays to access three-
dimensional stereoscopic graphics.
Initially, collaborative augmented reality
with personal Interaction Panel, a two-
handed interface system was
implemented which was later extended
to heterogeneous distributed
architecture to become useful in multiple
ways. MagicBook [Billinghurst et. al.,
2001] is a project in which digital 3d
models are embedded onto real book
pages. It’s Initial user feedback was quite
positive and even complete novices felt
that they could use the interface and
became part of the virtual scenes.
Construct3D is a three dimensional
geometric construction tool based on the
collaborative augmented reality system
‘Studierstube’ which is specifically
designed for mathematics and geometry
education [Kaufmann et. al., 2000 & 2003
]. Later on, its researchers went on
evaluate the system in terms of usability
[Kaufman & Dünser, 2007] and its
potential to train spatial of the students
[Dünser, Steinbügl et. al., 2006]. They
have reported that augmented reality
can be used to develop useful tools for
spatial ability training. But traditional
spatial ability measures probably do not
cover all skills that are used when
working in 3-D space. Thus new tools to
measure spatial ability directly in 3-D
would be desirable.In usability evaluation
study they found out that usability of
Construct3D was rated higher than the
usability of a desktop based geometry
education application. This may be due to
the more intuitive workflow when
working on 3D tasks.

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[Martín-Gutiérrez & Luís Saorín et. al.,
2010] presented an application of
augmented reality for improving spatial
abilities of engineering students. An
augmented book called AR-Dehaes has
been designed to provide 3D virtual
models that help students to perform
visualization tasks to promote the
development of their spatial ability
during a short remedial course. In their
next paper [Martín-Gutiérrez & Contero
et. al., 2010], researchers evaluated its
potential and usability. They suggested
AR-Dehaes as an efficient and effective
material for developing spatial abilities
and for learning engineering graphics
contents. In the usability assessment, AR-
Dehaes was scored very positively by
students with regard to both the
teaching material and the technology
used. ARIES [Wojciechowski and Cellary,
2013] is a very recent project towards
implementing augmented reality in
education in which learners’ attitude
towards the system was evaluated using
Technology Acceptance Model.
A recent study [Chen, 2006] investigated
how chemistry students interacted with
augmented reality and physical models
and evaluated the student perceptions
regarding these two representations in
learning about amino acids. Although
there were students who liked using AR
to learn about the amino acids because it
was portable and easy to make as well as
it allowed the students to observe the
structures in more detail others felt
uncomfortable using the AR marker
because it wouldn’t work if the student
flipped the marker since it works on
marker recognition. The study suggests
that using a cube to convey the AR
recognition pattern might be a solution
to addressing the issue associated with
flipping the marker. This research
provides guidelines concerning designing
the AR environment for a classroom
setting [Chen, 2006]. The application
shown in Figure 8 includes both an AR
marker and a physical model, which are
placed on the desk side by side. They
showed ball-and-stick models of the
acids. Participants could choose from the

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Department of Design, IIT Guwahati 17
AR marker or the physical model to learn
about the acids. One paper [Chen, 2006]
compares the use of AR marker and a
physical model to see which one is more
effective in helping students learn about
the acids.

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Week Dates Work
1, 2
Aug 19th
- Sep 1st
Literature Study +
Analysis
3
Sep 2nd
- Sep 8th
Need Finding,
How our project is
unique
4
Sept 9th
- Sept
15th
Testing with D
Fusion Studio
5
Sept
16th -
Sept
22nd
Report, PPT
6
Sept
23rd -
Sept
29th
Mid Sem Week +
User Research
7
Sept
30th -
Oct 6th
Getting started
with building AR
interfaces
10-Aug
Oct 7th -
Oct 27th
Development
11
Oct 28th
- Nov 3rd
Debugging
12
Nov 4th -
Nov 10th
Finishing Touches
13
Nov 11th
- Nov
17th
User Testing,
Report Submission
14
Nov 18th
- Nov
24th
Presentation,
Winding Up
Chapter 3 Project Timeline
The project has been divided into two
phases:
Phase 1 – Design Project III
August 2013 – November 2013
This phase would primarily focus on
development of the AR tool based on
identified content through research.
Phase 2 – Design Project IV
January 2014 – April 2014
This phase would focus on testing the
developed product in an experiment
against existing teaching modalities. This
would be followed by drawing inferences
from the experiment and arriving at a
conclusion about the use of augmented
reality in E learning.
Month Work
January
Literature review for
evaluation techniques
Prepare publication for
submission
February
Finalize design for
comparative analysis
Develop Web Interface
March
Questionnaire Design +
Pre pilot
Comparative analysis -
(Phase 1 + Phase II)
April
Analysis of results
Thesis report
Final Exhibition

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Figure 9: NCERT Chemistry Textbook,
Standard XII
Chapter 4 Methodology
4.1 Case Study Topic
To study the application of Augmented
Reality in E-Learning courses, we chose
Solid States, first chapter in Chemistry
book of class 12th according to NCERT
course curriculum as our case study topic.
This chapter mostly deals with 3d
arrangement of atoms of crystalline
metallic, non-metallic elements and ionic
and covalent compounds which need the
students to understand the concepts
sub-micro and symbolic level at the same
time. More importantly, it requires
students to visualize the atomic
arrangement in 3d space which deals
with Visio-spatial thinking capability of
the students.

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Figure 10: Dependent Variables
Figure 11: Independent Variables
4.2 Research Design
Target Participant Sample: As we chose
Solid States as our case-study topic, it
became very obvious for us to define our
target sample group as chemistry
students of class 11th and12th also with
the students who drop one year after
12th class for college entrance exams.
Variables: Our single independent
variable will be the manner in which
content is delivered to the students.
Basically, we will try to compare these
different manners of content delivery
and study the effects of them on
dependent variables. We are planning to
use Single way Multivariate ANOVA
(Analysis of Variance) test to analysis
purpose. There are four levels of this
independent variable:
1) Traditional face-to-face classroom
setting in which teacher use either
printed NCERT books and physical 3d
models (mostly balls) to teach the
students Solid State concepts.
2) Video: Videos can also be used to
explain the concepts. There can be
different types of videos also other than
basic camera recorded video: Interactive
or Animation videos
3) Mouse controlled 3d navigation web
apps
4) Augmented Reality (AR) based
solution: 3d rendered objects are
projected onto markers which are
tracked by the device camera. In contrast
with mouse controlled apps, these are
easier to learn and also give
sensorimotor feedback while using it.
Navigation from one view from another
is easy and quicker. There is more
directness in interaction with 3d object in
case of AR based solution.
We would study the effects of above
different levels on following dependent
variables :
1) Course Performance
2) User Perceived Satisfaction
3) User Engagement

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4.3 Design Guidelines
In [Wu and Shah, 2004], authors have
suggested five principles for designing
chemistry visualization tools that help
students understand concepts and
develop representational skills through
supporting their visuospatial thinking.
These principles are as following:
(i) Providing Multiple Representations
and Descriptions: As students faces
difficulty in representing chemical
concepts at the microscopic and symbolic
levels, comprehending representations
conceptually, it becomes important to
provide them the representations in
multiple along with descriptions.
(ii) Making Linked Referential
Connections Visible: Second principle is
to make linked referential connections
among representations visible so that
students could construct appropriate
conceptual connections among multiple
representations. One way to help
students visualize the connections is to
allow a representation to be changed by
manipulating its connected
representation or description.
(iii) Presenting the Dynamic and
Interactive Nature of Chemistry:
Students generally face difficulty in
visualizing the movement of particles and
develop a dynamic model of chemical
processes. The dynamic mental models
developed via viewing animation or
series of static diagrams could help
students learn advanced chemical
concepts and enhance their visuospatial
thinking.
(iv) Promoting the transformation
between 2d and 3d: Fourth design
principle is to provide features that
facilitate the identification of depth cues
and the transformation between 2D and
3D.
(v) Reducing Cognitive Load by Making
Information Explicit and Integrated:
Reducing cognitive load is an important
factor for making visualization tool
helpful for student with low visuospatial
abilities. This can achieved by providing
visual and verbal information
contiguously rather than separately.

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4.4 User Requirement Analysis
We conducted user research with the aim
to identify the problem points and needs
of teachers and students. Also, we
intended to select few concepts from
Solid States chapter for development
purpose on the basis of insights from
user research. With these objectives in
mind, we had semi-structured interviews
with five higher secondary class
chemistry teachers.
Teacher School/ Coaching Current Organization Interview Method City
A School Kendriya Vidyalaya Physically Guwahati
B School Mount Carmel Virtually Delhi
C Coaching Concept Education Physically Guwahati
D Coaching Oriental Tutorials Physically Guwahati
E Coaching FIITJEE Virtually Delhi

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4.4.1 Interview Questionnaire
We had six subjective questions in our
questionnaire as follows:
1) Do you find any relative difference in
teaching concepts of Solid States in
comparison to other chapters?
2) As a part of your teaching curriculum,
what is the standard division of the
chapter - could you please divide the
chapter into subtopics and modules
based on your teaching techniques For
example, if you cover the chapter in a
span of 3 classes, which topics are
broadly covered in which of the classes
3) Within these modules, are there any
specific topics which are relatively
difficult to explain / teach / make
students understand?
4) From a student's perspective, what are
the topics within the chapter in which
they face maximum difficulties / find
hard to grasp?
5) Is NCERT content sufficient to explain
all concepts of Solid States in a concise
manner? Is there any other reference
material that is recommended to
students?
6) Do you feel need of or use any
additional visualization tools to explain
the Solid States concepts to students
more constructively? If yes, what could
be they?

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Figure 12: Interview at Oriental Tutorials,
Guwahati
4.4.3 Insights from Interviews
1. Difference between Solid States and
other chapters:
Responses to this question are quite
consistent for all five teachers. They
describe Solid States chapter as more
demanding in terms of 3 dimensional
visualization and imagination for
students. Correlation among views of
different teachers can be easily seen in
their statements. One teachers said, “As
solid states involves 3d concepts, it
requires more visualization and
imagination skills of the students”.
According to another teacher: “It gives
help to understand 3-D structures of
metals and Ionic Compounds. Visualization
in 3-D is required.” These feedback gives
support to our assumption that there is
need of 3d visualization aiding for
students in Solid States and nurture our
motivation to design a Augmented
Reality based tool for the same.
2. Division of chapters into different
modules and sub-topics:
As some teachers are more focused
towards teaching school syllabus
whereas other are focused towards
teaching entrance exam syllabus.
Therefore, there are slight differences
across teachers in the content and the
modules in which the content is divided.
Even though, there is similarity in terms
in terms of teaching core concepts of the
chapter: different layer wise
3dimensional arrangement of atoms, unit
cells of Face Centered Cubic (FCC) and
Hexagonal Closed Packing (HCP) and
tetragonal and octahedral voids. We also
asked from some of the teacher’s most
important topic in the chapter. These
insights helped us to choose spatial
arrangement of atoms in unit cells and
voids formed inside them as content for
AR based pedagogical tool to start with.

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Figure 13: Interview at Kendriya Vidyalaya,
IIT Guwahati
3. Relatively difficult topics to teach and
learn
Teachers find it difficult make student
visualize and understand the spatial
arrangement of particles in 3d space.
One teachers informed, “For students it is
difficult to understand 3d crystalline
structure and where and how different
voids are present inside the structures.”
From different structures couple of
teachers found Hexagonal cubic packing
relatively difficult to visualize and so to
teach. A teacher said, “In hexagonal
packing, visualization is bit difficult and
then voids in hexagonal packing.”
Solid States chapter contains other
concepts also e.g. Voids, Cation-Anion
Ratio, Coordination number. There are
numerical problems in these concepts.
These concepts are associated with and
extension of basic concepts of 3d
structure arrangement and unit cells.
According to one teacher, “Once 3d
arrangement of atoms is clearly
understood by student, everything else is
easier.” This information motivated us to
start with spatial arrangement of atoms
in unit cells and voids as instructional
content.
4. NCERT is insufficient
Most of the teachers admire NCERT text
books because of the content and
instruction design. It somewhat helps
students understand the crystalline
structure with the help of colorful 2d
figures. But they do not find it sufficient
in terms of depth of content and its
effectiveness in provide clear 3d
visualization of structures and lattices.
One teacher stated, “NCERT books are
good and there are some diagrams and
explanations for 3d concepts but not
sufficient.” They generally refer foreign
author books or other guide books.
5. Use of additional tools
Teachers take help of ball - stick models
and animations to show how molecules
are arranged in a unit cell and voids are
created. One teacher provided us with
the details of the tools he has used. He

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informed, “I tried the following ball stick
models: Deluxe Version Solid State Model
Kit (http://ice.chem.wis
c.edu/Catalog/SciKi ts.html#Anchor-Solid-
31140). Currently I am using bits of J3D
animation from http://www.chm.davi
dson.edu/vce/ which are extremely
effective and students just enjoy them.”
There were opposite views also. 3d
physical models could be difficult to
make, store or carry. According to one
teacher, “It is time consuming to make
slides or use 3d models. There is non
availability of 3d models in market.” Also,
these models are just static 3d
representation of one state of lattices.
Animations are again dynamic 2d
representation of crystalline structure.
Another teacher shared his views,
“Unfortunately the videos and models are
not very useful and user friendly so they
also do not provide much help for teachers.
If we can have the visualization of the 3-D
structure that how a structure is formed
step wise it will help. It should be handy
and simple to use.” It was interesting to
find that most of the teachers use
example of room to teach arrangement
of atom in cubic unit cell and sharing
among different unit cells.

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Figure 14, 15: D Fusion Studio
Chapter 5: Development
5.1 D’Fusion
Initially, we did some explorations with
D’Fusion studio, a cross platform SDK for
building AR applications by Total
Immersion. It is more GUI based and one
can develop basic AR applications
(augmentation of single 3d rendered
supplement onto real world by tracking
single marker) without much
programming. Scenario intelligence
programming is done using Lua script. 3D
rendered objects can be directly
imported from Autodesk 3ds Max and
Maya using exporters provided in its
developer package. We were successful
in augmenting 3d molecular structure
over black and white marker. We also
tried adding interactivity to it by
changing the rendered supplement when
two markers are brought nearby.
But during the course of our exploration
with D’Fusion studio, we found following
issues in it:
1) Marker-Tracking is very unstable, a lot
of flickering was occurring while tracking.
2) It shows its trademark logo all the time
over display screen.
3) Interactive elements like on screen
buttons and animations were difficult to
add.
4) Weak developer community and
support.
5) One have to do a lot of steps just for
basic augmentation
Due to these issues, we decided not to
proceed with D’Fusion and switched to
Vuforia.

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Figure 16: Vuforia by Qualcomm
Figure 17: Unity software
Figure 18: SketchUp software
5.2 Vuforia, Unity, SketchUp
Vuforia by Qualcomm is an Augmented
Reality Software Development Kit (SDK)
for mobile devices that enables the
creation of Augmented Reality
applications. It uses Computer Vision
technology to recognize and track planar
images (Image Targets) and simple 3D
objects, such as boxes, in real-time. This
image registration capability enables
developers to position and orient virtual
objects, such as 3D models and other
media, in relation to real world images
when these are viewed through the
camera of a mobile device. The virtual
object then tracks the position and
orientation of the image in real-time so
that the viewer’s perspective on the
object corresponds with their
perspective on the Image Target, so that
it appears that the virtual object is a part
of the real world scene. Apart from
providing Image tracking capabilities,
Vuforia also gives developers the
flexibility to add interactions through
buttons, gestures, animation, sound etc.
in the mobile application. Tracking is very
stable in Vuforia in comparison with
D’fusion. Programming in Vuforia is done
on C sharp and Java script with unity.
SketchUp, marketed officially as Trimble
SketchUp, is a 3D modeling program for
applications such as architectural, civil
and mechanical engineering, film, and
video game design. It provides an
intuitive graphical user interface to
design 3D cad models similar to
softwares such as 3DS Max, Rhino etc.
Unity is a cross-platform game engine
with a built-in IDE developed by Unity
Technologies. It is used to develop video
games for web plugins, desktop
platforms, consoles and mobile devices.
Unity is of extreme importance to this
project because it provides a base
platform to use 3D models generated in
Sketchup with the Vuforia plugin.
Additional functionalities and
interactions such as GUI buttons, audio
support and virtual buttons can be built
on top of this using Unity.

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Figure 19: Virtual Buttons (in blue) and GUI
buttons (in black)
5.3 Virtual Buttons and GUI
5.3.1 Virtual Buttons
Virtual buttons are developer-defined
rectangular regions on image targets
that trigger an event when touched or
occluded in the camera view. For
example, in the sample picture, pointing
the hand or touching the rectangular
region triggers an action associated with
the button. Such buttons provide an
intuitive means of interaction since the
users are directly using the content (on
paper / surface) to navigate / as a button
rather than on screen buttons
5.3.2 GUI
The graphical user interface of
Augmented Reality Apps are primarily
simple because a major chunk of screen
space is dedicated to the camera for easy
viewing. Any additional content that
needs to be shown to the user is
subsequently placed on layers above the
camera layer.
In this project, we have used two GUI
buttons to allow users to navigate /
toggle between different views of the
same 3D model. The models are placed in
a chronological order - i.e, the next view
of the model is obtained from the
previous view.

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Figure 20: Task Flow Diagram, Module 1
5.4 Application
We divided our teaching into two
modules, based on the content finalized
through feedback from our qualitative
research. These modules are:
1. Understanding 3D Closed Packing
Structure
1a. Hexagonal Close Packing
1b. Cubic Close Packing
2. Understanding Voids
2a. Tetragonal voids
2b. Octahedral voids
5.5 App Flow
The flow of the app can be understood
through the following steps:
5.5.1 Module 1
1. User is reading the NCERT book and
comes across the concept of 3
Dimensional closed packing.
2. User turns on the application on his
mobile / tablet
3. The home screen of the application is
essentially live feed from the camera of
the device. The user points the device to
the page of the NCERT book.
4. The 3D model is augmented on the
device with audio feedback. Virtual
buttons to toggle between hexagonal
close packing and cubic close packing are
also augmented on the device. This 3D
model consists of two layers of atoms in
which placement of second layer is
shown through animation. The first layer
is white in color while the second is in
green. Different colors are used to for
different orientations of layer and easy
understanding.
5. The user points / touches the desired
concept to be explored on the NCERT
book.
6. Subsequently, the animation and
placement of third layer is shown

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Figure 21: App Screenshots, Module 1
6. a Hexagonal Close Packing
In case of hexagonal close packing, the
third layer is positioned exactly the same
way as the first layer, forming ABAB
structure. The placement of third layer
(white in color, same as first layer) is
shown through animation upon selection
of hexagonal close packing through the
virtual button on the book.
Also, once the user selects hexagonal
close packing, two GUI buttons appear on
screen (image here) namely ‘Next’ &
‘Back’. These buttons can be used to
navigate back and forth to subsequent
views of this packing. In the next view
(image here), additional atoms from each
layer are removed leaving out just one
unit cell, to be able to visualize the
hexagon formed through such a packing.
In the subsequent view, a a translucent
hexagon is augmented over the atoms to
show how the unit cell looks. Each of
these steps is accompanied with audio
feedback explaining the concept and
providing concepts.
Finally, for effective learning of these
concepts, the user is prompted with a
question related to the concepts shown
in the previous slides in the form of a
multiple choice question. In case a user
answers correctly, the user is prompted
again with a question about reasoning of
the correct answer / why other options
were incorrect. Only upon correctly
answering both these questions is the
user shown an explanation about the
actual answer of the question. Such a
twofold system of testing ensures that
the student approaches a problem from
different perspectives and identifies
different use cases (For example,
visualization of layering of atoms in a
different fashion / orientation). It also
helps complete the learning cycle of the
concept being communicated through
the application.

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Figure 22: App Screenshots, Module 1
Figure 23: AppTest Screenshots, Module 1
6. b Cubic Close Packing
In case of cubic close packing, the third
layer is not aligned either with the first
layer or the second layer. Thus, the third
layer has its own color (blue) the atoms
of which are placed such that they fit into
the octahedral voids formed by the
previous two layers. When the user
selects cubic close packing through the
virtual button, placement of this layer is
shown through animation over the first
two layers. Also accompanying the third
layer is the fourth layer in white, which is
aligned exactly with the first layer,
thereby forming ABCABC layering of
Cubic close packing.
Similar to hexagonal close packing, upon
selected of CCP through the virtual
button, two GUI buttons appear on
screen (image here) namely ‘Next’ &
‘Back’. These buttons can be used to
navigate back and forth to subsequent
views of this packing. In the next view
(image here), additional atoms from each
layer are removed leaving out just one
unit cell, to be able to visualize the cube
formed through such a packing. In the
subsequent view, a a translucent cube is
augmented over the atoms to show how
the unit cell looks. Each of these steps is
accompanied with audio feedback
explaining the concept and providing
concepts. This particular visualization of a
cube is of importance to us since it
involves rotation of the atoms at an
angle which is difficult to visualize. The
color coding used layers wise
accompanied with freedom to spatially
move in 3D helps students correlate this
form of ccp to the 1st state (ABCABC)
The user can navigate back to any of the
previous views through on screen
buttons. The user can also navigate to
other concept (Cubic Close Packing)
through virtual button. Also, these
models of CCP are accompanied by a test
question, followed by a question on the
justification of incorrect options.(Similar
to the model followed in hexagonal close
packing).

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Figure 24: Task Flow Diagram, Module 2
5.5.2 Module 2 Understanding Voids
Voids are the empty space created
between atoms when they arranged very
nearby. For students, understanding
different kind of voids, how they are
formed, their 3d positions in single unit
cells and how they are shared between
multiple unit cells are very important. In
ionic crystalline solid structures cations
are present on voids. Therefore, to
calculate cation anion ratio in a molecule,
it is important to know above mentioned
details about voids.
Therefore, in our second module we
chose voids in Face Centred Cubic (FCC)
as our content material. In a Face
Cantered Cubic unit cell, there are atoms
at each corner of the cube as well as on
the centre of each face. There are two
type of voids in FCC: (i) Tetragonal Voids
(ii) Octahedral Voids. These voids in FCC
unit cell are described on page 17 of 12th
class Chemistry NCERT book. There are
two diagrams on the page: upper one for
tetragonal voids and lower one for
octahedral voids.
When student starts the Clearn (AR
application) and bring the camera in front
of the page 3d model of FCC is
augmented on the screen. Also, there are
two virtual buttons on the page, one on
each diagram and so for void type.
Student can choose to learn any of the
void concept by point towards desired
virtual button.
Tetragonal voids
A tetragonal void is formed by placing
fourth atom over the depression among
three closely arranged face centred
atoms. Initially, all atoms of FCC unit cells
are colored grey. When tetragonal void’s
virtual button is pressed, the four
relevant atoms are colored orange to
distinguish them from other molecules.
These four atoms are joined and four
triangular green translucent faces are
shown to form the tetrahedron. Other
than these changes in 3d model, ‘Back’
and ‘Next’ are also shown on the screen.
Student can toggle between different
steps/models using these buttons. By
pressing next button small green sphere
is shown at exact center of the
tetrahedron. This sphere abstractly
represent the position of tetrahedral
void. So, tetrahedral voids are present on
the one-fourth of the body diagonal of

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Figure 25: App Screenshots, Module 2
FCC unit cell. In Sodium Oxide, Sodium
atoms in green are placed at these
tetrahedral voids. On pressing next
button, all 8 tetragonal voids are shown
as green spheres and all other spheres
are turned into orange. Instructional
audio related for each mode is also being
played.
Octahedral voids
Whenever three closely packed atoms
are placed directly over three oppositely
oriented atoms, an octahedral void (OV)
is formed within them. There are two
types of such voids in fcc unit cell. The
first formed at a body center is shown
here. When octahedral void’s virtual
button is pressed, octahedral void at
body center of FCC unit cell is shown with
three spheres of same layer as blue and
other three as orange. On pressing next
button, second type of octahedral void,
edge centered void is shown. This time
four unit cells are shown and one edge
centered OV is shared among these four
unit cells. After pressing next button,
small red sphere is appeared on the exact
center of the octahedron formed by 6
face centered atoms around center of
unit cell. This sphere abstractly represent
the position of octahedral void. In
Sodium Chloride, Sodium atoms in green
are placed at these octahedral voids. On
pressing next button, all 13 positions of
octahedral voids are shown which due to
sharing of edge centered atoms are
effectively four. Instructional audio
related for each mode is also being
played.

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Figure 26: 3D Models, Module 1
5.6 Audio components
To assist learning and provide instruction,
audio feedback was added into the
application to guide users through the
flow of the application as well as help in
instruction. A mute button to turn of
these instructions has also been provided
on the GUI.
The following is the audio feedback given
by the application at respective stages:
Module 1 : Understanding 3D Closed
Packing Structure
Stage 1
(Layer 1 + Animation of Layer 2 on top
of it)
“3Dimensional close packed structure can
be generated by placing layers one over
the other. Let us take a two dimensional
hexagonal close packed layer ‘A’ colored in
white and place a similar layer colored in
green above it such that the spheres of the
second layer are placed in the depressions
of the first layer. Let us call the second
layer B.
For placement of the third layer, point your
finger at either the diagram of hcp or ccp
on your NCERT book (Figure 1.18 b)”
Stage 2a
User selects hcp virtually
“In Hexagonal close packing, tetrahedral
voids of the second layer in green are
covered by the spheres of the third layer in
white, which is aligned exactly with the
first layer. Thus, the pattern of spheres is
repeated in alternate layers and is often
written as ABAB.
Toggle between different visual modes by
on screen buttons.”
Stage 2b
User toggles to next mode (hcp)
“One unit cell of such hexagonal close
packing can now be seen after removal of
atoms of other cells from each layer.”

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Figure 27: 3D Models, Module 1 and 2
Stage 2c
User toggles to final mode (hcp)
“The faces of this hexagonal unit cell can
now be seen. This sort of arrangement of
atoms is found in many metals like
magnesium and zinc.”
Stage 3b
User selects ccp virtually
“In Cubic close packing, octahedral voids of
the second layer in green are covered by
the spheres of the third layer in blue. When
placed in this manner, the spheres of the
third layer are not aligned with those of
either the first or the second layer. Only
when fourth layer in white is placed, its
spheres are aligned with those of the first
layer from which the pattern ABCABC
emerges.
Toggle between different visual modes by
on screen buttons.”
Stage 3c
User toggles to next mode (ccp)
“One unit cell of such cubic close packing
can now be seen after removal of atoms of
other cells from each layer. “
Stage 3d
User toggles to final mode (ccp)
“The faces of this cubic unit cell, known as
face centred cubic can now be seen. Note
how the original layers are oriented within
a cubic cell. Metals such as copper and
silver crystallise in this structure.”
Module 2: Understanding Voids
Stage 1: Cubic model
“In a Face Centered Cubic arrangement,
there are atoms at each corner of the cube
as well as on the centre of each face.
Point your finger at Figure 1 or Figure 2 to
know more about tetrahedral or
octahedral voids respectively.”
Stage 2a: Tetragonal void is selected
“Tetragonal void is selected.
A regular tetrahedron is formed
connecting three face centred atoms and
one atom at the corner of the unit cell

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Figure 28: 3D Models, Module 2
(Orange in color). This tetrahedron is
actually the tetragonal void within the
four atoms.
Toggle between different visual modes by
on screen buttons.”
Stage 2b: Next mode of tetragonal void
“Within this tetragonal void formed inside
the tetrahedron, an atom can be placed.
For example, in Sodium Oxide, Sodium
atoms in green are placed at these
tetrahedral voids.”
Stage 2c: Final mode of tetragonal void
“A total of 8 such tetragonal voids are thus
formed in each fcc unit cell, as shown.”
Stage 3a: Octahedral void is selected
“Whenever three closely packed atoms are
placed directly over three oppositely
oriented atoms, an octahedral void is
formed within them. There are two types
of such voids in fcc unit cell. The first
formed at a body centre is shown here.”
Stage 3b: Next mode of Octahedral
void
“Octahedral voids are formed on the
center of the edges as well. It can be seen
that one edge centered octahedral void is
shared amongst four unit cells.”
Stage 3c: Next mode of Octahedral
void
“Within this octahedral void formed inside
the octahedron, an atom can be placed.
For example, in Sodium Chloride, Sodium
atoms in red are placed at octahedral
voids.”
Stage 3d: Final mode of Octahedral
void
“Effectively there are 4 such octahedral
voids formed in each fcc unit cell.”

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Figure 29: Students using the prototype
Chapter 6: Initial Feedback
The prototype developed was tested for
qualitative feedback at Kendriya
Vidyalaya, IIT Guwahati amongst class XII
children. Aim of this study was to get the
initial feedback of concept and prototype
from its primary users i.e. students,
identify the major shortcomings in them
and then look for the scope for
improvement.
Some key insights from this study are:
 Wow factor and non familiarity
with technology major driving
force behind initial feedback.
 Some students pointed that they
would have liked to see rotation
and movement through touch
gestures on phone as well.
 One student wanted content to be
broken down to even smaller steps
(atom joining atom instead of layer
joining layer)

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Figure 30: Classification of A.R
publications by evaluation method /
approach
Chapter 7: Literature review (Phase II)
7.1 Evaluation Techniques
Although Augmented Reality (AR) has
been in studied for over forty years it has
only been recently that researchers have
begun to formally evaluate AR
applications. Most of the published AR
research has been on enabling
technologies (tracking or displays, etc.),
or on experimental prototype
applications, but there has been little
user evaluation of AR interfaces [Dunser,
et.al, 2007]. Existing literature [Dunser,
et.al, 2008] indicates that AR user
evaluation papers can be classified into
five types:
(1) Objective measurements
(2) Subjective measurements
(3) Qualitative analysis
(4) Usability evaluation techniques
(5) Informal evaluations
Objective measurements include task
completion times and accuracy / error
rates; other examples are scores,
position, movement, number of actions,
etc. In general these studies employ a
statistical analysis of the measured
variables, however, some only include a
descriptive analysis of the results.
Subjective measurements are those in
which users are studied using
questionnaires, subjective user ratings, or
judgments. With respect to analysis some
of these studies also employ statistical
analysis of the results, others only
include a descriptive analysis. Qualitative
analysis category comprises studies with
formal user observations, formal
interviews, or classification or coding of
user behavior. Usability evaluation
techniques are those that are often used
in interface usability evaluations such as
heuristic evaluation, expert based
evaluation, task analysis, think aloud
method, or Wizard of OZ method. Lastly,
informal user evaluations are those that
include informal user observations or
informal collection of user feedback.
It has been observed that the ratio of
formal user evaluations compared to

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informal evaluations has increased over
the years. Between 1995 and 2001 there
is an average of 57% formal evaluations,
whereas between 2002 and 2007 this
percentage is 76%. Thus there seems to
be a growing understanding for the need
to formalize the evaluation process and
conduct properly designed user studies.
7.2 Spatial Ability
Spatial ability can be described as the
ability to picture three-dimensional (3D)
shapes mentally. [Martin et. al, 2010].
Educational research of Potter and
Vander Merwe [Potter, 2003] concluded
that spatial ability influences academic
performance in engineering. But, every
student in the classroom doesn't have a
good spatial ability. Previous studies
have shown that students with lower
visuospatial abilities are unable to (i)
perform well in solving spatial and non-
spatial chemistry problems [Bodner &
McMillen, 1986; Carter, LaRussa, &
Bodner, 1987], (ii) identify the depth cues
of 2D models [Seddon, Eniaiyeju & Chia,
1985],(iii) form 3D mental images by
visualizing 2D structures [Tuckey,
Selvaratnam & Bradley, 1991] and (iv)
comprehend symbolic and molecular
representations conceptually [Ben-Zvi,
Eylon, & Silberstein, 1988].
There are multiple studies which divides
spatial ability in sub-domains [Guttman
et. al, 1990; Lohman, 1979]. These two
factors have consistent across these
studies: Spatial Relations which is
speeded mental rotation and Spatial-
Visualization which includes all complex,
multi-step spatial tasks [Lohman, 1979].
Tasks involving three-dimensional mental
rotation are somewhat intermediate and
have been grouped into each of these
two factors. Lohman, 1979] Tasks
requiring participants to imagine
different perspectives either form a third
factor or are grouped into Spatial
Relations. Mental Rotation Test
(Vandenberg, 1978) is a popular test to
assess spatial relation skills whereas
spatial visualization can be assessed by

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Figure 31: Technology Acceptance Model
Purdue Spatial Visualization Test (Guay,
1977).
7.3 TAM
Technology Acceptance Model (TAM) one
of the most widely accepted model which
explains the relations between user
attitudes, satisfaction and behavioral
intention to use the information systems.
[Davis, 1989] first introduced the TAM as
a theoretical extension of the theory of
reasoned action (TRA) [Fishbein and
Ajzen, 1975]. This model predicts user
acceptance based on the influence of two
factors: perceived usefulness and
perceived ease of use. Perceived
usefulness is defined as ‘‘the degree to
which a person believes that using a
particular system would enhance his/her
job performance’’, and perceived ease of
use is defined as ‘‘the degree to which a
person believes that using a particular
system would be free of physical and
mental effort’’ [Davis, 1989]. TAM posits
that user perceptions of usefulness and
ease of use determine attitudes toward
using the system which further
determines the behavioral intentions, in
turn leading to actual system usage. TAM
has been extended by addition of other
constructs called external variables which
perceived usefulness or perceived ease
of use such as self-efficacy [(Compeau
and Higgins, 1995], subjective norm
[(Taylor and Todd, 1995] or playfulness
[Moon and Kim, 2001].
Davis's original proposition of TAM has
more 1000 citations. Several attempts
have been made in the past by
researchers to consolidate the results
from these studies in terms of meta-
analysis [Yousafzai et. al., 2007; King and
He, 2006]. There are abundance of
studies which confirms TAM to be a good
theoretical tool to understand users’
acceptance of e-learning [Lee, Cheung
and Chen, 2005; Park, 2009]. [ŠUmak et.
al., 2011] have presented meta-analysis
of TAM studies in context e-learning
technologies. [Park, 2009] suggests e-
learning self-efficacy and subjective norm
as important factors to determine

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Figure 32: Sample PSVT Question
attitude and behavioral attention
towards e-learning. TAM has also been
used check the acceptance of mobile
augmented reality application with
historical photographs and information
about a historical street [Haugstvedt et.
al., 2012]. The results show that both
perceived usefulness and perceived
enjoyment has a direct impact on the
intention to use such mobile augmented
reality applications.
7.4 PSVT
Purdue Spatial Visualization of Rotations
Test (PSVT:R) is a common test to
measure spatial visualization ability of
chemistry students [Bodner, Guay, 1997;
Carter, LaRussa, & Bodner, 1987]. Actual
PSVT [Guay, 1977] consisted of three
sections: Developments, Rotations and
Views. Developments consisted 12
questions designed to see how well
subjects can visualize the folding of
developments into three-dimensional
objects. Rotations consisted 12 questions
designed to see how well subjects can
visualize rotations of three- dimensional
objects. Rotations consisted 12 questions
designed to see how well subjects can
visualize what three-dimensional objects
look like from various viewing positions.
There were also 30-items test booklets:
one each for Developments, Rotations
and Views.
Out of these 30 questions on rotations,
[Bodner and Guy, 1997] removed
question 6, 8, 11, 14, 20 ,21, 22, 24, 26
and 30 to reduce it to item-version. One
item from 20-item PSVT test is shown in
Figure. In this test, participants view two
rotated versions of one 3D figure, infer
the type of transformation between
them, and make the same transformation
with a new 3D figure.

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7.5 VARK
A learning style or preference is the
complex manner in which, and conditions
under which, learners most efficiently
and most effectively perceive, process,
store, and recall what they are
attempting to learn [James & Gardner,
1995]. One characterization of learning
styles is to define the learners’ preferred
mode of learning in terms of the sensory
modality by which they prefer to take in
new information. VAK is an acronym that
stands for three major sensory modes of
learning: visual, aural, and kinesthetic,
depending on the neural system with
which a learner prefers to receive
information. Thus VAK is a perceptual,
instructional preference model that
categorizes learning by sensory
preferences. Recently, Fleming [Fleming,
1995] expanded VAK to VARK to include
reading/writing as an additional type of
mixed sensory learning modality.
Although learners can use all of these
sensory modes of learning, one mode is
often dominant and preferred. For
example, visual learners learn through
seeing drawings, pictures, and other
image-rich teaching tools. Auditory
learners learn by listening to lectures,
exploring material through discussions,
and talking through ideas.
Reading/writing learners learn through
interaction with textual materials,
whereas kinesthetic learners learn
through touching and experiences that
emphasize doing, physical involvement,
and manipulation of objects. Students
have preferences for the ways in which
they receive information.
The visual, auditory, reading/writing,
kinesthetic (VARK) questionnaire
identifies student’s preferences for
particular modes of information
presentation. The following are internet
links to the VARK homepage
(http://www.vark-
learn.com/english/index.asp) and
questionnaire (http://www.vark-
learn.com/english/page.asp?pquestionna
ire). We administered the VARK
questionnaire to our participants as a
part of our pre questionnaire to be able
to draw inferences with their learning
styles and performance in spatial
visualization tests to be conducted as a
part of our main experiment.

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Figure 33: Octahedral void as seen in new
prototype
Chapter 8- Improvizations in AR
prototype
8.1 Introduction
Based on the initial qualitative feedback
received after showcasing our
application to high school students,
teachers & professors from our institute
(as discussed in chapter 6), we decided to
incorporate several changes in our
application. These improvisations were
completed before proceeding with our
experiment design which involved testing
through a comparative analysis with the
web counterpart of the applications.
Also, since the comparative analysis
involved an experiment design that
needed to completed within a fixed time,
we narrowed down our content even
further to voids - tetrahedral +
octahedral.

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Figure 34: Removal of virtual buttons and
changes in GUI
8.2 Changes
The improvisations in the application
include:
1) Removal of virtual buttons
Virtual buttons were used in our system
since they provided context specific use -
i.e, by pointing at a particular content on
the physical book, related content used
to be augmented on or application. Our
initial testing suggested that virtual
buttons were a hindrance for the users
since they had to switch their focus
between the screen and the textbook
regularly. While operating the application
the focus of the users is on the screen of
the tablet / mobile where the content is
displayed. However, when the user has to
choose a virtual button, he needs to shift
focus back on the book and regularly
switch between the tablet and the book
to be able to select the virtual button.
Also, since the virtual buttons were
placed as per the diagrams (content) in
the book, more than often, these buttons
were in close proximity to each other and
of smaller size. This resulted in tracking
issues since instead of the finger
sometimes the hand / arm used to false
trigger an option. Also, when the hand
was brought on top of the book to
choose a virtual button, the main image
tracker was also obstructed.
Keeping these points in mind, we decided
to replace virtual buttons by on screen
GUI buttons so that user’s concentration
is not diverted at any point of time and
there is no limitation because of tracking
errors and issues.
2) Changes in GUI
Since the virtual buttons were removed,
new buttons had to be added into the
GUI to provide the same functionality.
The option to choose between
tetrahedral void and octahedral void was
provided to users in the 1st screen where
an FCP model was shown. Once an option
was selected, the users now had three
options - to proceed to the next model
within the selected category (Tetrahedral
/ octahedral) or to switch to the other
category. These three buttons were
grouped together in conjunction with the
law of proximity (reference) since all of
them had similar functionality of
navigating between content .Apart from

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Figure 35: Addition of interactivity by
touch
this group, there was another button
placed at a distance for audio control.
3) Changes in Audio controls
Initially, the audio button had the
functionality of mute - i.e, the audio used
to play automatically and the users had
the option to mute it. The audio still used
to keep playing in the background but
was not audible. We observed that the
majority of the users preferred to mute
the audio in the beginning since they
were concentrating initially on the
augmented model and the interactions
surrounding it. After exploring the model
for a while, when they unmuted the
audio, the file had already played for a
significant amount of time and it difficult
for users to pick up from mid-way.
In the new interface, the audio did not
play automatically in the start. Instead,
the users had the option to tap on Play
audio to begin listening to the audio
content as and when they wanted to as
per their convenience. Also, instead of
providing of pause, the play button
transformed into a stop audio button
once the play was pressed. The reason
for choosing stop over pause is two fold .
The first reason is as discussed before -
the difficulty faced by users in grasping
content mid-way. The second reason is
that if users paused an audio and moved
to some other model for exploration,
upon returning to the original model it
was all the more difficult to be able to
understand the audio by resuming mid-
way.
4) Addition of interactivity by touch
The third major change in the interface
was the added interactivity of rotation of
models through swipe on the screen. A
majority of the users being accustomed
to touch screen devices expected to be
able to rotate the model through such an
on screen interaction. Also, this removed
the constraint of not being able to view a
3D model from below, as the model
could be rotated. The users now could
rotate the model as per their
convenience by swiping in the direction
of rotation. The swipe rotation also
included inertia so that the rotation
looked more natural - i.e, upon swiping in
a particular direction, the model rotated
for a particular angle and then came to a
smooth stop based on the speed of the

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Figure 36: Zoomed in view of prototype
swipe. A single tap on a rotating model
also brought it to a halt.
8.3 Final GUI walkthrough:
The final application thus has a total of
eight 3D models with their associated
audio files. The application begins when
the camera tracks the NCERT page on
voids. An FCP model is then augmented
on the surface. A total of 3 GUI buttons
appear on the top - two grouped
together (option to choose tetrahedral
or octahedral) and the third being that of
the audio. Upon selecting either tetra /
octa, the GUI shows the respective model
augmented on the NCERT. The buttons
are now changed - now grouped in three.
These buttons are that of previous
model, next model or the option to
switch between tetrahedral or
octahedral. The play audio button is
common throughout and can be used to
play audio content related to the model
being augmented.
The users can pan through by moving
around the tablet to view the augmented
model from all sides and angles. Bringing
the tablet closer to the NCERT booklet
serves as a zoom in and allows user to
explore the models from a closer angle.
Similarly, taking the tablet away serves as
zoom out. Also, apart from moving the
tablet device for zoom / pan, the NCERT
book or the image tracker itself can be
moved, brought closer or rotated to
serve pan / zoom features. Since the
model that is augmented is fixed to the
tracker, moving the tracker also moves
the model. Lastly, the users can use on
screen swipe gesture to rotate the model
in any direction they wish to.

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Figure 37: PSVT A.R Prototype
Additional application:
Apart from these changes in our
application, we also developed another
standalone application to be used for
conducting PSVT as a part of our
comparative analysis experiment. For this
application, we used a standard pebble
image default tracker provided by
Vuforia. The application consisted of
each of the twenty 3D models given as a
part of the PSVT. These are the models
which users have to finally rotate as per
the sample example given in the
question. The interface consisted of two
GUI buttons - previous question and next
question, which allowed them to
navigate between these 20 models. The
feedback about which model / question
they were currently on was also provided
on screen.
The users thus had the support of this
system to help them answer the PSVT
questionnaire - they could see in 3D the
model asked via the questionnaire and
also pan & rotate the model via finger
swipe to be able to help them visualize
the rotated view as asked in the
question.

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Chapter9 - Web Interface
9.1 Introduction
Along with Augmented Reality (AR)
based e-learning tool: Clearn, we
designed and prototyped a conventional
web based tool as well. Reason behind
having this interface is to compare Clearn
with web based tool and find the issues
in it. This web interface is completely
graphical user interface based, contains
multiple interactive 3d object viewers
and controlled by computer mouse.

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Figure 38: Web Interface (Voids)
9.2 Design
Web interface basically contains series of
multiple HTML pages linked together.
Each page contains a 3d object viewer in
which 3d digital model can be rotated in
any direction using mouse dragging.
There is also option of zooming in and
out the model (through mouse
scroll/wheel) along with full screen view.
We emphasized on keeping the two
interfaces (AR and Web) as similar as
possible in terms of interactions to avoid
any confounding variable i.e. effect on
results due to some extra feature. We
achieved so by taking following design
decisions for web interface:
(i) Visual design of web interface is kept
minimal having white background and no
extra visual element
(ii) Number of buttons, labels on them
and their functions are same as AR
interface
(iii) Maintaining the consistency with AR
interface in terms of 3d models and audio
instructions
(iv) Task flow of the interface is also
similar to that of Clearn interface
Similar to AR case, we designed and
developed two web apps: one for Solid
State concepts of voids and another for
Purdue Spatial Visualization Test of
Rotations (PSVT: R) assistance. In solid
state web-app, one can explore different
face centered cubic unit cells having
voids and navigate through these models
using GUI buttons. Second app was
intended to aid participants while
attempting PSVT: R. Interface is shown in
the figures.

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Figure 39: Web Interface (PSVT)
9.3 Development
Web Interface was developed by
programming in HTML and CSS. Basic
layout of the webpage was developed
using bootstrap framework
(http://getbootstrap.com/). HTML 5
audio player was used to add audio
instruction feature. 3d object viewers
were embedded in webpages using
Sketchfab (https://sketchfab.com/) and
p3d (http://p3d.in/) for solid state and
PSVT respectively. These are web-
services which allows you to upload your
3d models online and then embed them
on your web-pages. There is restriction of
rotation of models along vertical
direction after a particular angle in
Sketchfab. Therefore, we chose to use
p3d for PSVT web tool because flexibility
in rotation along all directions is very
important while solving PSVT. We
uploaded the Sketchup models same as
AR apps.

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Chapter 10 - Research Methodology
10.1 Aims & Rationale
Identifying the areas where Augmented
Reality based tool lacks in comparison
with conventional tools of e-learning, is
the main aim of this study. For this, we
decided to compare a web based e
learning tool with an augmented reality e
learning tool in a controlled experimental
setting. We compared the learnability of
students through questions that involved
spatial visualization and deep
understanding of the content. In this, we
expected students using A.R based e
learning tool to perform better as
compared to those using a web based e
learning tool. We believed that the
familiarity and comfort in usage of web
based systems would be outweighed by
the novelty and better 3D viewing in A.R
which would result in better
understanding and visualization of 3D
content.
We also compared the effect of such
systems on spatial rotation skills. The
goal was to discover whether interactive
3D applications both on web and A.r
would support similar level of spatial
skills to traditional mental rotation
scenarios. Apart from these, the goal also
was to compare web with A.R to discover
if there was any difference in their
support to spatial skills. Because of the
familiarity of users with mouse based
interactions, we expected web based e
learning tool to perform better in terms
of time taken to complete the task.
However, given same freedom to rotate
and view the models in both the systems,
we expected no significant difference in
the accuracy with which the questions
are answered.
Lastly, we compared both the A.R and
web systems on six parameters using the
technology acceptance model - perceived
usefulness, perceived ease of use,
attitude, behavioral intention, self-
efficacy and perceived enjoyment. We
believed both the systems to perform
equally well on all parameters, with A.R
performing slightly better in terms of
enjoyment. The novelty and the halo
effect associated with augmented reality
was expected to increase its enjoyment
scores.

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10.12 Experiment design
We conducted a between-subjects study
between group using AR tool and group
using web based tool due to two reasons:
first, to avoid issue of creating 2 content
quiz of same difficulty level and second,
reduce the experiment time for each
participant. Therefore, our independent
variable was e-learning tool with two
levels: Augmented Reality based tool and
web based tool. We did not incorporate
a gender variable since it is common in
spatial skills studies and was not the
focus of the study.
Our dependent variables were accuracy
of content related questions (scale, in %
of correct answers), response time for
PSVT (scale, in seconds), accuracy of
PSVT related questions (scale, in % of
correct answers) & behavioral intention
(Likert scale responses to 20 questions
for both respective systems) measured
through technology acceptance model.

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10.3 Research Questions
Stated formally, we formed the following
research questions:
RQ1: Would there be any differences in
content understanding, learnability and
application between A.R based E
Learning tools versus Web based E
Learning tools?
RQ2: Would there be any effect of A.R
and Web based E Learning systems in
PSVT performance as compared to
mental rotation alone?
RQ3: Would the accuracy in PSVT vary
with platforms ? (A.R and Web)
RQ4: Would there be any differences in
completion time for PSVT between Web
based systems and A.R based E learning
systems?
RQ5: Is there any difference in behavioral
intention in terms of using the system
between web and A.R?
RQ6: Would there be any correlation
between the learning styles of students
(VARK) and their PSVT performance as
well as their solid state scores?

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Figure 40: Participants filling Pre
Questionnaire
10.4 Participants
We recruited forty participants for our
study compromising mostly of first year
engineering students who had studied
the topic of our case-study, i.e. Solid
State chemistry in the past one year. We
conducted an initial phase of
questionnaire based survey with these
participants. This questionnaire consisted
of 3 parts:
1) The VARK questionnaire
2) Solid State chemistry related questions
3) PSVT:R
The VARK questionnaire was used to
provide us with insights into the learning
styles of our participants which could be
used in later stages to draw some
inferences. The second part of the
questionnaire involved 6 concept based
solid state questions to test the current
understanding and remembrance of
these concepts in the participants. The
last part was the standardized PSVT
conducted in a stipulated time limit of 15
minutes to gauze the spatial ability of our
participants. The PSVT test was
conducted on paper.
The forty participants were then divided
into two groups based on their PSVT and
solid state scores, such that the average
distribution of both the scores is same in
both the groups. These two groups were
then used for our comparative analysis
experiment wherein one group used a
web based e learning system whereas the
other group used an A.R based system.
Study duration varied per participant,
due to differing reaction times, but on
average participants took around an
hour, with 10 minutes to explore the
system, another 10 minutes for breaks
and questionnaires and 40 minutes for
PSVT. Participants were not paid for
involvement.

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10.5 Set up & materials
We implemented our A.R system on an
android tablet device as an application
and used standard XII chemistry NCERT
textbook as the image tracker. The
application was build using vuforia sdk on
unity platform. The Web system was built
using bootstrap framework with sketch
fab plugin used to embed 3D models. The
3D models used in both the web system
and A.R system were built using
sketchup.
Questionnaires: Two Solid State content
related quizzes (before and after using
tool) were developed which was later
validated by chemistry teacher. First quiz
which was given before the main study,
had basic and fundamental questions of
the chapter to just gauge their current
retention of the chapter knowledge.
Second quiz given to students just after
using the tool, had conceptual,
visualization based questions which were
related the content (octahedral and
tetrahedral voids) shown to them while
using the tool. We used the online service
of google forms to record responses for
content related questions as well as for
recording responses for technology
acceptance model. We used 20 items
sheet of Purdue visualization test of
rotation (PSVT:R) by [Bodner and Guy,
1997]. For having responses of PSVT, we
used an online service proprofs
(www.proprofs.com) which recorded the
time taken by each participant for each
of the questions in the background. In
TAM questionnaire, we had 3 questions
each on Perceived Ease of Use, Perceived
Usefulness and Attitude from [Davis,
1989], 3 questions on behavioural
intention from [Davis, Bagozzi et. al.,
1989], three on computer-self efficacy
from [Compeau et.al. ,1995], three on
perceived enjoyment adapted from
[Moon & Kim, 2001]. All questionnaires
are included in the appendix section.
Each trial was conducted in a quiet lab
environment. .

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Figure 41: Experiment with A.R (up) & Web
based system (below)
10.6 Procedure
The comparative analysis experiment was
conducted with 40 participants wherein
20 participants were given an A.R system
and the other 20, the web system. This
was done about a week after the pre
questionnaire was filled, so that there
was sufficient time gap between the
participant’s attempts at PSVT. All the
sessions were video recorded with prior
permission from participants. At the start
of the trial, participants were given a
hand-out describing different sections of
the study and the task for each. Having
read the instructions, the participants
were asked if they had understood how
the test would proceed, and any
questions that arose were answered. The
study began with a demonstration /
walkthrough of the system where
different interactions of the system were
shown and verbally explained. The
participants were told to pay focus on
the content since the subsequent
questionnaire involved questions related
to conceptual understanding of the
content showcased. The users were also
given assurance that there would not be
any memory based questions asked and
that they should only focus on
understanding and learning of the 3D
concepts rather than remembering them.
The participants were then given the
system for free exploration and content
viewing for as much time as they needed.
Headphones were provided with the
tablet to the A.R users and with the
laptop to the Web system users for audio
content. Once the users indicated they
had completed viewing the content, the
system was taken and they were asked to
answer an online questionnaire which
contained 8 conceptual questions related
to the concepts shown in the system.
Rough sheet and a pen were provided to
the users. This questionnaire also did not
have a time limit - users had the freedom
to take as long as they wanted. Once
completed, qualitative feedback about
the system and the questions was taken.
The demo, free exploration and solid
state questionnaire was followed by a
five minute break where we offered
chocolates to our participants. Post the
mid session break, the users began with
the PSVT test. Since the users had
already taken the PSVT as a part of the
pre questionnaire, they were familiar
with the format and types of questions.

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Figure 42: Experiment with A.R (up) & Web
based system (below)
This time though, there were 3 changes
in conducting PSVT, as explained below-
1. Users were given the system (A.R
application on a tablet device to 20 users
and Web to 20) to help them answer
these questions. Both these systems
contained the 3D models asked in PSVT
which could be rotated and viewed from
all angles in 3 dimension. Users were
expected to use these systems as a help
in answering.
2. The PSVT was conducted online
through proprofs instead of on paper like
the previous time. This helped in tracking
the time taken by each participant to
answer each of the twenty questions in
the background.
3. There was no time limit given to to
users (unlike last time) since they were
using a system to help them answer and
getting familiar with its use and
application is expected to take varying
time with users.
The users were allowed to take small
breaks between questions if required.
This was implemented by adding a “are
you ready to proceed?” question in the
questionnaire before each question.
Users were instructed that only when
they are ready to answer the next
question should they proceed and that
they could take breaks in between at
such questions.
Once the PSVT was complete, qualitative
feedback regarding the system and
questionnaire was again taken. The final
session consisted of the TAM
questionnaire which was given to the
users through an online Google form. The
users were instructed to answer each of
these questions independently without
any overall biasness.

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Mean SD
Solid State AR 4.2 1.15
Solid State Web 4.25 1.29
PSVT AR 14.7 3.08
PSVT Web 14.45 3.58
Figure 43: Pre Questionnaire mean &
standard deviation
Min Max Mean SD
Visual 1 13 5.63 3.14
Auditory 2 14 7.15 2.77
Reading 2 12 5.65 2.33
Kinesthetic 2 13 7.6 2.59
Total 16 43 26.03 6.98
Figure 44: Pre Questionnaire VARK mean &
standard deviation
Chapter 11 – Results
11.1 Quantitative
Pre-questionnaire results
Before main study, we collected Solid
state content quiz scores and Purdue
Visualization Test scores from forty
students to measure their content
retention and spatial ability and divide
them into two similar groups of twenty
each. We also gathered VARK learning
style scores for each participant to
explore relationship between learning
style and other test performances.
Means and Standard Deviation of content
quiz score and PSVT score for the two
groups are mentioned in the figure.
Mean values of solid state quiz scores of
all forty was 4.23 and of PSVT scores was
14.58.
Dominance of learning style was found
using mean and standard deviation
values of individual learning mode which
are mentioned in the table (). From the
table, it is clear that kinesthetic was most
strong learning mode in these participant
followed by auditory whereas Visual was
least dominant mode.
Main study results
After both of the groups had been
subjected to use two different e-learning
tools, new solid state quiz score, PSVT
accuracy score, individual PSVT question
response time, total PSVT completion
time, Technology Acceptance Model
responses for each participant were
collected and analyzed. An independent
t-test was
Solid-state accuracy
An eight questions quiz related the
content shown during tool usage, was
given to the participants to measure their
ability to understand and apply the
concepts after using the tool. Content
accuracy score was calculated as the
number of correctly answered questions
(out of eight). From Shapiro-Wilk Test
and skewness-kurtosis analysis, it was
confirmed that solid state score
distribution is non-parametric. Therefore,
due to non-parametric nature of data and
independent sample design, Mann-
Whitney U test was used to determine

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Figure 45: TAM Mean & Standard
Deviation
the significance of difference between
solid state accuracy scores of participants
using AR tool (M=5.8, SD=1.64) and Web
tool (M=5.64, SD=1.87). No significance
difference was found between the two
groups in terms of content accuracy
(U(38)=199.5,Z=-0.014, p>0.05). This
answers our first research question on
content understanding, learnability and
application through e-learning tool.
PSVT:R accuracy
A twenty question PSVT:R sheet similar
to pre-study sheet was given to
participants of both groups to solve with
help of e-learning tool. PSVT accuracy
score was calculated as the number of
correctly answered questions (out of
twenty). From Shapiro-Wilk Test and
skewness-kurtosis analysis, it was
confirmed that PSVT: R score distribution
is non-parametric. Therefore, due to non-
parametric nature of data and
independent sample design, Mann-
Whitney U test was used to determine
the significance of difference between
PSVT scores of participants using AR tool
(M=16.35, SD=3.18) and Web tool
(M=18.37, SD=1.83). PSVT score was
found to be significantly higher for web
tool in comparison with AR tool
(U(37)=115, Z=-2.14, p=0.032). This
answers our third research question
regarding difference in PSVT scores of
two groups. For measuring the effect of
using interactive e-learning tools in
comparison with mental rotation, we ran
Wilcoxon Signed Ranks Test (due to
paired design). PSVT score of mental
rotation was found to significantly lower
than both AR (Z=-2.12 , p= 0.034) and
web tools (Z=-3.63, p=2.8*10-4). This
finding answers our second research
question about effect on using E
LEarning systems on visualization ability.
PSVT:R completion time
PSVT completion time for each was
computed in seconds by summing up the
individual solving time for all twenty
questions. From Shapiro-Wilk Test and
skewness-kurtosis analysis, it was
confirmed that PSVT: R completion time
distribution is parametric. Therefore, due
to parametric nature of data and
independent sample design, independent
t-test was used to determine the
significance of difference between PSVT
completion times of participants using
AR tool (M=1887.21, SD=481.59) and
Tool Parameter Mean SD
AR
PEOU 5.42 1.14
PU 5.86 0.6
AT 5.82 0.67
BI 6.05 0.85
SA 5.47 0.84
PE 6.23 0.7
Web
PEOU 5.47 .94
PU 6 0.46
AT 6 0.51
BI 6.03 0.66
SA 5.65 0.93
PE 5.98 1.02

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Web tool (M=1293.53, SD=531.09).
Completion time was found to be
significantly lower for web tool in
comparison with AR tool (t(36)=3.61,
p=0.001). This answers our fourth
research question on difference in PSVT
completion time due to change in E-
learning system.
Technology Acceptance Model
Data of participants’ acceptance of the
two e-learning tools was collected on six
parameters: perceived ease of use
(PEOU), perceived usefulness (PU),
attitude (AT), behavioral intention (BI),
self-efficacy (SE) and perceived
enjoyment (PE). There were three likert
items on seven scales in each parameter.
Mean of the responses of the three
questions was taken as participant’s
response for a particular parameter.
Following table shows the mean and
standard deviation values of all seven
parameters for both groups.
From Shapiro-Wilk Test and skewness-
kurtosis analysis, it was confirmed that
distributions for these parameters are
non-parametric. Therefore, Mann-
Whitney U test was used to determine
the significance of difference between
behavior intention and other parameters
for AR and web tool. No significant
difference was found between the two
groups for any of the parameters. This
answers our fifth research question
regarding difference in behavioral
intention towards the two tools.
Correlational Results
Other than, variance analysis tests, we
performed correlation tests as well to
study the relationship between learning
style, content quiz and PSVT score. Due
to non-parametric data type, we used
spearman rho value to measure
correlations. Correlation values for both
of the tools are mentioned in the tables
below.
Correlated pairs in AR:
 VARK Visual and Solid state score: non
significant and weak-positive
correlation
 VARK Auditory and PSVT score: non
significant and weak-positive
correlation

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 VARK Reading and PSVT completion
time: moderate positive correlation
 VARK Kinesthetic and PSVT score:
moderate negative correlation
 Solid State score and PSVT score:
moderate positive correlation
 Solid state score and PSVT completion
time: moderate negative correlation
Correlated pairs in Web:
 VARK Visual and Solid state score:
moderate positive correlation
 VARK Reading and Solid state score:
moderate negative correlation
 VARK Reading and PSVT completion
time: moderate negative correlation
This answers our sixth question about
correlation between the learning styles
of students (VARK) and their PSVT
performance as well as solid state scores.

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Figure 46: Spearman Rho Corelation value table for Augmented Reality users
Correlations
VARK_Visual VARK_Auditory VARK_Reading VARK_Kinesth SS_Score
PSVT_New_Sco
re PSVT_Time
Correlation Coefficient 1.000 -.369 -.601**
-.452*
.320 .216 -.254
Sig. (1-tailed) . .055 .003 .023 .085 .180 .147
N 20 20 20 20 20 20 19
Correlation Coefficient -.369 1.000 .008 -.032 .077 .350 -.144
Sig. (1-tailed) .055 . .487 .446 .374 .065 .278
N 20 20 20 20 20 20 19
Correlation Coefficient -.601
**
.008 1.000 -.242 -.071 .063 .479
*
Sig. (1-tailed) .003 .487 . .152 .383 .396 .019
N 20 20 20 20 20 20 19
Correlation Coefficient -.452*
-.032 -.242 1.000 -.231 -.421*
-.066
Sig. (1-tailed) .023 .446 .152 . .163 .032 .394
N 20 20 20 20 20 20 19
Correlation Coefficient .320 .077 -.071 -.231 1.000 .411
*
-.433
*
Sig. (1-tailed) .085 .374 .383 .163 . .036 .032
N 20 20 20 20 20 20 19
Correlation Coefficient .216 .350 .063 -.421*
.411*
1.000 -.138
Sig. (1-tailed) .180 .065 .396 .032 .036 . .286
N 20 20 20 20 20 20 19
Correlation Coefficient -.254 -.144 .479*
-.066 -.433*
-.138 1.000
Sig. (1-tailed) .147 .278 .019 .394 .032 .286 .
N 19 19 19 19 19 19 19
VARK_Visual
VARK_Auditory
VARK_Reading
VARK_Kinesth
SS_Score
PSVT_New_Score
PSVT_Time

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Correlations
VARK_Visual VARK_Auditory VARK_Reading VARK_Kinesth SS_Score
PSVT_New_Sco
re PSVT_Time
Correlation Coefficient 1.000 -.369 -.601**
-.452*
.464* .249 .047
Sig. (1-tailed) . .055 .003 .023 .020 .152 .425
N 20 20 20 20 20 19 19
Correlation Coefficient -.369 1.000 .008 -.032 -.041 .093 .167
Sig. (1-tailed) .055 . .487 .446 .432 .352 .247
N 20 20 20 20 20 19 19
Correlation Coefficient -.601** .008 1.000 -.242 -.383* -.183 -.392*
Sig. (1-tailed) .003 .487 . .152 .048 .227 .048
N 20 20 20 20 20 19 19
Correlation Coefficient -.452*
-.032 -.242 1.000 -.079 .013 .305
Sig. (1-tailed) .023 .446 .152 . .370 .479 .102
N 20 20 20 20 20 19 19
Correlation Coefficient .464
* -.041 -.383
* -.079 1.000 .015 -.190
Sig. (1-tailed) .020 .432 .048 .370 . .476 .218
N 20 20 20 20 20 19 19
Correlation Coefficient .249 .093 -.183 .013 .015 1.000 -.040
Sig. (1-tailed) .152 .352 .227 .479 .476 . .436
N 19 19 19 19 19 19 19
Correlation Coefficient .047 .167 -.392
*
.305 -.190 -.040 1.000
Sig. (1-tailed) .425 .247 .048 .102 .218 .436 .
N 19 19 19 19 19 19 19
VARK_Visual
VARK_Auditory
VARK_Reading
VARK_Kinesth
SS_Score
PSVT_New_Score
PSVT_Time
Figure 47: Spearman Rho Corelation value table for Web users

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11.2 Qualitative
A.R
Qualitative feedback was taken from
students after completing both the solid
state chemistry task as well as the PSVT.
The feedback has been categorized in
terms of advantage of tool, positive
statements, negative statements,
strategy of answering etc:
Solid State
Advantages of tool:
 The system was found useful in
revising concepts and creating a
visual image of voids in the mind
of users which helped in answering
the questions.
 Users also felt its a better way to
retain the concept than reading
line by line in a textbook or
through 2D models.
 Users found the questions more
conceptual and a difficult than the
previous time.
Positive statements:
 The system made it easy for me to
create a mental picture & visualize.
 While answering the questions, I
was able to correlate and recollect
shapes from the system.
 The system helped recollecting
the positioning of voids in 3
dimension
 It was useful in visualizing beyond
4 atoms.
 This would be really helpful for
students with weak visualization
skills.
 I wouldn’t have been able to
answer any of these had I not used
the system despite knowing the
concepts.
Improvements:
 One of the users suggested that
GUI buttons should be shifted to
bottom of the screen.
 One user wanted the option to
pinch zoom on the tablet to be
able to scale up / down the model.
 One user suggested there should
be an option to be able to view

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entire lattice / neighbouring atoms so
as to be able to visualise sharing of
atoms and voids even better.
PSVT
Advantages of tool:
 The system helped in verifying and
matching the final answer for a
few users.
 Since the rotation of the figure
was being done on the tablet, the
mental load on the participant was
significantly less. However, the
physical load increased at the
same time.
Strategy:
 The most common methodology in
PSVT followed to answer the
questions were to first mentally
derive the angle of rotation (as
illustrated in the question) & then
use screen based touch rotation to
apply the same on the augmented
model.
 One of the users initially used the
system to rotate models but then
opted for mental rotation and
used the system for verification of
his answers.the tablet fixed and
rotated the paper to view
different views (90 / 180 degree)
of the models.
 One of the user used the image
marker / paper. He set the model
as per the question, kept Users
agreed and admitted that they
were just rotating on the tablet
and comparing with the options
instead of thinking.
Positive statements:
 While the users agreed that the
current interaction technique was
appropriate and useful for general
exploration and use, the added
option of choosing (and fixing) the
axis / point of rotation and fixing it
would immensely help in cases
where mental rotation is involved.
 The system was particularly useful
in questions that involved 3 steps
rotations since the user could use
the tablet in the intermediate

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state for reference and did not
have to rely on his memory.
Negative statements:
 Almost all the users faced
difficulties in rotation via touch - it
was not easy to rotate the model
for a fixed set of angles such as 90,
180 etc. The rotation thus wasnt
accurate / lacked precision.
 One of the users said it was
physically tiring to hold the tablet
for so long and use it for pan and
zoom movements.
 A lot of users did not find the
system useful for one rotation
qtns - preferred rotating mentally
even if the option to use the
system without any time
constraint was given to them.
 The users took some time to get
accustomed to the interaction
method and the system.
 A lot of users mentioned that it
took unnecessary time to first
allign the models as per that given
in the question before rotating
them.
Improvements:
 One user desired the option to
skip questions in PSVT.
 A lot of users said had there been
options to fix an axis and rotate
instead of rotation about centre, it
would have helped more.
 The options to rotate at fixed
angles was also desired by users.
 Two finger rotation (fixing axis of
rotation by one finger and rotating
by other) was also desired.

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Web
Qualitative feedback was taken from
students after completing both the solid
state chemistry task as well as the PSVT.
The feedback has been categorized in
terms of advantage of tool, positive
statements, negative statements,
strategy of answering etc:
Solid State
Many students found questions:
conceptual/ tough/ good and accepted
that there was need of
thinking/visualization involved in all of
them.
Advantages of tool:
 Imagination and visualization of
octahedral and tetrahedral voids
became easier
 Good for these kind of conceptual
and imagination based questions
because book is helpful for only
theoretical content
 Better Analysis of position of
atoms/voids
 In school, concept of voids would
have understood quicker
 No need of help from physical 3d
models/balls (one participant)
 Distinguishability between face-
centred atoms and corner atoms
during visualization became clear
 Participants informed that due to
the usage of tool, they could
understand the question quickly
and also while answering they
could recollect and visualize the 3d
models shown in the tool. In
conventional way, it might took
them 2-3 times to understand the
question.
 Gives freedom and flexibility. Can
rotate and see 3d object from any
angle. whereas there is just one
angle in the book
 One can confirm his mental
visualization of the hidden parts of
the 3d model shown in the book by
rotating the model.
 It creates a visual image in mind
 Can understand the 3d concept by
ourselves
 In contrast with mental rotation,
tool rotation is quick and precise
 There is less clarity in 2d figures

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Positive statements:
 I wish it would have been there in
my 12th class
 Retention of the concept would be
more after tool usage
 My previous knowledge about the
concept was quite low (forgotten),
this tool was very helpful in
recollecting all points and answer
the question
 In school, I had difficulty in
understanding the form and and
position of the voids.
 During school, Initially it took me
time to visualize and understand
the 3d concepts like voids and
sharing of atoms in unit cells. With
this tool, it would have been
quicker.
 Although I was good in mental
visualization in school but I had
friends who were poor in this
chapter because they couldn’t
visualize the 3d arrangement of
the atoms. I think this tool would
have been helpful for them
 Can learn by experiencing and
doing which is not there in the
case of book
 It is helpful in questions which
involves more than one unit cells
because there visualization is
difficult
 In book, there is lack of freedom
(figure from just one angle, have
to understand from that only).
Also need teacher.
 Concepts of voids etc. can be
understood 100% with the tool
whereas there was some doubt
remained in case of the book
 Visualization of edges and
boundaries is easier
 In case of 3d models, need of
explaining the model with the text
reduces.
 In case of 3d tool, it is more likely
that every student gets the
concept but in classroom, teacher
just says his experiences with
which every student might not be
able to visualize
 Helpful in concepts of
coordination number
 Liked the view from inside as well
 Instructional audio is good
 Edge centred ov figure was helpful
in understanding the sharing
 Good to see where exactly
atoms/voids are fitting

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 Helpful in radius calculation the TV
because exact positions of them is
confusing
Negative statements:
 After just one usage, I couldn’t
remember all many things. So, I
answered from my previous
memory
 Questions were tough and it was
difficult to answer just on the basis
content shown in the tool and had
to use previous knowledge.
 Zoom in feature didn’t help much
because there is not much
complexity inside the unit cell
Improvements:
 Tougher/more deep concepts can
be added
 Option full size atom view also.
(where atoms are touching)
 Interactivity with particular atom
or void (information tagged on
atoms/voids)
 Add multiple unit cells to
understand sharing
 Arrangement of atoms in ionic
compounds like FeO, Fe2O3 and
Fe3O4
PSVT
Most of the students faced difficulties in
rotation. When they intended to rotate
the model in just one plane, it got
rotated in other planes as well. Although
some students guessed right answer
from final rotated figure even it was not
perfectly aligned with the answer.
Strategy to solve:
 Analyze the reference rotation and
replicate the same in the tool on
the given model
 Most of them divided the rotation
of the reference model into
smaller 90 or 180 degree rotations
and perform those same partial
rotations with the model in tool
one by one.
 First vertical Horizontal (2 possible
rotation) and then Vertical
rotations (4 rotations)
 Few followed the rotation of
loose/unique edge or point of the
model

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Advantages of tool etc.:
 Was easier than previous time
because no need to mentally
rotate the model.
 No need to think much
 would have done it quicker if
familiar/handy with the tool
 Very helpful for multiple rotation
ques./complex figures/ very
symmetrical figures. Single
rotation can be done by mentally
itself
 can confirm the mental rotation in
the tool. So, more surety about the
answer.
 In multiple rotations, now there is
no need to remember in-between
steps because those can be seen in
the tool. (Previous step can be
seen in the tool) So, mental buffer
is not consumed.
 In case of multiple rotation, one
can reach to the answer quicker
 Can understand the dimensions
and shapes of the 3d figure from
all angles.
Positive statements:
 Rotation in full screen mode is
much easy to use and in-control
than short screen. More area to
rotate
 When we think of the rotation in
the mind, there is always a
dilemma about the rotated figure
 In mental rotation, I couldn’t do
multiple rotation questions but
now it was easier to do them
 Last time it took me a lot of time
but this time I was quicker
Negative statements:
 It was very difficult to rotate the
3d models in the models. I
preferred to use 3d models
instead for most of the question.
 One first need to
understand/learn how this
rotation interaction is working
 Inertia makes it difficult to handle
3d models
 Non cuboidal rotation are difficult
to judge (in both cases) that
whether 90 is done or not. e.g Q18

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Improvements:
 Option of rotation at some
predefined angles (90 or 180) and
direction.
 Option to rotate
reference/problem figure as well
 Feature of rotation in just one
particular plane by restricting the
motion/rotation in other axis. One
suggested method was to use 3
arrows like in unity or 3ds max
 Small thumbnails side-ways which
shows how model is appearing
from up, down, left or right in
current state.
 Reset Model button which brings
the model to its initial orientation
 Temporarily save in between step
images
 Colour of the voids/ forms etc.

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5.55
5.6
5.65
5.7
5.75
5.8
5.85
Web AR
AverageScore(Outof8)
System Used
Solid State Performance
0
1
2
3
4
5
6
7
8
9
2 3 4 5 6 7 8
Frequency
Score (Out of 8)
Frequency of Solid State Scores
Web A.R
Chapter 12 - Discussion
1. Solid State performance
Users using both web based systems and A.R
based systems performed well in a seemingly
tricky questionnaire . The average score for
users given a web system was 5.65 out of 8 as
compared to 5.8 compared to users using a A.R
system. The small difference indicates that both
the systems were at par in helping students
learn and visualize 3 dimensional chemistry
concepts of voids. Users using A.R. systems
could have performed better than web users
because of better visualization of 3D models
and increased interest because of novelty and
naiveness associated with the technology.
The frequency graph also shows a majority of
the students performing in the range of 6 – 8.
Overall, the performance of users having used
both web and A.R based systems is at par and
above average.

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0
20
40
60
80
100
120
140
160
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
AverageTimetaken(inseconds)
Question Number
Qtns wise time average ( Web, AR)
Web A.R
2. PSVT Time
The graph of average time taken per question
by users for web and A.R systems clearly
indicate that users using web based systems
required less time in answering all questions as
compared to users using A.R based systems. In
both the systems, there is a sharp dip after the
1st question which is because extra time taken
by users to familiarize themselves with their
strategy to answer the questions as well as
system usage while answering questions. There
is little fluctuation in the time for the next 7 - 8
questions since all of them are of the same
difficulty level (1 rotation of 90 degrees / 180
degrees). There is a sharp rise in the 9th
question for both web and A.R users because of
the tricky reference figure and a complex 3D
model. There is then a gradual rise between
questions 13 - 17 as the difficulty of the
questions increase.
It can be concluded that the time taken with
system usage increases with the difficulty &
complexity of 3D models and number of
rotations involved. Web based systems take less
time since students are more used to and
familiar with mouse based interactions. The
curiosity and inquisitiveness that comes with
viewing 3D models in an A.R systems could have
also increased in larger times for users took

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0
5
10
15
20
25
30
35
Web AR
TimeTaken(inminutes)
System Used
PSVT Time taken
more time in exploring the model in 3D before
attempting to rotate it as per the given
question.
If we have a look at the overall time average
taken by web and A.R users, we see a significant
difference. While web users took an average of
21.56 minutes to complete the PSVT, A.R users
took an average of 31.45 minutes. The
increased time in A.R could be again could be
attributed to the curiosity and inquisitiveness
that comes with viewing 3D models in an A.R
systems because of its novelty. Also, familiarity
with web based systems and mouse interactions
attributes to less time taken for users given the
web based e learning tool.

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0
2
4
6
8
10
12
14
16
18
20
Web AR Mental
AveragePSVTScore(Outof20)
System Used
Average PSVT Scores
3. PSVT Scores
The PSVT scores for users having used a web
based system while answering PSVT have
always been greater or equal to their scores
having using mental rotation while answering
PSVT. There is a sharp rise in the scores of users
who performed relatively poorly using mental
rotation. The difference between the scores
decreases with increase in mental scores. for
users with high mental rotation scores, the
PSVT scores with the system are almost equal.
In case of users using an A.R based system,
there are a few instances when the
performance of the users decrease as compared
with mental scores. Like web, we see a rise in
the scores of users having performed relatively
poorly earlier (with mental rotation). We also
see a decrease in performance using A.R
systems in the end, i.e, for users who’d
performed well earlier (with mental rotation).
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
PSVTscore(Outof20)
User Number ( in increasing order of mental scores)
PSVT Scores (Mental vs Web)
Mental Web

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It can be thus concluded that both A.R and web
systems are useful in assisting users for
answering questions that involve 3D rotation,
especially so for students with weak spatial
skills. (those who performed relatively poorly in
mental rotation). The dip in performance for
some users of the A.R system could be credited
to difficulties faced in rotation with the system
and unfamiliarity with such technology. The
overall average scores strongly reflect
increased performance for both web and A.r
users as compared with mental rotation, with
web users performing better than A.R users.
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
PSVTScores(Outof20)
User Number (in increasing order of mental scores)
PSVT Scores (Mental vs A.R)
Mental A.R

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0
1
2
3
4
5
6
7
4 6 8 10 12 14 16 18 20
Frequency
PSVT Scores (Out of 20)
Frequency of PSVT Scores
Mental Web A.R

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8
10
12
14
16
18
20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Noofcorrectresponses(outof20users)
Question Number
Question wide performance (Average)
Mental Web A.R
4. Question wise PSVT performance
The following graph illustrates question wise
performance of users in PSVT for 3 cases - when
solved with mental rotation without the help of
any system, when solved with the help of web
based E Learning based system and when
solved with A.R based system. The performance
here is the total number of correct responses to
the respective questions (out of twenty). In case
of mental rotation, since the total number of
users was 40, the score was divided by two. We
see that the performance remains almost
similar for questions 1 - 8, which involve only
one degree of rotation. This is in sync with the
qualitative feedback given by users, in which
they indicated that the system wasn’t too
helpful for the simpler questions that involved
just one rotation and that they preferred
mental rotation over physically rotating the
models in the system.
For later questions involving more than 1
rotation (10 and above), we see a sharp decline
in the performance by mental rotation, clearly
indicating difficulties faced by users in spatial

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rotation. The performance however remains
constant for web users and sees some
fluctuation with A.R users, indicating that these
systems were useful in general for questions
involving multiple rotations. In the later part
(qtns. 15 - 20) which were the toughest of the
lot and involved 2 or more rotations, we see
both web and A.R systems performing better
than mental rotation alone.
If we look at the performance based on
categorization of questions in PSVT, we see
similar results as discussed above. The
performance for questions involving one
degree of rotation (90 degrees) is same for
mental, web and A.R The performance then
decreases with increase in difficulty of
questions. The performance of web users
however does not decrease significantly and
remain constant indicating its usefulness in
answering difficult questions. The performance
with A.R systems too remains better than that
compared with mental rotation.
55
60
65
70
75
80
85
90
95
1(90) 1(180) 2(90+90) 2(90 + 180)
No.ofcorrectresponses(Outof100)
Category of Question [No. of rotations (Rotation Angle)]
PSVT performance question category wise
Mental Web AR

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200
250
300
350
400
450
500
550
1(90) 1(180) 2(90+90) 2(90 + 180)
Averagetimetaken(inseconds)
Category of Question [No. of rotations (Rotation Angle)]
PSVT time taken (in s) Question category wise
Web AR
Lastly, we also observe the time taken by users
in answering PSVT questions based on their
difficulty categories. As expected, the time
increases in general with increase in difficulty of
questions. It is interesting to note a decrease in
time for web users between questions involving
single rotation of 180 degrees as compared to
two rotations of 90 degrees each. This could be
attributed to the fact that users used the
system for the first rotation to obtain and
intermediate state and then mentally rotated
the next ninety degrees. As shown & discussed
before, time taken by users using an A.R based
system is greater than those using a web based
system for all categories.

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5
5.2
5.4
5.6
5.8
6
6.2
6.4
PEOU PU Attitude BI SA Enjoyment
TAMRating(Onascaleof7)
Attributes
TAM Ratings - Web vs AR
Web AR
Abbreviations:
PEOU – Perceived ease of use, PU – Perceived Usefulness, BI – Behavioral Intention,
SA – Self Efficacy
5. TAM
The comparison of TAM ratings (on a 7 point
lickert scale) with web and A.R systems show
positive scores for both web and A.R systems.
As expected, A.R scores more in enjoyment
attribute and at par with web in perceived
usefulness & behavioural intention. Web has a
slightly higher rating in perceived ease of use
and attitude. Increased rating for self-efficacy
for web users as compared to A.R users was
expected given that users are already
comfortable and used to web based
technologies.
These rating indicate that despite user’s
unfamiliarity with the technology and its
novelty, A.R system received almost at par
ratings with an established web based E.
Learning tool.

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5
5.2
5.4
5.6
5.8
6
6.2
6.4
PEOU
PU
Attitude
BI
SA
Enjoyment
TAM Ratings - Web vs AR
Web AR

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3
3.5
4
4.5
5
5.5
6
6.5
7
0 2 4 6 8 10 12 14 16 18 20
TamRating(Onascaleof7)
User Number
User Wise Tam Ratings (Web)
PEOU PU Attitude BI SA Enjoyment

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2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
0 2 4 6 8 10 12 14 16 18 20
TAMRating(Onascaleof7)
User Number
User Wise TAM Ratings (A.R)
PEOU PU Attitude BI SA Enjoyment

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Chapter 13 - Design Guidelines
Some proposed design guidelines based
on our experiment for A.R based E
Learning systems are as follows:
Virtual vs GUI - It has been consistently
observed that virtual buttons were a
hindrance for the users since they had to
switch their focus between the screen
and the textbook regularly. Issues with
tracking because of proximity of these
buttons was also seen as hindrance in E
learning tools. It is thus advisable to
avoid the use of virtual buttons and use
screen based GUI instead so that the
focus of the user at all times remains at
the tablet / screen and there are no
errors / tracking problems.
Don’t forget the context - The biggest
strength of A.R systems is its ability to
provide context to content, i.e, to be able
to augment content specific to the image
being tracked. This should never be
neglected while designing any A.R based
learning tools - i.e, there should always
be direct mapping between the tracking
image and the content being augmented.
Touch based Interactions: In general,
only tilt interactions are used to pan and
view the 3d models in AR apps from
different angles or zoom into the model.
But touch screen interaction like GUI
button tap, swipe and pinch zoom have
become conventions now a days in touch
devices which have made users very
much familiew and comfortable with
these interactions and they expect these
interactions in augmented reality
applications as well to interact with 3d
model. Therefore, it is advisable to
include on screen GUI buttons to access
context specific feature, swipe gesture to
rotate the model and pinch to zoom in-
out the model etc.
Two finger interaction - In case of on
screen interactions, users should be
provided at par gestures that are
available in common touch based
application these days. For example,
rotation should not only allow single
swipe, but the ability to rotate with two
fingers - by keeping one point fix with
one finger and rotate along that in the
given direction by second finger. Such
fixed axis rotation is useful for users

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desiring to view a 3D object from a
specific angle.
Content specific interactivity &
features- There should be added
functionality & interactivity to the
application based on the content. For
example, in applications in chemistry, the
feature to be able to view the entire
lattice in 3D should be provided. Also,
fixed axis rotation in content that
involved 3D visualization & rotation is
beneficial for the users.
Multimedia instruction: [Mayer, 2001]
indicated that students with low spatial
ability learn better when animation and
narration are presented in a coordinated
way. We also added audio instructions
and animation in our content on which
we got positive response from
participants. Therefore, it is always
advisable to couple your 3d models or
other main content with associated
narration and dynamic animations.
Ergonomics of handheld devices: Using
augmented reality applications on
laptops, mobile phones and tablets is still
not a very comfortable use case of it,
especially in case of e-learning apps
because it is very tiring and time-
consuming to keep the device and hand
up in the air for long time.
Collaborative learning- A.R applications
can be used as a collaborative learning
tool as well. For ex, applications can be
designed in a manner in which for a user
to correctly view the answer of the
question asked in the application, he
needs to augment his tablet over his
classmates tablet till he finds the correct
match. In this case, if our application is
used, students can collaborate with each
other for effective learning.
3D Models, Labelling: 3d models being
presented to the students should be
designed with extra care. One should be
able to distinguish them from the
background, its important parts should
be according highlighted through colors
and proper labelling should be done
wherever needed.

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Chapter 14 - Conclusion
We began off this project with immense
interest in two domains -augmented
reality and E.Learning. While we were
fascinated with the advancements and
accessibility of augmented reality in the
past few years, we saw its huge potential
in e learning especially for cases that
involved 3D visualization and spatial
thinking. We began off with literature
review of existing A.R projects in the
domain of e learning and education to be
able to identify our focus area and refine
our research aim. We consulted high
school teachers and students through
qualitative field studies to identify
specific topics in high school education in
India in which students face difficulty in
learning and visualization. We narrowed
down our area of interest to Solid State
chemistry, a topic taught to standard XII
students as a part of the NCERT
curriculum. We finalized the content of
our application and validated it with high
school chemistry teachers. Keeping A.R
design guidelines as well as E learning
guidelines in our mind, we developed an
android application that augmented 3D
models on NCERT textbooks and
provided features such as animation,
audio feedback, touch screen rotation,
virtual buttons etc. Once the application
was developed, we took qualitative
feedback about the same from high
school students, teachers and design
experts from academia to look for areas
in which it could be improved as an E
learning tool.
We were then intrigued to identify areas
in which A.R based E Learning tools could
lack if compared with existing E learning
paradigms. We designed a comparative
analysis research experiment in which we
aimed to test & compare our A.R
application with a web based E learning
tool. We therefore developed a web
based counterpart of our e learning tool
using the same 3D models, audio files,
interaction methods etc. We recruited 40
1st year engineering students for our
comparative study in which 20 users were
given an A.R based e learning system and
the other 20, a web based system. We
divided our 40 students into two groups
based on a pre questionnaire such that
both groups had similar average scores
of mental spatial ability as well as
content knowledge (solid state). All 40
users were tested across different
parameters - content learnability,

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performance & accuracy in spatial
rotation & behavioural intention for using
the systems. Apart from these
quantitative tests, we collected
qualitative feedback from our users for
both the areas to identify the system’s
strengths and weaknesses.
After analysing the data collected
through our comparative study, we
identified the strengths and weaknesses
of both A.R & Web based systems. We
then discussed areas in which A.R based
tools can be improvised and propose
guidelines that can be kept in mind while
designing E learning tools using A.R
based technologies.

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Appendix 1: Summary of Responses
Teacher Q1- Difference Q2 - Modules Important Topic
Q3 - Difficulty
(Teacher)
Q4 - Difficulty
(Student)
Q5- NCERT
sufficient?
Q6- Extra tools
A
As solid states
involves 3d
concepts, it
requires more
visualization and
imagination skills
of the students
1. Crystal lattice/ Bravis
Structure (14) 2.Cubic
structure -> Packing
Efficiency 3.Defects in
crystals
Packing
efficiency as it
involves lots of
numericals to
solve: density,
no. of voids, no.
of substituent
particles
3d concepts to
convey. HCP is
difficult
relative to CCP.
abc layer type
is tougher
Cation, anion
ratio. density.
numericals
sufficient for
12th board
syllabus but not
for competitive
exams
videos: to show how molecules
are arranged and voids are
created. 3d models: in abc-abc
both tetrahedral and octahedral
voids together. Presentations.
Spheres arrangement in
reference to room
B
Yes, as it requires
quite a bit of
visualization.
Module 1: Classification
of solids; Module 2:
Structure of Crystalline
solids-> Unit cells – close
packing – voids – rank of
unit cells – density of
cubic unit cell – density
of hexagonal unit cell;
Module 3: Structure of
simple ionic solids;
Module 4 : Defects,
Electrical and Magnetic
Properties
Module 2 and Module 3
From a simple
text book
perspective, it is
one of the best.
It tries to make
students
visualize quite a
bit.
Yes. I tried the following ball
stick models: Deluxe Version
Solid State Model Kit
(http://ice.chem.wis
c.edu/Catalog/SciKi
ts.html#Anchor-Solid-31140).
Currently I am using bits of J3D
animation from
http://www.chm.davi
dson.edu/vce/ which are
extremely effective and students
just enjoy them.
C
Need to visualize
and understand
molecular
structure in 3d
space whereas
other chapters
require lots of
calculation
Lattice, Unit cells,
arrangement, voids,
coordination no.
Arrangement ->
Visualization
In hexagonal
packing
visualization is
bit difficult and
then voids in
hexagonal
packing
To understand
3d
arrangement
and draw it on
paper.
NCERT is not
sufficient in
terms of depth
of concept.
foreign author
books can be
used for
reference
Time consuming to make slides
or use 3d models. non availability
of 3d models in market

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D
Unlike, other
chapters Solid states
includes 3
dimensional
structures and
students need to
first understand
these 3d structure
to grasp the other
concepts.
1. crystalline vs. amorphous
solids, 2. Basic 7 structure
in crystalline solid, 3.
Particle position in
structures, 4. Different Unit
cells, 5. Properties of
different crystalline
structures
How particles
are shared
among
multiple unit
cells. voids
are important
for ionic solid
Difficulty in understanding 3d
crystalline structure, voids.
Imperfection in solids
NCERT books are good
and there are some
diagrams and
explanations for 3d
concepts but not
sufficient. Other guide
books are referred.
Pictorial representation
are very good in
comparison with NCERT.
Takes help of animation and
ball- stick modals. Use
example of room to teach
arrangement of atom in
cubic unit cell and sharing
among different unit cells
E
it gives help to
understand 3-D
structures of metals
and Ionic
Compounds.
Visualization in 3-D
is required.
1. Class-1: crystalline and
amorphous solid, symmetry
elements, Formation of
unit cell, Bravias lattice,
Different types of unit cell;
Class-2: HCP and CCP
structure, Different types
of structures of ionic
crystal, Octahedral and
tetrahedral voids; Class-3:
Miller indices, Applications
defects
Topics
of class
-1 and
class -2
Visualization of
structures and how to
form a 3-D structure.
Spatial arrangement
understanding on
boards some time
become difficult for
many students
Although it is good but
not sufficient. help of
teacher is required to
interact
Unfortunately the videos
and models are not very
useful and user friendly so
they also do not provide
much help for teachers. If
we can have the
visualization of the 3-D
structure that how a
structure is formed step
wise it will help. it should be
handy and simple to use.

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Appendix 2 & 3: Image Trackers, Module 1 and 2

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Appendix 4: The VARK Questionnaire
• This section consists of 16 multiple choice questions.
• Choose the answer which best explains your preference and circle the letter(s) next to it.
• Please circle more than one if a single answer does not match your perception.
• This questionnaire is a measure of your preferences and not strengths.
Q 1. You are helping someone who wants to go to your airport, the center of town or railway station. You would:
a. go with her.
b. tell her the directions.
c. write down the directions.
d. draw, or show her a map, or give her a map.
Q 2. You are not sure whether a word should be spelled `dependent' or `dependant'. You would:
a. see the words in your mind and choose by the way they look.
b. think about how each word sounds and choose one.
c. find it online or in a dictionary.
d. write both words down and choose one.
Q 3. You are planning a vacation for a group. You want some feedback from them about the plan. You would:
a. describe some of the highlights they will experience.
b. use a map to show them the places.

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c. give them a copy of the printed itinerary.
d. phone, text or email them.
Q 4. You are going to cook something as a special treat. You would:
a. cook something you know without the need for instructions.
b. ask friends for suggestions.
c. look on the Internet or in some cookbooks for ideas from the pictures.
d. use a good recipe.
Q5. A group of tourists want to learn about the parks or wildlife reserves in your area. You would:
a. talk about, or arrange a talk for them about parks or wildlife reserves.
b. show them maps and internet pictures.
c. take them to a park or wildlife reserve and walk with them.
d. give them a book or pamphlets about the parks or wildlife reserves.
Q 6. You are about to purchase a digital camera or mobile phone. Other than price, what would most influence your decision?
a. Trying or testing it.
b. Reading the details or checking its features online.
c. It is a modern design and looks good.
d. The salesperson telling me about its features.
Q 7. Remember a time when you learned how to do something new. Avoid choosing a physical skill, eg. riding a bike. You learned best by:
a. watching a demonstration.
b. listening to somebody explaining it and asking questions.
c. diagrams, maps, and charts - visual clues.
d. written instructions – e.g. a manual or book.

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Q 8. You have a problem with your heart. You would prefer that the doctor:
a. gave you a something to read to explain what was wrong.
b. used a plastic model to show what was wrong.
c. described what was wrong.
d. showed you a diagram of what was wrong.
Q 9. You want to learn a new program, skill or game on a computer. You would:
a. read the written instructions that came with the program.
b. talk with people who know about the program.
c. use the controls or keyboard.
d. follow the diagrams in the book that came with it.
Q 10. I like websites that have:
a. things I can click on, shift or try.
b. interesting design and visual features.
c. interesting written descriptions, lists and explanations.
d. audio channels where I can hear music, radio programs or interviews.
Q 11. Other than price, what would most influence your decision to buy a new non-fiction book?
a. The way it looks is appealing.
b. Quickly reading parts of it.
c. A friend talks about it and recommends it.
d. It has real-life stories, experiences and examples.

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Q 12. You are using a book, CD or website to learn how to take photos with your new digital camera. You would like to have:
a. a chance to ask questions and talk about the camera and its features.
b. clear written instructions with lists and bullet points about what to do.
c. diagrams showing the camera and what each part does.
d. many examples of good and poor photos and how to improve them.
Q 13. Do you prefer a teacher or a presenter who uses:
a. demonstrations, models or practical sessions.
b. question and answer, talk, group discussion, or guest speakers.
c. handouts, books, or readings.
d. diagrams, charts or graphs.
Q 14. You have finished a competition or test and would like some feedback. You would like to have feedback:
a. using examples from what you have done.
b. using a written description of your results.
c. from somebody who talks it through with you.
d. using graphs showing what you had achieved.
Q 15. You are going to choose food at a restaurant or cafe. You would:
a. choose something that you have had there before.
b. listen to the waiter or ask friends to recommend choices.
c. choose from the descriptions in the menu.
d. look at what others are eating or look at pictures of each dish.

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Q 16. You have to make an important speech at a conference or special occasion. You would:
a. make diagrams or get graphs to help explain things.
b. write a few key words and practice saying your speech over and over.
c. write out your speech and learn from reading it over several times.
d. gather many examples and stories to make the talk real and practical.
Appendix 5: Solid States Questionnaire- Pre Questionnaire
Q 1. Number of atoms involved in making an octahedral void is ______________
Q 2. Number of atoms in a single face-centred cubic unit is ____________
Q3. What is co-ordination number in the context of Solid State Chemistry (Just one sentence, No need of exact definition)
Q4. If the of tetrahedral voids is N, then number of octahedral voids would be _____________
Q5. In a body centred cubic (BCC) unit cell, number of atoms which are not shared with any other BCC unit cell is _________
Q6. If length of a face centred cubic unit is A, then distance between two nearest atoms in the cell would be ___________.

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Appendix 6: Solid States Questionnaire- Main Study
Q 1. In a face centered cubic (FCC) cell, six atoms surround an octahedral void (OV). Considering an edge-centered OV in one FCC unit cell,
how many atoms (out of six) don’t belong to this unit cell?
Q 2. In an octahedral void present at the body center of the FCC unit cell, how many atoms(out of six surrounding atoms) are corner
atoms of the unit cell?
Q 3. In a face-centered cubic (FCC) cell, out of 8 tetrahedral voids, how many are shared among other FCC unit cells?
Q 4. One face- centered atom in a FCC unit cell is shared by how many tetrahedral voids? (out of the 8 tetrahedral voids in that unit cell)
Q 5. By how many face-centered unit cells is an edge centered octahedral void shared?
Q 6. Coordination number of an atom is defined as the number of touching atoms/nearest neighbor/closest surrounding atoms which is 12
in the case of a face centered cubic unit cell. In case of a corner atom in the FCC unit cell, out of these 12 neighboring atoms, how many of
them are corner atoms(of FCC unit cells) and not face centered atoms?
Q 7. Similar to the previous question, in case of a FACE-CENTERED (not corner) atom in the FCC unit cell, how many (out of of 12
neighboring atoms) are face centered atoms?
Q 8. In a face-centered cubic unit cell, 4 atoms are involved in the creation of a tetrahedral void (TV) and 6 atoms are involved in the case
of an octahedral void (OV). How many atoms in a FCC unit cell are common in both body centered octahedral void and any one tetrahedral
void (in the unit cell)?

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Appendix 7: The Purdue Visualization of Rotations Test
 This section consists of 20 questions designed to see how well you can visualize the rotation of 3D objects.
 This section has a time limit of 10 minutes.
 Each question has only one correct answer.
 Shown below is an example of the type of questions included in the test:

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Appendix 8: Technology Acceptance Model Questionnaire
(18 likert scale items on the scale of 7. Extremes labeled as “Highly Unlikely” to “Highly Likely”)
Perceived usefulness (PU)
This e-learning tool would improve my learning performance (U1).
This e-learning tool would increase academic productivity (U2)
This e-learning tool could make it easier to study course content (U3)
Perceived ease of use (PEOU)
I find this e-learning tool easy to use
Learning how to use this e-learning tool is easy for me
It is easy to become skillful at this e-learning tool
Attitude (AT)
Using this e-learning tool is a good idea
I like using this e-learning tool
It is desirable to use this e-learning tool
Behavioral intention (BI)
I intend to completely switch over to this type of e-learning tool.
I intend to increase my use of this type of e-learning tool in the future.
Assuming that I have access to this type of e-learning tool, I intend to use it.
Computer Self Efficacy (SA)
I could complete my learning activities using this e-learning tool if I had never used a system like it before
I could complete my learning activities using this e-learning tool if I had only the system manuals for reference
I could complete my learning activities using this e-learning tool if I had seen someone else using it before trying it myself
Perceived enjoyment (PE)
Using this e-learning tool is pleasurable
I have fun with using this e-learning tool
I find using this e-learning tool to be interesting

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Appendix 9: Web Quantitative Data (Part 1)
Name Roll No
VARK
Visual
VARK
Auditory
VARK
Reading
VARK
Kinaesthetic
Solid States Old
Score
Solid States New
Score
PSVT old
score
PSVT New
Score
Akkash Kohli 130205022 13 4 3 11 5 8 20 20
Vikram Aditya 130205042 5 7 5 5 4 7 19 19
Jithin Krishna CT 130205019 7 5 2 8 5 5 18 20
Ravi Kumar 130101064 7 10 9 6 4 7 18 19
Sarthak Dube 10010751 9 2 2 3 4 7
Kunj Tripathi 130108013 1 7 6 9 5 6 16 19
Pawan Kumar 130205025 6 7 5 4 4 7 16 20
Roopal Gupta 130205033 10 14 6 13 4 7 16 20
Rachit Chopra 130205028 7 11 4 9 4 7 16 18
Tarang Agarwalla 130205041 3 5 6 2 2 3 15 19
Manu Modi 130104036 9 6 4 6 6 7 16 17
J Chakri 130205017 6 7 7 8 5 2 14 18
Karale Ajinkya Ashok 130205020 1 8 4 8 3 5 14 20
Tushar Sircar 130123038 3 5 7 8 6 7 13 15
Abhishek Gupta 10010703 6 8 7 7 5 6 12 20
Rohit Yadav 130121031 3 5 5 5 6 4 12 14
Kanish Chaturvedi 130103035 1 5 3 7 4 7 11 17
Injarapu Pravalhika 130205016 5 6 8 9 1 2 11 20
Shubham Verma 130106044 3 8 3 7 3 3 10 18
Himanshu Bhatia 10010724 9 9 6 11 5 6 5 16
Mean 5.70 6.95 5.10 7.30 4.25 5.65 14.32 18.37
Standard Deviation 3.29 2.68 1.97 2.74 1.29 1.87 3.62 1.83

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Appendix 10: Web Quantitative Data (Part 2)
Name Roll No
PSVT
improvement %
PSVT total
time (sec) TAM PU TAM PEOU TAM Attitude TAM BI TAM SA TAM enjoyment
Akkash Kohli 130205022 0.00 993 5.67 6.33 6 5 7 6.67
Vikram Aditya 130205042 0.00 1546
Jithin Krishna CT 130205019 11.11 1203 6 6.33 6 6.67 6 3.33
Ravi Kumar 130101064 5.56 760 5.33 4.67 5.67 6.33 6.67 5.33
Sarthak Dube 10010751 5.67 4.67 5.67 6.33 5 5
Kunj Tripathi 130108013 18.75 651 6 5 5.33 5.67 4.67 6.33
Pawan Kumar 130205025 25.00 925 5.67 5.67 5.67 6 4.33 6.33
Roopal Gupta 130205033 25.00 2125 6.33 5.33 7 6.67 5 7
Rachit Chopra 130205028 12.50 1133 5.33 6 6.33 5 3.67 5
Tarang Agarwalla 130205041 26.67 919 6.67 5.33 6.33 6 5.67 7
Manu Modi 130104036 6.25 1151 6.67 6.67 6.67 6.33 6.67 6
J Chakri 130205017 28.57 1183 5.33 4.67 4.67 4.33 5.33 6.33
Karale Ajinkya Ashok 130205020 42.86 2403 5.67 6 6 6.33 5.33 6.67
Tushar Sircar 130123038 15.38 1019 6.33 6 6.33 6.33 7 5
Abhishek Gupta 10010703 66.67 727 5.67 3.33 6 5.67 5.67 5.67
Rohit Yadav 130121031 16.67 1044 6.33 5.67 6.33 6.33 6 6.33
Kanish Chaturvedi 130103035 54.55 1215 6.33 6.67 6 6.33 7 7
Injarapu Pravalhika 130205016 81.82 1595 6.67 3.67 6.33 7 5.33 7
Shubham Verma 130106044 80.00 1530 6 6 6 6.33 5.67 7
Himanshu Bhatia 10010724 220.00 2455 6.33 6 5.67 6 5.33 4.67
Mean 1,293.53 6.00 5.47 6.00 6.03 5.65 5.98
Standard Deviation 531.09 0.46 0.94 0.51 0.66 0.93 1.02

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Appendix 11: AR Quantitative Data (Part 1)
Name Roll No
VARK
Visual
VARK
Auditory
VARK
Reading
VARK
Kinaesthetic
Solid States Old
Score
Solid States
New Score
PSVT old
score
PSVT New
Score
Srijan Shailendra 130205037 3 9 5 11 4 8 20 18
Aditya Kaushal 130205002 11 9 7 7 4 6 19 20
Ranjan Arora 130205030 2 6 3 9 5 4 19 17
Harmeet Singh 130205015 9 12 9 9 6 6 17 18
Rajat Kumar 130205029 2 11 3 10 5 3 16 10
Jayant Jain 130205018 7 6 5 11 5 6 16 12
Chinmay Anand 130205010 9 9 4 11 5 7 16 20
Kande Rahul 130102029 6 4 8 6 4 7 16 19
Raunak Baranwal 130205032 9 13 11 9 3 7 16 18
Bidyut B. Changmai 10010716 10 3 5 6 3 7 16 19
Amarvaj Likhith 130205006 4 8 8 7 4 8 15 20
Nakul Yadav 130121019 2 5 4 5 5 7 14 14
Suprabho Dhenki 130205040 3 7 3 6 4 5 14 18
Vinay Kumar 130104076 3 6 12 7 2 6 14 17
Shivam Sachdeva 130205036 4 4 7 6 5 7 13 15
Charmie Kapoor 130205009 5 8 4 12 6 2 12 19
Mukul Chawari 130123013 2 9 5 8 4 6 12 15
Ankit Tamta 130104010 9 10 7 9 3 4 11 14
Pavani Suttaluri 10010752 4 4 7 2 2 4 10 14
Vineet Kumar 10010760 7 4 7 7 5 6 8 10
Mean 5.55 7.35 6.20 7.90 4.20 5.80 14.70 16.35
Standard Deviation 3.07 2.91 2.57 2.47 1.15 1.64 3.08 3.18

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d
Name Roll No
PSVT
improvemet %
PSVT total
time (sec) TAM PEOU TAM PU TAM Attitude TAM BI TAM SA TAM enjoyment
Srijan Shailendra 130205037 -10.00 1248 5.67 5.67 5.67 5.33 5.33 6
Aditya Kaushal 130205002 5.26 1891 5.33 6 6 6.67 5.67 6
Ranjan Arora 130205030 -10.53 1958 6 6.33 5 6.33 6.67 6.67
Harmeet Singh 130205015 5.88 2305 5.33 4 4.67 5 5.33 5.33
Rajat Kumar 130205029 -37.50 2283 6 5.33 5.67 6 6.33 6.67
Jayant Jain 130205018 -25.00 1964 5.67 5.33 5.33 6 4.33 6.33
Chinmay Anand 130205010 25.00 2312 7 7 7 7 6.33 7
Kande Rahul 130102029 18.75 1408 6 5.33 6.33 6.67 4.33 6.33
Raunak Baranwal 130205032 12.50 1209 7 7 6.67 6.67 7 7
Bidyut B. Changmai 10010716 18.75 1424 6 6 6.33 6.33 6.33 6.67
Amarvaj Likhith 130205006 33.33 1396
Nakul Yadav 130121019 0.00 2056 5 6 6 4.67 5.67 6.67
Suprabho Dhenki 130205040 28.57 2378 5.33 5.33 5.33 6.33 6 5.33
Vinay Kumar 130104076 21.43 6.33 4.67 5.33 6.67 6 5
Shivam Sachdeva 130205036 15.38 2013 6 5.33 5.33 6 5.33 7
Charmie Kapoor 130205009 58.33 2115 5 5 6.33 6 4.67 6.33
Mukul Chawari 130123013 25.00 2130 6 6 6 6.33 4.67 6.33
Ankit Tamta 130104010 27.27 2912 6.33 3 5.67 6.33 4.33 6
Pavani Suttaluri 10010752 40.00 1089 5 3 5 3.67 5 4.67
Vineet Kumar 10010760 25.00 1766 6.33 6.67 7 7 4.67 7
Mean 13.87 5.86 5.42 5.82 6.05 5.47 6.23
Standard Deviation 22.43 0.60 1.14 0.67 0.85 0.84 0.70