Download - TUFTS UNIVERSITYare Human Factors and Tangible User Interfaces respectively. Thankyou. At Tufts University in Boston I have collaborated with Ben Gemmill and Addie Sutphen, with assistance

The Development of Robotable

A Hands-On Tabletop Environment to Support

Engineering Education

A thesis submitted by

Paul S. Mason

In partial fulfillment of the requirements for the degree of

Master of Science

in

Mechanical Engineering

TUFTS UNIVERSITY

June 2005

Copyright 2005 by Paul S. Mason

All Rights Reserved

Adviser: Chris Rogers Ph.D.

Abstract

Citizens in the 21st century must keep pace with the ever-growing demands of an

increasingly technological society that is propelling the world towards a global econ-

omy. A global economy challenges all nations to increase industrial competitiveness,

and this is done primarily through innovation. Engineers, in particular, require inno-

vation for the great role they play creating wealth through the application of science

and technology in society.

The current education system is, however, not equipping students adequately for

their role in the 21st century. The pressure of today’s educational environment has

produced a “teach to the test” culture that is stifling student creativity.

One organization helping teachers bring passion, innovation, and independence

of thought into classrooms is Tufts University’s Center for Engineering Educational

Outreach (CEEO). The work presented in this thesis is closely associated with the

CEEO and documents the development of a compelling tabletop learning environ-

ment, the Robotable. The Robotable is a platform that supports new and existing

technologies to facilitate the kind of interactions that enhance learning. It is simply

a frosted tabletop that acts as a rear-projection screen. A computer screen is pro-

jected on to it via a mirror below. Instructional content can guide the learner through

hands-on activities that explore engineering concepts. Optical tracking enables the

position and orientation of objects, such as Lego robots, to be known at any time.

This enables participants at separate locations to share in an activity via the Internet.

They can see two dimensional projections of remote robots navigating their tabletop

with their own robot.

Preliminary tests have shown that the Robotable environment is significantly more

stimulating than conventional forms of delivering learning activities, and learning on

the Robotable significantly improves subsequent application of the given content.

ii

Acknowledgements

I sincerely thank all of the people who have been involved in the development of

Robotable’s concept, hardware and software. The initial concept for this work came

from Chris Rogers, who is academic advisor to all contributors at Tufts University. His

guidance has been essential to the project. Our collaborators at Lincoln University

in Canterbury, New Zealand, are lead by Alan Mckinnon and Keith Unsworth. Their

insight and judgement is always appreciated.

My committee members Caroline Cao and Robert Jacob have also been the lec-

turers for the two courses I have found most interesting, and useful, at Tufts. They

are Human Factors and Tangible User Interfaces respectively. Thankyou.

At Tufts University in Boston I have collaborated with Ben Gemmill and Addie

Sutphen, with assistance from Meredith Knight, Barbara Bratzel, Catherine Petron-

ino and Elissa Milto. At Lincoln University in New Zealand, Carl Pattie, Craig Oliver

and Jonathan Festing have done great work. I particularly appreciate their willingness

to bounce ideas back and forth.

Thank you to my friends from Tousey and McCollester houses, and everyone who

has studied at Tuftl or worked at the CEEO. Their good humor has, more than

anything else, made the past twenty two months a genuine pleasure.

I also wish to acknowledge my mother Valerie, my sisters Pam and Alice, and my

brother Tom for the example they set and the support they give.

Last but not least, thank you to Essie for being sole care giver to two black

labradors, Rousseau and Descartes, for three years while I am away.

iii

Contents

Abstract ii

Acknowledgements iii

List of Tables viii

List of Figures ix

1 Introduction 1

1.1 A Changing Society Challenges Nations and Engineers . . . . . . . . 1

1.1.1 Shortage of Engineers . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.2 An Increasingly Technological Society . . . . . . . . . . . . . . 2

1.1.3 Globalization . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1.4 How to Succeed in a Global Economy . . . . . . . . . . . . . . 3

1.2 Challenges to Education . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.2.1 Performance of the Current Education System . . . . . . . . . 5

1.2.2 Center for Engineering Educational Outreach . . . . . . . . . 6

1.2.3 Constructivism and Constructionism . . . . . . . . . . . . . . 7

1.2.4 The George Lucas Educational Foundation . . . . . . . . . . . 8

1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2 Literature Review 12

iv

2.1 Review of Distance Learning . . . . . . . . . . . . . . . . . . . . . . . 12

2.1.1 Technology and Distance Learning . . . . . . . . . . . . . . . 12

2.1.2 The Internet and Distance Learning . . . . . . . . . . . . . . . 14

2.2 Transactional Distance and Empathic Communication . . . . . . . . . 16

2.3 Tangible Interfaces: MIT . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.4 Augmented Reality: HITLab . . . . . . . . . . . . . . . . . . . . . . . 18

2.4.1 Communication Space and Task Space . . . . . . . . . . . . . 18

2.4.2 The HI-SPACE Table . . . . . . . . . . . . . . . . . . . . . . . 19

2.4.3 The ARToolkit . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.6 Specific Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3 Robotable: An Overview 24

3.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.2 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.2.1 Robotable Online (Ben) . . . . . . . . . . . . . . . . . . . . . 27

3.2.2 Image Processing (Carl) . . . . . . . . . . . . . . . . . . . . . 29

3.2.3 Activity Card Toolkit (Craig) . . . . . . . . . . . . . . . . . . 29

3.2.4 Calibration, Whiteboard, Activity Prototyping, and General

Integration (Paul) . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4 Hardware 36

4.1 Table Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.2 Table Top . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.3 Mirror and Projector . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.4 Cameras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

4.4.1 Software Access to Cameras . . . . . . . . . . . . . . . . . . . 47

4.4.2 Tracking from Above vs. Tracking from Below . . . . . . . . . 47

v

4.4.3 Tracking with IR vs. Tracking with Visible Light . . . . . . . 50

5 Software 54

5.1 Conferencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

5.2 Whiteboard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.3 Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5.3.1 Electronic Activity Cards . . . . . . . . . . . . . . . . . . . . 59

5.3.2 Web-based Activity Cards . . . . . . . . . . . . . . . . . . . . 63

5.4 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

6 Testing and Evaluation 67

6.1 Experiment Description . . . . . . . . . . . . . . . . . . . . . . . . . 67

6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

6.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

6.3.1 Times to Task Completion . . . . . . . . . . . . . . . . . . . . 73

6.3.2 Subjective Data . . . . . . . . . . . . . . . . . . . . . . . . . . 79

6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

7 Future Work 82

7.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

7.1.1 Tabletop screen . . . . . . . . . . . . . . . . . . . . . . . . . . 82

7.1.2 Mirror . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

7.1.3 Tangible Devices and Augmented Reality . . . . . . . . . . . . 83

7.1.4 Portability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

7.2 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

7.2.1 Development of Instructional Content . . . . . . . . . . . . . . 84

7.2.2 Integration and Testing of the Robotable Internet Server . . . 85

A Data and Analysis 86

vi

Bibliography 92

vii

List of Tables

4.1 Epson PowerLite S1 specifications. . . . . . . . . . . . . . . . . . . . 43

6.1 Summary of statistics of the times to task completion. . . . . . . . . 75

6.2 ANOVA: Two-actor with replication for times to task completion. . . 76

6.3 Differences of means for comparing with Q(σd) values. . . . . . . . . . 78

6.4 Calculating Q(σd). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

6.5 Excerpt from a table of Q-values. . . . . . . . . . . . . . . . . . . . . 79

6.6 Critical values of ±z. . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

A.1 Wilcoxon signed rank test for data from the question rating the activ-

ities from Dull to Stimulating. . . . . . . . . . . . . . . . . . . . . . . 91

viii

List of Figures

2.1 Classroom2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.2 Maratech. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3 Comparing face-to-face collaboration with computer supported work

(Billinghurst). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

(a) The task space is contained within the communication space. . 19

(b) The task space is separate from the communication space. . . . 19

2.4 The HITLab’s virtual dig exhibit at the Seattle museum (HITLab). . 20

2.5 3D virtual object overlaid on the real world (HITLab). . . . . . . . . 21

2.6 Tracking based on ARToolkit. . . . . . . . . . . . . . . . . . . . . . . 21

(a) Incoming video stream. . . . . . . . . . . . . . . . . . . . . . . . 21

(b) Threshold and find squares. . . . . . . . . . . . . . . . . . . . . 21

(c) Calculate 3D position and orientation. . . . . . . . . . . . . . . 21

3.1 Schematic of the Robotable showing key features. . . . . . . . . . . . 25

3.2 Activity cards inspired by a variety of metaphors. . . . . . . . . . . . 33

(a) eBook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

(b) Electronic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.3 Web based activity card. . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.1 Mimio capture bar and pen. . . . . . . . . . . . . . . . . . . . . . . . 37

4.2 The basic Robotable. . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.3 Cross-section of 1530 and 1530-Lite. . . . . . . . . . . . . . . . . . . . 41

ix

4.4 Ghosting is more noticeable with a larger angle of incidence near the

top of the mirror. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

(a) Case 2 - mirror at 45◦. . . . . . . . . . . . . . . . . . . . . . . . 45

(b) Double reflection causes ghosting. . . . . . . . . . . . . . . . . . 45

4.5 Case 3 - projector aimed down (used at Tuftl). . . . . . . . . . . . . . 45

4.6 Cameras currently used on the Tuftl Robotable. . . . . . . . . . . . . 46

(a) iSight with iChat for conferencing (Apple). . . . . . . . . . . . . 46

(b) Channel Vision 5124 B&W night vision for IR tracking (Channel

Vision). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.7 SightFlex (MacMice). . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.8 Marker placement for tracking from above and below. . . . . . . . . . 49

(a) Marker attached to the topside. . . . . . . . . . . . . . . . . . . 49

(b) Marker attached to the underside. . . . . . . . . . . . . . . . . . 49

4.9 The quality of the image deteriorates as distance from the tabletop

increases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

(a) Marker 1 to 2mm from frosted tabletop. . . . . . . . . . . . . . 49

(b) Marker 16mm from frosted tabletop. . . . . . . . . . . . . . . . 49

4.10 850nm longpass filter. . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.11 Non-uniform intensities viewed from above the table. . . . . . . . . . 52

4.12 Image processing is easy without a projection. . . . . . . . . . . . . . 52

(a) Image without a projection. . . . . . . . . . . . . . . . . . . . . 52

(b) Marker detection successful. . . . . . . . . . . . . . . . . . . . . 52

4.13 Image processing fails with a projected texture. . . . . . . . . . . . . 53

(a) Image with a projected texture. . . . . . . . . . . . . . . . . . . 53

(b) Marker detection fails. . . . . . . . . . . . . . . . . . . . . . . . 53

5.1 Robotable whiteboard (March 2005). . . . . . . . . . . . . . . . . . . 58

5.2 Electronic activity cards enable rich media content. . . . . . . . . . . 60

x

5.3 Greyscale image plus lookup table equals terrain. . . . . . . . . . . . 61

5.4 Viewing pages of a Fable. . . . . . . . . . . . . . . . . . . . . . . . . 62

(a) Title with credits. . . . . . . . . . . . . . . . . . . . . . . . . . 62

(b) Story in images and text. . . . . . . . . . . . . . . . . . . . . . 62

(c) The moral of the fable. . . . . . . . . . . . . . . . . . . . . . . . 62

5.5 Tracking based on ARToolkit. . . . . . . . . . . . . . . . . . . . . . . 64

(a) Defining a reference for the projection. . . . . . . . . . . . . . . 64

(b) Construction for finding normalized coordinates. . . . . . . . . . 64

5.6 Finding position and orientation from the marker’s ordered vertices. . 66

6.1 Frustrating to satisfying. . . . . . . . . . . . . . . . . . . . . . . . . . 71

6.2 Dull to stimulating. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

6.3 Difficult to easy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

6.4 Comparing data for the time to completion of the first activities. . . . 74

6.5 Comparing data for the time to completion of the second activities. . 74

6.6 Graph of mean times to completion. . . . . . . . . . . . . . . . . . . . 77

A.1 The age and gender of participants in the study. . . . . . . . . . . . . 87

A.2 Comfort with computers. . . . . . . . . . . . . . . . . . . . . . . . . . 87

A.3 Experience with ROBOLAB™. . . . . . . . . . . . . . . . . . . . . . . 87

A.4 Experience with spreadsheets. . . . . . . . . . . . . . . . . . . . . . . 88

xi

Chapter 1

Introduction

1.1 A Changing Society Challenges Nations and

Engineers

1.1.1 Shortage of Engineers

Many people believe that the United States is facing a shortage of scientists and en-

gineers. In 2001 the U.S. Bureau of Labor Statistics predicted that the number of

jobs in science and engineering would increase 47% by the year 2010. Meanwhile,

the National Science Board (NSB) reported undergraduate engineering enrollments

declined by more than 20% from 1983 to 1999[1]. Two years later a National Science

Foundation (NSF) study revealed that this trend has reversed and that enrollments

have increased every year since 1999[2]. The NSF report also states that full-time,

first-time graduate enrollment of foreign students in these fields declined by about

8% in 2002, which is thought to be due to a restriction on temporary visas since the

9/11 terrorist attack. In contrast to this, full-time, first-time science and engineering

graduate enrollment increased almost 14% for U.S. citizens and permanent residents.

So it seems that trends are volatile and, it turns out, there are a number of critics

1

CHAPTER 1. INTRODUCTION 2

do not agree with forecasts of an impending dearth of scientists and engineers. How-

ever, even the critics agree “that America’s science-and-engineering machine faces

significant challenges in a world much altered by global competition and increasing

diversity at home.”[3].

1.1.2 An Increasingly Technological Society

In an increasingly technological society, engineering and science skills can help people

to function more effectively and adapt to change. All citizens should have the oppor-

tunity to develop these skills, irrespective of attributes such as gender, ethnicity, or

socio-economic background. Currently, women and minorities are under-represented

in engineering professions, although an NSF document[2] shows that enrolments based

on gender and ethnicity have shown gains in recent years. Susan Staffin Metz, Presi-

dent of Women in Engineering Programs and Advocates Network, says there is a need

to encourage all students to “pursue a career in engineering so the United States can

continue to meet the ever-growing needs of its technology based society.”[4] Legal

Affairs Editor for The Economist, David Manasian, says, “Despite the dotcom boom

and bust, the computer and telecommunications revolution has barely begun. Over

the next few decades, the internet and related technologies really will profoundly

transform society.”[5] All citizens need to have a basic understanding of the processes

and uses of engineering and technology to make informed choices[6]. In England,

Chair of the Royal Society Education Committee, Sir Alistair MacFarlane, said in

a statement on 17 December 2003, “We live in an increasingly technological world,

and we need, as a nation to have a workforce that includes highly skilled scientists,

engineers, doctors and technicians, and a citizenry able to understand, appreciate

and act upon the consequences of advances in scientific knowledge.” Although he

was speaking in the United Kingdom, his words are just as relevant to the United

States. While the general population must cope with an increasingly technological


world, the greatest challenge to scientists and engineers, and to the prosperity of all

nations, over the next few decades comes from globalization.

1.1.3 Globalization

One inevitable and irreversible consequence of the forward march of technology is

globalization[7]. Globalization refers to the spread of free-market capitalism through-

out the world. In a global economy research, manufacturing, and skilled professionals

will go where the economic climate is best. According to the President of the Ameri-

can Society of Mechanical Engineers (ASME) Harry Armen, the disturbing feature of

globalization is that there appears to be no rules; some nations will suffer and some

will thrive. The challenge for all nations, including the United States, in today’s

global economy is to increase industrial competitiveness[8].

The true wealth of an organized entity, be it a company or a country, resides

in its human capital. Engineers play a great role in the creation of wealth through

the application of science and technology in society[9]. In June 2000, President Bill

Clinton said “Our passion for discovery, our determination to explore new scientific

frontiers, and our can-do spirit of technological innovation have always driven this

Nation forward.” It is this capacity to innovate that will determine leadership in the

era of globalization.

1.1.4 How to Succeed in a Global Economy

To ensure the United States remains competitive in a global economy requires invest-

ment in its human capital, which includes:

• Research and development,

• education,

• increasing the diversity of our science and engineering workforce,


• attracting the brightest youngsters into the profession,

• and collaborative partnerships between industry, academia and government[8].

In an article titled, Making Connections: The Role of Engineering and Engineering

Education, the Deputy Director of the National Science Foundation, Dr. Joseph

Bordogna suggests what engineers need to succeed:

“To be successful and to promote prosperity, engineers must exhibit more

than first-rate technical and scientific skills. In an increasingly competitive

world, they must help us make good decisions about investing enormous

amounts of time, money, and human resources toward common ends. I

like to think of the engineer as someone who not only knows how to do

things right, but also knows the right thing to do. This requires that he

or she have a broad, holistic background. Since engineering itself is an

integrative process, engineering education must likewise be integrative.

For example, engineers must be able to work in teams and communicate

well. They must be flexible, adaptable, and resilient. Equally important,

they must be able to employ a systems approach in their work, to make

connections within the context of ethical, political, international, environ-

mental, and economic considerations.” (Joseph Bordogna, 1997)[9]

Revered management thinker, Peter F. Drucker, defines innovation as making and

profiting from new things, as opposed to productivity, which implies simply making

existing things more efficiently. It is innovation that drives economic growth and

determines a nation’s competitiveness in a global economy. Therefore, education

needs to cultivate this quality in today’s students to produce tomorrow’s leaders.

Engineers will also require a commitment to lifelong learning in order to hone their

intellectual skills and revitalize their talents for innovation and creativity[8]. Dr.


Bordogna says, “A critical element in the innovation process is scientific inquiry, an

analytic, reductionist process that involves delving into the secrets of the universe to

discover new knowledge.” He also believes that engineers should participate in the

process of engineering throughout their educational experience. In addition to the pre-

requisite mathematical and scientific skills, engineers should have an understanding

of risk, and enjoy the excitement of facing an open-ended challenge and creating

something new[9].

1.2 Challenges to Education

The challenge for the education system is to provide all students with the kind of

training that will equip them to be confident and effective citizens throughout their

lives. This section looks at the performance of the current education system, and

introduces some theories and practices that can bridge the gap between what exists

and what is required.

1.2.1 Performance of the Current Education System

The Department of Education does not feel that the existing system is producing the

required results:

“Upon graduating from high school, few students have acquired the math

and science skills necessary to compete in the knowledge-based econ-

omy.”(U.S. Department of Education, 2004)[10]

So current educational practices are not producing the required results. Educa-

tional policies driven by standards and the No Child Left Behind (NCLB) law have

put pressures on State Education Departments, schools and teachers to deliver higher

test scores on limited budgets[11]. Critics of these policies say President Bush was


ill advised and has ushered in an age of rigidity in education. Classrooms are full of

teachers who “teach to the test”, which has the effect of stifling student creativity[12].

A fall 1998 survey by the National Center for Education Statistics found that

47% of teachers don’t use software at all for instruction. Nearly four out of every 10

of these teachers said they don’t have enough time to try out software, and almost

as many said they don’t have enough training on instructional software. Most K-5

teachers only hold a general education degree and their awareness of, and comfort

level with engineering and science is minimal. One third of the teachers who don’t

use software do not have a computer in their classroom and 40% have just one or

two. However, since 1998 things have improved. Between 1998 and 2003 the ratio of

students to instructional computers with Internet access in public schools decreased

from 12.1:1, to 4.4:1[13]. Although, to make a difference in learning, teachers must

know how to use the digital content in their classrooms. This underlines the need for

professional development and teacher support. One organization that offers a range of

programs to meet this need is Tufts University’s Center for Engineering Educational

Outreach (CEEO).

1.2.2 Center for Engineering Educational Outreach

The CEEO’s mission is to increase people’s knowledge and awareness of, and comfort

with, science and technology. It provides workshops, institutes, conferences, and sum-

mer programs for children and teachers. It has created research and degree programs

at Tufts University that have combined disciplines such as engineering, education,

child development, computer science, and psychology. One project investigates how

children and teachers learn engineering, and another looks at how to create opti-

mal learning environment for engineering education. The Student Teacher Outreach

Mentorship Program (STOMP) and the GK-12 programs pair graduate and under-

graduate engineering and computer science fellows with school teachers to help them


infuse engineering concepts and activities into their lessons. The GK-12 fellows serve

as a technical resource in the classroom, helping the teachers to create and imple-

ment hands-on engineering-based projects and curricula. An emphasis is placed on

creating activities that appeal to both genders, and the program aims to encourage en-

gineering students to make educational outreach an ongoing commitment throughout

their lives. The CEEO’s efforts to bring engineering to the classroom are grounded

in constructionist philosophy, which maintains that people learn better when they

are working with materials that allow them to design and build artifacts that are

meaningful to them[14].

1.2.3 Constructivism and Constructionism

Constructivism is a theory of knowledge developed by Jean Piaget. He argues that

children are not simply empty vessels into which knowledge can be poured, but they

are theory builders who actively construct and rearrange knowledge based on their

experiences in the world. Constructivism is regarded as a learning theory more than

a teaching approach[15]. The “open system” approach of constructivism, where the

content is not pre-specified, the learner determines the direction of the lesson, and

assessment is more subjective, does not appeal to teachers under pressure to “teach

to the test”.

Seymour Papert was a colleague of Piaget’s in the late 1950s and early 1960s. In

1993, he wrote that education “remains largely committed to the educational philoso-

phy of the late nineteenth and early twentieth centuries.”[16]. By this, he is referring

to the objective behaviorist/cognitive learning theories that readily lend themselves to

instructional design. He was convinced of Piaget’s theory of knowledge but wanted

to extend it to learning and education. Papert called his theory constructionism,

which asserts that constructivist learning happens particularly well when people are

engaged in building something external to themselves. In this way they are not only


constructing their own knowledge, they are simultaneously constructing a product

such as a sand-castle, machine, computer program, or a book. Piaget recognized

what he called concrete thinking, which is thinking with and through physical ob-

jects. He believed concrete thinking was used by children, who replaced it with more

abstract formal thinking when they grew up. Papert, however, believes concrete

thinking is complementary to formal thinking and applies to adults as well as chil-

dren. In fact he says, “that a prevailing tendency to overvalue abstract reasoning is

a major obstacle to progress in education”[16]. Constructionism is a way of making

formal, abstract ideas and relationships more concrete, more visual, more tangible,

more manipulative, and therefore more readily understandable.

1.2.4 The George Lucas Educational Foundation

The George Lucas Educational Foundation (GLEF) is a nonprofit operating foun-

dation that documents and disseminates information about exemplary programs in

K-12 schools to help these practices spread nationwide. The foundation uses the

word Edutopia to refer to their vision of an ideal educational landscape, where stu-

dents are motivated to learn and teachers are energized by the excitement of teaching.

GLEF’s mission and goals are included here because they support those of the CEEO,

NSF, ASME, the Department of Education, the Federal Government, and many more

organizations who wish to see education revitalized. The GLEF advocates commu-

nity partnerships and the need for volunteers to connect students to the real world.

There are 13 topics that the GLEF believes represent the critical elements in public

education. They are included here because they foster the skills needed by future cit-

izens, engineers and scientists. They also serve as guiding principles while developing

learning tools and instructional content.

• Assessment: Use real-world performance assessments in addition to standard-

ized tests.


• Business partnerships: Make learning more challenging and relevant.

• Community partnerships: Community volunteers connect students to the real

world.

• Digital divide: Bridge the divide by allowing all individuals and communities

access to technology resources and training.

• Emotional intelligence: Helping students develop skills to manage their emo-

tions, resolve conflict non-violently, and respect differences.

• Mentoring: Provides benefits for student teachers, new teachers, and veteran

teachers.

• Ongoing professional development: Opportunity to learn from other teachers,

and exposure to the latest research, knowledge, and technology.

• Parents Involvement: Increases learning and self-confidence for students, morale

and support for teachers, understanding for parents.

• Project-based learning: Increases self-direction and motivation, improves re-

search and problem solving skills, results in deeper understanding of subject

matter.

• School-to-career: Provide career exploration opportunities such as job-shadowing,

internships, mentoring, and career counseling.

• Teacher preparation: More practice to supplement theory in schools of educa-

tion.

• Teacher integration: Internet and multi-media.

• Technology professional development: Providing teachers information, training

and assistance to ensure that new technology tools benefit student learning.


When effectively integrated into the curriculum, technology tools can extend learn-

ing in powerful ways (www.edutopia.org). The Internet and multi-media can provide

students and teachers with:

• Access to up-to-date primary source material.

• Ways to collaborate with students, teachers, and experts around the world.

• Opportunities for expressing understanding via images, sound and text.

1.3 Summary

An increasingly technological society and a global economy have challenged the na-

tion’s citizens, particularly engineers and scientists, to achieve a higher level and a

more diverse range of skills. Apart from having first-rate technical and scientific skills

engineers will need to be creative and innovative. They will need to enjoy the ex-

citement of facing an open-ended challenge, and to work in teams and communicate

well. They will need to be flexible, adaptable and resilient, and to make connections

within the context of ethical, political, international, environmental, and economic

considerations.

The current education system is not providing graduates with the skills they need

to excel in the increasingly technological 21st century. The education system has

issues which need to be addressed in order to produce the quantity and quality of

engineers that can lead the world in a global economy. Standards driven educational

policies produce an environment that does not foster essential qualities such as pas-

sion, innovation, and independence of thought. A revolution is required to move

into an ”age of learning”. This huge and essential change will require involvement of

community, encouragement of educational ”diversity”, decentralization, fostering of

personal teaching styles, and the involvement of parents, teachers and students[16].

The fact is that teachers need help. The CEEO is one organization that is providing


help. The CEEO advocates and delivers practices that can make classrooms an excit-

ing and empowering learning environment that will equip students to live confidently

and successfully in the new millennium. This thesis describes the development of a

hands-on interactive tabletop environment (Robotable) that is designed to support

the outreach mission of the CEEO. Robotable is a platform that supports learning

activities in Papert’s constructionist style. It can provide collaborative online learn-

ing, or independent training for community volunteers in preparation for outreach

work.

Chapter 2

Literature Review

Topics have been selected for review if they contribute towards the understanding

or enhancement of distance education, communication, learning, collaboration, or

human-computer interaction.

2.1 Review of Distance Learning

2.1.1 Technology and Distance Learning

Distance learning and technology have been closely related for thousands of years. The

first significant technology was paper. A variety invented in China, using pounded

mulberry bark, was superior to the Egyptian’s papyrus because ink could penetrate

the surface. This made it suitable for legal documents. It took about 400 years for

Chinese paper to make its way through the Arab world to Europe, carrying mathe-

matics with it.

Gutenberg’s printing press revolutionized distance education. Until the printing

press the only kind of document were manuscripts, either originals or carefully copied

by scribes. The printing press enabled the rapid diffusion of knowledge throughout

the world. It ushered in an Age of Enlightenment, paved the way for democracy, and

12

CHAPTER 2. LITERATURE REVIEW 13

facilitated the international communication and co-operation of scientists[17].

An important mechanism for the movement of documents was the postal system.

A number of older civilizations, including China’s Chou dynasty and the Roman

Empire, had very good postal systems. With the collapse of the empire in the west,

the postal system became fragmented, but lingered until finally falling into disuse

around the 9th century. The growth of commerce during the Renaissance and the

need for business correspondence motivated the re-emergence of the postal system.

The modern postal system, using a fixed rate pre-paid stamp rather than cash on

delivery, appeared in the early 19th century and soon after correspondence courses

appeared.

Radio was invented in the early 20th century. Within a few decades programs,

including news, could be broadcast almost instantaneously across nations. By the

mid-20th century television was established and promised great things as an instruc-

tional tool. However, radio and television suffered two major drawbacks, they were

a “live” medium and they were one-way[18]. Other technologies such as the phono-

graphs, audio and video tapes, and copying equipment allowed a variety of course

materials to be produced and duplicated with ease.

With the introduction of microwave and satellite technologies, radio and television

could be broadcast much further and at a cheaper rate than with previous systems.

As the cost of reception equipment has reduced in the last couple of decades, the

number of distance education courses using these mediums has increased. However

they are still one-way, which prompts critics to complain that education should be

”more than a passive transmission of academic information”[19].


2.1.2 The Internet and Distance Learning

Beginning in the late 1960s the U.S. Department of Defense began funding research

on networking using a variety of technologies. By 1982 the Defense Advance Re-

search Projects Agency (DARPA) had a prototype Internet in place running TCP/IP

software. DARPA negotiated a contract with Berkeley so that the next distribution

of UNIX incorporated TCP/IP software. The Internet quickly became popular at

other universities and proved invaluable to scientists and engineers. NSF then took a

leadership role in funding the development of the Internet. Since the mid 1980s the

Internet has grown exponentially, from approximately 2000 computers on the Internet

in 1985 to 73,000,000 in the year 2000[20].

With the growth of the Internet in the 1990s came proclamations of a new era

in distance learning. Unfortunately, distance learning applications have fallen short

of many of their initial promises[21]. Presented here are two case studies of Internet

based learning applications that illustrate two different approaches. Classroom 2000

is typical of the type of one-way distance learning applications that appeared during

the 1990s, while Maratech is designed to be a self-contained, fully-interactive online

learning environment.

Case Study I: Classroom 2000

Classroom2000 is primarily a way of using technology to supplement a standard class.

Students attend a lecture in the usual way, except that the room is equipped with

video cameras, microphones, a screen for projecting slide shows, and an electronic

whiteboard (ZenPad) that allows the lecturer to annotate slides. The different media

streams are time-stamped and automatically integrated into a web page (see Figure

2.1). Later, students can use the page to review certain parts, or they can play the

entire lecture if they missed class. While viewing a slide, students can click on the

teacher’s annotations to replay the audio and video at the time the ink was written.


Figure 2.1: Classroom2000.

Classroom2000 does not claim to be a distance learning application although they

do say they can “later recreate the lecture experience”. Its format is very similar

to other, earlier, applications that did claim to be distance learning tools. As a

distance learning tool, this type of application can be criticized for being one-way.

Classroom2000, however, assumes that students are present in class where they have

an opportunity for two-way interaction.

Case Study II: Maratech

Maratech provides two-way interaction with an application that is specifically de-

signed for distance learning1. Remote students are able to join classes from their

homes or schools using a laptop or PC with a headset and webcam. The groups can

see and talk with each other, interact with their teacher, and share documents and

applications over the Internet (see Figure 2.2). It can run on Mac, Linux or Windows,

incorporates echo cancelation, and provides end-to-end encryption, group or private

1It is also designed for managing administration, training, and collaborative international researchprojects.


Figure 2.2: Maratech.

chat, and the facility to record meetings and lectures for archiving, distribution, or

playback. Maratech gets around firewall issues by hosting public or private meeting

rooms that participants can join.

2.2 Transactional Distance and Empathic Commu-

nication

The criticism of most distance learning technologies is that they are one-way. This

is true for television and, to a lesser extent, for radio because the cost of two-way

communication in these mediums is largely prohibitive. The attraction of Internet

based technologies is that two-way audio and video is possible at a greatly reduced

cost.

The role of interaction in learning has been highlighted by education researchers.


In 1983 Michael Moore introduced the concept of Transactional distance, as opposed

to geographic distance. To enhance learning one must reduce transactional distance,

which depends on structure and dialog[22]. Structure is a measure of an educational

program’s responsiveness to learners individual needs, and dialog is the extent to

which learner and educator are able to respond to each other. A rigid lesson structure

inhibits interaction, while dialog depends on the quality of interaction technologies.

Moore identified three key interactions:

• Learner - content,

• Learner - instructor,

• Learner - learner.

Transactional distance is reduced and learning is enhanced by facilitating these kinds

of interactions.

Holmberg theorized that a more personal/conversational style is more conducive

to learning. In other words, if you explain things in simple language then everyone

can understand it. He notes that some educators do not like this approach because

they are afraid that they will not appear “scholarly” and will lose some “academic

dignity”. Nevertheless, students prefer it and it is more effective.[23]

2.3 Tangible Interfaces: MIT

Pioneering work developing Tangible User Interfaces (TUIs) has been done by the

Tangible Media Group at MIT. A goal of the group is to bridge the divide between

the physical world and cyberspace so that one can seamlessly interact with objects

from both worlds [24]. A TUI is one in which real world objects are used as com-

puter input and output devices. A tangible input device is a physical object whose

manipulations of mapped one-to-one to virtual object operations [25]. Tangible input


devices are generally space-multiplexed, which means that each object has a single

function. Because these tools are dedicated to a specific task they are typically very

good at that task. Objects in a toolbox are space-multiplexed devices, for example,

a hammer, a screw-driver, or a pair of pliers. These objects can be extremely intu-

itive because their have physical properties naturally suggest how they can be used.

An advantage afforded by space-multiplexed devices is that several devices can be

manipulated simultaneously. This facilitates collaboration and teamwork in a way

that a single input device cannot. Input devices can be categorized as being either

space-multiplexed or time-multiplexed. A time-multiplexed device is a single physi-

cal object that controls different functions at different points in time. For example,

a computer mouse.

2.4 Augmented Reality: HITLab

The Human Interface Technology Lab at Washington University in Seattle, and now

in New Zealand, have pioneered new ways for humans to interact with computers.

They have investigated some of the social factors and unique technical challenges

presented when using computers to enhance collaboration. It is worth taking note of

the communication space, the task space, and the display space.

2.4.1 Communication Space and Task Space

In a typical face-to-face collaboration around a table the task space encompasses the

volume on and above the table top. The communication space is a little broader and

includes all the participants (see Figure 2.3(a)). While an object on the table may

be the focus of attention, it is easy to maintain good communication with others who

are across the table, or in our peripheral vision. Our communication “bandwidth” is

broad because a rich variety of communication cues are present. Visual cues include

gaze, gesture, facial expression and body position. Audio cues include everything


(a) The task space is contained within the communi-cation space.

(b) The task space is separate from thecommunication space.

Figure 2.3: Comparing face-to-face collaboration with computer supported work(Billinghurst).

verbal such as words, inflection, pitch, emphasis, pace, rhythm, pause, volume, and

sounds that are not words such as ”uh-huh”. There are also environmental cues

such as object manipulation, writing and drawing, spatial relationships and object

presence.

By comparison, computer supported collaborative work is generally characterized

by a separation of the task space and the communication space (see Figure 2.3(b)).

Participants are usually facing the same way; towards the computer monitor. This

introduces a functional seam in the workspace and reduces the number of effective

communication cues[26].

The lesson from this is that good communication is best facilitated by providing

an environment that supports the greatest number of communication cues.

2.4.2 The HI-SPACE Table

The HITLab proposes that the key to developing the next generation human to

information interface is to move beyond the limitations of small computer monitors

as our only view into the electronic information space and keyboards and mice as

the only interaction devices. Our physical information space, which includes walls,


Figure 2.4: The HITLab’s virtual dig exhibit at the Seattle museum (HITLab).

tables, and other surfaces, could also be our view into the electronic information space.

This line of thinking resulted in construction of the HI-SPACE table. The top is an

interactive display surface and the physical table environment affords collaboration

and natural face-to-face communication. An example application of the HI-SPACE

table is the Virtual Dig exhibition, which appeared at the Seattle Museum from May

to August 2001. In Figure 2.4 participants are asked by the narrator to help in

excavating a new archaeological site in the Sichuan province. As the brushes move

over the table, the virtual grass and dirt are removed to reveal a layer of artifacts.

2.4.3 The ARToolkit

The ARToolkit is an open source, cross-platform software library for building Aug-

mented Reality (AR) applications. AR applications overlay virtual content on to our

view of the real world as shown in Figure 2.5. The core of ARToolkit is an optical

tracking system that tracks markers. The markers are squares containing a unique,

asymmetric identifying pattern. Each frame of the incoming video stream is processed

as shown in Figure 2.6. First, the image is converted to greyscale and a threshold is

applied (Figure 2.6(b)). This image is then searched for squares and, knowing the

size of the marker and the camera parameters, it is possible to extract the 3D position


Figure 2.5: 3D virtual object overlaid on the real world (HITLab).

(a) Incoming video stream. (b) Threshold and findsquares.

(c) Calculate 3D positionand orientation.

Figure 2.6: Tracking based on ARToolkit.

and orientation of the markers with respect to the camera (Figure 2.6(c)). A virtual

object can then be overlaid on the image by rendering it with respect to a virtual

camera placed at the same position and orientation as the real camera[27].

This is a type of tracking is known as Outside-In and is characterized by having

a fixed optical sensor, that is the camera. It requires the entire marker to be within

view and sufficiently large to obtain a reliable pattern match. The ARToolkit has the

advantage of being inexpensive, since it is open source and requires only a camera.


2.5 Summary

The learning theories reviewed in this chapter agree that interaction as the key to

enhancing learning. Moore speaks of interactions between learner and content, learner

and instructor, and learner and learner. Holmberg says a more natural, conversational

dialogue between learner and instructor is more effective. Piaget notes that we are

not passive learners, rather we actively construct knowledge through our experiences

in the world. Papert adds that by constructing something external to ourselves we

enhance our internal construction of knowledge, and that this is true for adults as

well as children.

The technologies reviewed in this chapter offer ways to implement these interac-

tions. Tangible user interfaces offer a more intuitive and collaborative alternative

to the traditional, keyboard and mouse, way of interacting with computers. The

HITLab suggests bringing computer supported collaborative work back to the table,

which allows greater communication bandwidth between participants. They also sug-

gest broadening our view into the digital world by turning parts of our physical world,

such as a tabletop, into displays. The ARToolkit enables low cost optical tracking,

which can be used to implement tangible interface devices and augmented reality

applications.

2.6 Specific Objectives

The goal of this work is to utilize these theories and technologies to produce a power-

ful tabletop learning environment that can be used to promote engineering education.

Tables can be connected via the Internet to provide remote collaboration and compe-

tition. A tabletop environment affords face-to-face style interactions, and it provides

a convenient work space for hands on constructionist style learning. The tabletop

will be a display surface that can be used with tangible devices. Video and audio


conferencing will allow communication between remote tables, and will support re-

mote collaborations. A camera will be used for optical tracking using the ARToolkit.

Keeping in mind that simplicity and manageability are the keys to introducing tech-

nology, one of the foremost goals will be to make the tabletop environment intuitive

and easy to use. The hardware needs to be robust and aesthetically pleasing. The

software needs to be reliable, flexible, and easy to use.

Chapter 3

Robotable: An Overview

The Robotable is a tabletop learning environment that is designed to integrate new

and existing technologies and learning theories to create a powerful learning expe-

rience. This chapter presents an overview of the Robotable to show readers where

others have contributed to this project. Detailed discussion of the author’s work

appears in Chapter 4 for hardware, and Chapter 5 for software.

3.1 Hardware

The hardware is determined by the requirements of the technologies, which have been

chosen to enable interactions that are richer, more intuitive, and more collaborative

than traditional computer supported work.

Occasionally, implementing a technology has undesirable side effects. In this case

the benefits must be weighed against disadvantages to evaluate if the technology

should be included. For example, wearing head-mounted displays (HMDs) enables

tangible-augmented reality, which eliminates a functional seam between the display

space and the task space. However, the expense and awkwardness of using HMDs

effectively vetoes their inclusion. A schematic of a prototype Robotable is shown in

Figure 3.1. This shows the key hardware components.

24

CHAPTER 3. ROBOTABLE: AN OVERVIEW 25

Figure 3.1: Schematic of the Robotable showing key features.


The physical components of the table are:

• Frame: The frame is constructed from 15 Series aluminum extrusions manufac-

tured by 80/20 Inc, and is sturdy and vibration-proof.

• Tabletop: Half inch Plexiglas provides a strong tabletop that can support a

computer and a piece of frosted glass that acts as a rear projection screen. It

is also possible to produce an equally good rear projection screen by applying

a translucent adhesive vinyl coating directly to the Plexiglas.

• Projector: The projector is fixed to the table frame so it will remain in the

required position, even if the table is moved.

• Mirror: The current version of the Tuftl Robotable uses a mirror that lies on

the floor and reflects the projected display back to the table surface. This

arrangement is required to increase the path length traveled by the projected

image so that it is sufficiently large when it arrives at the tabletop.

• Computers: Different versions of the Robotable have used different computer

configurations. To increase performance, tasks can be shared between two ma-

chines. The computers run video and audio conferencing, optical tracking, the

table display, shared applications, and anything else that may be required to

support a learning activity.

• Camera: Two cameras are used; one for video conferencing and one for optical

tracking. For video conferencing the camera is mounted to the vertical moni-

tor on the tabletop, and for optical tracking the camera has been tested both

above and below the table. Discussion of the relative merits of different camera

positions is given in Chapter 4.

• Electronic pen (not shown): An input device alternative to the mouse and

keyboard - it allows natural point/click and drag functionality.


3.2 Software

The people who have helped to develop Robotable’s software are identified in this

section. In addition to the work done by Paul Mason, there are three main bodies

of work; a Masters thesis by Ben Gemmill, an Honors dissertation by Carl Pattie,

and an Honors dissertation by Craig Oliver. Also, Addie Sutphen spent the winter of

2004-2005 developing electronic activity cards and prototype activities, and Jonathan

Festing spent the summer 2005 (southern hemisphere) investigating tracking using

infrared light. The following sections present descriptions of the main bodies of work.

For more in-depth information relating to this material you will need to contact

Professor Chris Rogers at Tufts University Department of Mechanical Engineering.

3.2.1 Robotable Online (Ben)

The Robotable Internet Server was designed by Ben Gemmill to connect Robotables.

Ben’s thesis is titled Design and Construction of a Physically Controlled, Online,

Persistent 3D World and was completed as part of a Masters of Science in Mechanical

Engineering at Tufts University, Medford. There are two parts to Ben’s work; the

Internet server and the interface to a 3D rendering engine. Ben’s work is described

below.

Internet server

In order to create a physically controlled, online, persistent 3D world, Ben designed a

custom internet server capable of handling multiple users and their objects transpar-

ently. Users can log on and off at will, and are synchronized to the current goings-on

in the world every time they rejoin. The world itself is managed like a library where

users can write in the books: a user would check an object out, modify it, and later

check it back in again for others to modify. In this way many people can collaborate

on tasks, even if they’re across the world from one another. The system keeps track of


the changed data and only gives the clients what changed, so even users on slow con-

nections can be kept up to speed. Using this server, our counterparts in Christchurch,

New Zealand, have successfully run activities and collaborated with Tuftl laboratory

in Boston.

The server was designed to be general purpose, supporting multi-user applications

from simple chatting to immersive 3D worlds.

Spyglass

Say two Robotables are connected and involved in a competition where each table has

a Lego robot involved in some task. Each table uses optical tracking to determine their

robot’s position and orientation. This data is then sent to the Robotable Internet

Server, which forwards changes to the other table. After the data arrives, a two

dimensional virtual representation of the remote robot is projected onto the tabletop

so that participants can follow their competitors progress. Spyglass is the term applied

to an environment that uses data from all connected Robotables to reconstruct a 3D

virtual view of the competition. For this task Ben integrated an open-source 3D

graphics engine with Robolab™and the Server. This can enable children to create

and share their own 3D worlds with their friends. This system could also be used

to show remote users how to construct a Lego model in 3D, have kids show off their

creations to others, or to play 3D movies. It works by sending commands out of

Robolab™to an external graphics program, combining Robolab’s ease of use with the

power of the open source Ogre3D.

Both of the systems are fully modular, so that both users and developers of Robo-

lab™can include them in their own programs without having to “re-invent the wheel”.


3.2.2 Image Processing (Carl)

The ARToolkit tracking system was adapted for use with Robolab™(hence Lab-

VIEW™) by Carl Pattie. Carl’s dissertation is titled Optical Tracking for the Ro-

botable Project and was completed as part of a Bachelor of Applied Computing with

Honors at Lincoln University, New Zealand.

The task is to track objects, usually robots, on the Robotable. Since the table

surface is a plane, only four degrees of freedom are required to fully specify position

and orientation. The ARToolkit, however, returns six degrees of freedom. Therefore,

Carl selected the parts of ARToolkit source code that were required for the task

and eliminated the rest. This improved performance because there was no need to

iterate for a solution to the 3D transformation matrix. The stripped-down code was

interfaced with Robolab™data structures and compiled on PC and Mac. The DLL

or Shared Library can then be accessed using the LabVIEW™Call Library Function.

The system identifies markers in the video stream at rates up to 15 Hz depending

on the image complexity and lighting conditions. A marker’s position is accurate to

within 1.4% of the size of the table surface1. All blobs found in the image are returned

in an array including blob number, blob area, whether it is a square, whether it is a

marker, pattern number, confidence factor, coordinates of the first vertex found, and

a list of the squares’ vertices in order.

In addition to the image processing, Carl wrote code to load pattern files, which

are used to recognize markers, and he wrote code that does pre- and post-processing

of the video stream. (Carl Pattie, 2004)

3.2.3 Activity Card Toolkit (Craig)

Craig Oliver carried out this work so that teachers, volunteers, and others with lit-

tle or no LabVIEW™programming experience could easily develop content for the

1As Carl mentioned in his dissertation, there are ways to improve upon this accuracy.


Robotable. Craig’s dissertation is titled A LabVIEW™Tool for Creating Electronic

Activity Cards and was completed as part of a Bachelor of Applied Computing with

Honors at Lincoln University, New Zealand.

Traditional Activity Cards consist of a set of cards that contain a series of iterative

steps designed to provide students with an independent learning environment. Elec-

tronic Activity Cards are based on the format of traditional activity cards, but they

add rich media such as video, audio, and interactive content to further enhance stu-

dents’ learning. Using LabVIEW™, Craig developed a system to simplify the creation

of electronic Activity Cards and enable the result to be viewed with LabVIEW™or

Robolab™. The solution employs LabVIEW™Express VIs, which allow users to add

code chunks in a single step, and then automatically configure them to achieve the

desired result.

3.2.4 Calibration, Whiteboard, Activity Prototyping, and

General Integration (Paul)

This section presents an overview of Paul’s work with software and how it fits with

others’ contributions. Names are included in braces where their work appears. A

more detailed description of Paul’s code is given in Chapter 5.

Calibration

Calibration is the process of mapping the physical environment to an internal repre-

sentation, so that the computer’s model matches the real world. Inaccuracy in the

estimation of position and orientation causes a lack of precision in the projection of

virtual content to the tabletop, which results in a loss of realism and usability. In our

case, accurate calibration enables us to project a 2D representation of a robot on a

remote table in the position that exactly corresponds to the real robots position on

the local table. Image processing with calibration allows robots to be tracked on a


Robotable. Optical tracking may be used with offline or online activities.

Optical Tracking

Pseudo-code of a simple tracking application is presented here. For a collaborative

activity involving remote tables, Server-related tasks would need to be included.

1. Calibrate

2. Initialize

◦ Camera

◦ Load pattern files (Carl)

◦ Load calibration information

3. Grab frame from camera

4. Image Processing (ARToolkit) (Carl)

5. Derive normalized position and orientation

6. Close

◦ Camera

Steps 3 through 5 are repeated until the activity is stopped.

Whiteboard

It is assumed that the Whiteboard is typically used collaboratively, so Server related

tasks are included in this pseudo-code.

1. Start the Server (Ben)

2. Initialize

◦ Calibrate electronic pen


◦ Connect to server (Ben)

◦ Clear whiteboard, initialize variables, etc.

◦ Start monitoring for user events

3. Get remote data from server (Ben)

4. Handle user events

5. Send local data to server (Ben)

6. Close

◦ Connection to server (Ben)

◦ Camera

Again, steps 3 through 5 are repeated until the activity is stopped.

Activity Prototyping

An activity refers to the content delivered by the Robotable. A variety of metaphors

have been used to create different styles of activity. These designs will be elaborated

upon in Chapter 5. Activities can be categorized as being either offline or online. An

offline activity does not use an Internet connection to another Robotable and is used

for independent learning. An online activity refers to a collaborative exercise using

connected Robotables. Pseudo-code for a remote activity is similar to that given

above for the whiteboard, except that the user events are specific to the activity

rather than the whiteboard. Robotable development has, up to now, explored three

main types of offline activity:

1. eBook (Figure 3.2(a)): This idea had each activity presented as a booklet pro-

jected onto the tabletop workspace. It was implemented with Robolab™.

2. Electronic activity card (Figure 3.2(b)): Like the traditional activity card only

including multi-media content. This design incorporates Craig’s Activity Toolkit


(a) eBook. (b) Electronic.

Figure 3.2: Activity cards inspired by a variety of metaphors.

for easy content creation. Addie created many activities based on this design.

These activities are implemented with Robolab™.

3. Web based (Figure 3.3): Currently being investigated, these are implemented

with Robolab™and html, javascript, asp, etc.

Thought has been given to library structures for storing the activities, and in-

terfaces that allow users to browse the libraries. An initial idea involved separating

activities from references. In this case, a reference is a short tutorial on some specific

aspect of building, programming or theory that may help in the completion of an

activity. For example, a user who is building a Lego robot may need to consult a

reference on ways to attach a motor, or the theory of gear trains and gear ratios.

The activities may also be categorized in a variety of ways to facilitate easy access.

A user may wish to search for an activity by grade2, by subject3, or relating to specific

standards or curriculum units.

2For example, All or specific such as K-5.3For example, mathematics, engineering, social studies.


Figure 3.3: Web based activity card.

3.3 Discussion

Integrating the technologies presented in this chapter enables the Robotable to be

a self-contained learning environment. Instructional content can guide the learner

through hands-on activities using Lego Bricks™to explore engineering concepts. Lego

parts are available in bins attached to the side of the table. Design, testing and

evaluation are iterative steps in the engineering product development cycle, which

is advocated for most tabletop activities. By attaching an ARToolkit marker to a

Lego robot, its position and orientation can be tracked. This information can be used

to modify the tabletop display interactively both locally and remotely. This enables

participants at separate locations to share in the same activity. They will see two

dimensional projections of remote robots navigating their tabletop. By using head-

mounted displays and augmented reality techniques, it is even possible for them to

see 3D virtual representations of remote robots navigating their tabletop.

Creating online activities tends to be more labor intensive than offline activities.

Online activities typically involve custom coding, which reduces re-useability, and


makes their development unsuitable for novice programmers.

Until recently the focus for prototype activities has been on producing something

that can be used to test Robotable’s hardware and software capabilities. During this

process it has become clear that many factors including content, structure, layout,

language, assumed prior knowledge, and mechanism of delivery are important to the

success of the resulting learning experience. These factors are being investigated now

and are a part of future work for the Robotable.

Chapter 4

Hardware

This chapter discusses issues surrounding design and construction of the Robotable.

Sections are given to the table frame, the table top, the mirror and projector, and

the cameras. The computers and the electronic pen do not warrant a section of their

own, so they are discussed now.

Computers

During development of the Robotable a number of different machines have been used.

The choice of computer determines options for the peripheral hardware. Robotable

is currently run by an iMac G5, which allows us to use iChat AV for conferencing.

When running Internet activities the Robotable Server needs to be run on a separate

machine because it is CPU intensive. On the client side, the number of concurrent

applications and the nature of their tasks determines how the whole system behaves.

In some prior incarnations of the Robotable, two machines have been used to spread

the workload. Although these issues have hardware implications, they are really

software issues and are discussed in Chapter 5.

36

CHAPTER 4. HARDWARE 37

Figure 4.1: Mimio capture bar and pen.

Electronic pen

The electronic pen gives Robotable users a more natural way to manipulate tabletop

content, and an alternative to keyboard and mouse input. It is particulary effective

if the user does not have to switch back to the keyboard or the mouse during an

activity. If it is necessary to return to the mouse to interact with the vertical screen,

or to use the keyboard to input text, then we are simply forcing the user to juggle

three input devices instead of two.

The Mimio™pen works like this; it has a capture bar, which is fixed to one side of

the whiteboard area (see Figure 4.1). It has infrared and ultrasonic sensors at each

end of the capture bar. The pens1 emit synchronized pulses of ultrasonic sound and

IR light so that by comparing the delays recorded at the sensors, the position can be

triangulated.

Care must be taken that the working environment does not have too much ambient

infrared or ultrasonic noise, because this can interfere with detection and makes

calibration impossible. We have tested two pens; an eBeam™and a Mimio™. The

Mimio™has a facility to check ambient noise levels. In Tuftl Lab it was necessary to

disconnect the ultrasonic motion detectors that are a part of the power saving lighting

1And the eraser.


system.

4.1 Table Frame

Design

When in its operating position, the table needs to be rigid enough to prevent vibration.

Vibration can interfere with optical tracking and the stability of the projection on

the tabletop. It is assumed that, once in position, the table will not require moving.

Therefore, the Robotable has been built for stability rather than portability. At one

time castors were considered for the legs of the table. Although this would have

enabled a single person to easily move the table, this idea was also rejected in the

interests of stability. Robotable may be dragged by one person but lifting requires

two people. The dimensions of the table have been decided to ensure that:

• People standing on either of the three open sides are easily able to reach the

center of the table.

• The height of the table allows the average adult to work comfortably while

standing, and to clearly see what is projected on to the tabletop.

• There is enough space beneath the table for the image from the projector to

attain sufficient size after reflecting off a mirror on the floor.

The table frame will have a projector, a camera, bins for Lego bricks, and perhaps

other things attached to it so the chosen material needs to enable this.

Implementation

The frame of the TUFTL Robotable (see Figure 4.2) is built using the Industrial

Erector Set, which is manufactured by 80/20 Inc. This product is convenient to use

because it is versatile, has excellent online catalogs and price-lists, can be cut or


Figure 4.2: The basic Robotable.


machined to order, and is easy to assemble. Also, it is easy to add, adjust, or move

attachments. 80/20 has a large network of distributors across North America2 which

helps reduce shipping time for all destinations. One disadvantage with this product

is that it takes some time to become familiar with their huge range of parts and to

know which is most appropriate to use for a given task.

The first prototype of the table was constructed using 80/20’s 15 Series as the

basic component. That is, the legs and the perimeter of the table-top used the

”1530” extruded profile. This extrusion is called 1530 because it has a cross section

that measures 1.5” x 3.0” (see Figure 4.3). This material was used initially since it

was available in the laboratory from a previous project. In an attempt to lower costs

and reduce the weight, a later version of the table was built using 80/20’s 10 Series,

where the basic component was a 1.0” x 2.0” extruded section. This version of the

table proved to be too prone to vibration. Even after bracing the table with extra

parts, a vibration problem persisted. Interestingly enough, the extra parts used in

the attempt to eliminate vibration caused the total price to exceed that of the 15

Series table. Therefore, the 10 Series was abandoned. The next, and current, version

of the table was constructed from ”1530-Lite”. 1530-Lite has the same cross-section

as 1530 (see Figure 4.3), is advertised with the same vibration proof properties as

1530, but is 83% lighter.

2In other countries, New Zealand for example, 80/20 products are not available and anothersolution must be found.


Figure 4.3: Cross-section of 1530 and 1530-Lite.

4.2 Table Top

Design

The two essential requirements are that the tabletop is strong enough to support

computers, monitors and miscellaneous equipment, and part of it can act as a rear-

projection screen. It was learned at the beginning of the project that HITLab’s HI-

SPACE table used frosted glass as a tabletop/rear-projection screen. Initial testing

included a variety of glass finishes to see if there was something better. Trials were

done with sand-blasted glass, white-laminated glass, satin-etched glass, and frosted

glass. Sand blasted was considered to be too coarse and did not produce a fine image.

White-laminated was too opaque and blurred the image. Satin-etched was not opaque

enough and did not produce a bright image. As a result, frosted glass was chosen

above other varieties.

Implementation

There are currently two slightly different approaches to implementing the tabletop.

Tuftl laboratory uses frosted glass as a screen and Lincoln uses translucent adhe-

sive vinyl. The first Robotable, built at Tuftl, uses a sheet of half-inch thick, clear

Plexiglas as the basic tabletop. A smaller sheet of quarter-inch frosted glass rests on


top of the Plexiglas to act as a rear-projection screen. The size of the frosted glass

was chosen to be a few inches larger, on both dimensions, than the image from the

projector appears after being projected twice the table-height. This makes the sheet

of frosted glass at Tuftl 37” by 28.5”. This is convenient because it allows approxi-

mately six inches of space around the projection area for placing things such as mouse

pads, robots, measuring tape, and Lego pieces. Concerns about the weight, durability,

portability, and safety of using glass led Lincoln to first use a sheet of Plexiglas with

an abrasive blasted finish. In this case the abrasive particles used were perhaps too

fine resulting in a finish that took a long time to apply, was not opaque enough, and

was uneven. Staff at Lincoln University discovered a 3M™product, Dusted Crystal,

that was a translucent adhesive vinyl coating. Initial experiments proved this product

to be very effective as a rear-projection screen, so they polished their Plexiglas back

to transparent and applied a sheet of 3M™Dusted Crystal. Subsequent trials at Tuftl

with other 3M™adhesive vinyls revealed Milano to be more opaque and a slightly

better rear-projection material, however it did not work so well when attempting to

track markers using a camera underneath the table3. This is because the more opaque

material reduces contrast in the image of the marker. Comparing the two methods,

one could say that glass is more resistant to wear and tear. On the other hand vinyl

is lighter and more portable. Although this is not an issue right now, future plans

include a portable Robotable that can be taken to visit schools.

When judging the performance of materials used for rear-projection screens, one

considers factors such as uniformity, brightness, sharpness, and color shift. Frosted

glass and translucent vinyl score well in sharpness, brightness and color shift. How-

ever, uniformity is an issue that plagued optical tracking until switching to infrared

light4.

3More about camera positioning in section 4.4.2.4More about tracking with IR light in section 4.4.3.


Pixel number 800 × 600Brightness (Typical) 1400 ANSI lumensContrast Ratio 500:1Screen Width Ratio (Distance/Width) 1.45 to 1.8:1Aspect Ratio 4:3 (supports 16:9)Keystone ±15◦

Lamp Life (Typical) 2000HLamp Price $200Total Price $1000

Table 4.1: Epson PowerLite S1 specifications.

4.3 Mirror and Projector

When choosing a projector consideration was given to the number of pixels, bright-

ness, screen width ratio (distance/width), aspect ratio, lamp life, cost of lamp re-

placement, and price. At the time of building the Tuftl table an Epson PowerLite S1

was chosen based on these factors. Some of the projectors specifications are given in

Table 4.1. This projector was brighter than most of its competitors and the contrast

ratio was relatively high. These factors combine to give a better image in a lighted

room.

The information provided with screen-width-ratio is sometimes given as throw-

distance. It refers to the “field of view” of the projected image and determines how

rapidly the image grows larger as distance to the screen increases. This Epson pro-

duces an image measuring approximately 35” × 26” after being projected a distance

of 51”. The pixel number is a parameter of the LCD. With the Epson, this is 800 ×

600. Input resolutions higher than this are compressed to fit, which results in a loss

of clarity. Keystone adjustment is essential and a larger angle is prefered.

The resulting quality of the projected image also depends on the mirror and the

relative arrangement of both. There are three possible basic arrangements:

1. Projector: placed on or near the floor and aimed vertically up at the tabletop.


The path from the projector to the tabletop is limited to the height of the table

less the depth of the projector. Even a projector with a small Distance/Width

ratio cannot produce a very large image.

Mirror: Not required.

2. Projector: placed near the floor, slightly away from the table and aimed hori-

zontally towards the table.

Mirror: Set at a 45◦ angle. This requires a large mirror since, near the tabletop,

the mirror needs to be almost as wide as the image on the table.

3. Projector: Projector placed near the tabletop and aimed (almost) vertically

down. It can’t be aimed precisely vertical or the projector would get in the

way. The slight angle required in this case produces a keystoned image but it

should be possible to correct it by making an adjustment with the projector.

Mirror: Set horizontally on or near the floor, reflects the image back up to the

tabletop. The mirror can be much smaller than the one used in the second case

since the longest path from the projector to the mirror is shorter.

The first two Robotables, the ones at Tuftl and the one at Lincoln, used the

second method. However this produces a ghosting problem at one end of the image

(see Figure 4.4). The angle, 45◦, is measured where the projector’s centerline meets

the mirror. This means the light at the bottom of the image has an angle of incidence

smaller than 45◦, and the light at the top of the image has and angle of incidence

greater than 45◦. This is compounded by the fact that projectors typically project

above the centerline at a much greater angle than below the centerline (see Figure

4.4(a). Ghosting occurs because the light is reflected from the glass at the front of

the mirror, and again at the silvered surface at the rear of the mirror (illustrated in

Figure 4.4(b)) . A thicker mirror accentuates ghosting because the distance between

the two reflected images increases.


(a) Case 2 - mirror at 45◦. (b) Double reflection causesghosting.

Figure 4.4: Ghosting is more noticeable with a larger angle of incidence near the top of themirror.

Figure 4.5: Case 3 - projector aimed down (used at Tuftl).

Lincoln University solved their ghosting problem by getting a front-surface mir-

ror5, whereas Tuftl switched to mirror-projector arrangement number 3 where the

projector is aimed downwards and a horizontal mirror is near the floor as in Figure

4.5.

4.4 Cameras

The are two main functions requiring cameras; video-conferencing and optical track-

ing. Currently an iSight camera (Figure 4.6(a)) is being used with iChat AV for

conferencing, and a Channel Vision 5124 (Figure 4.6(b)) black and white night vision

camera is being used for optical tracking. This camera is used with an IR long-pass

5Sometimes called a first-surface mirror.


(a) iSight with iChat for conferencing(Apple).

(b) Channel Vision 5124 B&W night vi-sion for IR tracking (Channel Vision).

Figure 4.6: Cameras currently used on the Tuftl Robotable.

filter enables tracking with infrared light. The Unibrain Fire-i fire-wire camera is

very good and works with Mac, PC and Linux, but does not have the convenience of

a built-in microphone. The Logitech Quickcam (a.k.a. LEGO Cam) USB has been

used successfully with a PC6 for tracking with visible light, but does not work with

infrared light.

The night vision camera is an analog line scanning video camera and is used

because it can see into the infrared range, whereas a standard webcam can not. Also,

the Channel Vision camera comes with 10 built in, high intensity IR LEDs which

act as an infrared flashlight. To use the night vision camera with Robotable, an

XLR-8 Video-USB adapter is used since it is compatible with Robolab’s QuickTime

drivers. This setup can snap still images at 640 × 480 pixels, and deliver video at 320

× 240 pixels. There is no doubt that tracking is improved when detecting markers

in an image of size 640 × 480, however the cost in processing time outweighs the

improvement in tracking.

6It can also be used on a Mac with a free USB webcam driver by Macam.


4.4.1 Software Access to Cameras

If an application requires a particular hardware resource, say a camera, then it must

acquire the camera from the operating system. If another application has already

acquired the camera, it will not be available. In this case the application will probably

pop up a dialog box to inform the user, and possibly continue with a black rectangle

where the image ought to be. For example, say, a video conference is in progress

and one wishes to snap an image for the whiteboard, the problem arises. Robolab

will attempt to acquire the iSight camera but it will not be available since iChat is

using it. One option is to close the video conference so that the iSight camera is

released. It may be easier to aim the iSight camera to show your remote counterpart

what you wish them to see. This is not easily done if the iSight camera is fixed to a

mounting and stuck to a computer. However, a company called MacMice (through

DVForge) sells two flexible “gooseneck” firewire holders7 for the iSight; an iFlex, and

a SightFlex that comes with a stand (see Figure 4.7). Another option is to replace the

iSight with a digital video camera. This has been tried with a Sony DV camera fixed

to tripod. The camera is then easy to swivel around, up and down, it has zoom, and

excellent image and sound. However, the position of the tripod can be restricted by

the table frame, and the length of the fire-wire cable determines the camera’s range

of motion.

4.4.2 Tracking from Above vs. Tracking from Below

One of the requirements for optical tracking is that the camera must have a clear

view of the target. To track a Lego robot using the ARToolkit, markers need to be

fixed to the robot. With a camera placed above the table, the marker is placed on

top of the robot as shown in Figure 4.8(a). A camera placed under the table requires

the marker to be attached underneath the robot as in Figure 4.8(b).

7These are popular items and currently hard to get.


Figure 4.7: SightFlex (MacMice).

Tracking from below offers two main advantages; the robot is clearly visible to

users from above, and access to the RCX is not obstructed. On the other hand, care

must be taken to obtain a clear image of the marker from below. This is because of

the effect of the frosted tabletop. The clearest image is produced when the marker

is lying directly on the tabletop. Since the marker is attached to the robot there

will inevitably be at least a millimeter or two of clearance, which introduces minor

blurring (see Figure 4.9(a)). Blurring increases the further the marker is above the

table surface. Figure 4.9(b) shows the result when the robot is raised 16mm on Lego

bricks. This image is not good enough for tracking. Another disadvantage is that

the contrast in the marker is significantly reduced when viewed through a frosted

tabletop. This can be seen by comparing Figure 4.8(a) with Figure 4.9(a). Although

the images in Figure 4.9 were taken using visible light, the effect is the same with

infrared light.

One issue when tracking from beneath the table concerns the field of view of

the camera. The Channel Vision 5124 has a field of view similar to most standard

cameras. If set near the base of the table and aimed upwards, it does not “see” the

entire tabletop. Our solution is to attach it to the table frame and aim it down so


(a) Marker attached to the topside. (b) Marker attached to the underside.

Figure 4.8: Marker placement for tracking from above and below.

(a) Marker 1 to 2mm from frosted table-top.

(b) Marker 16mm from frosted tabletop.

Figure 4.9: The quality of the image deteriorates as distance from the tabletop increases.


Figure 4.10: 850nm longpass filter.

that it sees the tabletop through the mirror. This works well but it requires a larger

mirror because it must be shared with the projector.

4.4.3 Tracking with IR vs. Tracking with Visible Light

The reason for tracking with IR light is to avoid problems posed by visible light

such as non-uniformity of intensities, interference from the texture being projected,

artificial and natural lighting. To “see” in IR alone requires a filter to block visible

light, and an IR light source. The filter currently in use is 12.5mm in diameter and

fits nicely into a recess in the night vision camera (see Figure 4.10). This filter, from

Edmund Optics, has a cutoff position8 of 850nm, a stop-band limit9 of 700nm, and

a pass-band limit10 of 950nm. It works very well. The two main reasons for using

infrared light for tracking are non-uniform intensities and texture projection. These

two are now explained.

8Specified at 50% internal transmittance.9Specified at 0.001% internal transmittance.

10Above 99% internal transmittance


Non-uniform intensities

Variable lighting conditions pose problems for any image processing application. Part

of the problem is that most cameras have an automatic gain control, which adjusts

according to lighting conditions. Tracking with infrared light is used because many

of the factors that affect visible light cannot be controlled. One of the main problems

is due to non-uniform intensities on the table’s screen. This is particularly apparent

when the camera is above the table. Commercial rear projection screen manufacturers

strive to produce a screen that has good uniformity, brightness, contrast, color, and

a wide viewing angle. These features are determined by the optical properties of the

material used for the screen. Non-uniformity is often manifested as a “hot spot” and

is related to the transmittance and diffraction of the incident light. The center of the

hot spot occurs at the point on the screen that intersects a line from the viewer to

the projector (see Figure 4.11).

Interference from texture projection

Another problem with visible light is that any texture projected onto the tabletop

interferes with recognition of squares and identifying patterns. This can cause the

image processing routine to fail to detect a marker in the image. Figure 4.12 shows

the input image and the result of applying a threshold when there is no texture in

the projection. Compare this to Figure 4.13 which shows the same image processing

applied to an image that includes black and white lines projected onto the table.

Hence, tracking from beneath the table is not possible using visible light.


Figure 4.11: Non-uniform intensities viewed from above the table.

(a) Image without a projection. (b) Marker detection successful.

Figure 4.12: Image processing is easy without a projection.


(a) Image with a projected texture. (b) Marker detection fails.

Figure 4.13: Image processing fails with a projected texture.

Chapter 5

Software

A requirement with all software used for this project is that it is cross-platform.

Occasionally this has been ignored in the interests of convenience while prototyping,

but ultimately it is a must. An example serves to illustrate this requirement. Ron

Daniels, chief information officer for the School District of Philadelphia, reports they

have about 35,000 computers within the district, and 80 to 85 percent of them are

Macs. It is also interesting to note that their philosophy is to develop more platform-

independent, web-delivered applications of software.

5.1 Conferencing

Simplicity and manageability are the keys to introducing technology and this thought

was foremost while choosing a conferencing tool. The Internet has no quality of service

guarantees, so an important requirement was to find a solution that not only delivers

good quality, but is robust and recovers well when hiccups occur. A number of options

have been considered and this section presents a brief description of them.

• Mbone and Darwin Streaming Server (DSS): Combining these two open source

tools can produce a multi-point conferencing application. The DSS receives

54

CHAPTER 5. SOFTWARE 55

two1 unicast streams from Mbone and sends two2 unicast relay streams and

two3 multi-cast relay streams. The streams can be received using Real Me-

dia player or QuickTime player. Mbone tools, VIC for video and RAT for

audio, are available as open source and binaries for Solaris, SunOS4, Irix 6.2,

Linux, FreeBSD, Windows. Macintosh uses can use Coolstream, which is a

QuickTime streaming file server that allows you to upload, to connected work-

stations, QuickTime files in streaming mode. Unfortunately, the Mbone tools

are not currently maintained, so everyday they support fewer audio and video

devices. This is one reason why these technologies were not chosen. However

the main reason was simply that developing a conferencing application from

these tools was considered to be “re-inventing the wheel” and not good use of

available time.

• Microsoft NetMeeting : Provides video and audio conferencing, chat, file trans-

fer, program sharing, remote desktop sharing, security, and whiteboard. How-

ever, it is strictly PC.

• VRVS : Provides public or private chat, it is cross-platform, has desktop sharing,

allows you to pop up web pages on others’ desktops, conferences held in booked

meeting rooms, is multi-point, and it supports Windows, Linux, Mac OS X with

iSight, and UNIX. It also has a voice-switched view mode which can receive

video of the participant who is speaking by default. One potential drawback is

that it relies on the Mbone tools, which are currently not maintained. For some

reason, we could not get VRVS to work although we tried everything suggested

in documentation and by email.

• iVisit 3.4.3 : Multi-point, supports chat, web co-browsing, share files, pictures,

1One for video and one for audio.2One for video and one for audio.3One for video and one for audio.


videos, music and PowerPoint presentations, and runs on Windows and Mac.

Experiments with iVisit went very well.

• iChat AV : Easy to use, full screen, and the best quality of all applications

tested. With Tiger operating system you can video chat with three others or

audio chat with 9 others. It is compatible with AIM 5.5/5.9 or later which gives

it cross-platform capabilities.

Currently the Robotable is using iChat AV because it provides best quality and

is robust and easy to use. At this time there is no need to video chat with more than

three other participants but, should the need arise, there are other applications that

can support more.

It would be desirable to have software that allows an instructor to observe the

desktops of one or more remote learners, to control a remote desktop, or to distribute

software. These features are provided by Apple Remote Desktop (ARD). ARD, how-

ever, is not cross-platform and requires static IP addresses to connect to computers

outside the local network. One option might be to use Virtual Network Computing

(VNC), which is a cross platform solution that allows one computer to view and in-

teract with another computer anywhere on the Internet. In an educational context it

can allow a distributed group of students simultaneously to view a computer screen

being manipulated by an instructor, or to allow the instructor to take control of the

students’ computers to provide assistance.

5.2 Whiteboard

Some video conferencing applications include a shared whiteboard, however these are

generally proprietary and not customizable. We would like Robotable’s whiteboard

to be an integral part of the workspace with connections to other applications such

as Robolab™.


The first Robotable whiteboard was programmed in Robolab. It was very basic

and provided freehand drawing without options to undo, move, or delete. It used

LabVIEW’s Open Application Reference VI to connect to a specified IP address, and

then Open VI Reference to draw to the front panel picture control on the correspond-

ing whiteboard program on the remote machine. This required the remote machine

to have a static IP address, so it could not cope with firewalls or network address

translation (NAT).

Later versions improved on the Internet connection method, the number of draw-

ing features, the interface and usability. The current connection method allows one

of the machines to be behind a firewall, although the other machine must have a

static IP address. The machine with the static IP address begins “listening” prior

to the other machine “calling”. The machine that is calling specifies the IP address

of the machine that is listening. This opens up a “pipe” through the firewall that is

used for the remainder of the session. If the connection is terminated for any reason

the applications must be restarted. Since the Listener must start before the Caller,

it is convenient to use video or audio conferencing to arrange the connection. The

reader may refer to the screenshot shown in Figure 5.1 as the features are explained.

A list of layers is in the top left corner and a new layer is created for each new object

added to the whiteboard. Objects can be deleted by highlighting them4 in this list

and clicking the Delete Layer button. Beneath the list of layers is where the current

whiteboard can be saved5 as a page, new pages can be added, and existing pages can

be reviewed. The Clear Whiteboard button deletes all layers. The toolbar along the

top allows the user to:

• Snap an image from a camera,

• import an image of the diagram of a Robolab VI,

4In Figure 5.1 layer three is highlighted - new layers are added to the top of the list.5Pages from a whiteboard are saved as JPG files.


Figure 5.1: Robotable whiteboard (March 2005).

• import an image from disk,

• Begin a new drawing,

• edit pen parameters - style (solid, dashed, etc.) and width,

• colors - black, blue, green or red,

• add text,

• edit font - font, size, text orientation, bold, italics, and underline,

• drag - the default mode is to draw so if you wish to move a drawing you need

to hit the drag button first.

There are a number of options for optimizing this version, the main one being to

pre-draw lower layers. Since Ben’s Internet Server provides mechanisms for efficiently


managing shared data, the next version of whiteboard will use the Internet Server to

handle those tasks. This will allow multiple people to share a whiteboard simultane-

ously, whereas the current version can accommodate just two participants. Since the

development of the Internet Server occurred in parallel with this work, integration

and testing of the whiteboard and the Server will be a part of future work.

5.3 Activities

The eBook style activity, mentioned in Chapter 3, was soon superseded by the idea

of an electronic activity card. A more recent approach has been to use web-based

activity cards. The focus of this section is on the electronic activity cards.

5.3.1 Electronic Activity Cards

Inspired by a standard activity card, an electronic activity card can include audio,

video, images, links to web pages, and interactive content. The prototype shown in

Figure 5.2, is a learning activity about the “Factors that Influence Climate”. This

activity included interactive 3D models of the sun and earth to explain the seasons,

and a Robotable application that allowed users to create their own continent. The

virtual sun and earth were implemented using C and the OpenGL graphics library.

User controls were added with GLUI6, the GLUT7 based C++ user interface library.

GLUI, GLUT and OpenGL are available on Windows and Macintosh so this is po-

tentially cross-platform, although a Mac version was not produced. The reason that

the 3D content was coded using OpenGL is that LabVIEW (hence Robolab) does

not support 3D hardware accelerated graphics. This is the motivation for Ben’s work

with the Ogre 3D rendering engine.

When creating electronic activity cards, key issues concern time and skills. It is

6OpenGL User Interface.7OpenGL Utility Toolkit.


Figure 5.2: Electronic activity cards enable rich media content.

not reasonable to expect an activity to take a week to make, and require someone

who is an experienced graphics programmer on both platforms. Ideally, content for

the Robotable can be created by teachers and volunteers who may have little or no

programming experience. In this way they can share their work and contribute to

the knowledge-base. If having special skills was a pre-requisite, too few people would

be able to make activities, and accumulating an activity library would take too long.

Also, the replacement and updating of activities would be slow process, which would

make the library slow to adapt to changing needs. This criticism is true for this

method of adding 3D content, and also for applications like the Continent Creator.

Activity prototype: Continent Creator

The Continent Creator is intended to be run on the tabletop using the electronic pen

as an input device. It begins with the whole tabletop as a blue sea. Then, as the pen

is moved over an area, the land is gradually raised above the surface of the water.


Figure 5.3: Greyscale image plus lookup table equals terrain.

The more the pen is used over an area, the higher the land becomes, until mountains

are formed. The pen can also be used to remove land to form lakes, valleys, and

rivers.

Actually, moving the pen over an area just changes the intensity of that part of

an 8-bit greyscale image. When the image is displayed with a color lookup table, it

looks like terrain(see Figure 5.3). After learning about the factors that affect climate,

the activity asks learners to create their own continent and add at least two mountain

ranges, at least two rivers, global wind pattern arrows, and six cities - one interior,

one coastal, one high altitude, one at sea-level, one windward of a mountain, and one

leeward of a mountain. The final task is to predict temperature and precipitation for

each city and explain reasons why.

Activity prototype: Fable Maker

This activity was produced to show that Robolab can be used for subjects other

than engineering, science and mathematics. The task is to adapt a well known fable

or to create an entirely new story. Preparation requires reading some fables and

discussing the idea of a ”moral” and how it works as a theme in a story. Other

elements of writing such as characterization and plot should also be discussed. A


(a) Title with credits. (b) Story in images andtext.

(c) The moral of the fable.

Figure 5.4: Viewing pages of a Fable.

moral is chosen as the focus of the fable, and planning is essential to tell the story

in 6 to 10 pages. Thought should be given to characterization and care taken to

make the fable readable and interesting. The story can be illustrated by constructing

scenes from Lego and other materials, and snapping the scene with a webcam. The

application, FableMaker, takes the user through the steps of producing the fable. The

steps include the making the title, credits, text, an illustration for each page, and the

moral. When finished, the Fable Viewer presents the result as an old style book (see

Figure 5.4).

Activity prototype: Habitat

This activity was produced to investigate more complex interactions with the table.

The idea is that your robot is a creature exploring its habitat (the tabletop). Initially

you choose your creature. The habitat is populated with other creatures and objects,

some of which are food and some are not depending on the creature you have chosen.

The robot is programmed to walk in a random manner, searching for food. There

is a marker attached to your creature which is tracked by the camera. When the

position of your creature is compared with objects in the habitat and is found to be


at some food, a message is sent to halt the creature. This message is sent in direct

mode from a Lego tower suspended overhead. While the creature is stopped at the

food, the amount of food gradually decreases until it is gone. The creature is then

sent a message to begin searching for more food. The learning is about ecosystems

and creatures that inhabit them, food chains, and how to program your creature to

find the most food.

5.3.2 Web-based Activity Cards

The focus is currently on web-based activity cards. These have the advantage of

being held at a central location (a server), so they are easy to update and maintain.

They can be coded to work with the widest possible number of browsers to ensure

consistent performance across platforms. Since this work has only recently begun, it

will simply be mentioned here and will be the focus of future work.

5.4 Calibration

Since all the markers appear on a plane (the tabletop) and there is no visible radial

distortion, it is reasonable to consider calibration as a 2D problem. This involves

mapping the 2D camera image coordinates to the 2D tabletop coordinates, which

requires specifying the coordinate systems.

Defining coordinate systems

The origin for the tabletop coordinates can be specified arbitrarily and it is convenient

to consider it as being the left-top corner of the projection (see Figure5.5(a)), since it

is the projected image that will be constructed from these coordinates. The origin for

the camera image is simply the left-top corner of the image, with x-values increasing

to the right and y-values increasing towards the bottom. Figure 5.5(b) shows a


(a) Defining a reference for the projec-tion.

(b) Construction for finding normalizedcoordinates.

Figure 5.5: Tracking based on ARToolkit.

schematic of a camera view. Viewed through the mirror from under the table, the

projection area appears horizontally flipped8. This means the point P in Figure

5.5(b) corresponds to the left-top in Figure 5.5(a). The entire projected area and a

margin around it must be visible from the camera. The margin is required because

it is necessary to recognize markers whose centers may be inside the projected area

although parts of the marker extend beyond it.

In our case, the goal of calibration is to derive normalized projection coordinates

and orientation vectors for each of the markers seen in the camera image. Using

normalized coordinates allows the resolution of the projector to be altered without

requiring modification of the code. For example, if a marker’s position is determined

to be (0.5, 0.75) in normalized coordinates and the resolution of the projector is 800

× 600, then the virtual object will appear at pixel position (400, 450) in the projected

image.

To improve the response of the system it is important to make the transformation

from one set of coordinates to the other to be as fast as possible. For this reason

the calibration routine calculates a set of lookup tables which can be accessed by

the application while it is running. This way the normalized projection coordinates

8Of course, this depends on the position and orientation of the camera.


can be obtained by indexing the lookup table using the x and y pixel positions as

subscripts.

The lookup tables are computed by iterating along each row of the camera image

and calculating the normalized x and y values for each pixel position. The lookup

table consists of three 2D arrays of 8-bit values, one for the x-parameter, one for the

y-parameter, and one to flag whether the given pixel is within the projection region.

The size of the lookup tables is determined by the size of a video frame from the

camera, which in our case is 320 × 240. Accuracy will be improved in future versions

by using floating point, instead of 8-bit, values for lookup tables.

Calculating x -parameters

Referring to Figure 5.5(b) the task is to find the normalized x coordinate for the

marker whose center is at the head of vector C. The vectors A and B can be written

as:

A = P + x (Q - P), B = S + x (R - S)

The value x is found so that the vectors C and D are collinear. This can be

done by considering the value of the 2D cross product of C and D. These vectors are

collinear when their 2D cross product is zero. This value has opposite sign depending

on which side of the line, from the head of A to the head of B, the marker lies.

This makes the problem suitable for solving using the Bisection Method. Iterations

are stopped when the value of the 2D cross product becomes smaller than 10−4.

Experiments have shown this can be achieved with an average of 22 iterations for a

320 × 240 image.

Calculating the y-parameter

Once the x -value is known and vectors C and D are collinear, the normalized y value

can be found from the ratio of the magnitudes of vectors C and C + D.


Figure 5.6: Finding position and orientation from the marker’s ordered vertices.

Determining the in-bounds flag

The in-bounds flag is true if both the x and y parameters lie within the projection

region, which is within the quadrilateral PQRS in Figure 5.5(b). If the resulting x

and y parameters do not both lie between 0 and 1.0, inclusive, then the image pixel

is out of bounds.

Finding Position and Orientation

The position and orientation is determined from the ordered list of the markers ver-

tices returned by Carl’s image processing code. Referring to Figure 5.6, the center of

the marker’s front edge can be found by averaging vertices 0 and 1, while the center

of the back edge can be found by averaging vertices 3 and 2. The corresponding

normalized coordinates, F and B, can be found by accessing the lookup tables. The

direction vector is then, F −B, which should then be normalized. The average of F

and B gives the marker’s center in normalized coordinates.

Chapter 6

Testing and Evaluation

6.1 Experiment Description

Twenty undergraduate and graduate students were recruited from Tufts University.

Age, gender, and ethnic background was not part of the selection process.

Purpose and rationale

The primary purpose of this experiment was to evaluate one aspect of the Robotable

as a learning tool. The experiment compared the involvement and performance of a

subject in an activity that was presented in two different ways. One way presented

the activity in the form of a worksheet, and the other way delivered the same activity

as web pages augmented by interactions with the Robotable. From the results it is

hoped to determine the success of design goals relating to the provision of an engaging

environment, and also to guide future development of the Robotable.

The constructivist learning theory advocates that learning is an active process in

which meaning is constructed from experience. Consequently, one of the design goals

of Robotable has been to facilitate this kind of learning by providing a compelling

environment that will motivate the learner to action. In an age of technology literate

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CHAPTER 6. TESTING AND EVALUATION 68

children, digital media can engage children who lose interest in traditional instruction

methods. The purpose of the experiment is to gain some insight into how successful

we have been in achieving this design goal.

Task

Subjects were asked to program a Lego car to run for 2, 4, and 6 seconds, and measure

the corresponding distances traveled. They recorded the data in the table provided

and generated a line graph based on the data. Using the graph, they then determined

the speed of the car. A target1 was set at a random distance from the start line and

subjects used the car’s calculated speed to predict the time required to run the car

as close as possible to the target without knocking it over. Successful completion of

the activity was judged to be when the car stopped within one inch of the target.

Subjects were asked to ”think aloud” during the activity to gain an insight into

their cognitive processes. A video recording of the task space was made for the purpose

of capturing the think-aloud and linking those comments to the corresponding actions.

The videos were reviewed to identify issues relating to performance of the activity.

At the end of the activities, subjects were asked to complete a short questionnaire

on their general experience with computers, and their subjective evaluations and

impressions of the two methods of presenting an activity. The entire experiment took

between approximately 20 and 30 minutes.

Apparatus

Lego Car : Two pre-built Lego cars were provided. Each car was geared to have a

different speed. Subjects used the slower car for the shorter distances on the Robot-

able, and the faster car for the worksheet activity which is run on the floor. Another

important reason for using cars with different speeds is to ensure that subjects do not

simply transfer the numbers from one trial to the next.

1A Lego person.


Computer : A computer was made available for programming the car using ROBO-

LAB’s simplest level, Pilot 1. All subjects were given a short tutorial (a couple of

minutes) and practice with Plot 1 before the experiment began. This enabled them

to learn and practice everything they need. It was proposed that the input device for

the tabletop would be the Mimio™electronic whiteboard pen but, due to problems

with calibration, this idea was abandoned part way through the first trial.

Miscellaneous : Two tape measures were provided, one for measuring on the table-

top and one for measuring on the floor. For the case where the activity is presented in

worksheet form, writing instruments and graph paper were provided so that subjects

could record the data in tables and plot a graph.

Procedure

The experiment was explained in detail to the subjects, who were given time to

ask questions before starting. The experiment was counter-balanced to avoid any

confounding effects, such as learning, caused by the order of presentation. Of the 20

subjects, 10 were presented with the Robotable first and 10 were presented with the

worksheet first. Participants were informed that they would not be offered help, but

that requests for help would be answered if required. This was to allow the number

of requests for assistance to be included as a performance measure.

Variables

1. Objective variables:

• Time to task completion.

• Successful task completion: This turned out to be irrelevant since all sub-

jects completed the task.

• Number of times the investigator is consulted: This also turned out to be

irrelevant. Each person consulted me twice to inform me that they were


ready for the target to be set. Apart from that there were two occasions

where subjects, who were not familiar with Macintosh computers, lost

one of their windows behind another and didn’t know how to get it back.

These were issues that I considered to be peripheral to the relationship of

the subject to the content of the activity.

2. Involvement is derived from answers to the subjective questions in the question-

naire.

• Satisfaction

• Stimulation

• Ease of use

3. Concurrent verbal protocol: used to detect issues not revealed through obser-

vation.

6.2 Results

Attributes

There were 11 female subjects and 9 male subjects, ranging in age from 18 to 30.

Questions 1 to 3 asked the subjects to rate their comfort with computers, their ex-

perience with Robolab, and experience with spreadsheets such as Microsoft Excel.

Five point rating scales were used for these attributes. Graphs of the results, which

appear in Appendix A, show that most subjects judged themselves to be very com-

fortable with computers. Experience with ROBOLAB™was more evenly distributed.

Four subjects reported having no experience at all, while six reported being very ex-

perienced. With regard to the question on spreadsheets, all but one of the subjects

judged themselves to be either experienced or very experienced.


Figure 6.1: Frustrating to satisfying.

Subjective Data

Questions 4 to 6 asked subjects for a comparison of the two ways of presenting an

activity by rating their impressions for each of the following items. Question 4 (see

Figure 6.1) rated from frustrating to satisfying, question 5 (see Figure 6.2) rated from

dull to stimulating, and question 6 (see Figure 6.3) rated from difficult to easy. These

three questions used a nine-point rating scale.

These graphs appear to show that the Robotable version of the activity was consid-

ered to be more satisfying and more stimulating than the worksheet version. However,

these graphs do not consider the paired nature of the data, so the data will be tested

in more depth in Section 6.3.

Subjects’ Comments

Question 7 gave subjects an opportunity to express any other comments, or opinions.

These responses are given in Appendix A. To summarize, one could say that approx-

imately one half of these comments were positive or complementary, while the other

half reflected problems. Many subjects said that the Robotable environment was


Figure 6.2: Dull to stimulating.

Figure 6.3: Difficult to easy.


more convenient because the activity could be done in one place, without the need

to bring additional equipment. Some said Robotable was more precise. This may

be due to the reduced error that is a consequence of the slower speeds and shorter

distances required on the Robotable. Some enjoyed the automatic plotting of data,

saying this was more accurate and time saving. Others did not like things to be

automated and would rather have maintained control and done things by hand. A

couple of comments noted that tick marks on Robotable’s graph were not clear, and

their intervals could change depending on the range of data graphed. Users found

it annoying to be required to switch input devices, from electronic pen to mouse to

keyboard. Other comments related to unfamiliarity with the environment, such as

locating the mouse pointer with dual monitors (the vertical monitor and the table

screen), or recovering hidden windows on the Macintosh platform.

6.3 Analysis

There are two main parts to the analysis. An analysis of variance (ANOVA) and a

Multiple Range Test on the times to task completion, and a Wilcoxon signed rank

test to find significant differences in the subjective data from questions 4, 5, and 6.

6.3.1 Times to Task Completion

The raw data is graphed with means and bars to indicate the 2σ (95%) range. The

first activities are compared in Figure 6.4, and the second activities are compared in

Figure 6.5.

It is difficult to tell whether the differences between the times to completion for

the two first activities, or the two second activities, are significant so, to establish

this, an analysis of variance was done.


Figure 6.4: Comparing data for the time to completion of the first activities.

Figure 6.5: Comparing data for the time to completion of the second activities.


SUMMARY Robotable Worksheet TotalGroup 1Count 10 10 20Sum 118.4168 69.2500 187.6668Average 11.8417 6.9250 9.3833Variance 8.0501 2.5809 11.3972SE 0.8972 0.5080

Group 2Count 10 10 20Sum 93.5834 124.4169 218.0003Average 9.3583 12.4417 10.9000Variance 5.4167 13.6961 11.5553SE 0.7360 1.1703

TotalCount 20 20Sum 212.0002 193.6669Average 10.6000 9.6833Variance 8.0019 15.7191

Table 6.1: Summary of statistics of the times to task completion.

ANOVA

Statistical significance was investigated by analyzing the data in Microsoft Excel using

Tools→ Data Analysis→ ANOVA: Two-Factor With Replication. There are four sets

of times with ten values each; the Robotable activity done first and the Worksheet

activity done second, and the Robotable activity done second and the Worksheet

activity done first.

Table 6.1 gives the summary statistics for the four sets of data; two methods

of delivering the activities, and two orders in which the activities were carried out.

Group 1 refers to the Robotable first, followed by the Worksheet. Group 2 refers to

the Worksheet first, followed by the Robotable. The mean times for the four groups

are graphed in Figure 6.6. The error bars are set at ±SE (standard error of the mean)


ANOVASource of Variation SS df MS F P-value F-critSample 23.0030 1 23.0030 3.0935 0.0871 4.1132Columns 8.4027 1 8.4027 1.1300 0.2949 4.1132Interaction 160.0012 1 160.0012 21.5173 4.5072E-05 4.1132Within 267.6940 36 7.4359

Total 459.1010 39

Table 6.2: ANOVA: Two-actor with replication for times to task completion.

for each data point. That is, there is a 68% chance that the population mean for each

dataset lies within this region. The x-axis represents the method by which the activity

was delivered. The chronological order of activities in each case is represented by the

direction of the arrow.

Table 6.2 gives the analysis of variance for p = 0.05. The P -values in this table

exceed p = 0.05 in the rows labeled Sample and Columns, which means there is no

overall difference between times for Group 1 and Group 2, and there is no overall

difference between times for the Robotable and the Worksheet. However, the Inter-

action with P -values = 4.5072E−05 indicates there are highly significant effects due

to specific (order, method) pairs over and above differences based on order alone or

method alone.

The two-factor ANOVA test says there is a significant interaction between average

times in the experiment as a whole, but it does not say which times differ from one

another. There are two ways of comparing individual sets of times that might provide

more information. One way is to calculate the least significant difference (LSD), and

another way is to use the multiple range test. The multiple range is chosen because

it is more reliable and must satisfy stricter criteria.


Figure 6.6: Graph of mean times to completion.


MeansWk 1st 12.4417Rb 1st 11.8417 0.6000Rb 2nd 9.3583 2.4833 3.0834Wk 2nd 6.9250 2.4333 4.9167 5.5167

6.9250 9.3583 11.8417 12.4417 MeansWk 2nd Rb 2nd Rb 1st Wk 1st

Table 6.3: Differences of means for comparing with Q(σd) values.

Multiple Range Test

To implement the multiple range test a table of differences between mean times is

built. The means are ranked from highest to lowest and the differences are calcu-

lated (see Table 6.3). These differences are then compared with calculated values for

Q(σd). If any are greater, then they are significant. To calculate the required values

for Q(σd), the first step is to find σ2d and then σd.

σ2d = 2 × residual mean square ÷ n

= 2× 7.4359÷ 10

= 1.4872

σd = 1.2195

The residual mean square is actually a variance and is the MS value in the

“Within” row of the ANOVA table. The next step is to get Q-values from a ta-

ble using the number of datasets being compared as one index, and the degrees of

freedom for the residual mean square as the other index. The relevant part of the Q

table, sometimes referred to as the “studentized range” table, is given in Table 6.5.

Since there is no row for 36 degrees of freedom, the values for 30 degrees of freedom

are used, which constitutes an even stricter test. The required Q(σd) values are found

by multiplying the tabulated Q-values by σd (see Table 6.4).


Q(σd)2 = Q2× σd = 2.89× 1.2195 = 3.5244Q(σd)3 = Q3× σd = 3.48× 1.2195 = 4.2439Q(σd)4 = Q4× σd = 3.84× 1.2195 = 4.6829

Table 6.4: Calculating Q(σd).

df Number of Datasets2 3 4

30 2.89 3.48 3.8440 2.86 3.44 3.79

Table 6.5: Excerpt from a table of Q-values.

The only two differences of interest are between the Worksheet first (Wk 1st) and

the Robotable second (Rb 2nd), and the Robotable first (Rb 1st) and the Worksheet

second (Wk 2nd). From Table 6.3, the difference between Wk 1st and Rb 2nd is

3.0834 minutes. Since this is in column four of the data, Q(σd)4 = 4.6829 is used for

comparison. In this case the value of Q(σd)4 is not exceeded so the difference is not

significant. Again from Table 6.3, the difference between Rb 1st and Wk 2nd is 4.9167

minutes. Since this is in column three of the data, Q(σd)3 = 4.2439 is used, and it

is exceeded. Therefore, a significant difference in times to completion of the activity

exists when subjects did the Robotable first and the Worksheet second.

6.3.2 Subjective Data

Wilcoxon Signed Rank Test

The Wilcoxon Signed Rank Test is used to establish significance in the subjective

rating scales. The Wilcoxon test is appropriate because the subjective ratings are

ordinal, the scale intervals are not equal, and because each subject provides two rat-

ings, one for the Robotable and one for the worksheet, which makes them a matched

pair. The analysis was done in Excel and significance was found in one case; the data


Level of Significance for aDirectional Test

0.05 0.025 0.01 0.005 0.0005Non-Directional Test

– 0.05 0.02 0.01 0.001zcritical

1.645 1.960 2.326 2.576 3.291

Table 6.6: Critical values of ±z.

comparing the two methods on a scale from Dull to Stimulating. For this data the

z-value was calculated to be z = 2.93. Using critical z-values from Table 6.6, this is

better than 99% significant for a directional test. The z-values for the other responses

were z = 0.72 for question 4, and z = 0.09 for question 6.

6.4 Discussion

There are two key results from this experiment:

1. Statistically significant effects due to interaction were found. That is, specific

order, method pairs were found to be different over and above differences based

on order alone or on method alone. Further analysis with the multiple range

test, which applies stricter criteria, revealed that the most significant improve-

ment in performance was for the case where the Robotable activity was done

first.

2. When analyzing subjective impressions of the two methods of delivering an

activity, the Robotable was found to be more stimulating with high statistical

significance.

If the effectiveness of the learning experience is judged by the amount of improve-

ment in times to completion, then one could say the Robotable was a more effective


learning experience. This is because the Robotable activity followed by the Worksheet

activity resulted in a average improvement of 4.9 minutes. An average improvement

of 3.1 minutes occurred when the Worksheet activity was followed by the Robotable

activity. This observation is verified statistically by the multiple range test.

While speculating on the reasons for this result, there is another factor that may

be relevant. That is, the novelty of the Robotable environment. It is reasonable to

suppose that a subject would proceed cautiously with an unfamiliar environment,

resulting in greater times to completion. This might explain why the mean time

for the 2nd Robotable activity is approximately 2.5 minutes greater than that for

the 2nd Worksheet activity. If the novelty factor was having an effect, one would also

expect the 1st Robotable activity to be proportionately longer than the 1st Worksheet

activity. However, the mean time for the 1st Robotable activity is slightly less than

that for the 1st Worksheet activity, which contradicts the supposed effect of novelty.

Recalling that the difference in mean times of the first activities, and the difference

in mean times of the second activities were not statistically significant, one should

not indulge in too much speculation. This topic could be explored in future tests.

The subjects were all university students, of both genders, ranging in age from

18 to 30 years old. This implies that any conclusions should apply only to the same

demographic. One of the intended uses of the Robotable is as an environment for

training teachers and professionals for outreach work to schools. It cannot be assumed

that all teachers and professionals have similar age to the subjects in our study. It

would, however, be reasonable to assume that most professionals are experienced

with computers and technology. This assumption would not be reasonable for most

teachers. Therefore, it would be wrong to deduce that most teachers would have a

similarly positive attitude towards Robotable based on this study.

Chapter 7

Future Work

7.1 Hardware

7.1.1 Tabletop screen

Initially, the tabletop screen was implemented using a sheet of frosted glass because

it was recommended by staff at the New Zealand HITLab. Frosted glass works well

as a rear projection screen, and its hard surface is resistant to scratching and is easy

to clean. However, glass is also heavy and brittle, which reduces portability and, in

case it is broken, has possible safety issues.

For this reason adhesive vinyl film was investigated. It is possible to obtain this

material that has optical properties at least as good as frosted glass. Additionally,

it is safe, lightweight1, cheap relative to glass, and far more portable. It is tough

and, although it will not break, it is more prone to minor surface damage such as

scratching.

The technology of commercial rear projection screens has advanced over recent

years and, although they are relatively expensive, they have the potential to greatly

1Nearly all of the weight associated with adhesive vinyl is contributed by the surface it is attachedto, rather than the vinyl itself.

82

CHAPTER 7. FUTURE WORK 83

improve the quality of the tabletop image. To be used as a table surface they would

need to be resilient enough to operate as a workbench as well as a screen. An ad-

vantage is that they are almost certain to be lighter and more durable than glass. It

would be worthwhile to keep a lookout for a commercial screen that might be suitable

for the Robotable. An attempt was made to salvage a screen from a discarded rear

projection TV, but such an item could not be found.

7.1.2 Mirror

Just as a frosted glass screen is heavy and breakable, so is a glass mirror. In the

setup at Tufts University, the optical properties of the mirror are not critical. This is

because Tuftl’s, almost vertical, projector arrangement does not have a problem with

a ghost image, which means a front silvered mirror need not be required. Therefore, it

may be possible to replace the glass mirror with, for example, something like mirrored

lucite. A mirror made of this type of material would be tough, portable, and safe.

7.1.3 Tangible Devices and Augmented Reality

The greatest advantage to be gained from using tangible interfaces would be to elim-

inate the functional seam caused by a separation of the display space and the task

space. Space-multiplexed input devices can be more intuitive to use because they

are designed specifically for a single function and their physical shape indicates this.

However, the Robotable needs to be a flexible environment, which can make a single

purpose tangible input device unsuitable. Therefore, incorporating tangible input

devices needs to be done carefully. Perhaps, as more content is created for use on

the Robotable, a common use for certain tangible devices will become apparent and

their inclusion will be warranted. At this time, no tangible input devices are used

because a real need has not yet been established. Tangible user interfaces and aug-

mented reality applications are still new, and it seems that most applications of these


technologies are labor intensive to produce, require skilled people to produce them,

and generally the code is not particularly re-usable. Hence, every new activity would

require a significant investment of time from skilled people. Augmented reality con-

tent is very compelling and if a suitable use can be found for the Robotable, it should

be included. It was mentioned earlier that tangible-augmented reality requires head

mounted displays. Unfortunately, it is not feasible at this time to include a set of

head mounted displays with the Robotable.

7.1.4 Portability

Using glass for the tabletop screen or the mirror reduces portability of the table since

glass is brittle and heavy. One issue that arose during preliminary testing serves to

motivate the development of a more portable Robotable. Because the Robotable was

constructed in Tuftl and is not easily moved, the experiments were also conducted

in Tuftl. This was not an ideal environment for a study of this nature. Other stu-

dents share the laboratory and, although they were considerate, there were difficulties

because of this. One difficulty was that some subjects felt uncomfortable doing the

think-aloud in this environment. These subjects did not verbalize much despite being

encouraged. Having other students working nearby also raised ambient noise levels.

7.2 Software

7.2.1 Development of Instructional Content

The development of the Robotable is a work in progress and instructional content

is currently being produced. The focus is on Internet based activities that will be

connected to an existing knowledge-base. Besides activities there will be access to

technical support, and building and programming help. Ways of organizing and

delivering these activities is also being investigated. For example, being able to search


the database by grade level or by subject. In addition to this, work should continue

on creating, prototyping and testing new concepts in instructional content for the

Robotable.

7.2.2 Integration and Testing of the Robotable Internet Server

Since Robotable’s Internet Server was developed in parallel with other software, there

is still integration and testing to be done. The whiteboard, and existing online activ-

ities, should be converted to run on the Internet Server and tested between at least

three tables, since any testing to date has been done with just two tables.

Currently, the ARToolkit-based image processing code has not been compiled for

use on a Macintosh. Also, Ben suspects that one of the reasons for poor update rates

when using optical tracking is because the DLL, on the Windows platform, has been

compiled in Debug Mode. Apparently, compiling in Release Mode greatly improves

performance.

Apart from testing the various software components of the Robotable individually,

they also need to be tested together by running multi-user online workshops and

activities. An application may perform splendidly on its own, but when sharing the

CPU with many other processes its performance may seriously suffer. In the past,

this has happened with just two or three separate applications such as trying to use

the whiteboard while video conferencing and optical tracking. Managing access to the

CPU may yet turn out to be very important. In this respect, a product like Maratech

has an advantage because all of the essential distance education tools are provided in

a single application. This enables the application to manage those tools sensibly.

Appendix A

Data and Analysis

Attributes

Age and gender data is given in Figure A.1.Figure A.2) gives comfort with comput-

ers, Figure A.3) gives experience with Robolab, and Figure A.4 indicates subject’s

experience with spreadsheets.

Subject’s comments

General comments

• The task was simple enough, the Robotable didn’t add anything.

• Robotable is better because you can stay in one place.

• Robotable does not require you to find pen and paper.

• Robotable reduces the need for additional equipment.

• Robotable is more convenient.

• Robotable was more exact.

• Data entry on Robotable was easier and accurate and allowed you to focus on

what it means.

86

APPENDIX A. DATA AND ANALYSIS 87

Figure A.1: The age and gender of participants in the study.

Figure A.2: Comfort with computers.

Figure A.3: Experience with ROBOLAB™.


Figure A.4: Experience with spreadsheets.

• Robotable was more precise.

• Robotable was easier because everything was right there.

• Not sure how to get the mouse from the table to the computer monitor.

• The Lego car is traveling in curves, which isn’t a problem on the Robotable.

• Want pop-up help on Robotable screen objects, wasn’t sure what things did.

• Should project a ruler on Robotable, then a click where the car stops could drop

a perpendicular line to the ruler.

Graphing comments

• Robotable was easier with automatic plotting.

• Did not like automatic plotting, would rather graph manually.

• The worksheet version allows you to add notes, rule lines, and feel more in

control.

• Switching from electronic pen to mouse to keyboard is annoying.


• Robotable lines are difficult to see, tick marks are not accurate.

• Robotable’s connecting lines are good because you didn’t have to use equations

or rulers.

• Robotable’s automatic graphing is more accurate than by hand.

• Liked Robotable’s automatic graphing.

• Wanted a scratch pad on Robotable to do some calculations.

• Could do calculations more accurate by hand.

• When drawing lines, they can be adjusted many times on Robotable without

making a mess.

• As points are added on Robotable, the tick marks change which makes the

intervals confusing.

• Plotting by hand is more familiar but less exciting.

• Sick of plotting manually, so Robotable is good.

Wilcoxon’s Signed Rank Test

The procedure used for this analysis is given here and the result is shown in Table

A.1. The table was initialized with three columns; Subjects, Robotable and Work-

sheet. Subjects contained numbers 1 to 20. Robotable contained subjects’ ratings

for the Robotable, and Worksheet contained subject’s ratings for the worksheet. The

worksheet rating was then subtracted from the Robotable rating to create a column

of differences labeled Diff.. The absolute value of these differences were then put in

the fifth column and labeled ABS. All five columns were sorted by the absolute values

of the differences, ABS, in ascending order. Ranks were then assigned to the absolute

values of the differences in the following way. Ranking begins at the first non-zero


value. When two values are the same, the same rank is assigned to both and is an

average of the ranks that would otherwise have been assigned. Referring to Table A.1

for example, rows 3 through 6 all have a value of 1. If the values were increasing, they

would take ranks 1 through 4. Therefore the average rank, (1 + 2 + 3 + 4)/2 = 2.5,

is assigned. Finally, a column of signed ranks is created by copying the ranks to this

column and giving them signs equivalent to the signs in the difference column.

The number of signed ranks, ns/r = 18. The sum of the signed ranks, W = 135.

The distribution of possible values for W has a mean of zero and a standard deviation

given by σw = ns/r(ns/r +1)(2ns/r +1)/6, hence σw = 18(18+1)(2∗18+1)/6 = 45.92.

The z-value can then be calculated, including a ±0.5 correction for continuity, using

the formula z = (W − 0.5)/σw. Therefore, z = (135− 0.5)/45.92 = 2.93.


Subject Robotable Worksheet Diff ABS Rank Signed rank5 4 4 0 015 8 8 0 09 6 5 1 1 2.5 2.512 7 6 1 1 2.5 2.516 8 7 1 1 2.5 2.519 8 7 1 1 2.5 2.53 7 5 2 2 7 78 7 5 2 2 7 710 5 7 -2 2 7 -718 7 5 2 2 7 720 7 5 2 2 7 72 5 8 -3 3 11 -117 5 2 3 3 11 1114 7 4 3 3 11 111 7 3 4 4 14 146 8 4 4 4 14 1417 8 4 4 4 14 144 8 3 5 5 16.5 16.511 8 3 5 5 16.5 16.513 8 2 6 6 18 18

Table A.1: Wilcoxon signed rank test for data from the question rating the activities fromDull to Stimulating.

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