Invited keynote presentation given at Virtual Worlds Best Practices in Education conference in July 2013.
Website http://www.vwbpe.org/ai1ec_event/keynote-speaker-michael-vallance-sl-dafydd-beresford?instance_id=413
Learning by TKF to promote computational participation in Japanese education. Michael Vallance
Vallance, M. & Goto, Y. (2015). Learning by TKF to promote computational participation in Japanese education. In Proceedings of 18th International Conference on Interactive Collaborative Learning and 43rd International Conference on Engineering Pedagogy. World Engineering Education Forum. 20-24 September 2015, Florence, Italy.
Note:
T (Tsukutte つくって) is Making/ Creating.
K (Katatte かたって) is Talking/ Sharing.
F (Furikaeru ふりかえる) is Reflecting/ Discussing.
Engineering active learning: LEGO robots & 3D virtual worldsMichael Vallance
Robot -mediated Interaction (RMI)
Research: Design an evidence-based framework of learning when undertaking tasks of measurable complexity in a 3D virtual world.
The students’ aim is to communicate solutions to problems which involve the programming of a robot to navigate specific circuits.
# Experiences lead to personal strategies for teamwork, planning, organizing, applying, analyzing, creating and reflection.
# Measured as Essential Skills for Wales Baccalaureate Qualification, UK.
Evidence required by UK Education Authority for post-16 qualification.
The 3-week project focuses on how traits are passed from parents to offspring through genes. Students will determine genotypes and phenotypes of common traits, create Punnett squares and genetic crosses to illustrate inheritance. The driving question is how traits are passed through gene inheritance. Students will address life science standards on genetics and inclusion of math. Products include a display board and presentation. Formative assessments include quizzes and reviews, while summative assessments consist of a written report and oral presentation with rubrics. Resources and support include after school time, technology, and instruction on research, software, and presentation skills.
The document discusses using educational robotics to help students with cerebral palsy. It describes objectives of analyzing how users with cerebral palsy interact with robotic tools, identifying any difficulties. The study used Lego Mindstorms with two participants to see if robotics could improve concentration, interest, and learning. Results found the users actively participated in building, programming and interacting with the prototype robot, demonstrating they acquired new learning.
From programming to software engineering: ICSE keynote slides availableCelso Martins
Meyer's blog:
"In response to many requests, I have made available [1] the slides of my education keynote at ICSE earlier this month. The theme was “From programming to software engineering: notes of an accidental teacher”. Some of the material has been presented before, notably at the Informatics Education Europe conference in Venice in 2009. (In research you can give a new talk every month, but in education things move at a more senatorial pace.) Still, part of the content is new. The talk is a summary of my experience teaching programming and software engineering at ETH."
Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning, constantly and efficiently updating our biased understanding of the external world. On the contrary, current AI systems are usually trained offline on huge datasets and later deployed with frozen learning capabilities as they have been shown to suffer from catastrophic forgetting if trained continuously on changing data distributions. A common, practical solution to the problem is to re-train the underlying prediction model from scratch and re-deploy it as a new batch of data becomes available. However, this naive approach is incredibly wasteful in terms of memory and computation other than impossible to sustain over longer timescales and frequent updates. In this talk, we will introduce an efficient continual learning strategy, which can reduce the amount of computation and memory overhead of more than 45% w.r.t. the standard re-train & re-deploy approach, further exploring its real-world application in the context of continual object recognition, running on the edge on highly-constrained hardware platforms such as widely adopted smartphones devices.
Pre-service teachers views of technology for teaching and learning foreign la...Shona Whyte
This document summarizes research on pre-service teachers' views of technology for teaching foreign languages. It involved 137 students in teacher training programs who completed a questionnaire and 32 who participated in interviews. The findings show that while students had positive beliefs about language learning, their technology skills varied considerably between groups. After instruction, most felt able to perform basic tasks but fewer felt confident creating online content like wikis and slidecasts. The results suggest teacher education should provide both theoretical and hands-on training to help future teachers integrate technology effectively.
1) The study explored how teacher training institutions (TTIs) support pre-service teachers' development of technological pedagogical content knowledge (TPACK) through various strategies.
2) A survey of 688 pre-service teachers found that while they perceived receiving support through the strategies, feedback was seen as the least apparent. The strategies were also found to significantly relate to perceptions of TPACK.
3) Interviews with 17 participants revealed that authentic experiences and feedback from mentor teachers most impacted skills, but not all strategies were fully addressed during training. Both quantitative and qualitative evidence suggest TTIs can influence TPACK through strategic support, but challenges remain in fully implementing all strategies.
Learning by TKF to promote computational participation in Japanese education. Michael Vallance
Vallance, M. & Goto, Y. (2015). Learning by TKF to promote computational participation in Japanese education. In Proceedings of 18th International Conference on Interactive Collaborative Learning and 43rd International Conference on Engineering Pedagogy. World Engineering Education Forum. 20-24 September 2015, Florence, Italy.
Note:
T (Tsukutte つくって) is Making/ Creating.
K (Katatte かたって) is Talking/ Sharing.
F (Furikaeru ふりかえる) is Reflecting/ Discussing.
Engineering active learning: LEGO robots & 3D virtual worldsMichael Vallance
Robot -mediated Interaction (RMI)
Research: Design an evidence-based framework of learning when undertaking tasks of measurable complexity in a 3D virtual world.
The students’ aim is to communicate solutions to problems which involve the programming of a robot to navigate specific circuits.
# Experiences lead to personal strategies for teamwork, planning, organizing, applying, analyzing, creating and reflection.
# Measured as Essential Skills for Wales Baccalaureate Qualification, UK.
Evidence required by UK Education Authority for post-16 qualification.
The 3-week project focuses on how traits are passed from parents to offspring through genes. Students will determine genotypes and phenotypes of common traits, create Punnett squares and genetic crosses to illustrate inheritance. The driving question is how traits are passed through gene inheritance. Students will address life science standards on genetics and inclusion of math. Products include a display board and presentation. Formative assessments include quizzes and reviews, while summative assessments consist of a written report and oral presentation with rubrics. Resources and support include after school time, technology, and instruction on research, software, and presentation skills.
The document discusses using educational robotics to help students with cerebral palsy. It describes objectives of analyzing how users with cerebral palsy interact with robotic tools, identifying any difficulties. The study used Lego Mindstorms with two participants to see if robotics could improve concentration, interest, and learning. Results found the users actively participated in building, programming and interacting with the prototype robot, demonstrating they acquired new learning.
From programming to software engineering: ICSE keynote slides availableCelso Martins
Meyer's blog:
"In response to many requests, I have made available [1] the slides of my education keynote at ICSE earlier this month. The theme was “From programming to software engineering: notes of an accidental teacher”. Some of the material has been presented before, notably at the Informatics Education Europe conference in Venice in 2009. (In research you can give a new talk every month, but in education things move at a more senatorial pace.) Still, part of the content is new. The talk is a summary of my experience teaching programming and software engineering at ETH."
Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning, constantly and efficiently updating our biased understanding of the external world. On the contrary, current AI systems are usually trained offline on huge datasets and later deployed with frozen learning capabilities as they have been shown to suffer from catastrophic forgetting if trained continuously on changing data distributions. A common, practical solution to the problem is to re-train the underlying prediction model from scratch and re-deploy it as a new batch of data becomes available. However, this naive approach is incredibly wasteful in terms of memory and computation other than impossible to sustain over longer timescales and frequent updates. In this talk, we will introduce an efficient continual learning strategy, which can reduce the amount of computation and memory overhead of more than 45% w.r.t. the standard re-train & re-deploy approach, further exploring its real-world application in the context of continual object recognition, running on the edge on highly-constrained hardware platforms such as widely adopted smartphones devices.
Pre-service teachers views of technology for teaching and learning foreign la...Shona Whyte
This document summarizes research on pre-service teachers' views of technology for teaching foreign languages. It involved 137 students in teacher training programs who completed a questionnaire and 32 who participated in interviews. The findings show that while students had positive beliefs about language learning, their technology skills varied considerably between groups. After instruction, most felt able to perform basic tasks but fewer felt confident creating online content like wikis and slidecasts. The results suggest teacher education should provide both theoretical and hands-on training to help future teachers integrate technology effectively.
1) The study explored how teacher training institutions (TTIs) support pre-service teachers' development of technological pedagogical content knowledge (TPACK) through various strategies.
2) A survey of 688 pre-service teachers found that while they perceived receiving support through the strategies, feedback was seen as the least apparent. The strategies were also found to significantly relate to perceptions of TPACK.
3) Interviews with 17 participants revealed that authentic experiences and feedback from mentor teachers most impacted skills, but not all strategies were fully addressed during training. Both quantitative and qualitative evidence suggest TTIs can influence TPACK through strategic support, but challenges remain in fully implementing all strategies.
This document discusses a study that compared the effects of using constructivist versus behaviorist assignments in a programming class. The study found that the constructivist assignments, which gave students more freedom and flexibility, required significantly more effort from students compared to the behaviorist assignments. Various metrics were used to measure the complexity of programs developed in each condition. Students who completed the constructivist assignments performed slightly better in subsequent programming courses on average. The findings provide preliminary evidence that constructivist assignments may help students learn more, but larger and longer-term studies are needed.
Educators Bonanza – Discovering Resources and Getting Started with Robotics E...MecklerMedia
The document discusses robotic STEM education projects from a robotics quest to robot school. It describes the background and experience of Bill Lovell, the primary instructor. The quest project aims to take students through the full process of designing, building, and testing a robot. It proposes using a computer game, sandbox, and robots to engage students in STEM education in an innovative way. The sandbox could be used in the classroom to remotely operate robots. It also outlines a technical high school curriculum focused on robotics, automation, and manufacturing.
Farkhatdinov Robotics education for children 2017 Accepted.pdfMonesseKHAMISSIA1
The document describes an experience teaching robotics to secondary school students using a TRIK robotic platform. A set of 8 robotics exercises were designed and evaluated based on difficulty, time to complete, and student interest. Exercises involved programming the robot for tasks like navigation, line/wall following, and parking. Feedback was positive and exercises involving navigation, sensors, and computer vision were most interesting to students. Teaching robotics contributed to developing skills in programming, mathematics, physics, and engineering concepts.
A case study of LEGO Mindstorms suitability for artificial intelligence and...Emily Smith
This document examines the suitability of using LEGO Mindstorms kits for robotics projects in an artificial intelligence course. It discusses student backgrounds, the course context, the Mindstorms equipment used, and 6 sample projects completed with the Mindstorms kits. The projects included designing simple reflex robots, evaluating sensor accuracy, building autonomous robots, implementing intelligent agents, and remotely controlling robots with a Lisp interpreter. The document evaluates both the strengths and limitations of using Mindstorms kits for college-level AI education.
This document discusses research on assessing collaboration in virtual worlds. It focuses on using robotics programming tasks with remotely located science students communicating and working together synchronously and asynchronously in virtual environments. The goal is to develop metrics and frameworks for evaluating the effectiveness of collaborative tasks in virtual worlds by measuring factors like student engagement, task completion, and the quality of interactions and communication. Preliminary observations suggest looking at affective factors, metacognition, cognitive load, and discourse. An ongoing study involves groups in Japan and the UK completing robotics tasks in Second Life and other virtual worlds to iteratively analyze the data.
The document summarizes a student project to build a model that can efficiently represent nodes in large social networks as low-dimensional vectors. The model is based on the LINE algorithm presented in the baseline paper. The students implement both first-order and second-order proximity models in Torch, using the same node representations for both. Their model achieves F1 scores between 39-42% on the BlogCatalog dataset. The project took 5 weeks and challenges included understanding the baseline paper's mathematics and debugging neural networks in Lua.
Understanding Large Social Networks | IRE Major Project | Team 57 | LINERaj Patel
The document summarizes a student project to build a model that can efficiently represent nodes in large social networks as low-dimensional vectors. The model is based on the LINE paper, which learns embeddings by optimizing for first-order and second-order proximity. For their project, the students implemented the LINE approach in Torch, using the same node representations for both proximities and evaluating on the BlogCatalog dataset. Their model achieved F1 scores between 39-41% for node classification.
Talbot Knighton is seeking a career that allows him to develop new computational tools across multiple disciplines. He has a strong technical background, including experience programming in Mathematica, LabVIEW, Python, C++ and other languages. As a PhD student in experimental physics, he designs experiments, analyzes data, and has published papers. He also teaches Mathematica to students and tutors physics and math. His skills include solving differential equations, simulating quantum systems, modeling chaotic systems, and analyzing rocket trajectories.
Robotic hand prototype as a didactic model.IRJET Journal
This document describes the development of a robotic hand prototype designed to be used as a didactic (teaching) model for students. The prototype was designed using 3D CAD software and 3D printed. It is controlled using an ATmega 328P microcontroller and flex sensors to read finger positions and servo motors to drive finger movement. The prototype was tested with students through classroom lessons focusing on electronics, microcontrollers, programming and robotics concepts. Students then programmed a binary counting sequence using the hand. The prototype was able to execute the programmed sequence but some fingers had slower movement times than desired due to the weight and flexibility of the 3D printed material. The robotic hand prototype was intended to motivate students and support their learning of robotics
The document describes a study that developed a framework for designing effective tasks in virtual worlds. Researchers had one group program a LEGO robot to follow a circuit and then teach another group how to do it by communicating through Second Life. Student interactions were video recorded and analyzed using Bloom's Taxonomy to code cognitive processes and knowledge dimensions. The analysis found that conceptual knowledge tasks engaged higher-order cognitive processes over time, and that task design, not just difficulty, impacted learning.
IRJET- A Real Time Yolo Human Detection in Flood Affected Areas based on Vide...IRJET Journal
This document proposes a method for real-time human detection in flood-affected areas using video content analysis and the YOLO object detection algorithm. It trains YOLO on the COCO Human dataset to detect and localize humans in video frames from surveillance cameras. The results show that YOLO can accurately detect multiple humans, even with occlusion, and single humans under varying illumination. This approach aims to help rescue operations quickly identify affected areas and prioritize aid.
Ivan Khomyakov's portfolio summarizes his skills and experience. He has extensive knowledge of programming languages like C++, C#, Python, and technologies including OpenCV, SQL, machine learning, AWS, and Unity 3D. Some of his projects include developing a fast cubemap filter for rendering environments, a real-time locating system for tracking objects, and a dynamic map module for navigation systems. He also has experience with route editing tools, augmented reality applications, medical image segmentation, and machine learning algorithms. His background includes both academic and professional work on computer vision, image processing, statistics, and more.
Understanding Large Social Networks | IRE Major Project | Team 57 Raj Patel
This document summarizes an undergraduate project to build a model that can efficiently represent nodes in large social networks as low-dimensional vectors. The project uses the LINE algorithm from the baseline paper as a starting point. Specifically, the project implements LINE's first-order and second-order proximity models in Torch and combines the learned embeddings, unlike the baseline paper which trains the models independently. The project aims to represent over 10,000 nodes from the BlogCatalog dataset within a scalable neural network model written in Lua using the Torch framework.
Deep Learning Hardware: Past, Present, & FutureRouyun Pan
Yann LeCun gave a presentation on deep learning hardware, past, present, and future. Some key points:
- Early neural networks in the 1960s-1980s were limited by hardware and algorithms. The development of backpropagation and faster floating point hardware enabled modern deep learning.
- Convolutional neural networks achieved breakthroughs in vision tasks in the 1980s-1990s but progress slowed due to limited hardware and data.
- GPUs and large datasets like ImageNet accelerated deep learning research starting in 2012, enabling very deep convolutional networks for computer vision.
- Recent work applies deep learning to new domains like natural language processing, reinforcement learning, and graph networks.
- Future challenges include memory-aug
This document discusses constructing and utilizing a peer-to-peer network system using Nintendo DS devices. It proposes using PC servers as a "cloud" middle layer to connect DS devices and allow them to share data and applications over WiFi. An algorithm called DSChord is presented, which is based on the Chord algorithm but improved for DS. As an example, an application called DS Drawr is mentioned that allows drawing on DS devices over the DS network. The document concludes that the feasibility of using DS devices in a networked environment has been verified.
Robot Localisation: An Introduction - Luis Contreras 2020.06.09 | RoboCup@Hom...robocupathomeedu
The document summarizes Luis Contreras' upcoming lecture on robot localization using particle filters. The key points covered are:
1. Robot localization is the process of determining a robot's pose (position and orientation) over time using motion and sensor measurements within a map.
2. Particle filters represent the robot's uncertain pose as a set of weighted particles, with each particle being a hypothesis of the robot's state.
3. As the robot moves and senses its environment, the particles are propagated and weighted according to the motion and sensor models to estimate the posterior probability distribution over poses.
A Beginner's Guide to Monocular Depth EstimationRyo Takahashi
Mono-depth estimation uses a single camera to produce depth maps. Recent works have made progress using self-supervised learning from video. Key methods include SfMLearner which pioneered this approach, struct2depth which models object motion explicitly, and Depth from Videos in the Wild which learns camera intrinsics from YouTube videos. PackNet directly estimates depth in metric units using a 3D packing network that preserves spatial details better than traditional upsampling. TRI has achieved state-of-the-art results using these techniques.
Transfer learning allows machine learning models to recognize and apply knowledge learned from previous tasks or domains to new related tasks or domains. It involves identifying commonalities between tasks or domains and transferring knowledge, such as feature representations or model parameters. Common transfer learning approaches include instance-based methods that reweight source domain data, feature-based methods that learn shared feature representations, and parameter-based methods that learn shared parameters across related tasks. The goal is to leverage labeled data from source tasks or domains to improve learning on a related target task or domain with few or no labels.
Establishing meta-learning metrics when programming Mindstorms EV3 robotsMichael Vallance
Presentation at LTEC 2016 in Hagen, Germany. July 2016.
Abstract. Recently, wider issues of social relationships, contexts, feelings and personal goals have been recognized as impacting upon learning. Moreover, as the Higher Education paradigm appears to be shifting towards students as consumers, there is added pressure on academics to ensure students evaluate and subsequently ‘make sense’ of their educational experiences. This has been termed ‘meta-learning’ but there has been little research on meta-learning compared to the more recognized cognitive science term of metacognition. The paper describes a project in a Japanese university where meta-learning was promoted among first-year Systems Information Science students learning to program LEGO Mindstorms EV3 robots. Students were engaged in a collaborative, creative cycle termed TKF (Tsukutte つくって- Create)/ Katatte かたって- Share)/ Furikaeru ふりかえる- Reflect) to build and program robots to solve systematic problems. This paper will demonstrate that learners actively engaged in iteratively challenging robot-mediated interactive tasks can develop generic, declarative and epistemic competencies, with a consequential development of meta-learning.
Google SketchUp for Media Architecture CommunicationMichael Vallance
Second year undergraduate students in Japan studied alternative energy sources for the country's future. They conducted a SWOT analysis, researched facts and opinions, compared new ideas to existing energy, gathered feedback, and designed 3D representations using Google SketchUp over six weeks. The students presented their ideas through an app developed for the iPad to provide an interactive experience for viewers. Their projects were assessed based on design, justification, exercises, and exceptional work. An evaluation found that students were very positive about the transmedia approach to developing skills in design, communication, and higher-level cognitive processes.
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The document summarizes a student project to build a model that can efficiently represent nodes in large social networks as low-dimensional vectors. The model is based on the LINE paper, which learns embeddings by optimizing for first-order and second-order proximity. For their project, the students implemented the LINE approach in Torch, using the same node representations for both proximities and evaluating on the BlogCatalog dataset. Their model achieved F1 scores between 39-41% for node classification.
Talbot Knighton is seeking a career that allows him to develop new computational tools across multiple disciplines. He has a strong technical background, including experience programming in Mathematica, LabVIEW, Python, C++ and other languages. As a PhD student in experimental physics, he designs experiments, analyzes data, and has published papers. He also teaches Mathematica to students and tutors physics and math. His skills include solving differential equations, simulating quantum systems, modeling chaotic systems, and analyzing rocket trajectories.
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The presentation was shown at the Interdisciplinary Social Sciences conference at Cambridge University, UK in August 2010.
See Michael's website for publication reference athttp://web.mac.com/mvallance/DRVALLANCE/Publications.html
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This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Virtual World simulations to support Robot-Mediated Interaction
1. Virtual World simulations
to support
Robot-Mediated Interaction.
Dr. Michael Vallance
Future University Hakodate, Japan
http://www.mvallance.net
2. Research aim (long term): To design an evidence-‐based
framework
of
learning when undertaking tasks
of
measurable
complexity in a 3D
virtual
world.
How?
(i) procedural processes,
(ii) learning reflections,
(iii) collate data of students collaborating in-world when programming
a robot
# A successful task consists of a robot and program solution to solve
specified circuit challenges.
In
this
presentation, the focus is upon the
development
of
measuring
complexity
of
tasks
involving
robot-‐mediated
interactions
(RMI).
3. March 11, 2011 Fukushima Japan nuclear plant
disaster.
Earthquake and tsunami damaged cooling
systems to reactors.
Four reactors exploded and radioactivity was
released to the atmosphere.
Currently:
evacuees cannot return home and depression is
becoming prevalent among the strained residents [1];
the Japanese government has changed its
criteria for dangerous levels of radioactivity so
leaving residents confused [2];
workers are struggling to maintain the safety of
the plant [3];
deformities have been discovered in local wildlife
[4].
Why?
Our motivation for context
4. Lack of robots in Japan to assist with the recovery operations!!!
Less than a week iRobot USA donated two PackBot 510 robots and
Warrior 710 robots, and iRobot engineers trained Japanese operators.
3 weeks for TEPCO to authorize their use [5].
5. 1. People need to be better informed and equipped to make sense
of information.
Give students learning opportunities: reflecting, organizing,
negotiating and creating.
A challenging project like programming robots also provides
opportunities for learning content in the Science, Technology,
Engineering and Maths (STEM) subjects.
2. International collaboration is essential communication for now
and the future.
A virtual world as a future 3D space.
A safe medium for communication and experiential learning.
The tasks in this research aim to support (1) and (2).
As educators, what can we learn from this
disaster?
6. The students’ aim is to communicate solutions to
problems which involve the programming of a LEGO robot
to follow specific circuits.
This is undertaken by
1. designing circuits
- with robot maneuvers and sensors
2. experiencing collaboration
- students in Japan and UK within 3D space.
Experiences lead to personal strategies for teamwork,
planning, organizing, applying, analyzing, creating and
reflection.
# Measured as Essential Skills for Wales Baccalaureate Qualification, UK. Evidence
required by Education Authority for post-16 qualification.
About the research ...
8. CTC
=
Σ
(d
+
m
+
s+
o)
for
example
CTC = Σ (4 + 3 + 2 + 2) = 11
There is no consensus in the discipline of Robotics or Human-Robot Interaction for
accurately measuring task complexity [6].
Given the specific purposes of the robot in our research, task complexity was calculated
according to the number of sections that make up a given maze [7] [8].
Circuit Task Complexity (CTC) = number of directions + number of maneuvers +
number of sensors + number of obstacles.
Circuit
Task
Complexity
9. We found that the logic of assigning task complexity to circuits was
inadequate.
For instance, complexity values were assigned to distinct maneuvers
such as forward – turn – back.
Over the course of our previous research, as circuits became more
challenging, the NXT programming became more complex.
Especially adding sensors to maneuver around and over obstacles.
Simply counting the number of obstacles in the circuit task
complexity was flawed because the programming required to
maneuver over a bridge using touch sensors, for instance, was far
more complex than maneuvering around a box using touch
sensors.
CTC
=
Σ
(d
+
m
+
s+
o)
Circuit
Task
Complexity
10. In the NXT Mindstorms software, the
Move block controls the LEGO robot
direction and turns.
Move block contains 6 variables:
NXT ‘brick’ port link - direction - steering -
power - duration - next action.
In other words, the students have to
make 6 specific decisions about the
values which make up the programmable
block. Therefore, we assign v1 a value of
6.
This was repeated for sensor, switch and
loop.
Robot Task Complexity
RTC
=
Σ
Mv1
+
Σ
Sv2
+
Σ
SW
+
Σ
Lv3
11. RTC
=
Σ
Mv1
+
Σ
Sv2
+
Σ
SW
+
Σ
Lv3
where,
M
=
number
of
moves
(direcHon
and
turn)
S
=
number
of
sensors
SW
=
number
of
switches
L
=
number
of
loops
for
example
RTC
=
Σ
Mv1
+
Σ
Sv2
+
Σ
SW
+
Σ
Lv3
RTC = (8 x 6) + (3 x 5) + 0 + 3
RTC = 66
v
=
number
of
decisions
required
by
user
for
each
programmable
block
v1
=
6
v2
=
5
v3
=
2
Robot
Task
Complexity
We acknowledge that, at present, our modified Robot
Task Complexity metric applies only to the Mindstorms
NXT software and LEGO robot, but it does provide a
useful indicator in our attempts to analyze the
experiential learning during the collaborative tasks. The
CTC problem can now be evaluated against the RTC
solution.
12. Students in one country
1. provided with task specification
2. work on a solution to the task
3. construct their circuit in the virtual world + in their real-world lab
4. develop a NXT program to maneuver the physical LEGO robot
appropriately.
The problem and the
proposed solution are then
communicated in real-time
to
students
in
the
other
country via the 3D virtual
world.
Task implementation
13. Task specification examples
Task
Task: robot
actions
CTC/ target CTC only /
objective is to iteratively
increase CTC/
Collabo
ration
STEM/ anticipated
Essential
Skills (Wales
Baccalaureat
e)/
anticipated
RTC/ post
task
calculation
based upon
students’
solution.
T1
Movement:
follow the
line.
Sensors: light
and touch
CTC = Σ (d + m + s+ o)
CTC= 1+2+2+1 = 7
Japan
teach
UK
S: Recognition of light sensor values. What
happens when trigger point increased/ decreased?
T: Learn how to organise NXT program blocks
logically.
E: Construct a robot. Connect software to
hardware.
M: Recognise spatial movements and the
problem of friction. Change surface to see if
robot works the same. Calculate coefficient of
friction.
Identify
Plan/
manage
Explore/
Analyse
(organize)
Evaluate
(checking)
Reflect
T2
Movement:
follow the
line.
Sensors:
colour and
action.
CTC= 1+2+2+2 = 8
UK
teach
Japan
S: Recognition of light sensor values. What
happens when trigger point increased/ decreased?
How does the NXT sensor recognise colour R, G
or B? Try different colour variations and observe
subsequent robot actions.
T: Learn how to organise NXT program blocks
logically.
E: Construct a robot. Connect software to
hardware.
M:
Identify
Plan/
manage
Explore/
Analyse
(organize)
Evaluate
(checking)
Reflect
T3
Movement:
square.
Sensors:
touch and
sound.
CTC = 4+3+1+1 = 9
Japan
teach
UK
S:
T: Learn how to organise NXT program blocks
logically.
E: Construct a robot. Connect software to
hardware.
M: Calculate distance, speed and force (touch).
Identify
Plan/
manage
Explore/
Analyse
(organize)
Evaluate
(checking)
Reflect
14. Resources.
• LEGO Mindstorms NXT software version 2.1
• LabView 2010 with NXT module.
• LEGO robot 8527 kit
• LEGO blocks and similar workspaces/lab in Japan university + 2 UK schools
• All have same Apple technologies (MacBook Pro + OSX 10.7)
30. Task Task description
T1 Assemble LEGO robots. JPN + UK students introductions
T2 NXT program + circuit. JPN teaching UK
T3 NXT program + circuit (90 degree turns + measured length). UK teaching JPN
T4 Circuit + NXT program. Move. Touch sensor. Turn 90 degrees. JPN teaching JPN.
T5 Circuit + NXT program. Around obstacles. JPN teaching JPN.
T6 Circuit + NXT program. Around obstacles. JPN teaching JPN.
T7 NXT program + touch sensors + circuit. Locate and press switch off. JPN teaching JPN.
T8 Over an obstacle. NXT program + sensors + bridge building (cardboard). JPN teaching JPN.
T9 Over an obstacle. NXT program + sensors + bridge building (wood). JPN teaching JPN.
T10 Robot arm + scoop. UK teaching JPN
T11 Robot arm + NXT program. JPN preparation
T12 Robot arm + scoop + NXT program. Streaming video. JPN teaching UK.
T13 Programming LabView for remote control.
T14 Programming LabView for remote control.
T15 Programming LabView for remote control.
T16 UK teaching Japan. Robot construction + NXT program + stop and swing arm to hit ball.
T17 Suika robot. Rotate + follow line+ sensor + chop down. Japan preparation 1.
T18 Suika robot. Rotate + follow line+ sensor + chop down. Japan preparation 2.
T19 Suika robot. Rotate + follow line+ sensor + chop down. Japan preparation 3.
T20 Robot construction + NXT program + + obstacles + sensors.
T21 Suika robot. Rotate + follow line+ sensor + chop down. Japan teach UK.
T22 Programming LabView for remote control.
T23 Programming LabView for remote control.
T24 Remote control for search & rescue circuit A.
T25 Remote control for search & rescue circuit B.
T26 Remote control for search & rescue circuit C.
T27 Remote control for search & rescue circuit D.
T28 Move to black line, stop and throw ball to hit over obstacle. UK teaching Japan.
Tasks
33. Immersion ( flow ) - how immersed students become within
the process of each task.
To record immersion (or flow), a virtual FlowPad appears in front
of the virtual world avatars.
At regular intervals during the task procedures each avatar has to
answer two questions, with four options:
Q1. How challenging is the activity?
• Difficult (score 4)
• Demanding (score 3)
• Manageable (score 2)
• Easy (score 1).
Q2. How skilled are you at the activity?
• Hopeless (score 1)
• Reasonable (score 2)
• Competent (score 3)
• Masterful (Score 4).
These questions were chosen based upon research in flow by Pearce
et al. [9].
35. If we look at the data of Task Fidelity and
immersivity, we suggest that T10 and T28 would be
considered most successful tasks when students are
engaged in robot mediated interactions.
TF value for T28 was only + 0.08; slightly above
the optimal Task Fidelity line. T28 was slightly below
the optimal path of immersivity.
Similarly for T10 with immersivity slightly above
optimal path of immersivity and Task Fidelity at +0.01.
The challenge for instructors is to seek tasks
similar to T28 and T10 where immersivity is close to
or on the optimal path of immersivity, and task
complexity is close to or on the optimal line of Task
Fidelity.
The challenge for researchers is to seek ways to
transfer these observations to further tasks with
different participants in order to develop more
reliable optimal learning tasks when engaged in robot
mediated interactions in a virtual space [10].
36. This applied research is developing metrics for learning when conducting virtual world
tasks.The motivation to implement this research was the nuclear disaster of 3-11.A
virtual Fukushima nuclear plant and an OpenSim training space have been iteratively
designed and built. International collaboration by students as non-experts has highlighted
the benefits and challenges posed when engaged in constructing robot-mediated
interactions (RMI) within the context of distance-based communication in 3D spaces.
Students’ immersion (or flow), Circuit Task Complexity, and Robot Task Complexity have
been calculated. Optimal learning tasks have been highlighted.A new metric is suggested
for measuring tasks involving robots, which we term Task Fidelity [10].
Many thanks to UK collaborators and students at University of South Wales and CynonValley
schools, my students at Future University, Japan, and metaverse designers at Firesabre and
Reaction Grid.
Conclusion
Next question
How can a better taxonomy be designed to identify specific learning when students are
engaged in mixed reality (real and 3D virtual world) Robot Mediated Interactions?
Acknowledgements
37. References
(1) T. Morris-Suzuki, D. Boilley, D. McNeill and A. Gundersen. Lessons from Fukushima. Netherlands: Greenpeace
International, February 2012.
(2) J. Watts. “Fukushima parents dish the dirt in protest over radiation levels.” The Guardian, May 2, 2011. [Online].
Available: http://www.guardian.co.uk/world/2011/may/02/parents-revolt-radiation-levels [Accessed August 20, 2012].
(3) L. W. Hixson. “Japan’s nuclear safety agency fights to stay relevant.” Japan Today. [Online]. Available: http://
www.japantoday.com/category/opinions/view/japans-nuclear-safety-agency-Fig.hts-to-stay-relevant [Accessed August
20, 2012].
(4) N. Crumpton. “Severe abnormalities found in Fukushima butterflies.” BBC Science & Environment. [Online].
Available: http://www.bbc.co.uk/news/science-environment-19245818 [Accessed August 20, 2012].
(5) E. Guizzo. “Fukushima Robot Operator Writes Tell-All Blog.” IEEE Spectrum, August 23, 2011. [Online]. Available:
http://spectrum.ieee.org/automaton/robotics/industrial-robots/fukushima-robot-operator-diaries [Accessed August 20,
2012].
(6) M. Vallance and S. Martin. “Assessment and Learning in the Virtual World: Tasks, Taxonomies and Teaching For
Real.” Journal of Virtual Worlds Research Vol. 5, No. 2, 2012.
(7) S. B. Barker and J. Ansorge. “Robotics as means to increase achievement scores in an informal learning environment.”
Journal of Research in Technology and Education, Vol. 39, No. 3, pp. 229-243, 2007.
(8) D.R. Olsen and M.A. Goodrich, “Metrics for evaluating human-robot interactions.” [Online]. Available: http://
icie.cs.byu.edu/Papers/RAD.pdf [Accessed March 14, 2009].
(9) M. Pearce, M. Ainley and S. Howard. “The ebb and flow of online learning.” Computers in Human Behavior, Vol. 21,
pp. 745–771, 2005.
(10) M. Vallance, C. Naamani, M. Thomas and J. Thomas. “Applied Information Science Research in a Virtual World
Simulation to Support Robot Mediated Interaction Following the Fukushima Nuclear Disaster.” Communications in
Information Science and Management Engineering (CISME). Vol. 3 Issue 5, pp. 222-232.