Learning in a Smart City

Environment

Elena Shoikova, Prof. DScUniversity of Library Science and

Information Technologies

Federation of the Scientific-Technical Unions in Bulgaria 27 -28 October 2016, Sofia

In this talk

1. Context: Digital Transformation & Innovation

2.Smart City Concept

3.Conceptualizing the smart learning

environments

4.Smart Learning Design Model

5.Projects

6.Case study SmartSantander: IoT

experimentation over a smart city testbed

7. Conclusion

1. Context: Digital

Transformation & Innovation

Digital Transformation & Innovation

Gartner's 2016 Hype Cycle for Emerging Technologies

Gartner Hype Cycles provide a graphic

representation of the maturity and adoption

of technologies and applications, and how

they are potentially relevant to solving real

business problems and exploiting new

opportunities

Gartner's 2016 Hype Cycle for Emerging Technologies

Gartner's 2016 Hype Cycle for Emerging Technologies

Gartner's 2016 Hype Cycle for Emerging Technologies

Докладът на Gartner 2016 Hype Cycle for Emerging Technologies (Distills Insight From More Than 2,000 Technologies )разкрива три различни технологични тенденции, които са на път да бъдат от най-висок приоритет за организациите, изправени пред бързо ускоряващите се дигитални иновации.

Gartner's 2016 Hype Cycle for Emerging Technologies

Три основни технологични тенденции създават коренно нови преживявания с ненадмината интелигентност и предлагат платформи, които позволяват на организациите да се свързват с нови бизнес екосистеми.

Прозрачно завладяващи преживявания(Transparently immersive experiences)

Интелигентни машини с възприятие (Perceptual smart machine age)

Революция на платформите (Platform revolution)

1.Прозрачно завладяващи преживявания (Transparently

immersive experiences)

Прозрачност между хората, бизнеса и устройствата - все по-адаптивни, контекстуални и безпрепятствани на работното място, в университета и у дома, взаимодействайки с бизнеса и другите хора.

Tехнологии:

4D Printing Brain-Computer Interface

Human AugmentationVolumetric Displays

Affective Computing Connected Home

Nanotube Electronics Augmented Reality

Virtual RealityGesture Control Devices

1. Прозрачно завладяващи преживявания (Transparently

immersive experiences)

2. Интелигентни машини с възприятие(Perceptual smart machine age)

Умните машини ще бъдат най-пробивната класа технологии през следващите 10 години, поради радикалната изчислителна мощност, близки до безкрайност количества данни и безпрецедентни постижения в дълбоки невронни мрежи, което ще позволи на организациите с технологии на интелигентни машини да извличат максимална полза от данните, за да се адаптират към нови ситуации и решаване на проблеми, които никой не е срещала преди.

Machine Learning Virtual Personal Assistants

Cognitive Expert AdvisorsSmart Data Discovery

Smart Workspace Conversational User Interfaces

Smart RobotsCommercial UAVs (Drones)

Autonomous VehiclesNatural-Language Question Answering

Personal AnalyticsData Broker PaaS (dbrPaaS)

Context Brokering (контекст посредничество).

2. Интелигентни машини с възприятие(Perceptual smart machine age)

3. Революция в платформите

Преминаването от техническата инфраструктура към интегрирани хибридни платформи, позволяващи създаването на екосистеми, полага основите за изцяло нови бизнес модели, които образуват мост между хората и технологиите.

В рамките на тези динамични екосистеми, организациите трябва активно да разберат и да предефинират стратегията си, за да създадат платформено-базирани бизнес модели и да използват външни и вътрешни алгоритми, за да генерират стойност.

Ключовите платформени технологии включват:

IoT PlatformSoftware-Defined Security

Software-Defined Anything (SDx)Neuromorphic Hardware

Quantum Computing Blockchain

3. Революция в платформите

Интернет на нещата (IoT)IERC-European Research Cluster on the Internet of Things

разглежда IoT като неразделна част от Бъдещия интернет:

Динамична глобална мрежова инфраструктура с възможности за самостоятелно конфигуриране, базирани на стандартни и оперативно съвместими комуникационни протоколи, където физическите и виртуалните "неща", които имат своя идентичност, физически качества и виртуални личности, използват интелигентни интерфейси и са безпроблемно интегрирани в информационна мрежа.

IoT - Characteristics

Unique IdentitySelf adaptation

Everything is connected

Self-configuration

Ubiquity

Ubiquity

Programmability

SensingActuation

Embededintelligence

Interoperablecommunication

The new Intel Edge Management Systems for IoT Networks

is a pre-integrated, cloud-based middleware stack that facilitates device configuration, file transfers, data capture, and rules-based

data analysis and response

The new Intel Edge Management Systems for IoT Networks

GATEWAYSHardware verificationSoftware verification

The new Intel Edge Management Systems for IoT Networks

CLOUD MANAGEMENT

DATACENTER STORAGE

The new Intel Edge Management Systems for IoT Networks

END-TO-END SECURITYSecure HW & SW & Data

Secure device managementSecure policy managementSafeguard scalable compute

The new Intel Edge Management Systems for IoT Networks

Connect Things and Devices• Capture sensor data• Machines take action

The new Intel Edge Management Systems for IoT NetworksTurn data into insight

• Process and store data• Perform cloud analytics• Manage devices and policies • Manage networks

The new Intel Edge Management Systems for IoT Networks

Intelligence at the edge• Filter data• Perform edge analytics• Data informs and directs devices

INTEL

IoT layered architecture

What is stopping the IoT?Vertical Silos

10 технологии , които ще отключат пълния потенциал на Интернет на

нещатаIoT Security

Analytics

IoT Device Management

Low-Power, Short-Range IoT Networks

IoT Processors

IoT Operating Systems

Low-Power Wide-Area Networks

Event Stream Processing

IoT Platforms

IoT Standards and Ecosystems.

GARTNER’s View of the IoT Ecosystem

GARTNER’s View of the IoT EcosystemThe desired outcome is an ecosystem where everyone benefits – Standards – Security – Interoperability – Breakdown of silos –Horizontal layers – Data commons with open access The IoT ecosystem is in its early stages of evolution and is characterized by a high degree of complexity:

IoT Ecosystem Network Service Providers Cloud Service Providers (CSP)

Hardware Makers Device Manufacturers

IT services vendors Middleware vendors

Software vendors Standards Bodies

Industry Groups Regulators /Govt

Introduction to FIWARE Open Ecosystem

2. Smart City Concept

Smart City Definitions

Smart City is a term denoting the effective integration of physical, digital and human systems in the built environment to deliver a sustainable, prosperous and inclusive future for its citizens

•

Smart City Definitions

A Smart City can be viewed as a combination of

four Internets or networks: Internet of Things,

Internet of People, Internet of Data and

Internet of Services.

The Smart City as a set of ‘Internets’

An Enterprise Architecture View of Smart City

• The emphasis is put on the system integration and

synergistic characteristic of a smart city

• Such a view illustrates briefly the system integration

property that ICT provides in smart cities

An Enterprise Architecture View of Smart City

Smart City Components

Logical and Virtual Level

Technology Platforms and Components

3. Conceptualizing the

emerging field of smart

learning environments

• The new forms of industries and new types of jobs require future personnel to be well equipped to meet the need of the expansion requirements of these industries and keep up with their development needs

The needs

Competence based

education

Competence assessment & development

market

labor

Demand and supply of employees with specific knowledge, skills and competences

Information systems for the labor market

• Constant improvements in and re-evaluation of the curriculum taught to the learners has to be done regularly to keep the learners up-to-date in fulfilling the requirements of these industries

The needs

• Today, as education systems are currently undergoing significant change brought about by emerging reform in pedagogy and technology, our efforts have sought to close the gap between technologies as educational additive to effective integration as a means to promote and cultivate student centred, inquiry based and project based learning

The needs

Smart Learning Environments

The International Association for Smart Learning Environments embraces a broad interpretation of what constitutes a smart learning environment.

• A learning environment can be considered smart when it makes use of adaptive technologies or when it is designed to include innovative features and capabilities that improve understanding and performance. In a general sense, a smart learning environment is one that is effective, efficient and engaging.

efficient

Smart Learning Environment

is

engaging

effective

Smart Learning Environments

• Broadly defined, smart learning environments represent a new wave of educational systems, involving an effective and efficient interplay of pedagogy, technology and their fusion towards the betterment of learning processes

Smart Learning Environments

Various components of this interplay include but are not limited to

Pedagogy

• learning design

• learning paradigms

• teaching paradigms

• environmental factors, assessment paradigms, social factors, policy

Technology

• emerging technologies, innovative uses of mature technologies, interactions, adoption, usability, standards, and emerging/new technological paradigms (open educational resources, learning analytics, cloud computing, smart classrooms, etc.)

Fusion of pedagogy & technology

• transformation of curriculum, transformation of teaching behaviour, transformation of learning, transformation of administration, transformation of schooling, best practices of infusion, piloting of new ideas

Considerations of smart learning environments development

• A learning environment can be considered smart when the learner is supported through the use of adaptive and innovative technologies from childhood all the way through formal education, and continued during work and adult life where non-formal and informal learning approaches become primary means for learning

Considerations of smart learning environments development

1. Full context awareness

2. Stacking vs. Replacing the LMS

3. Big data and learning analytics

4. Autonomous Decision Making and Dynamic Adaptive Learning

Considerations of smart learning environments development

– can combine a physical classroom with many virtual

learning environments.

– by combining smart learning environments with

holistic Internet of Things and ubiquitous sensing

devices, e.g., wearable technologies such as smart

watches, brainwave detection, and emotion

recognition

1. Full context awareness

Considerations of smart learning environments development

– accepting the role of the existing LMS as the base system

– adding Stacks or Layers on top that will create added and more targeted functionality: • Competency or Talent Management Layers;

• Assessment or Feedback Layers;

• Compliance or Regulatory Layers;

• Career Development Layers;

• Collaboration and Social Networking Layers,

• Gamification or Engagement Layers

2. Stacking vs. Replacing the LMS

Considerations of smart learning environments development

– employing big data and learning analytics to collect, combine and analyze individual learning profiles

– monitor individual learners’ progress and behavior continuously in order to explore factors that may influence learning efficiency and effectiveness

3. Big data and learning analytics

Considerations of smart learning

environments development

– precisely and autonomously analyze learner’s learning behaviors in order to decide in real time, for example, what interactions with the physical environment to recommend to the individual learners to undertake various learning activities, the best location for those activities, which problems the learners should solve at any given moment, which online and physical learning objects are the most appropriate, which tasks are the best aligned with the individual learner’s cognitive and meta-cognitive abilities, and what group composition will be the most effective for each group member’s learning process

4. Autonomous Decision Making and Dynamic Adaptive Learning

Smart learning environments foundation areas

Social constructivism, psychology and technology are the foundation areas that provide meaningful and convergent input for the design, development and deployment of smart learning environments

Smart learning environments foundation areas

Social co

nstru

ctivism

Psych

olo

gy

Tech

no

logy

Smart Learning Environments

The hierarchy of revised Bloom’s Taxonomy supported by technologies

4. Smart Learning Design

Model

Innovative learning scenario supported by smart learning environments

Physical environments that are enriched withdigital, context-aware and adaptive devices

to promote better and faster learning

Learning activity

Smat Learning

environment

Roles

Learning objective

Services

Web 2.0 & Social

3.0

MOOCs Learning resources

Learning activities

Management activities

Administrative activities

Learning scenario

Roles

TeacherStudent

Learning Analytics

SECI 2.0 - Dynamic knowledge conversion processes in technology

enabled smart learning environments

SECI

processes

and web 2.0

Web 2.0:

Glogster;

Flippingbook;

Animoto; etc.

Web 2.0:

MindMeister;

Bubbl.us; etc.

Brainstorming

Conceptualization

Internationalization Socialization

Externalization

Web 2.0: Prezi;

YouTube; Blog; ect.

Web 2.0: Weebly;

Voci; ProProfs;

Jimdo; etc.

Create

Web 2.0: Ning; Wiki;

Facebook; etc.

Networking

Learning

activity

Learning

activity

Learning

activity

Learning

activity

Sharing

Sharing

5. PROJECTS

Projects• SOC PROJECT – School On the Cloud: Connecting Education

to the Cloud for Digital Citizenship, 2016

• FP7 FORGE PROJECT "Forging Online Education Through Future Internet Research and Experimentation“, 2016

• FETCH PROJECT - Future Education and Training In Computing: How to Support Learning at any Time Anywhere, 2016

• FP7 PROJECT ELLIOT Experiential Living Lab for the Internet of Things, 2013

• ФНИ, Концептуално и симулационно Моделиране на Екосистеми за Интернет на Нещата (подаден проект)2016

6. FP7 FORGE SmartSantander: IoTexperimentation over a smart city testbed - Monitoring the environmental parameters in Smart City

SmartSantander: IoT experimentation over a smart city testbed

• Synergy with the FP7 FORGE project "Forging Online Education through FIRE“

– The FORGE project introduces the FIRE experimental facilities to the eLearning community, in order to promote experimentally driven research in education by using experiments as an interactive learning and training channel both for students and professionals

– FORGE provides learners and educators with access to world-class experimentation facilities and high quality learning materials via a rigorous production process.

Smart Santander

IoT Specialization in the Software Engineering Master Program

• The University delivers the IoT Specialization in the Software Engineering Master Program, which heavily relays on present research and the FORGE eLearning methodology and tools having the opportunity to study in depth various aspects of networking protocols and infrastructure, watch instructional movies and screencasts, as well as conduct experiments using the FIRE infrastructure

IoT Specialization in the Software Engineering Master Program

• In the IoT lab sections students will learn hands-on IoTconcepts such as sensing, actuation and communication

• The FORGE model and methodology employed for the development of interactive lab in the field of Internet of Things is aimed at fostering remote experimentation with real production system installed in the Smart City, such as SmartSantander

Learning scenario: Monitoring the environmental parameters in Smart City

• As an illustration of experimental learning on IoT , a learning scenario entitled “Monitoring the environmental parameters in Smart City“is presented, which proofs the power of FORGE methodology and infrastructure for building remote labs and delivering them to students

Learning scenario: Monitoring the environmental parameters in Smart City

• Aim: In this experimental lab students will learn hands-on IoTconcepts, such as sensing and communication in the Internet of Things experimentation facility being deployed at Santander city

• Smart environment: SmartSantander infrastructure and its interactive online site, which is conceived as a 3-tiered approach: IoT node; Repeaters; Gateways. Within the SmartSantander project more than 2,000 environmental monitoring sensors have been already deployed. These sensors are monitoring CO index, temperature, noise level and light intensity

Smart Santander

SmartSantander

Learning scenario: Monitoring the environmental parameters in Smart City

• A hybrid cloud infrastructure has been established to support the learning process , which integrates variety of collaboration platforms and eLearning systems

• The cloud based infrastructure enables innovative learning scenario execution and monitoring

Learning scenario: Monitoring the environmental parameters in Smart City• Learning activities:

– Navigate through the various parts of the SmartSantander (http://maps.smartsantander.eu/) and become familiar with the capabilities of the freely accessible platform tags - IoT infrastructure, Mobile Sensing, Pace of the City, Augmented Reality POIs and play with consideration of various parameters

– Explore the SmartSantander IoT infrastructure and examine the set of parameters of the environment, the system is able to monitor

– Find on the Internet intelligent sensors or complete devices that can measure the same set of parameters.

– Explore the features of smart sensors for air pollution, for example C02, O3, particulate matter and ZO2.

– Design of a “Network of sensors and system for continuous

Learning scenario: Monitoring the environmental parameters in Smart City

SmartSantander

Conclusion

The Future Learning Infographic

The Future Learning Infographic

These changes point the way toward a diverse learning ecosystem in which learning adapts to each Learner instead of Learner trying to adapt to school.

The Future Learning Infographic

• Learning will no longer be defined by time and place — unless a learner wants to learn at a particular time and in a particular place.

• Learners and their families will create individualized learning playlists reflecting their particular interests, goals, and values.

The Future Learning Infographic

• Those learning playlists might include public schools but could also include a wide variety of digitally-mediated or place-based learning experiences.

• Whatever the path, radical personalization will become the norm, with learning approaches and supports tailored to each learner.

The Future Learning Infographic

• Educators’ jobs will diversify as many new learning agent roles emerge to support learning.

• A wide variety of digital networks, platforms, and content resources will help learners and learning agents connect and learn.

The Future Learning Infographic

• Some of those tools will use rich data to provide insight into learning and suggest strategies for success.

• At the same time, geographic and virtual communities will take ownership of learning in new ways, blending it with other kinds of activity.

The Future Learning Infographic

• As more people take it upon themselves to find solutions, a new wave of social innovation will help address resource constraints and other challenges.

• Diverse forms of credentials, certificates, and reputation markers will reflect the many ways in which people learn and demonstrate mastery.

The Future Learning Infographic

• Work will evolve so rapidly that continuous career readiness will become the norm.

• “School” will take many forms. Sometimes it will be self-organized.

Elena Shoikovae.shoikova@unibit.bge.d.shoikova@gmail.com