Further Readings: Internet of Things

 How data management unlocks the internet of things

https://www.itu.int/pub/T-TUT-SMARTCITY-2016-2

Solution: Internet of Things without ecological disaster

Readings: Faculty Activism Education Laggards, Big Data ..

Northwestern orders professor to stay away.



Indian Education- Late by 50 years?


On Student Data Practices


Model Policy' for Handling Student Data

Building Teacher Skills in Emerging Economies-1

There is a worrisome perception among parents and other stakeholders about the declining quality of school education in emerging economies.  This is attributable to numerous factors but the more serious among faculty is a lack of adequate and contemporary post-entry training or continuous professional updating  for teachers.

Poor quality of  teachers means children are provided with weak inputs. Student learning is sub-optimal. Teachers may have the basic skills, but not much more. The cost of this failure to meet student needs on monetary and development and psychological angles could be substantial.  Thus investing in teachers'human resource development is the single most important challenge. 

There is a learning crisis at hand unless the teachers are adequately trained. There is need for focused attention on updating professional competences through technological intervention among teachers so that quality delivery of education is possible. 

Steps may include:

• Establish an IT based information system  to inventory skills of teachers. 
• Estimate demand and supply of skilled workforce in relation to education needs at a macro level and at the granular level.    
• Match supply with demand at granular level. Identify deficits and work towards rebuilding teacher competences. 
• Leverage modern technology to ensure  reaching out to teachers, particularly those needing scaling up in course content. 
• Technology to be leveraged for designing  and developing tech driven pedagogical techniques.
• An open platform for e-content on skill development of teachers to be aimed at. 
• Crowd source content for this platform from among the practitioners and experienced to keep costs low. Affordability has to be ensured. 
• High quality content aggregation by an expert team essential prior to release of content. 
• Teachers must utilize the contents of this platform through Massive Open Online Courses (MOOC) and virtual classrooms . 
• Have teacher innovation hubs at  regional and administrative unit levels. Learning has to be simultaneously decentralized. 
• The Internet of Things (IoT)—the networked connection of people, process, data, and things—must be exploited to become the Internet of Learning Things. 
• The target should be improvements in infrastructure / device availability which make  24/7 connectivity possible  for teachers to benefit and for the teachers to use techniques ranging from Cloud Computing to Big Data integration with the IoT 
• Ease of delivering content to be ensured. 

Note: This is the first of a series on Teacher Skill Building in Emerging Economies by the author. 

How will Big Data help educational institutions?

• Students’ voluminous data are easily captured in all its volumes on an ongoing basis.
•  Data can be studied for the shortest time frame. Even intra-day analysis is possible. This shortens time to action. Prompt, proactive action is possible.  
• Study patterns of students can be documented and examined on a historic basis.
•  Predicting Student Performance. Rating them - Which students are slow in tasks assigned? Who are low performers and least performers?
• Drawing up a Risk Matrix for Cohorts.
• LP (Low Performing) student needing help some online help or offsite. 
• A student in classroom session or on online learning:  Is there disinterestedness, boredom visible?  Is there frustration creeping in?
• How often and in what manner do students use educational software? (Blackboard, EBSCO, Turn-it-in, any internal intranet?) When do they really submit; the pattern of submissions.
• What is the inventory of faculty skills?
• Predicting student progression. 
• What courses and pedagogical modes attract students?
• Which courses and pedagogical modes deter students?
• Patterns in enrollment.
• Deciphering patterns in student progression.
• Analysis of student dropout ratios and causative factors.
• Student retention trends.  
• Predictive models using this type of unconventional data to assess teaching risk. 
• Knowing and monitoring the opinion and attitude of the students and stakeholders as opinions, feelings and attitudes about the EI, as discussed on the world wide web.
• Developing a sentiment analysis tool to monitor student reflections.
• Monitoring social platforms and social media websites. Reconnaissance of the social sphere, including social networks, blogs, Facebook and twitter and other relevant sites.  
•  Leveraging on valuable feedback and insights to improve offerings and services.
• Student profiling to suit personalization.
• Data would reveal the student interests.
• Using student data for cluster analysis
• Student’s propensity towards a certain subject can be measured.
• Data from online usage- from cookies, URL and software metrics – could to identify which online channels the students are using and what they are using them for.  

Smart Education and New Skills Thru Internet of Education

Seeking Value in the New Education Model in Emerging Economies

Deriving Value concept in education is what leaders in emerging economies must embrace: Value is adding benefits at lesser costs (V = B-C).  The new educational model must  look to network innovations which are interconnected - connect  to  create value.

Cisco predicts that by 2020 there will be 50 billion “things” connected to the Internet, up from 25 billion in 2015. The future is one of data analytics - the need to mine and draw inferences on student performances. The new order provides an integration of faculty (human ) and the digital elements. The Educational Internet of Things (IoEd) would enable educational institutions to utilize software strengthened sensors, machine-to-machine conversations and learning. It will exploit technologies to harness and analyze data from the student world  and then use those analyses to add value to educational organizations.  

Enhancing value applies to any educational product or service:  An education institution might offer a product like degree/ diploma  or a given service in the form of enhancing knowledge, competency, or skill sets or just plain offer a given social good (churning out model citizens)- in all of these categories, there has to be value generation in the logistic chain. 

Tomorrow's educational entities will be in smart buildings (Smart education city) . There will be smart academic infrastructure. Educational development will be integrated with new smart cities. Such moves will generate value through greater economies of scale.

Steps essential:

·         The psyche build up to usher in innovative change.

·         Thinking  outside the usual framework- thinking technologically right.

·         Looking  beyond current knowledge base of teachers

·         Looking beyond current functional skill sets of teachers

·         Working with a technologically proficient team and partnering with innovative institutions to strengthen the capacity and to induce technological motivation

·         Have a cross disciplinary approach: What works in patient care works in student service. - Tender, loving care (TLC) - So one could borrow from other silos.

Hardware:

The new model has  to draw from the potential well of technology. Technology is time constraining and resource intensive. Technology intervention points are:

·         the use of laptops for elucidation in class rooms,( .ppt presentations)

·         watching videos/ you tube in classrooms,

·         coping with learning management systems,

·         engaging with peers on line for self learning by teachers,

·         engage with students on line, accepting assignments and course work on line.  

·         conducting on line tests

·         declaring results on line

There could be a host of devices deployed to facilitate e studies:

·         Laptops,

·         Chrome books,

·         Macs,

·         iPads,

·         Windows machines

For data analytics and predictive usage, centralization of data and of accompanying software is of essence.

Balancing between costs and mass drives at technology accessed 'in to' schools is essential to ensure the preparedness of students for tomorrow's jobs. Drawing in support from corporate entities to  usher in greater technological value build up in education is the challenge as resources are a constraint. .

Cisco identifies the following Skills in the times of Internet of Things 

Cisco - 21st Century Skills

•         Collaboration

•         Communication

•         Creativity

•         Problem solving

•         ICT proficiency

•         Critical thinking

Cisco - Global leadership skills

•         Global mindset

•         Languages proficiency

•         Cultural awareness

•         Team player

•         Professionalism

•         Work ethics

Cisco - Entrepreneurial Skills

•         Opportunity recognition

•         Self-direction

•         Persuasion

•         Planning skills

•         Risk taking

•         Resourcefulness

Cisco quotes top 10 skills for the future workforce

•         Sense making

•         Social intelligence

•         Novel and adaptive thinking

•         Cross-cultural competency

•         Computational thinking

•         New-media literacy

•         Transdisciplinarity

•         Design mindset

•         Cognitive load management

•         Virtual collaboration.

 The new model is about getting the people and process  in the act .

This is a part of the research work on the Internet of Education by the author. He can be contacted  at jaynayar@gmail.com.

Advantages Using Sensors and Big Data to help the Internet of Education (IoEd)

 1.      A sensor—something that senses, captures, and reports information[1] can be used by the education industry to improve student monitoring on an ongoing basis.

2.    It adds to immediate care of students by constant although through remote connectivity.

3. Cell phones could be used to effectively track student locations through using global positioning systems (GPS). Radio frequency identification (RFID) sensors are already in use in educational context.            

4.      Schools can mine big  data for segmentation, growth trending, to track student progress,  assessment patterns and deviations if any.

5.      Understand gauge student reactions through interpreting voluminous data.

6.      Be conversant with the sources of data available to the decision makers.

7.      View industry level data for averages' computation and benchmarking.

8.      Gleaning relevant information.

9.   Big Data brings together  statisticians, IT data specialists and business analysts for adding to business value.

10.  Schools obtain an insight to decision making. Teachers can achieve insight based on analysis of real data.  

11.  Schools get a macro perspective; there is the development of a mind-set and behaviour against silos.

12.  There is an added emphasis on shared data.

13.  The school management has to perforce assimilate basic understanding of analytical techniques so that they can utilize the results of big data analytics effectively.

14.  Aggregated datasets help evolve norms for  access, security and privacy. 

15.  There is more professionalization in the education industry with  requisite analytic skills being  at a premium.

16.  Decisions are not intuitive but based on confirmed big data.  

17.  Big data require schools to align their operational strategy and even their mission to big data. Information needs of the school is viewed in totality.

18.  Big data can improve consistency, ensure accuracy

19.  Big data necessitates and accelerates standardization of data, processes for data usage, consequential communications and best practices across the  industry.

20.  Schools need to redesign their existing architecture and move to component based architecture.

21.  Develops School information strategy.

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The copyright vests with the author. He can be contacted at jaynayar@gmail.com

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[1] Hewlett Packard (2015) The disruptive power of big data Business white paper | HP Haven big data platform

Make in India: The Need for Game Changers in Indian Education -skill building

1. An emerging economy like India  has to accept the role of Internet of Things (IoT) in  education- beyond information and communication technologies (ICT)  . With  the global spread of the internet and the attraction  that the web holds for the youth, education needs to capitalize on IoT if it is to seek skill development to meet the Make in India objectives.  The reach of technology should help strengthen relevant skills for the twenty-first century.

2. Education has to move to eliminate  the obsolete and archaic methods of delivery. It has to upscale conventional methods to integrate ICT with learning and teaching processes. Good teachers make good students. There is a serious need to put teachers in India back on teachers’ professional development. The purveyors of knowledge  have to be equipped with new sources of information as much as the learners.

3. New technologies must lead to teacher and student motivation. Capacity building has to be accompanied by objective assessment and an ongoing learner monitoring mechanism. Technology has to continuously  interact with the learner and the teacher over a range of areas. Accessibility into remote and sociologically , geographically or economically weaker sections need the support of technology.[1] There is an element of 'educational inclusion' called for.  

4. Corporates like Infosys seem to be waking up to the need to leverage on the technological shift to the Internet of things.[2]  Infosys is set to identify and incubate about a dozen new ideas that could potentially bring $100 million each and a total of over a billion dollars in incremental revenue annually over the next few years, as part of a deliberate innovation strategy . Cisco has advanced quite a bit on education in the scheme of IoT.  

5. The Government of India has to seize the technological moment. It must evolve an organized mechanism by which it identifies new Internet of education ideas based on artificial intelligence, school delivery automation and Internet of Things. The Government of India needs to have national policies in education encompassing::

 a - Big Data Analytic:. With the advent of big data, the accumulation, inventorying and interpretation of education statistics would be a key issue.  Student behaviour and  teacher value building all would be converted to data bytes which need interpretation from a futuristic and additive perspective.

b - Robotics- there is likely to be a dis-intermediation of human intervention in teaching in more routine areas. Subjects that need vast tutorial elements like mathematics can better be handled by artificial intelligence. This is likely to excite young minds and avoid monotony to the deliverer of learning.

c-  Internet of Education : Smarter classrooms through sensors measuring receptivity and feedback dashboards. According to Cisco, the increased deployment will affect  in many ways. The Internet of Everything (IoE) "is revolutionizing the way ... It's resulting in better, more reliable service, consumer empowerment and improved capacity and efficiency ..."[3] The Internet of Education has to capitalize on the digital developments. 

d - Microelectromechanical Systems (MEMs) – transfering information between the worlds of the physical and the digital.

e-  Nano-technology : Miniaturization of devices. As nano-material availability increases, miniaturization will thrive.  

f-  Working towards a cloud platform for the education industry.   

g - An ecosystem of partners   from education providers to technology providers

h-  Applying Mobile technology to spread education.

Countries like Taiwan, Korea are able to become leaders in the electronic industry but India has to cover a longer ground although it has more entrepreneurs than these countries, along with skilled labor force.[4] Taiwan, Korea and China have massive government funding of strategic industries. Government and corporates need to finance IoEd university research, finance breakthrough research, help in education patents,   and help in setting up a massive ecosystem for education.  

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[1] http://www.unicef.org/education/files/Making_Education_a_Priority_in_the_Post-2015_Development_Agenda.pdf

[2] http://articles.economictimes.indiatimes.com/2015-04-07/news/60902939_1_ceo-vishal-sikka-michael-reh-navin-budhiraja. Vishal Sikka's strategy sees Infosys shift focus to Internet of Things and artificial intelligence, Anirban Sen, ET Bureau Apr 7, 2015, 02.49AM IST

[3] http://www.smartgridnews.com/story/50-billion-connected-iot-devices-2020/2015-04-21

[4] India Insight, Ecosystem for “Make in India” does not exist: Rajeev Karwal of Milagrow

By Reuters Staff http://blogs.reuters.com/india/2015/02/25/ecosystem-for-make-in-india-does-not-exist-rajeev-karwal-of-milagrow/ February 25, 2015

Internet of Things will bring about the dis-intermediation of bankers.

 Internet of Things: The dis-intermediation of bankers.

In banking, the intervention of  technology has reached a critical threshold. The Internet of Things (IoT) revolution will accelerate the age of disruption ushered in by the  digitalization of  banking. IoT will reduce dependence on bankers but add to the customer accessibility of banking.

The components that would capture the attention of IoT in banking include (a) development of sensor platforms, (b) development of communication and connectivity systems,(c) cloud computing (d) big data  (e) analytics, and (f) cyber security.[1]

Moore’s Law states that there is a doubling of computer processing capacity every 18 months or so   The “internet of things” uses multiple sensors (like the brain’s neurons) connected through the web (like the brain’s synapses) to create, in effect, a machine-brain.[2] It is estimated that by 2020 there would be 30 billion wireless devices connected to the Internet of things.[3]  

These requires that IoT reduces if not nullifies subjectivity. All processes would be driven by integrated networking of technology and less by people. As banking moves towards total technology driven banking, and as people operate from a host of hardware interfaces through rapidly evolving software,  there will be fewer bankers and more of machines.

These mathematics driven processes will cover the following functional areas through  predictive models (using advanced analytics like data modelling, data mining, statistical analysis, text mining and visualization can be used by banks to ensure compliance to regulatory requirements by identifying patterns and predictive trends.

a) Capital adequacy management;

b) Risk management - Evaluation of risk exposure for internal and external  - Risk Mitigation.

c) Real time  assessment of factors like the external market conditions, balance sheet changes.

d) Balance Sheet Management

e) Project performance management.

f) Fraud detection.

g) Credit risk

h) Customer profiling

i) Compliance

j) Payment / Settlement systems

k) Trading of financial instruments  (forex, bonds, other market instruments )

 Without any risk or responsibility.

Copyright of this article and its contents vests with the author of this blog: Jayaram Nayar. 

He can be contacted at email: jaynayar@gmail.com  

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[1] http://theinstitute.ieee.org/ieee-roundup/opinions/ieee-roundup/your-questions-answered-the-internet-of-things

[2] Haldane, A. G. 'Growing Fast and Slow' Bank of England Speech at the East Anglia University, 17 February, 2015

[3] Padmanabhan, G 'Emerging Issues In Cyber Security in The Financial Sector' Reserve Bank of India, at   the State Bank of Travancore, , 28 February 2015.