A description on how to incorporate data analytics for K-16 classes.

1. Information entanglement: future developments of cognitive
based knowledge acquisition systems based on Big Data.
by
Willard Van de Bogart – Bangkok University
12th International Conference on Intellectual Capital, Knowledge Management & Organizational
Learning – ICICKM 2015 Nov. 5-6
The Institute for Knowledge and Innovation Southeast Asia (IKI-SEA) of Bangkok
University, Bangkok, Thailand

2. Background
As part of its Technology Utilization Program—a program designed to transfer new aerospace
knowledge and innovative technology to nonaerospace sectors of the economy—NASA operates a
network of Industrial Applications Centers.
These centers serve U.S. industrial clients on a fee paying basis by providing access to literally
millions of scientific and technical documents published by NASA and by other research and
development organizations. Using computers, the NASA Industrial Applications Centers conduct
retrospective and current awareness searches of available literature in accordance with client
interests, and assist in the interpretation and adaption of retrieved information to specified needs.
1970 - 2015
Studied Information Counseling with Dr. Anthony Debons, Information Scientist, Univ of Pittsburgh

3. Getting from here to there. Where is there?
A teacher-researcher’s perspective
A compressed presentation
Immersive vectors
Multi-disciplinary trajectories
= Teaching point
Pair word syntax
Reform, Re-structure, Re-organize, Re-evaluate learning, Revolution
Innovative language usage
The question
Operational Schema

5. How to keep up?
Making choices

6. Fig. 1. Frequency distribution of documents containing the term “big data” in ProQuest Research Library.Amir Gandomi, Murtaza Haider
Beyond the hype: Big data concepts, methods, and analytics
International Journal of Information Management, Volume 35, Issue 2, 2015, 137–144
http://dx.doi.org/10.1016/j.ijinfomgt.2014.10.007
Acceleration of data will require a new approach to retrieval

7. Getting Prepared for Big Data Instruction
Data Analytics verses Subject Learning
“Big data is high volume, high velocity and
high variety information assets, innovative forms
of information processing for enhanced
insight and decision making.” (Gartner IT glossary”)

8. Big Data for Kids – K-16 - Where do we begin?

9. Preparing the cognitive landscape for big data analytics
Applying data acquisition strategies in the classroom

10. Data distribution pedagogy – Learning vectors
Provide Multiple source locations for information input

11. Preparing for big data analytics – Cognitive enhancers
The Use of Metapatterns for Research into Complex Systems of Teaching, Learning, and Schooling— Part II: Applications
Jeffrey W. Bloom, Tyler Volk http://ejournals.library.ualberta.ca/index.php/complicity/article/view/8759
Visual-Analytics.EU http://www.visual-analytics.eu/faq/

12. Iconic Teaching Methodology
Basic visualization techniques to develop concepts
The rationale for the
Iconic Teaching
Technique
was to find an easier way
to convey and idea to an
ESL student.
https://www.academia.edu/4185603/Iconic_teaching_techniques_using_cognitive_enhancers

13. Tools to evaluate
Digital Conceptualization
• cognitive

14. Critical thinking tools – what is a concept?
It’s a concept map and not a mind map.
http://cmap.ihmc.us/

15. NASA uses Cmap

16. Eight elements of thought –
the question is the most difficult beginning to knowledge

17. Can Mollusks whistle? Yes, if you blow into them
http://www.wired.com/2010/11/conch-trumpets-peru
The questions guide
our thinking to:
1. Exploring sound
2. Exploring air
3. Cultural integration

18. QGT and Answer Retrieval Technology (ART).
• Question Generation Technology (QGT) in Deep Learning -
Why defining new questions is more important than finding
answers in Data Analytics. (Scot Forshaw)

19. So, where is there? It’s here. Man-Machine Interface
The goal of autonomic computing is to create systems that run
themselves, capable of high-level functioning while keeping
the system's complexity invisible to the user.
AI
Quantum
Computing
Disruptive technology

20. Cognitive architecture - Where do we go from here?
Can the human make effective decisions?
http://www.vernon.eu/cognition/08_Duch_Oentaryo_Pasquier.pdf

21. Data mining – Can you find it? Can the algorithm?
Generally, data mining
(sometimes called data or
knowledge discovery)
is the process of analyzing
data from different
perspectives and summarizing
it into useful information.

22. Data Analytics – Who sees the patterns?
Data analytics (DA) is
the science of examining
raw data with the
purpose of drawing
conclusions about that
information.

23. Data patterns – Data analytics
Patterns are useful to data because they:
Document simple mechanisms that work.
Provide a common vocabulary and taxonomy
Enable solutions to be described concisely as
combinations of patterns.
Enable in the designing and implementation of
decisions.

24. Visualization tools for data analytics
The way we look at things is changing
MeshLab is an advanced 3D mesh processing software system which is well
known in the more technical fields of 3D development and data handling.

25. In education

26. Information Entanglement – Data Analytics – Quantum algorithms
Toridion quantum algorithms – encoding data
Will the human add to the autonomous system or be supplanted by it?
http://www.visicomscientific.com/web/en/rss/wp/developers-make-first-unaided-encoding-transmission-and-extraction-of-a-toridion-quantum-compressed-file/

27. Deep Learning

28. Getting Prepared for Big Data Instruction
Data Analytics verses Subject Learning
Subject identification verses non linear processing using an unsupervised algorithm
Will the computer discover concepts on its own
or
will the human be able to extrapolate more?
Neural Network
Deep Learning Issue

29. Data Summary for Information Entanglement
• Distributed Learning
• Cognitive Learning
• Iconic Learning
• Machine Learning
• Deep Learning
• Hybrid Learning
• Data Analytic Learning

30. Promote Data Literacy
Oceans of Data Institute
• Call for Action
• Data drives discovery, decision-making, and innovation. The quantity of data
created globally is growing exponentially, and data is everywhere. Data touches
all aspects of our lives, including our health, our environment and our role as
citizens.
• We are all perpetually producing streams of data, which we need to shape and
manage to ensure our privacy and personal security;
• Effective use of data empowers us to make objective, evidence-based
inferences and fundamental decisions affecting our lives, both as individuals
and among societies.
• In light of this, we are calling for a revolution in education, placing data
literacy at its core, integrated throughout K-16 education nationwide and
around the world. By enabling learners to use data more effectively, we prepare
them to make better decisions and to lead more secure, better-informed and
productive lives.
http://www.oceansofdata.org/projects