KINROM: Knowledge: its nature, representation,organisation and Management

About this course :

I have been a great admirer of the Theory of Knowledge ( ToK) course in the IB Diploma program. I was not able to understand why no Indian School Education Board gave any importance to its students first having some idea of what ‘Knowledge’ is before completing their journey of School Education. This is perhaps only one of the many failings of the School education system that as we are about to enter the age of the “ Knowledge and Innovation Economy” we have the reality of much of our youth being labelled as inadequate and untrainable for the future driven by Artificial Intelligence and allied technologies. 

Another source of inspiration for this course is the The Harvard Universal Classics, the 51-volume anthology of classic works from world literature compiled and edited by Harvard University president Charles W. Eliot and first published in 1909.

This weeklong introductory ( taster) course builds upon these two and attempts to align it the needs of contemporary concerns in the age of Artificial Intelligence. 

The day-wise themes are : 

Day 1: What is Knowledge?

Day 2: Knowledge Representation

Day 3: Knowledge Organisation

Day 4: Mathematical Modelling

Day 5: Knowledge Management


Detailing the flow:

Day 1: What is Knowledge?

1.1: The nature of Knowledge

1.2: Personal and shared Knowledge 

1.3: Science : as public knowledge 

1.4: Paradigms and frameworks

1.5: The opposite of knowledge is not ignorance

Day 2: Knowledge Representation

2.1: Numbers: Roman, Hindu/ Arabic, Bits, Qbits

2.2: Language : alphabets

2.3: Images: icons, emojis, road signs, airport signs, maps, musical notation

2.4: Bar codes, QR code and target images for augmented reality

2.5: Diagrams in Science : chemical equations to Feynman diagrams

Day 3: Knowledge Organisation 

3.1: Areas of Knowledge 

3.2: Taxonomies

3.3: Mind maps

3.4: Knowledge Graphs

3.5: Ontologies

Day 4: Mathematical Modelling

4.1: Why Mathematical modelling?

4.2: Classification of types of Mathematical models

4.3: Growth models

4.4: Predator prey systems

4.5: Epidemics

Day 5: Knowledge Management

5.1: Key ideas in Knowledge Management

5.2: Capturing and sharing knowledge 

5.3: Semantic Networks

5.4: Leveraging Knowledge

5.5: Trends in Knowledge Management