Next-Gen E-Learning Ideas

Knowledge Base with 3D Graphical Environment for Learning,
Distributed Collaboration and Critical Thinking
Jan-2010 MacArthur Foundation Proposal +
Supporting Mockup Diagrams +
More...
Richard Creamer
2to32minus1@gmail. com
Copyright © 2010-2013 Richard Creamer
All Rights Reserved

Copyright © Richard Creamer 2010 - 2013 – All Rights Reserved

1

Why I’m publishing this slide deck
I am currently in the middle of a job search (have any openings?) and want to place the ideas in this slide deck in the public domain prior
to accepting a new position so that any IP contained herein is ‘clean’ as well as in the hope that I can find developers who would like to
help me form an open source project to evolve and implement the ideas presented.
I believe that with the right tweaks, these ideas can form the bulk of an ideal MOOC platform of the future.
Also, this isn’t just about online/MOOC Education - I feel that there are also many potential applications for these ideas in both traditional
Education as well as the Commercial sector.
By the way, most of these ideas were previously publicly published on my personal website in January, 2010 and remained online for
about two years as an unofficial supplement to a 300-word-limit proposal I submitted to a MacArthur Foundation RFP in January, 2010
with a ‘Reinvent Learning’ theme. (This proposal is included in this slide deck.)
The bulk of the brainstorming which went into the mockup diagrams was done in about a 2-week period in January-2010.

Richard Creamer
October 22, 2013
PS: This deck was necessarily assembled in a hurry and as a result, is incomplete, has errors, and some redundancies.
PPS: Some of the foundational ideas are derived from the W3C’s Semantic Web standards.
PPPS: I can remove the copyright notices at any time - they are included now only as a preliminary precaution.


2

- Begin Intro Slides -


3

Preface
Overview
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There are many important problems in Education.
Many of these problems, perhaps the most important problems, remain unrecognized or ignored.
Hint: There are much more effective and expedient ways to present the material in many textbooks and videos.
This under-development slide deck presents ideas which I hope uniquely integrate the following:
• Knowledge representation and carefully-designed graphical visualization
• Learning
• Long-term memory retention of learned material
• Collaboration and discourse
• Computational argumentation
• More
A large node-edge graph and the integrated use of novel metadata coupled with new UI components, underlie the presented approach.
Most of the UIs for this project are either graphical, or new forms of instrumented, multi-layer hypermedia.
Important: The architecture described in this deck enables, via metadata, any form of external information, URI, highly-specialized
learning apps, and even 3D immersive environments to be associated with, and launched from, any node in the graph.
Most of these ideas were developed in Jan-2010 while developing a proposal for a MacArthur Foundation RFP.
This technology has many important applications beyond Education.

Goals
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Understanding: Provide technology which ensures a deep, cohesive, cross-disciplinary understanding of topic materials.
Difficult Learners: Rapidly resolve students’ confusion on any topic, statement, problem, equation, symbol, etc.
Time Efficiency: Enable substantially more time-efficient learning  learn more in less time  learn more
Retained Knowledge: Solve the Forgetting Curve problem and develop efficient ways to graphically review, refresh, and maintain all
salient knowledge from current and prior courses throughout one’s education and career.
Collaboration: Greatly improve upon today’s poorly-indexed, disorganized, repetitive, non-scalable discussion thread models.
Non-Technical Subjects: Develop technology allowing many non-technical subjects, such as History, to be effectively taught and
tested/graded in online environments.
UI/UX Presentation Components: Develop new, more powerful, UI components which can be instrumented to support new and
existing types of hypermedia (in multiple layers).
Community Contribution: Enable community voting on, and contributions to, problem sets, curriculum, and explanatory materials.
4

Projected Benefits
Projected/Anticipated Benefits*
• Double the amount of knowledge taught and assimilated in typical online (and traditional) courses.
• Deepen the level of students' understanding of course materials, concepts, how they fit together, and are applied.
• Dramatically reduce the time often spent by students resolving their confusion on difficult concepts such as mathematical formula notation and
conventions.
• Effectively solve the 'forgetting curve' problem. Initial thoughts:
• Create a UI component which visualizes a complete, highly-minified sub-graph visualizing a complete subject in a single view where the nodes
are just tiny dots
• Color-code portions of this course-holistic sub-graph view according to a student’s mastery level of each course vicinity.
• Employ a scheduler to queue/revisit each vicinity and test a student’s level of mastery until a high level of recall has been demonstrated the last
n times.
• Perhaps on an annual basis, re-activate the above steps to sample and refresh accordingly, course knowledge and keep it from fading over time.
• This same UI component could also be used by students to review/prepare for tests and final exams.
• Develop more efficient and precise curriculum authoring and curation tools.
• Develop more effective presentation UX paradigms.
• Develop a common foundational data model on which to base all:
• Knowledge
• Curriculum
• Metadata:
• Statement provenance
• Interactive visualization component UX behavior
• Content curation
• And more
• Problem sets and problem solutions
• Collaboration/discourse/voting
• Embed critical thinking, statement provenance, points of contention, and precise, scalable, distributed collaboration into MOOC platforms.
• Integration of arbitrary, external topic-specific apps so that the best instructional tools can be utilized at any point.
• This project can be implemented in an evolutionary manner.
• A coherent, phased project plan could be designed to gradually incorporate various aspects of this project, so this path need not be ‘disruptive.’
• And more...
*These are big claims, but my 20+ years of developing software and novel, effective user interfaces suggests to me that they are, in fact, achievable.

5

Introduction
• In the long term, express all knowledge as a node-edge graph using a 4-tuple graph data model [ subject, predicate, object, uuid ] called Quads.
• This graph will include not only knowledge and concept-related data, but also novel types of useful metadata including:
• Provenance, UX behavior, permissions, vote tallies, links to related materials, and the expansion predicate vocabularies available to a node/edge.
• Typically, only one or two small, interactive salient subsets of this graph will be viewable at a once, usually via special-purpose knowledge viewers.
• Even though a large node-edge graph underlies the knowledge, over-cluttered conventional visualizations of this graph will never be seen by students.
• Courses will be defined as paths through this knowledge graph and pause points (‘vicinities’) corresponding to curriculum chapters/sections/sidebars.
• Each vicinity may be viewed at user-controllable level-of-detail settings: Low, Medium, and High, and branches may be interactively expanded/collapsed.
• Vicinities will be rendered differently for different grade levels (driven by metadata). E.g., a 5th grader may see factoring differently than a 10th grader.
• This large underlying graph will drive multiple types of higher-level, interactive graphical UI components. These multi-layer UI components include:
• Slide Deck Viewer
• An advanced slide deck viewer which can be ‘instrumented’ to link to arbitrary graph vicinities, external hypermedia, animations, and more
• Much of the narration and ‘scribbling’ in video lectures could be built into such a hypermedia component, again, via underlying graph metadata.
• Knowledge Graph Viewers
• Several highly interactive knowledge graph viewers which allows users to efficiently control the amount and type of information displayed as well
as to explore ad hoc branches in the graph as-needed, for example, in order to learn more about a side topic often found in a different textbook.
• Unlike traditional graph visualization, these viewers:
• Will not overwhelm users with too much information - too many nodes and crossing edges
• Will not simply display ‘pixels’ but rather, each node, edge, and node ‘grouping element’ will be interactive objects
• Both 2D and 3D (including immersive 3D environment) viewers are anticipated.
• Computer Screen Viewer (and Video Viewer)
• A tool which allows a recording of a computer’s screen to be displayed, but which also supports interactive (id, dx, dy, dt) hot spots so that
students can pause the viewer and overlay other viewers in order to explore supplementary/explanatory materials related to the in-view screen
as well as make notes and sharable bookmarks.
• Argument Diagram Viewer
• An argument diagram viewer visually groups related graph statements (Quads) together to form premises which may then be linked into a
formal, complete, discussion- and voting-enabled argument. All statements, premises (and their provenance) may be viewed, voted on, have
adjacent vote tallies, and in general, be precisely critiqued in a collaborative environment.
• Distributed Collaboration Environment
• This component is similar to the Argument Diagram Viewer except that ad hoc sub-graphs, not necessarily part of a formal argument, may be
viewed, discussed, and voted on (at the individual Quad level).
• Storyboard Viewer
• An advanced storyboard component will be developed which can be used to visually represent story-like subjects such as History which have
timelines, scenes, actors, relationships and more. Storyboards might also offer a good way to portray temporally-varying data and processes such
as chemical, political, and assembly/repair processes.
• Tools
• Finally, efficient content authoring and curation tools will be developed. This would include revisioning, metadata authoring, grading, etc.
Note: I feel this direction has great potential and that a phased, evolutionary approach to implementing these ideas both in MOOCs and classrooms is possible.
6

Comments
Time Efficiency
• In today’s multi-disciplinary, lifelong learning world, both students and adults need to learn more skills/courses and even new careers.
• But we still need to have a life and spend time with our children!
 Therefore, we need to reduce the time needed to learn new subjects.
• Too much of students’ time is unnecessarily wasted (a traditionally unrecognized area of potential improvement):
• Text books are too verbose and disconnected (students must take lengthy tangents to learn related topics in other books).
• Many university texts almost appear to have intentionally been made difficult to read.
• Videos take too long to view, have low information density, and are just ‘pixels’ and audio narrative - not interactive, multi-layer
hypermedia, but they do have their place. (Some people learn best from different presentation types than others.)
• Many MOOC courses give difficult assignments without enough preparatory, illustrative examples. (Compare w/any Calculus book.)
• For the most part, MOOC curriculum lecture slides rarely take advantage of even ordinary hypermedia such as lecturer pen
scribbling, let alone the new, advanced hypermedia which would be possible with new presentation technologies. This:
• Adds to the time it takes a student to understand a slide
• Does not provide the deeper understanding and linkage to other related materials which advance hypermedia could provide
• MOOC forums have very little organization and are poorly indexed making it difficult for students to navigate and find answers to
their questions or find threads they have previously visited.
• Student collaboration on MOOCs, based primarily upon forums, is cumbersome and slow because it is linear, unstructured text.
Retained Knowledge
• Textbooks contain a lot of material. If each page contains 10 units of knowledge and there are 500 pages in the book, then 5,000 units
of knowledge are contained in the book. (A hypothetical, illustrative example which probably underestimates the fact count by 5x-10x.)
• Think about what your brain actually retains after completing a course in, say, Algebra. Do you remember:
• Every word on every page? No, of course not.
• 5,000 things? No, of course not.
• What your brain does store is a small collection of concepts, techniques, and knowledge of when and how to apply those techniques.
• And even for engineers who use Algebra throughout their careers, this concise subset of retained knowledge is quite sufficient!
• The brain distills those 500 pages of text into a small, concise subset of relevant material, logically organized as a network.
 This suggests that it may be possible to condense and formulate curriculum into a form more akin to how it is stored in the brain.


7

Comments
Knowledge Representation
• The W3C’s Semantic Web has a fundamental data type called an RDF Triple: [subject, predicate, object], for example: (see slide 18 for more details)
• [ rick, WeightInKg, 88.1826 ] (after his mid-day snack;-)
• The above RDF statement states that ‘rick’ weighs 88 Kg.
• ‘rick’ is the subject, ‘WeightInKg’ is the predicate (analogous to an attribute), and 88.1826 is the object (analogous to an attribute value).
• ‘WeightInKg’ is the predicate and is similar to an attribute/property.
• The three elements in actual RDF triples are URIs (like URLs) - globally unique IDs (‘object’ can also be a string literal, also see slide 18).
• Since each tuple element is globally unique, ambiguity can be completely avoided. Think about how many different meanings exist for ‘thread.’
• An RDF triple is a ‘statement’ and collections of triples can be visualized as a node-edge graph/diagram.
• Except for the smallest graphs, simple/naive visualization of such a graph is usually not effective.
• Sometimes, you want to know information related to a statement called ‘metadata’ (see Provenance below).
• To do this, you need to write a ‘statement about a statement’ formally called ‘reification.’ (also see slide 27)
• With reification, anyone can state anything about anything. This is a very powerful capability supporting explanations, contention, uncertainty, etc.
• The W3C provides a way to do reification, but a perhaps simpler way is to create 4-tuples (Quads): [ subject, predicate, object, uuid ].
• With this simple foundational data structure, arbitrarily complex node-edge graphs may be defined (networks).
• This graph structure can encode any type of information: curriculum, problem sets, solution sets, multimedia, arguments, permissions, etc.
• Not only can knowledge be represented in such a graph, but also any type of associated metadata (data about the data):
• Provenance
• Who or which entity made the statement?
• For what moment or time span is this statement valid (e.g., temporal data such as city population or temperature)?
• If the statement was a data measurement in a scientific experiment, what was the experiment ID, date-time, equipment, and uncertainty?
• If the statement was made by a politician, what special interests groups which benefit from this statement contributed to this politician’s
campaign?
• If the statement was someone’s opinion/vote in an online student forum, what were the vote tallies? On which statements?
• Contention - Show all perspectives on issues
• Clearly/graphically display any individuals or groups who disagree with any statement.
• With which specific statements do they disagree, and on what basis (links to additional statements and their provenance)?
• Visualization - Graphical knowledge graph browser visualization/UX hints/behavior (explained later)
• More - Additional metadata can be specified such as geolocation, associated statements (as in argument diagrams).
• So, knowledge, its metadata, its visualization, interrelationships between the data, permissions for content curation, collaboration, voting, external
Web links, problem set aggregation, problem solutions, solution explanations, and more can all be integrated into a single data model and network.

8

- End Intro Slides -


9

Begin
January-2010 MacArthur Foundation Proposal
Theme: ‘Reinvent Learning’
(Converted to 5-slide slide deck)


10

Knowledge Base with 3D Graphical
Environment for Learning, Distributed
Collaboration and Critical Thinking
Richard Creamer
2to32minus1@gmail.com
Copyright © Richard Creamer 2010 - All Rights Reserved


11

Brief Project Description

Perform the R&D necessary to create a unique educational
knowledge base enabling students, world-wide, to graphically
explore most subjects, grades 4-16. The included 3D graphical
environment will also enable distributed student groups to
collaboratively view, compose, discuss, critique, and vote on:
topics, arguments and premises, ideas and thought processes.


12

Primary Goals
• Graphical Knowledge Exploration: Provide all children with a free, multilingual,
complementary/supplementary educational knowledge repository enabling them to
view and explore knowledge in the form of intuitive graphical diagrams (enhanced
mathematical node-edge directed graphs). Graph nodes may contain text, charts,
multimedia, HTML documents, etc. as well as virtual 3D environments and other
graphs/diagrams. Students will be able to “jump into” many types of node content.
Unlimited levels of supplementary/explanatory information such as Why,
ExplainFurther, ExplainDifferently, BasedUpon, and RealWorldUses will be able to
be associated with facts/statements and interactively navigated.
• Collaborative Critical Thinking: Enable distributed student groups to collaboratively
discuss, critique, and vote on: topics, arguments and premises, ideas and thought
processes--all from within the 3D graphical environment. Argument premises will be
clearly linked to their supportive facts in the same view. This will enable students to
critically discuss/chat and vote on the validity and merit of any fact or premise. As a
result, students will learn to judge the logical strength of arguments while
collaborating on a variety of socially-relevant topics such as Global Warming.


13

Details
• Data Model: For many reasons, the data model for this project will likely be based
on the W3C Semantic Web’s RDF "triple" format.
• Knowledge Base Content: The goal is to motivate scholars from around the world to
contribute to the knowledge base’s content, in parallel.
• Rationale for Graphical Visualization:

• Clear, concise, relational depiction of knowledge, its categorization, and its
provenance.
• Efficient, selective navigation and display of information.
• Ability to avoid disruptive context switches when browsing multiple side topics.

• Graphical knowledge representation has been shown to improve student
knowledge retention.
• Provides skeletal framework/context for organizing/assimilating knowledge
gained in coursework.
• Expertise: Multiple expert academics/consultants will be engaged during this
project.
• Example preliminary diagrams: tinyurl.com/DMLExamples

(Note: this link no longer valid)

14

Knowledge Base with 3D Graphical
Environment for Learning, Distributed
Collaboration and Critical Thinking
Richard Creamer
Copyright © Richard Creamer 2010 - All Rights Reserved


15

End
January-2010 MacArthur Foundation Proposal
Note: Since this proposal was submitted, I believe that grades K-3 could also partially benefit from this
approach.


16

Begin Preliminary Illustrative Diagram
Screenshots and Explanations


17

Preface

- A hastily-prepared primer for understanding the diagrams to follow Briefly:

• First, this document is very under-developed and preliminary. Much refinement, evolution, and elaboration on benefits and motivations has yet to
be developed.
• What is not included are more recent ideas such as:
• The utility for Storyboard applications (similar to how movies are initially planned) for converting subjects such as History into a graphical,
interactive, electronic format.
• Various ways in which traditional slide decks may be augmented and ‘instrumented’ to become a much more effective form of ‘hypermedia’.
• All knowledge can be represented as a large, single network of statements, which in turn can be modeled as a node-edge graph.
• Courses are simply paths through this network graph with stopping points along the way for viewing the local vicinity.
• These local vicinities could, in general, represent a chapter/section/sidebar in a traditional text book.
• Unlike a text book, if a math symbol or concept is not explained, the graph network will always include links/branches to other vicinities where these
things are defined, recursively, if necessary.
• Ultimately, a child may navigate all the way back to fundamental axioms of a Greek mathematician when exploring a side topic/area of confusion.
• In most of the following static screenshot mockups, please realize that the amount of information in view at any time will be completely
controlled by the viewer/student. The reader of this slide deck may feel these diagrams are too cluttered, but that is only because they have been
substantially ‘expanded’ to illustratively show what branches may be available to be viewed.
• I anticipate offering students the ability to set the level of detail in ‘vicinities’ to Low, Medium, and High, and allow them to toggle back-and-forth as
desired.
• To make a graphical learning experience as effective as possible, I have/will develop several advanced User Experience techniques to make the
navigation of this graph very intuitive and efficient - much more efficient than current graph visualization tools.
• The fact that I use a formal graph data model (RDF triple/quad) means that any statement can be ‘decorated’ with additional RDF
statements/information. Here are but a few useful, illustrative ‘predicates’:
• ExplainFurther, ExplainDifferently, RealWorldUses (these are self-explanatory, may have cardinality > 1, and may form arbitrarily long chains)
• Provenance: Who made this statement
• Validity: Who agrees that this statement is valid
• ProbabilisticValidity: What is the probability that a statement is valid/factual (typically used for scientific or imperfect observations)
• Contention: Who disagrees with a statement, and what are their reasons
• GeoLocation: A geographical point or area associated with a statement or statement element
• VoteTally: Students will be able to collaboratively vote on any statement and these results can be displayed in the graphical browser
• Temporal History: Some knowledge/facts are not static - they change with time. Hence, graphical ‘time slider’ controls will be provided so that
students can view information at different times. For example, a city’s population could be viewed at any time in the past or future (projected).
• UX: Statements and groups thereof can be decorated with metadata hints for how the actual graphical knowledge browser should render the
statement(s) and what operations they support (such as ‘jump into,’ available expansion predicate vocabularies, and display time slider).
I hope this is enough to permit readers to better understand what follows and some of the motivation for why things were designed this way.

18

Introduction to RDF Triples*
One of the simplest spoken language sentence structures is the pattern: Subject + Predicate + Object.
For example, in the sentence “Jim lives in Seattle.”, we have:
• Subject = Jim
• Predicate = lives in
• Object = Seattle

This simple sentence structure is the basis for the unit of information used in the W3C’s Semantic Web Resource Description
Framework (RDF). Any and all complex information can be decomposed into this basic, atomic format called an RDF “triple.”
Often, a triple is visualized as a mathematical directed graph:
Subject

Predicate

Object

Or, for the above example:

Jim

lives in

Seattle

The above graph is a bit simplified. In the Semantic Web, the elements of a triple are semantically precise and unique strings**
(URIs to be specific). For example, “Jim” might actually be defined as http://bigonto.org/earth/us/humans/093a5e21-988e-449fb89d-55b09d1c2b3b. Similarly, the predicate “lives in” might be defined as http://world.eduweb.org/predicates/human/geo/livesin. Fortunately, non-unique human-friendly labels may be associated with RDF resources to make graphs more readable.

The basic idea is that globally-unique URIs can be assigned to concepts, such as “Jim” or “lives in.” This enables unambiguous
statements to be asserted that refer to specific concepts using specific (globally-unique) predicates. Thus, using semantically-precise
triples, intricate networks of semantically precise statements can be created to richly describe any area of knowledge. Due to the
inherent tree structure of URIs, RDF triple elements are often derived from nodes in taxonomic classification trees.
Benefits resulting from the selection of RDF triples as the basic unit of information for the educational knowledge base include:
• Automatic addressability and incorporation of everything on the Internet
• Intuitive visual/graphical representation of knowledge and information
• Unrestricted decoration of knowledge to facilitate learning (e.g., predicates such as Why or ExplainDifferently)
• Ability to represent and adapt to any information regardless of its structural characteristics (unlike DBMS schemas)
• Basic multi-lingual support due to RDF’s support for language tags (“Biology”@en vs. “Biologie”@fr)
• By using language tags and very brief text in graph nodes, translation to any language becomes much simpler.
* Note: In this project, ‘Quads’, not Triples, are used, where a 4th tuple element (uuid) is added to support efficient reification (statement about a statement).
** The object element may be either a URI or a string literal such as “98.6 F”.

19

Example: Browsing First Grade Curriculum (extremely preliminary)
(This is not what a child would see - this design work is under construction.)
Numbers

Handwriting
Category
Alphabet
First Grade

Subject[ ]

Category
Category

Arithmetic

Letter Sounds

Lessons[ ]

...

Upper-Case
Lower-Case

Reading
Hours
Spelling
Telling Time

Lessons[ ]

...

Minutes

Lessons[ ]

...

Category
Category

[1]
Category
Hours & Minutes

Lessons[ ]
[2]

In general, Predicates will be blue.

...

...

Predicates corresponding to a
collection of statements (a ‘bag’)
will sometimes be displayed as a
node with [ ] brackets.

Clicking on this
node launches an
external, custom,
highly-specialized
time -telling
practice application

(Nodes can
contain text, URLs,
HTML,
applications,
multimedia, etc.)


20

Example: Astronomy/Solar System Browsing Session
(simplified, intentionally incomplete, under-construction, very preliminary)

Astronomical
Body

definition

“A naturally-occurring, gravitationally bound
aggregation of matter located in space“@en

is-a
definition

Star
is-a
The Sun

“An astronomical body that radiates energy
resulting from sustained, internal thermonuclear
reactions” @en
definition

meanDiameter

Major
Planet

1,390,000 km
is-a

[1]

is-a

[2]
majorPlanet []

“An astronomical body orbiting a
star, or n-ary star system, which is
not itself a star, has sufficient mass
to have a nearly round shape, and
has cleared the neighborhood
around its orbit” @en

[3]

The Earth

moon

The Moon

meanDiameter

[4]

3,474 km

meanOrbitalRadius
12,742 km

meanOrbitalRadius
[5]

meanDiameter

Moon

384,403 km

149,600,000 km

[6]
[7]
[8]
planetarySummary
minorPlanet []

[1]

Pluto

is-a

Minor
Planet

definition

“An astronomical body orbiting a star, or n-ary star
system, which is not itself a star and has sufficient
mass to have a nearly round shape” @en

[2]
...
[Tabular summary
of selected
planetary data]
Please note the use of @en tags using language tags will make
translation of graph data much
simpler than translating entire
textbooks.

In all diagrams, students
interactively collapse and expand
the graph branches/nodes to quickly
view just the content of interest.


21

Example: Interactive browsing a topic with distributed group voting and collaboration (very preliminary)
Global Warming – Synopsis 

X

Interactive hovering and node
expansion user session...

Earth – Temperature  Increasing


X

Voting...

Mouse hover popup displays
available predicates

Caused By...

(Optionally, user could first select
desired predicate vocabulary)

Open in new panel...


Basis...

Distributed Collaboration and
Decision Making (group)

Effects...
Contention...


X
Agree

Voting Data

Note: RDF
triples do not
necessarily have
to be rendered
as a distinct
node-edgenode.

Voting Action
Voting Action

Caused By

Earth – Atmosphere.CO2  Increasing

Caused By

Vote Tally

Disagree
Unsure

Vote Agree

84

3
1

Vote Disagree

User Actions

Humans – Generate  CO2
Popup GUI
Controller

Caused By

Humans – Engage In  Deforestation
Relevance

Type []

Source []
(weighted set)

1900 1950 2000 2050 2100

Relevance

Burning – Reduces  Tree Population

Harvesting – Reduces  Tree Population

Humans.Power Plants

33%

22%

Temporal Data
(Displayed information
interactively changes in
real-time based on
selected year via slider)

Humans .[ Factories, Home Heating ]
Humans .[ Cars , Trucks ]

12%

Burning – Produces  CO2

Scope of slider(s) could
encompass entire graph or
subset

Year: 2000

33%
Trees – Convert  CO2

Tally nodes could be
linked to interactive chat
session view so voters
could debate premise.

Humans.Major Transportation

Ref

umich.edu/~gs265/society/greenhouse.htm


22

Example of Social/Scientific Topic RDF Graph (extremely preliminary)
Purpose: Show that graphs/diagrams can be very useful vs. narrative text/HTML
Global Warming

Related To

Synopsis

Known As

Solutions

Sources
Global CO2
Reform
Policies

Improved
Energy
Efficiency

Asserted By

1900 1950 2000 2050 2100

Projected
Effects

Solutions

Increase in global
temperatures primarily
caused by increases in
atmospheric CO2 levels arising
from multiple human activities.

Year: 2100

Climate
Change

Best Case

Set [ ]

Major Sources

Basis

Targets

Category

Category

Intermediate

...
Worst Case

Industries [ ]
Country

Greenhouse
Effect

IPCC
Technology

(2100)
3.2° F surface temperature rise
7.8” ocean level rise

(2100)
5.0 ° F surface temperature rise
(2100)
6.48 ° F surface temperature rise

Coal Burning
Technology

Could easily be line graphs
depending on visualization
metadata

Set [ ]
Oil Burning

Electric Cars

Lighting

China

Deforestation

US
Type

Incandescent

Disadvantage
Energy
Inefficient

Type

Type

CFL Lights

Disadvantage

Advantage
Safe

Possibly Unsafe: Mercury is
released into homes and
environment, high cost
Relevance

Increased probability of toddler
neurodevelopmental diseases
(autism, Down syndrome)

LED Lights

Advantage
4X > efficiency vs.
incandescent
lighting

Disadvantage

Higher initial cost,
more R&D needed

An example of a statement
‘grouping element’
Advantage
Safe, 10X >
efficiency vs.
incandescent
lighting

Contention

Validity of research
concluding released
mercury levels are safe
is currently disputed.

Relevance
Current autism rate in
U.S. alone > 29,000
children per year


23

Example Knowledge Graph - Argument Graph Hybrid (very preliminary)
Argument:
World should decrease its
consumption of fossil fuel

B

F

D

Infrared Light

Absorbed By

Re-Emits

Atmosphere.CO2

E
D

G

Observed increase in
global surface
temperature

Infrared Light

To

Earth.Surface

Warms

• Sunlight warms Earth’s surface
• Earth’s surface radiates infrared light
• The infrared light is absorbed by the atmosphere’s CO2
• The atmosphere’s CO2 re-emits the infrared light
• ...to the Earth’s surface
• ...which warms the Earth’s surface
• ...which contributes to Global Warming

C

C

Radiates

Earth.Surface

Graph should be read as:

A

A

Warms

Sunlight

Earth.Surface
Global
Warming

Contributes To

ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/monthly.land.9
0S.90N.df_1901-2000mean.dat

Ref

Graph

B

Note: At this very early stage, it is
thought that the above type of
argument diagram will support inplace expand/collapse of nodes to
permit exploration of hidden,
hierarchically-contained subargument elements, their premises,
and their underlying facts and
associated metadata (voting,
uncertainty, etc.)

Home Heating

Defined As

C

Gradual increase in
Atmospheric CO2

Caused By

Deforestation

Human
Activity

Factories

Type []
Fossil Fuel
Consumption

Type []

Cars, Trucks
Major Transportation

Caused By

Power Plants
Global
Warming

E

Arctic Ice Melt
Projected
Effect [] *

F

1 Meter Ocean Rise by 2100

Species Extinction
Major Coastline Recession

This slide also illustrates how the
(many) corresponding pages of
Wikipedia’s unstructured text
may be compressed into a single
diagram which allows ‘bigger
picture’ structure and
interrelationships to be
displayed.

Validity
Endorsed By

Solution []

10% Humans Lose Housing by 2100
Temperature Rise > 10 °F

40+ Scientific
Societies

World

Consumption of
Fossil Fuel

Should
Decrease

G


Distributed student groups would be able to vote on any
fact statement and cite other facts, arguments or
argument premises, as the basis for their vote.
Distributed students could then discuss their votes and
the underlying premises in a virtual group environment.
Essentially all nodes have further expansion predicates
available to ultimately get to the root validity, pedigree,
basis, or origin of the fact/premise under
discussion/referenced.

*Some illustrative statements
are approximations

24

Visualization of Rationale for Voting w/Discussion (very preliminary)
Major benefits: 1) specificity (precise statement discussed), and 2) aggregation of many equivalent opinions (scalability)
IPCC
40 Scientific
Societies

Agrees With
Trend

Agrees With

Earth.Temperature

Trend

Caused By

Increasing

Graph

Increasing
Natural Geologic
Fluctuation

Disagrees With

Chat Group: G&G 120B

X

Ian: Mike, I disagree—I think GW is the
result of natural environmental
fluctations. See my thought bubble.
Mike: Ian, whether or not that’s the case,
don’t you think we should be reducing our
carbon emissions? The polar ice is
melting and the sea levels are going to
rise regardless of whether GW is caused
by humans or due to natural causes.
Ian: Mike, you have a point, the cause is
unimportant – we need to take action,
and now.

Human.Activity

Caused By

Unsure

Vote Tally

Voting Data

1

Agree

Vote Agree

Voting Action

3

Disagree

Undecided About

Note: Student name
labels in thought
bubbles correspond to
names and colors in
chat window.

84

Vote Disagree

Voting Action

Agrees With

User Actions

- Alternate Idea (Graphical Thought Bubbles)

IPCC

Ian

Agrees With
Trend

40 Scientific
Societies

Agrees With

Earth.Temperature

X

Increasing

Caused By

Trend
Graph

Human.Activity

Caused By

Natural Geologic
Fluctuation

Increasing
Disagrees With

Caused By
Distributed student groups would be able to vote on any
fact statement and cite other facts, arguments or
argument premises, as the basis for their vote.
Distributed students could then discuss their votes and
the underlying premises in a virtual group environment.
Essentially all nodes have further expansion predicates
available to ultimately get to the root validity, pedigree,
basis, or origin of the fact/premise under
discussion/referenced.

Voting Data
Voting Action

Human.Activity
Vote Tally
Vote Agree

Disagree
Unsure

Agree

3

?

1
84

Mike
Voting Action

Vote Disagree

User Actions

Increasing

Caused By


Trend

Human.Activity

25

X

RDF Graph for Example Algebra Problem (very preliminary)

Solve

This shows the result of a user interacting with and
exploring the problem/graph by successively
expanding desired predicates. This verbose detail
(and more) would be available if the user elects to
display it.

2x – 3 = 5

Note: Advanced mouse hovering
techniques will be used to make
navigation and expand/collapse
of graph nodes efficient and
intuitive.

Problem
Answer

x=4

Solution

Solution 1

View
Options

Step Expansion Labels:
 Operation
 Before
 During
 After

Control Graph Visualization
This, for example, would allow
a user to see only During
predicates for steps, if desired.

Author

Steps

...
Set [ ]

Add 3 to both sides

Operation
Step 1

Before
During

2x – 3 = 5

Why

To move the 3
to the opposite
side of equal
sign.

Why

To solve Algebra
problems, one needs
to get x on one side
of the equal sign by
itself.

Explain
Further

2x – 3 + 3 = 5 + 3

After
2x = 8

Allowable
Operations

Divide both sides by 2.
Operation
Step 2

Before

During

Algebraic equations are solved by
manipulating the equation until
the variable, x, is on one side of
the equal sign while the numeric
values are placed on the opposite
side. This is done by performing
operations, one at a time, on both
sides of the equal sign so that
equality is preserved. By doing
this step-by-step, one gradually
simplifies the equation until the
last operation yields the answer.

+
-

2x = 8
x
2x / 2 = 8 / 2

After

÷

Exception

x=4

It is illegal
to divide by
zero.

Logarithm
Raise to Exception
Power


It is illegal to
take square
root of
negative
number.

26

Older Example of Previous Algebra Problem
Exception

Begin

Solution

Exception

2x – 3 = 5

It is illegal to take the
square root of a negative
number.

It is illegal to divide by zero.

Valid Operations
+, -, /, x, raise to power, logarithm
Step 1

Operation

2x – 3 + 3 = 5 + 3

Add 3 to both sides.
As long as you do the same
operation to both sides of an
equation, both sides will remain
equal.

Explain Step

Algebraic equations are solved by manipulating the equation until the variable, x, is on one side of the
equal sign while the numeric values are placed on the opposite side. This is done by performing
operations, one at a time, on both sides of the equal sign. By doing this step-by-step, you gradually
simplify the equation until the last operation yields the answer.

Why

Step 1 Result

Step 2

2x = 8

2x / 2 = 8 / 2

Operation

Explain
Why
Step 2 Result

Divide both sides by 2.

Expand…
Expand…

Note: Hovering the
cursor over a predicate
will show a popup
Collapse… button.

x=4


27

Preliminary Thoughts on Graph Data & Metadata Model
Predicate

Subject

Single, logical data structure type

Object

•Subjects can be associated with zero or more types.
•Subjects (and Types) can be associated with zero or more predicate vocabularies.
•Predicate vocabularies allow filtered graphical expansion of graphs and easier query formulation GUIs.
Notation for displaying
predicates which denote
collections of related objects

Subject

Predicate[]

[0]

Object

( Composed Subject)
Metadata/reification:
Statement about Statement
aka: Composition of Subject

Predicate

Subject

Predicate

Object

Object

(All metadata are triples as well)
1240463339800

rdf:statement
rdf:type
Unique Identifiers (uuids)
enable metadata

rdf:subject
rdf:predicate

(statement ) uuid

x:MeasurementTime
subject uuid
(statement ) uuid1
predicate uuid

x:AssertedBy

rdf:object
(This is how data will tentatively
be stored “under the hood.”)

NOAA

[0]

[0]

[1]
type1

0.27

Example metadata statements
describe statement

object uuid or value

Vocabulary Definition: A group of related predicates.

x:Uncertainty

type2

Domain []

Range[]
type5

type3

type6

type4

Examples:
Fetus, Infant, Child, Human,
Mammal

[0]

[1]

[0]

[2]

Type[]

Object

Predicate

Subject

MemberOfVocabulary
Polymorphic Type uuids
(Allow Polymorphic
Queries/Patterns)

[0]

vocabulary1

[1]

vocabulary2

[2]

Associated
Predicate
Vocabularies[]

[1]

Type[]

[0]
Elements[]

vocabulary3


[1]
…

28

Example Astronomy RDF Ontology (with 3 instances: Sun, Earth, and Moon)
(simplified, intentionally incomplete, under-construction, very preliminary)
Type (Class) Definitions (RDF Triples)

Property (Predicate) Definitions (RDF Triples)

Instance Definitions (RDF Triples)

// Type: Astronomical Body
kb.astro.o:AstronomicalBody, rdf:type, rdfs:Class
kb.astro.o:AstronomicalBody, rdfs:label, “Astronomical Body” @en
kb.astro.o:AstronomicalBody, kb.gen:definition, “A naturally-occurring,
gravitationally bound aggregation of matter located in space“@en

// Property: majorPlanet
kb.astro.o:majorPlanet, rdf:type, rdf:Property
kb.astro.o:majorPlanet, rdfs:label, “majorPlanet” @en
kb.astro.o:majorPlanet, rdfs:domain, kb.astro.o:AstronomicalBody
kb.astro.o:majorPlanet, rdfs:range, kb.astro.o:MajorPlanet

// Instance: The Sun
kb.astro.i:Sun, rdf:type, kb.astro.o:Star
kb.astro.i:Sun, rdfs:label, “The Sun” @en
kb.astro.i:Sun, kb:meanDiameter, “1,390,000 km” @en
kb.astro.i:Sun, kb.astro.o:majorPlanet, kb.astro.i.Earth

// Type: Star extends Astronomical Body
kb.astro.o:Star, rdf:type, rdfs:Class
kb.astro.o:Star, rdfs:subClassOf, kb.astro.o:AstronomicalBody
kb.astro.o:Star, rdfs:label, “Star” @en
kb.astro.o:Star, kb.gen:definition, “An astronomical body that radiates
energy resulting from sustained, internal thermonuclear reactions”
@en

// Property: moon
kb.astro.o:moon, rdf:type, rdf:Property
kb.astro.o:moon, rdfs:label, “moon” @en
kb.astro.o:moon, rdfs:domain, kb.astro.o:AstronomicalBody
kb.astro.o:moon, rdfs:range, kb.astro.o:Moon

// Instance: The Earth
kb.astro.i:Earth, rdf:type, kb.astro.o:MajorPlanet
kb.astro.i:Earth, rdfs:label, “The Earth” @en
kb.astro.i:Earth, kb.astro.o:meanDiameter, “12,742 km” @en
kb.astro.i:Earth, kb.astro.o:meanOrbitalRadius, “149,600,000 km” @en
kb.astro.i:Earth, kb.astro.o:moon, kb.astro.i:Moon

// Type: Major Planet extends Astronomical Body
kb.astro.o:MajorPlanet, rdf:type, rdfs:Class
kb.astro.o:MajorPlanet, rdfs:subClassOf, kb.astro.o:AstronomicalBody
kb.astro.o:MajorPlanet, rdfs:label, “Major Planet” @en
kb.astro.o:MajorPlanet, kb.gen:definition, “An astronomical body
orbiting a star, or n-ary star system, which is not itself a star, has
sufficient mass to have a nearly round shape, and has cleared the
neighborhood around its orbit” @en
// Type: Minor Planet extends Astronomical Body
kb.astro.o:MinorPlanet, rdf:type, rdfs:Class
kb.astro.o:MinorPlanet, rdfs:subClassOf, kb.astro.o:AstronomicalBody
kb.astro.o:MinorPlanet, rdfs:label, “Minor Planet” @en
kb.astro.o:MinorPlanet, kb.gen:definition, “An astronomical body
orbiting a star, or n-ary star system, which is not itself a star and has
sufficient mass to have a nearly round shape” @en
// Type: Moon extends Astronomical Body
kb.astro.o:Moon, rdf:type, rdfs:Class
kb.astro.o:Moon, rdfs:subClassOf, kb.astro.o:AstronomicalBody
kb.astro.o:Moon, rdfs:label, “Moon” @en
kb.astro.o:Moon, kb.gen:definition, “An astronomical body orbiting a
planet” @en

// Property: definition
kb.gen:definition, rdf:type, rdfs:Property
kb.gen:definition, rdfs:Label, “definition” @en
// Property: meanDiameter
kb.astro.o:meanDiameter, rdf:type, rdfs:Property
kb.astro.o:meanDiameter, rdfs:Label, “meanDiameter” @en
kb.astro.o:meanDiameter, rdfs:domain, kb.astro.o:AstronomicalBody
kb.astro.o:meanDiameter, rdfs:range, rdfs:Literal

rdf:type
The Sun
meanDiameter

1,390,000 km

rdfs:subClassOf

Star
Major
Planet
Minor
Planet
Moon

[0]

[2]
[3]

majorPlanet []

Astronomical
Body

// Property: meanOrbitalRadius
kb.astro.o:meanOrbitalRadius, rdf:type, rdfs:Property
kb.astro.o:meanOrbitalRadius, rdfs:Label, “meanOrbitalRadius” @en
kb.astro.o:meanOrbitalRadius, rdfs:domain, kb.astro.o:AstronomicalBody
kb.astro.o:meanOrbitalRadius, rdfs:range, rdfs:Literal

rdf:type

[1]
Star

// Instance: The Moon
kb.astro.i:Moon, rdf:type, kb.astro.o:Moon
kb.astro.i:Moon, rdfs:label, “The Moon” @en
kb.astro.i:Moon, kb:meanDiameter, “3,474 km” @en
kb.astro.i:Moon, kb.astro.o:meanOrbitalRadius, “384,403 km” @en

The Earth

moon

Major
Planet
The Moon

meanDiameter

meanOrbitalRadius

rdf:type
meanDiameter

Moon
3,474 km

meanOrbitalRadius
12,742 km

384,403 km

[4]

149,600,000 km
[5]
[6]
[7]


29

Example Astronomy RDF Ontology – Actual RDF/XML
<?xml version="1.0"?>
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns:kb="http://dmlkb.org/kb/gen#"
xmlns:kb.astro.o="http://dmlkb.org/kb/astro.o#"
xmlns:kb.astro.i="http://dmlkb.org/kb/astro.i#">
<rdfs:Class rdf:about="http://dmlkb.org/kb/astro.o#AstronomicalBody">
<rdfs:label xml:lang="en">Astronomical Body</rdfs:label>
<kb.astro.o:definition xml:lang="en">A naturally-occurring, gravitationally bound aggregation of matter located in space</kb.astro.o:definition>
</rdfs:Class>
<rdfs:Class rdf:about="http://dmlkb.org/kb/astro.o#Star">
<rdfs:subClassOf rdf:resource="http://dmlkb.org/kb/astro.o#AstronomicalBody" />
<rdfs:label xml:lang="en">Star</rdfs:label>
<kb.astro.o:definition xml:lang="en">An astronomical body that radiates energy resulting from sustained, internal thermonuclear reactions</kb.astro.o:definition>
</rdfs:Class>
<rdfs:Class rdf:about="http://dmlkb.org/kb/astro.o#MajorPlanet">
<rdfs:label xml:lang="en">Major Planet</rdfs:label>
<kb.astro.o:definition xml:lang="en">An astronomical body orbiting a star, or n-ary star system, which is not itself a star, has sufficient mass to have a nearly round shape, and has cleared the neighborhood around its orbit</kb.astro.o:definition>
</rdfs:Class>
<rdfs:Class rdf:about="http://dmlkb.org/kb/astro.o#MinorPlanet">
<rdfs:label xml:lang="en">Minor Planet</rdfs:label>
<kb.astro.o:definition xml:lang="en">An astronomical body orbiting a star, or n-ary star system, which is not itself a star and has sufficient mass to have a nearly round shape</kb.astro.o:definition>
</rdfs:Class>
<rdfs:Class rdf:about="http://dmlkb.org/kb/astro.o#Moon">
<rdfs:label xml:lang="en">Moon</rdfs:label>
This is the actual XML needed to assert the triples in the previous slide.
<kb.astro.o:definition xml:lang="en">An astronomical body orbiting a planet</kb.astro.o:definition>
</rdfs:Class>
If you are unfamiliar with RDF, try this:
<rdfs:Property rdf:about="http://dmlkb.org/kb/astro.o#majorPlanet">
<rdfs:domain rdf:resource="http://dmlkb.org/kb/astro.o#Star" />
<rdfs:range rdf:resource="kb.astro.o:MajorPlanet" />
1.
Copy the XML text from here. (PDF text on this page loses its formatting when copied.)
<rdfs:label xml:lang="en">majorPlanet</rdfs:label>
2.
Paste it into the Check by Direct Input text area on the W3C RDF Validator web page
</rdfs:Property>
<rdfs:Property rdf:about="http://dmlkb.org/kb/astro.o#meanDiameter">
(http://www.w3.org/RDF/Validator/).
<rdfs:domain rdf:resource="kb.astro.o:AstronomicalBody" />
3.
In the Display Result Options drop-down list, select: Triples and Graph.
<rdfs:range rdf:resource="rdfs:Literal" />
4.
Click on the Parse RDF button.
<rdfs:label xml:lang="en">meanDiameter</rdfs:label>
</rdfs:Property>
5.
Browse the resulting parsed RDF triples as well as the (large) generated graph image.
<rdfs:Property rdf:about="http://dmlkb.org/kb/astro.o#meanOrbitalRadius">
<rdfs:domain rdf:resource="kb.astro.o:AstronomicalBody" />
<rdfs:label xml:lang="en">meanOrbitalRadius</rdfs:label>
</rdfs:Property>
<rdfs:Property rdf:about="http://dmlkb.org/kb/gen#definition">
<rdfs:label xml:lang="en">definition</rdfs:label>
</rdfs:Property>
<kb.astro.o:Star rdf:about="http://dmlkb.org/kb/astro.i#Sun">
<rdfs:label xml:lang="en">The Sun</rdfs:label>
<kb.astro.o:meanDiameter>1,390,000 km</kb.astro.o:meanDiameter>
<kb.astro.o:majorPlanet rdf:resource="http://dmlkb/kb/astro.i#Earth" />
</kb.astro.o:Star>
<kb.astro.o:MajorPlanet rdf:about="http://dmlkb.org/kb/astro.i#Earth">
<rdfs:label xml:lang="en">The Earth</rdfs:label>
<kb.astro.o:meanDiameter>12,742 km</kb.astro.o:meanDiameter>
<kb.astro.o:meanOrbitalRadius>149,600,000 km</kb.astro.o:meanOrbitalRadius>
<kb.astro.o:moon rdf:resource="http://dmlkb/kb/astro.i#Moon" />
</kb.astro.o:MajorPlanet>
<kb.astro.o:Moon rdf:about="http://dmlkb.org/kb/astro.i#Moon">
<rdfs:label xml:lang="en">The Moon</rdfs:label>
<kb.astro.o:meanDiameter>3,474 km</kb.astro.o:meanDiameter>
<kb.astro.o:meanOrbitalRadius>384,403 km</kb.astro.o:meanOrbitalRadius>
</kb.astro.o:Moon>
</rdf:RDF>


30

Example Astronomy RDF Ontology – Actual RDF Triples
(After parsing the previous slide’s XML on the W3C RDF Validator web page)
Number
1
2
3
4
5
6

Subject
http://dmlkb.org/kb/astro.o#AstronomicalBody
http://dmlkb.org/kb/astro.o#Star

Predicate
http://www.w3.org/1999/02/22-rdf-syntax-ns#type
http://www.w3.org/2000/01/rdf-schema#label
http://dmlkb.org/kb/astro.o#definition
http://www.w3.org/2000/01/rdf-schema#subClassOf

7



8
9
10

http://dmlkb.org/kb/astro.o#MajorPlanet


11



12
13
14

http://dmlkb.org/kb/astro.o#MinorPlanet


15



16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

http://dmlkb.org/kb/astro.o#Moon
http://dmlkb.org/kb/astro.o#majorPlanet
http://dmlkb.org/kb/astro.o#meanDiameter
http://dmlkb.org/kb/astro.o#meanOrbitalRadius
http://dmlkb.org/kb/gen#definition
http://dmlkb.org/kb/astro.i#Sun
http://dmlkb.org/kb/astro.i#Earth
http://dmlkb.org/kb/astro.i#Moon

http://www.w3.org/2000/01/rdf-schema#domain
http://www.w3.org/2000/01/rdf-schema#range
http://dmlkb.org/kb/astro.o#moon

Object
http://www.w3.org/2000/01/rdf-schema#Class
"Astronomical Body"@en
"A naturally-occurring, gravitationally bound aggregation of matter located in space"@en
"Star"@en
"An astronomical body that radiates energy resulting from sustained, internal thermonuclear
reactions"@en
"Major Planet"@en
"An astronomical body orbiting a star, or n-ary star system, which is not itself a star, has sufficient mass
to have a nearly round shape, and has cleared the neighborhood around its orbit"@en
"Minor Planet"@en
"An astronomical body orbiting a star, or n-ary star system, which is not itself a star and has sufficient
mass to have a nearly round shape"@en
"Moon"@en
"An astronomical body orbiting a planet"@en
http://www.w3.org/2000/01/rdf-schema#Property
kb.astro.o:MajorPlanet
"majorPlanet"@en
kb.astro.o:AstronomicalBody
rdfs:Literal
"meanDiameter"@en
kb.astro.o:AstronomicalBody
rdfs:Literal
"meanOrbitalRadius"@en
"definition"@en
rdfs:Literal
"The Sun"@en
"1,390,000 km"
http://dmlkb/kb/astro.i#Earth
"The Earth"@en
"12,742 km"
"149,600,000 km"
http://dmlkb/kb/astro.i#Moon
"The Moon"@en
"3,474 km"
"384,403 km"


31

The End
(for now)

This is the end of this quick snapshot in time of my ideas on
‘reinventing learning.’ I have many other ideas but haven’t
the time to evolve them or write them down.
If you want to help, feel free to contact me at:

Richard Creamer


32

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