You are a mommy congrats congrats congrats...that is so cool.

Hello, Stephanie! My congratulations to you! I am russian student of Ulyanovsk State Technical University. I am interested in applying artificial intelligence in education. Can I communicate with you via E-mail and ask you some questions?
My E-mail: a.romanov@ulstu.ru

Hi Anton. Sure, I will e-mail you!

Hi,

This is a nice page. The organization is nice. I sometimes visit this page to solve some of my statistical queries.

Me: A Cognitive Scientist from India, working as Post Doc in Indian Institute of Science,Bangalore in Super Computer Education and Research Centre.

My prime interests are: Natural Language Processing, Machine Learning.

Wishing you a happy and prosperous new year.

Regards,
Subhabrata Banerjee.
PDF-SERC,IISc,Bangalore.
Advisor to: India Software Lab, IBM, Bangalore.

Hi Stephanie,
Hope things are fine.
Getting up slowly after machine crash.
Did I send my comment on your report?
Presently busy with semantic network and few days back some of my friends gave me a problem on how to build a q/a machine? Seems I got the algorithm.
Lets see.
Trying to write a blog.
Bit inspired by you.
Our MT problem is more or less solved.
So may be migrating from Bangalore on a job offer.
Let me see where I can land up. USA is my guide's choice he is a faculty of Carnegie Mellon but I am equally thinking of your place not bad.
Presently bit busy I'll talk to you sometime later.
Please do not get upset abt the virus in your site, I installed a good Antivirus now and it was my luck what you can do, sometimes virus even gets from our own machines.
Hope you are fine.
Please Take care.
Best Regards,
Subhabrata.

Hi Steph,

Got into Grad School? Congrats,then!
I am bit busy with lot of things. Being assigned as an advisor of Project Molto-supposed to be Europe's answer to Google Translation.
Wrote around 14 papers, 10 conference, 4 journals.
Travel will be a big issue now. Looking for some people, who can go into some conferences on my behalf as some dates are clashing.

Rest is so and so. I would get back to you soon I get back to you soon I install Linux in my machine. I heard that it does not give any virus.

Hope you and your family members are fine.

Kindly convey my best regards to them.

Please Take Care,
Subhabrata.

Thanks for the quote from Dante's Inferno you posted. I believe we all go through the Gates of Hell, regardless, every time we sleep and dream. It is how we get our human-ness.

Index to Steph's Notes

1. Representing meta-data (fuel) & the different kinds of "hooks" that intelligent systems can use (how fuel is injected into the motor of the engine)
1. Motivation: Semantic net / Rationalizable to a machine
1. Semantic network
2. Genetic graph
3. Prerequisite AND/OR graph
4. Constraint Satisfaction Problems
5. Bayesian networks / causal graphs
2. Technology & Philosophy: RDF, modus ponens,
1. Predicates, Logic & situation calculus
1. When in doubt, do some math
3. What kinds of data? - What kinds of meta-data would an AIEd system possibly need, and how is it represented?
1. task domain knowledge
2. "is-prerequisite-to"-type knowledge
1. Jackpot! A pedagogical ontology
3. interactions with learning objects & other learners - (location, composition is-a/part-of, sequencing by restricting navigation, personalization, ontologies for LO context)
1. Types of 'Ecological' data
4. lesson plans, curriculum plans, practicing sessions (What is stored, what is generated on the fly? What is remembered?)
1. Agent memory
4. How to organize it - When is it stored in a database? Meta-data? Agent memory banks? Protocols? Repositories? XML files? Home-servers? WSDL services? Frameworks? Portable banks? P2P access?
1. Database of object-agent interactions
2. Concept of "Home" on a P2P network -- maybe the bulk of a learning object's usage data is on its home server and can be queried using WSDL or something ? Similar homes for each student's usage history, etc. Baggage problem.
5. Links to the ontologies
1. referring to a concept/relationship - ex. AgentOwl?
1. Using Vocabularies in JENA
2. Referring to a concept/relationship in an ontology
3. Improved: Referring to a concept/relationship in an ontology
4. Using OWL to reference constraints in tutoring systems
6. Generation of this data
1. Rationalization: For use by other AIEd systems
2. What is generated - discuss items under part I.C.
3. When it's generated - describe procedural model, which parts of the engine generate what (isa-part-of data, XML feeds, web services, meta data bout groups and collaboration, protocols, examples Friend of A Friend FOAF project)
1. Thinking about the system's RDF output
4. Technical notes of HOW it's generated: JENA, issues of implementation demo, my Hermione & Ron agent examples, lol
5. Usage of this generated data - see part IV. A.
2. Given the engine, who uses it?
1. Students / Learners / "Me"
1. instructional planning, student model, pre-requisites, tutoring, coaching, collaboration,constructivism
2. Teachers / Educators / "Me"
1. putting together lessons
2. be able to browse through task domain knowledge in an objective / encyclopaedia format, then be able to pick-and-choose what you need for your students
3. compose examples, design explanations, pull together diagrams, learning objects, etc. Haystack Relo?
3. Administration / Governement / Structure / Crowd Control
1. as restrictions/obstacles/sand pit to the robot in agent environment
2. can't just have a swarm of students and teachers out there -- need structure of courses, curriculum, objectives, requirements (at least, we do in this day and age!) - Report cards, evaluation, feedback
3. government, marks, certificates, requirements, funding, curriclum, attendance, delinquent, non-attending, motivation
4. school''s images, goals, strengths, payroll, HR, security, accounts, permissions, privacy
5. registration, failed courses
3. User Environment -- How does this engine work? What does the user see on the screen?
1. Introduction - Given a background in educational psychology, how does the system present itself -- what does the user see, and were does this data come from? Links to thoughts from part I.)
2. Task Domain Browsing - Suppose you're you're just idly browsing through the "raw" content. How would it look when it's not wrapped around a learning-context or lesson or tutorial or anything. 'Cross between browsing a raw task domain ontology and browsing a learning object repository.
   1. Cleaning up the data -- Visualizing the data for humans to pick through the task domain and work on it. Suppose the "Subject Expert" discovers an advancement in science and needs to update the "world's" domain knowledge. (I used the "Subject Expert" terminology from Ontologies to Support Learning Design Context - Thanks Chris) How would they make corrections to ontologies and learning objects, or at least point the users of "old" objects towards adopting the newer ones.
   2. "Modes" - Learning & Lessons / Checklist - Homework, Assignments, Courses being taken / Collaborative mode / Teaching mode / Calendar- email -adminisrative mode -- See also the different kinds of scenarios in the ActiveMath system
   1. Educating myself about Education
4. Evolution of this engine
1. target some key implementation hooks discussed in part I - design an experiment/demo
1. scrape a page - (Note, scraping can only give objective data, not in-context dat)
2. LO repository - related to browsing the task domain?
3. a learners "To Do" list - where does it come from? Assignments, courses.
4. sample group scenario
5. sample teacher lesson planning
6. sample data "left behind"
7. sample use of that data
2. Data mining (for what? lol )
1. discovery / generation of ontologies - when do you need to hunt for them, and when do you have to have a solidly-known & predictable ontology?
1. Ontological Engineering: taking a first bite
3. I/O - where it happens, which languages, protocols, which agents perform i/o and when, precepts, actuators
1. Role Assignments
1. Levels of authorization in web applications
2. My Environment Adapts to me
1. Displaying feedback from the server on JSP pages (Software engineering considerations)
2. Sketching out a design (Content planning vs. Delivery planning)
3. agent negotiations / social structures / ummm... Web 2.0 ?
   1. Towards student modelling
   2. Anatomy of an agent
4. garbage collection of meta data
1. Artificial Intelligence & Evolution
1. Memory Culling: Necessary part of intelligence? (artificial or human)
2. Applications for the Genetic/Evolutionary algorithm
2. open learning environments
5. Agents, pets, grouping, Community modelling
1. Protocols - finding groups, cyber dollars, state diagrams (?)
2. "Community Studies" - graphs & communication hubs, types of communities (free-for-all, hierarchy of authority, etc.)
3. implications of joining a community - what do you share, which parts of your student model are relevant
4. Walls & sand traps -- deliberate restrictions as problem-solving for learning
5. Communication channels - individual-to-individual, individual-to-community, chat channels, agent-only "administrative" communications, ex. requests for related learning objects in a particular community, etc.
6. Educational/Pedagogical focus (this part probably shouldn't be its own section but rather incorporated into the whole picture, but it's separate for me right now because I'm still only just starting to learn about it.)
1. Semantics - what there is to talk about in Education
   1. ex. Merril's First Principles of Instruction, linking educational terms to AI terms
      1. Educating myself about education
2. Pedagogical skills for tutors -- supporting human *and* artifical tutors
   1. Modelling teaching strategies
   2. What is teaching?
   3. Decision theory for teaching strategies
   4. My pedagogical issues
   5. Ontological comparisons as spatial relationships
3. Student modelling - what the machine needs to know about the student, pedagogically-speaking, about learning history/preferences
4. Roles - Simulated students, Coaches, Tutors, Teachers,