February 01, 2009
So I've been reading some theses in the area of my research because I want to do an M.Sc. of my own and wanted an idea of the kind of scope I'm looking at for a research/contribution project. I really enjoyed how a lot of the theses summarized and beautifully laid out all of the background information upon which they build their contribution. That was part of my motivation for plowing through the "Sweep of Representation Techniques" from the last few posts. I figured if I blogged about the necessary background information, then I'd be able to reference these concepts in future postings that would hopefully build up from these ideas. I have so many head-in-the-clouds ideas and I feel like I can't make any progress with them unless I build a foundation for myself to grab a hold of!
But, I'm having some trouble with my plan -- I'm struggling with my "Sweep"! I haven't even scratched the surface of all the things that are in my head and all of the jot notes I have on paper. It takes SO LONG to type stuff out (probably because I get about 30 minutes per day at the computer... grumble grumble!!!) So I am feeling discouraged. I also have to remind myself that I'm the mother of a 5-month-old baby and that it's silly to expect that I should be getting a lot of research done.
I'm also struggling with the format of this blog. To me, a linear set of postings released in time (reminiscent of a Markov model??) is not an appropriate format. I'm more interested in focusing on the overall content first. The timestamps on each entry are helpful, I guess, but I don't like how the order-in-time of my entries is emphasized in this blog format. Maybe I should have used a wiki. Except that it would be "just me" authoring. I guess that was the idea behind the black box you see if you scroll to the bottom of this page. *Choke*, it has been TWO YEARS since I updated that. heh
Alrighty, I've figured out how to make this better. I'm going to go clean up my "Look Down on All Those Notes With Some Grand Vision of Organization". Then maybe I can keep up with my slugging through of knowledge representation techniques. Or maybe I'll have a completely different angle to tackle. Fuddle wuddle! :P :)
Frozone, don't get discouraged...it does take a long time to read and digest papers. For a paper submission I did a few months ago, I spend like 2 full days reading and parsing a VERY small sub-area of work to discuss in a related work section. I couldn't believe how long it took me.
So don't worry, just keep chugging away. The important thing is to not stop. Then you'll always make progress.
Good organization helps too, so good for you for doing what you need to be organized about your thoughts.
Posted by: PhizzleDizzle at February 5, 2009 09:11 AM
Hi Phizzle (may I call you Phizzle?) :) :P
Thanks for the encouragement: I'm lucky that there are other women like you out there, burnin' the path!
This conversation reminds me about a book called "The Dip" by Seth Godin. http://sethgodin.typepad.com/the_dip/ It's about quitting and not quitting -- knowing when to stick with what's most important to you, while learning to let go of what isn't. (Or at least, that's my impression. I haven't read the book, but my husband has.)
Posted by: Steph (a.k.a. Frozone) at February 5, 2009 02:28 PM
Of course you may call me Phizzle :). The Dip philosophy sounds spot on :).
BTW, do you use a Mac? I saw you had a post about Mac OSX and Java earlier, so I just thought I'd ask. There is a great paper organizing piece of software out there called Papers, and it's just for Mac. It's truly awesome and helps a lot with literature reviews and stuff.
Posted by: PhizzleDizzle at February 5, 2009 02:33 PM
Yup indeedie doo, I am a happy Mac user, and also a happy Papers user -- thanks for the tip! I love how you can export to *.bib for easy \cite-ing in LaTeX!
Posted by: Steph (a.k.a. Frozone) at February 5, 2009 10:18 PM
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Index to Steph's NotesFeb. 24th 2007 - Weee! This new part of my website is not an entry, but rather a permanent fixture whose purpose is to "Look Down on All Those Notes With Some Grand Vision of Organization". Wish me luck. LOL
- Representing meta-data (fuel) & the different kinds of "hooks" that intelligent systems can use (how fuel is injected into the motor of the engine)
- Motivation: Semantic net / Rationalizable to a machine
- Semantic network
- Genetic graph
- Prerequisite AND/OR graph
- Constraint Satisfaction Problems
- Bayesian networks / causal graphs
- Technology & Philosophy: RDF, modus ponens,
- Predicates, Logic & situation calculus
- What kinds of data? - What kinds of meta-data would an AIEd system possibly need, and how is it represented?
- task domain knowledge
- "is-prerequisite-to"-type knowledge
- interactions with learning objects & other learners - (location, composition is-a/part-of, sequencing by restricting navigation, personalization, ontologies for LO context)
- lesson plans, curriculum plans, practicing sessions (What is stored, what is generated on the fly? What is remembered?)
- 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?
- Database of object-agent interactions
- 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.
- Links to the ontologies
- referring to a concept/relationship - ex. AgentOwl?
- Generation of this data
- Rationalization: For use by other AIEd systems
- What is generated - discuss items under part I.C.
- 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)
- Technical notes of HOW it's generated: JENA, issues of implementation demo, my Hermione & Ron agent examples, lol
- Usage of this generated data - see part IV. A.
- Given the engine, who uses it?
- Students / Learners / "Me"
- instructional planning, student model, pre-requisites, tutoring, coaching, collaboration,constructivism
- Teachers / Educators / "Me"
- putting together lessons
- 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
- compose examples, design explanations, pull together diagrams, learning objects, etc. Haystack Relo?
- Administration / Governement / Structure / Crowd Control
- as restrictions/obstacles/sand pit to the robot in agent environment
- 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
- government, marks, certificates, requirements, funding, curriclum, attendance, delinquent, non-attending, motivation
- school''s images, goals, strengths, payroll, HR, security, accounts, permissions, privacy
- registration, failed courses
- User Environment -- How does this engine work? What does the user see on the screen?
- 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.)
- 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.
- 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.
- "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
- Evolution of this engine
- target some key implementation hooks discussed in part I - design an experiment/demo
- scrape a page - (Note, scraping can only give objective data, not in-context dat)
- LO repository - related to browsing the task domain?
- a learners "To Do" list - where does it come from? Assignments, courses.
- sample group scenario
- sample teacher lesson planning
- sample data "left behind"
- sample use of that data
- Data mining (for what? lol )
- discovery / generation of ontologies - when do you need to hunt for them, and when do you have to have a solidly-known & predictable ontology?
- I/O - where it happens, which languages, protocols, which agents perform i/o and when, precepts, actuators
- Role Assignments
- My Environment Adapts to me
- Displaying feedback from the server on JSP pages (Software engineering considerations)
- Sketching out a design (Content planning vs. Delivery planning)
- agent negotiations / social structures / ummm... Web 2.0 ?
- garbage collection of meta data
- Artificial Intelligence & Evolution
- Memory Culling: Necessary part of intelligence? (artificial or human)
- Applications for the Genetic/Evolutionary algorithm
- open learning environments
- Agents, pets, grouping, Community modelling
- Protocols - finding groups, cyber dollars, state diagrams (?)
- "Community Studies" - graphs & communication hubs, types of communities (free-for-all, hierarchy of authority, etc.)
- implications of joining a community - what do you share, which parts of your student model are relevant
- Walls & sand traps -- deliberate restrictions as problem-solving for learning
- 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.
- 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.)
- Semantics - what there is to talk about in Education
- ex. Merril's First Principles of Instruction, linking educational terms to AI terms
- Pedagogical skills for tutors -- supporting human *and* artifical tutors
- Student modelling - what the machine needs to know about the student, pedagogically-speaking, about learning history/preferences
- Roles - Simulated students, Coaches, Tutors, Teachers,