## April 24, 2010

### Britney Spears

I admire Britney Spears. Here's why.

Over the last 10 years or so, I've been aware of the ups and downs of her very public life. I think it's because I was born the same year that she was - I'm only about 2 months older. It sets a normalization of sorts, making me think about what diverse lifestyles people can have.

Today I bought one of her albums partly because I enjoy her music, but mostly because of this. Britney released "before" and "after" digitally-altered photos from a magazine shoot. Britney wanted to highlight the pressure exerted on women to look perfect.

Good job, Britney. I'm proud of you, and I admire you!

Posted by Frozone April 24, 2010 07:07 PM | Comments (0)

## November 23, 2009

I'm starting a new category on this blog called "People I admire". If I learn about someone whose work I admire or if they have experienced a similar lesson in life that I can relate to and I appreciate the wisdom that they have shared in some way, I might write a note about them on this blog under this new category. :-)

I posted a while back about Vera Rubin, and I'm moving that entry into the new part of my blog.

Posted by Frozone November 23, 2009 08:37 AM | Comments (0)

## November 22, 2009

### Yoky Matsuoka

Yoky Matsuoka is a scientist at the University of Washington who is working to create a prosthetic arm that can be controlled with your brainwaves. SO COOL! I admire her because I watched a segment about her on NOVA ScienceNow where she shared a lesson she had learned in her life, which was that loving science is something that gives you strength, and not something that is "uncool". Also, most of all, a message I took away is that it's okay to let your interests evolve and change... you don't have to be a superstar in your field from a very young age in order to make a good contribution; you don't have to find your research area "early" and "stick" there forever!

Posted by Frozone November 22, 2009 05:13 PM | Comments (0)

## August 10, 2009

### Vera Rubin

I was watching a science show on television the other day, and my eyes a-lit with stars when astronomer Vera Rubin was interviewed during one of the segments. Rubin was interviewed because of her discovery that bodies on the outside of galaxies move faster than expected. It was supposed that bodies towards the centre would move relatively quickly and bodies towards the outside would move relatively slowly, but Rubin found evidence that showed otherwise. (Or, at least, that's the understanding I got from watching the TV show. I don't know very much about physics or astronomy!)

Anyway, the reason why I wanted to mention Vera Rubin on my blog is that during her interview about how she selected her research topic, she said something to the effect of, "I had children, and I didn't want to compete with what other researchers were working on," (paraphrase).

Her words really struck a chord with me. As a new mother and as an aspiring scientist, I sometimes feel "left out" because I'm unable to dive in and work on research with the same ferocity as people without children or family obligations. I just don't have the time. And I worry that I'm not doing science "the right way". But I can't do science the way "everyone else" does it. At least, not right now. But I still want to do science.

So it warmed my heart to see that this successful, validated scientist has shown that, "being a good scientist" $\neq$ "working on your problem 24 hours per day for months on end". You CAN weave these different braids of life together: family, work, research, etc.. But like a braid, sometimes each strand is very prominent in front, and other times they're hiding in the background. :)

Posted by Frozone August 10, 2009 10:48 AM | Comments (0)

## Index to Steph's Notes

Feb. 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
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
2. Technology & Philosophy: RDF, modus ponens,
1. Predicates, Logic & situation calculus
3. What kinds of data? - What kinds of meta-data would an AIEd system possibly need, and how is it represented?
2. "is-prerequisite-to"-type knowledge
3. interactions with learning objects & other learners - (location, composition is-a/part-of, sequencing by restricting navigation, personalization, ontologies for LO context)
4. lesson plans, curriculum plans, practicing sessions (What is stored, what is generated on the fly? What is remembered?)
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.
1. referring to a concept/relationship - ex. AgentOwl?
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)
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
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?
3. I/O - where it happens, which languages, protocols, which agents perform i/o and when, precepts, actuators
1. Role Assignments
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 ?
4. garbage collection of meta data
1. Artificial Intelligence & Evolution
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
2. Pedagogical skills for tutors -- supporting human *and* artifical tutors
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,