Index - academia
- Research Methodology #2 (March 20, 2010)
- Finding kindred spirits in academia (March 10, 2010)
- Academic papers online: Reading them, sifting though them: One researcher's strategies (February 28, 2010)
- Managerial mindedness (February 05, 2010)
- On being Part-time (January 06, 2010)
- Open Research, Collaboration & Proper attribution (December 26, 2009)
- Upheaval (December 17, 2009)
- Registration update (December 17, 2009)
- First Advisor Meetings (December 13, 2009)
- Statement of purpose #2 (December 06, 2009)
- Statement of purpose (December 06, 2009)
- Changing attitudes (November 14, 2009)
- One step at a time (November 13, 2009)
- Research methods (November 01, 2009)
- Conference proceedings (May 15, 2009)
- Journals galore! (February 26, 2009)
- Research Blogging (February 07, 2009)
March 20, 2010
I have mused on this topic before.
I was just reading "What the Best College Teachers Do" by Ken Burns. I admired how they constructed their research methodology.
I am still not sure where my own work fits, quantitatively- and qualitatively-speaking.
March 10, 2010
I'm sure I have written before about how "research lonely" I get sometimes. I have so many ideas, and I want to share them with other researchers in related fields. I've spent well over a year now signing myself up for social networks and being aggressive about making connections with people.
I don't think that there is actually anyone else working in "exactly" my area. So the best that you can do is keep working on your own stuff and share with others as much as you can. Although you will never find anyone with exactly the same set of goals, you *will* find someone with a subset of common goals, and these cases are where you can start looking for collaboration. I know I have found several others lately and I just love emailing back and forth with ideas. I only wish I had more time to do so!
Anyway, the point of this entry is to share a remark. After scrounging the World Wide Web for Artificial Intelligence researchers (Twitter, Blogs, Academia.edu, Yahoo Groups, etc.) I have been amazed off my feet that the most active group has been LinkedIn.
Like, WTF? Isn't that service for job seekers?
But no, there's some really great talking and sharing going on about AI research. I am flabberghasted.
February 28, 2010
The following describes my personal strategy as a creative person to cope in a world of "too much input". I also put a finger on the density of idea-sharing (twitter, blog entries, PDF papers) and how this affects my idea-processing strategies. In this article, I describe two scenarios about reading academic papers, and then I formulate a rule-of thumb for the everyday E-Academic. (heh, I just made that up. E-Academic.)
Scenario One: Papers on the Mobile Device
I am always trying to optimize my usage of time. I would like to read more papers. I have a great iPhone app that allows me to do this on the go. Most of the time, I use my mobile device when I am nursing my young daughter (and can't really be doing anything else). However, after several months of trying to use "nursing time" for "getting more papers read", I noticed that most of the time I was not browsing through academic papers. Instead, I was Twittering and skimming Google Reader headlines1. Why? Why? Why? I thought maybe this was a personal discipline problem, or procrastination. But it's more than that.
Here is my hypothesis: The more "dense" the material, the more "output" is required by the information consumer. When I read a paper, I am most productive when I have it printed out, and I can write on it, and when I can blog about how I think some of the ideas in the paper relate to my own work. Mixing and matching ideas is difficult on the mobile device, because it requires lots of window switching: the PDF viewer, the paper annotations, my blog post in progress, and so on. On the other hand, Tweets do not require much "output": most of the time you simply read it, other times you click a link, other times you Re-Tweet it, etc.. My point is that to process a tweet, most of the time you can do this with a short set of atomic operations.
It's great to have papers handy on the mobile device, but it's not the best medium for reading a new paper (especially a technical one) for the first time. Reading papers on a local scale requires artifact-production, where an artifact is an annotation, a tweet, a blog post, a conversation, or anything like this.
Scenario Two: So many papers! How can you read them all?
Let N = the number of cool papers with possible links to your work.
I figure that N is pretty much infinite. So how do you cope? If you want to publish something, how do you make sure that you have read everything that's relevant and made sure to figure out how your work compares to others and that you are not duplicating something that has already been done?
How do you read strategically? I figure I've got the basics - i.e. learn to read the abstracts, conclusion, etc. without getting bogged down by details. But on a grander scale, how do you deal with the sheer number of papers? And, (last question) how do you select your "depth" in research? In my relatively short career as an amateur researcher (4 years) I've been a breadth-first sort of researcher. While I attempted to go into depth 2 or 3 times I never got as far as programming anything or doing anything too specific. This is the second challenge E-Academics face today: *Which* papers do you read? There are so many!
To be successfully creative in depth, Todd Henry at Accidental Creative suggests Closing Doors. (Thanks, via @JohnDCook) To guide your work to completion in suffictient depth, you must limit your input and commit to some decisions.
And how do you "close doors"? How do you force yourself to be specific? I think that the key is to put your energy and effort towards *producing* something: a blog article, a software system, giving a talk, organizing a group activity on the topic -- anything like this. By starting along this path, your work will generate new questions, and, the sheer volume of these new questions will drive away the too many other distractions.
In this way, you continue to lead a life full of exciting newness, but, it is guided towards some depth. And when you produce your artifact (the blog article or whatever), you will have gone into sufficient depth to judge whether this is a good path to continue onwards, or whether you ought to backtrack and pick a different direction. Reading papers on a global scale requires a filter.
The rule-of-thumb that I have adopted for myself is this: Work to turn your ideas into artifacts. Artifacts can include tweets, blog posts, face-to-face conversations, publications. Keep producing, and keep sharing. By creating artifacts, you keep your interest "tuned in" which creates a better filter for today's bombardment of input. Continue to browse those headlines, and stay creative.
1 I don't want to imply that Twittering and reading RSS headlines is wrong: in fact, it is one (of many) important source of ideas for my work. It's just that in this case Twittering was going against my goal, which was to read more papers.
February 05, 2010
Here is something new that I've learned about myself: when a new project idea or some work request or favor or anything comes my way, I'm able to determine if I can do it myself (with my current set of resources), or, if I have to say, "that's a great idea, but we'll need additional funding for that." This happens frequently, several times per week. (I live in a world full of ideas and new initiatives! It's great, but, if you don't know how to say no you will wear out. Also, I am one of those people who'll shoot out the new ideas in all directions, too, so. heh.)
I was feeling bad this morning for my recent trend of saying "No" (or, "that's a great idea, but will need additional funding," heh) but then I caught myself and realized this is GOOD. It's GOOD to stop and think and not dive into some source code at every opportunity. And I do still say yes sometimes.
I'm not a pessimistic killjoy. I'm a good manager.
January 06, 2010
Here are some challenges I have faced so far, being a part-time working mommy graduate student.
(1) For my graduate studies program, the tuition model at my university charges a flat rate per term (say $1030.00), regardless of the number of classes you're taking. My program requires 5 classes and a thesis. I only have capacity to take 1 class at a time. Therefore, at two terms per academic year, my course work will take 2.5 years, which means I pay for 5 terms, which would be $5150.00. A student who is able to take 3 classes at a time (full-time) could finish classes in 1 year, which is 2 terms, which would only cost them $2060.00. Do you see? It costs me, a part-time student, Five Thousand bucks and it would cost a full-time student Two Thousand bucks, for taking the same number of classes.
(2) Time. Department seminars are usually (always?) scheduled from 3:30-4:30 in the afternoon. This is stressful for me because daycare closes at 5:00 p.m. and I have to make sure I pick up my baby before closing. Thirty minutes is not enough time for me to get from the lecture theatre to my car and to the daycare. I leave the talk a few minutes early and I miss out on the "good stuff", questions and networking afterwards.
December 26, 2009
I think that citing your work is important because it gives due credit to people's efforts, and, it also allows researchers to "trace" their way through the evolution of an idea through published works.
Proceeding with my MSc work, I've been thinking about how inevitably a lot of it will end up on this blog. How do I cite properly? How much? What if I get inspired by a twitter conversation, should that get referenced, too?
In Open Research, I see ideas evolving more rapidly due to increased collaboration. To me that implies denser citation. Then, optimism turns into fear that I will be so busy with documentation that I will not have time or freedom for creativity.
I figure as long as I link when applicable I should be cool to go back and do up a proper reference list according to APA or whatever if needed. In my perfect world, all of this would be done automatically!
For now, I think I will just tell folks or email them or whatever (or link to them if it was a digitized interaction) if I refer to our conversations here.
December 17, 2009
This was a big week for my research life due to my advisor meetings. After sharing my research ideas and getting some feedback, I feel like I'm looking at my problem in a different way. I wish I could have done this more often over the years, but. Alas. :)
Last week, I would have told you this: I want to pick a tool from AI, and adapt it and extend it for creative application to my problem. I'm less confident about this approach right now. This week, I'm playing catch up towards "normal" research from my 3-and-a-half year long independent project.
The other thing I have to tell myself is that I'm still at the beginning. The whole point is to evolve and change.
I finished my statement of purpose and I met with two possible advisors. Next I have to finish my CV. I started preparing my full CV, but then I read on the application package that they only want a short resume, so I will have to select a subset. This shouldn't be hard, but I had an oops moment when As usual I had barreled forward full steam ahead and took the long way.
I do want to finish the full CV because it's good for me to have. I'm surprised at the difficulty I had over remembering details of some of my past projects!
Both professors I spoke with had a caution about being a part time student. I'd never thought about this before. I'm ALWAYS thinking about my research so I didn't really identify with the term "part-time" just because I plan to keep my day job. I guess I'm just surprised that my status is a bigger deal than I thought. Maybe grad school is not for working mommies, and maybe I'm being naive. But I want research to be a part of my lIfe so much (and it is) but I am craving mentorship and the companionship of other researchers and students, at to me grad school seems like the best option.
I don't know. It feels weird to be having doubts already, when I'm still early in the formal process, but I understand that this is normal.
December 13, 2009
Tomorrow I'm meeting with a possible advisor for my M.Sc. program. I am actually a little nervous. I have no idea why, because this is something I want to do, a lot. I think I associate nervousness with things that make me uncomfortable, like public speaking, in some cases. But nervousness can be associated with good things, too, like being nervous about going on your first date with someone that you really like.
There is a second professor I want to speak with as well, another possible advisor. I don't know if I will be able to meet with him before the holidays, but it will be relatively soon. I've given myself the goal of having my application package submitted during the last week of January. Need to finish the resume / CV, get my transcript, ask for reference letters (and I want to leave a good month or so to give my references enough time for the letters), write the statement of purpose....
This grad school thing is looking like it's actually going to happen.
I had a moment today when I was briefly paralized with fear about being able to find the time to work on my thesis and course work. But I have thought about this for years, and I have a plan, and I really want to do it. So I'm going to try.
December 06, 2009
Here's the thing. I'm having trouble with the Statement of Purpose because the stuff I'm most excited about is the stuff I want to study, the stuff I want to learn more about, the stuff I want to BEGIN work in. But the problem is that I'm such a newbie, I don't know anything about it, and I sound like a nitwit when I start using technical terms that I clearly have no background in using. So I find myself shying away from the really interesting stuff and talking about things that I DO know about.... but then the writing comes out boring because I really am less excited about the things I know about already.
There. *heaves shoulders*
As I put together my application for grad school, these days I am trying to write my Statement of Purpose. Gosh, it's hard!
I consider myself to be an articulate person with a lot of ideas. But I feel like my writing comes out like mush when I take a stab at this important document.
November 14, 2009
As I'm making serious progress towards my dreams of grad school, I find that my attitudes are changing a little bit as I conduct my research.
Ideas that are foggy and unexplored, especially long-held ones, are causing a little voice inside my head to say, "you are going to have to face this (i.e. explain it) so you might as well tackle this now". Just in the last day or two I find that I've been putting more effort into fully articulating my ideas. I'm thinking ahead about what sorts of questions someone else might ask as they read, and I'm trying to address them.
The fact that I have been researching on my own has definitly had some damaging effects on my work. Rarely does anyone ask me any questions which means that I rarely get to explain my thinking behind my work. Despite my twittering and GoogleReadering and Yahoo Grouping and supposedly OpenResearching, I am so "research lonely". I wish there were more people in my subfield who are also blogging so that I could talk to them about their research and maybe they would be interested in my work, too.
I should also say that researching on my own for the last several years hasn't been ALL bad. For someone who hasn't even started their masters, I know my field exceptionally well. It's just that my contribution to the field has sucked.
Finally, on the matter of being the odd one in the pack (or at least perceived being odd) I invite you to go and check out Seb Paquet's posting, How to Deal With Your Weirdness.
November 13, 2009
In the long road towards getting into grad school, I made a big step this week. I put together my CV! I've never prepared a CV before. Every morning before work I get to my office early and try to spend a few minutes plugging away at it.
I had a lot of questions... for example, I received several scholarships but that was over 10 years ago: should I include those? I learned that Yes, you can, because a CV is supposed to be like a record of significant points showing your background, and the idea is that as you grow older and gain more experience, your CV grows too. I also didn't know if I should say anything about my maternity leave in the Employment History section. I've decided to omit it. I have mixed feelings about that, but, meh. It's not really employment, so, there. But at the same time I feel like this year was a hugely beneficial thing and a transforming piece of my life. It seems stupid to omit it. But I am omitting it. *shrug*
Anyway, the CV is not quite ready for printing because I have some tidying up to do, but, I've made huge leaps in getting it written up. Also, I was very impressed with the consultant at the Student Employment & Career Centre where I went for a drop in "Quick Talk" (hours are listed on this page). The consultant was professional, knowledgeable and had extremely good interpersonal skills. Also, he predicted that I would need to assemble a Letter of Intent or Statement of Purpose, and that I was welcome to bring that in for a consultation, too. I intend to do so.
The deadline for grad school applications is February. I'm doing okay. I think I can do it! Gotta do the statement of purpose, gotta have some conversations with the professors who know me best and ask for reference letters, gotta fill out several online forms....
.... One step at a time! Woooo!
November 01, 2009
I want to do a study about instructional planning. It's hard to know where to start because I can't pinpoint what, exactly, it is about instructional planning that I want to create, to measure, to experiment with, to optimize... Nor do I know very much about conducting proper scientific studies. Well, that's not exactly true. I know quite a lot about science from general interest reading across the disciplines (cognitive science, psychology, sociology, medicine, optimizing algorithms...). I just want to note that I've never actually conducted a study myself.
I thought that this page was useful, from Birkbeck University of London. From this, I could kind of list or sketch out different types of research methodologies. (Simulations, case studies, empirical studies, proofs...).
I'm struggling a lot because I don't have an advisor with whom I can talk about these things. Sure, I have my friends who have done grad studies before, and I can surf around and read the blog entries of other students, or read "How To Be a Successful Grad Student" type articles written by professors, but it's not the same as having a real person or persons who know your work, whose job it is to talk to you about your work, and can share their wisdom with you. I am craving mentorship.
I think I'm going in the right direction... I mean, I'm working on my application for grad school by dusting off the ol' resume, planning to approach some professors for reference letters.... It just seems so FAR AWAY right now to me. I want to do research, but I feel like I have to jump through a million hoops before I can actually get down to business. The barriers seem very high to me. Perhaps I'm just feeling sorry for myself! Part of the problem is that I have very little (okay, zero) computer time at home, and I just have to get better at using my lunch hours and coffee breaks at work to get this stuff done.
Anyway, getting back on topic: Research methods. In the area of planning, your planning algorithm is successful if your agent reaches its goal while performing the fewest number of steps. This measure (the number of steps taken by the agent) isn't the right measure for my area. For instance, if an instructional planner is guiding a student through a guided exploration of some course material, throwing in some games or exercises here and there, it doesn't matter so much how many "steps" it takes for the student to "understand". I've talked about this and have put my thumb on this before: Utility, in my problem, is meaningfulness of the user's experience. (See related entries: My friend, the utility function, and This dichotomy)
So, is this an empirical study? Or am I doing qualitative research? Probably some of both.
I guess I really do need to define my problem better before I can design a study around it!
May 15, 2009
So I thought I was officially a hotshot researcher because I was staying up-to-date with all the latest publications in my area. I figured out how to subscribe to the RSS feeds to a bazillion different journals. Whenever my baby is nursing, I sit down with the ol' iPod touch and skim through paper titles, occasionally reading through the abstracts, and for the really relevant ones I star them and download the full paper into my Papers app. Doubly cool of me, I figured out how to dig deep down in my university library's many links in order to access the full PDF of just about any paper I want. This is working REALLY well. Even when I go back to work, and if baby is still interested, I can see myself keeping up this habit of skimming through research papers while nursing the baby every evening. Staying abreast of the latest research, with a baby abreast, if you will. (ho ho ho, I am so funny!)
So yes. As I was saying. I found this awesome research rhythm, using all the latest technology to stay ahead of the game. I was, how do you say, "the shiz"!
But then I read this article in the latest Communications of the ACM, "Conferences vs. Journals in Computing Research" by Moshe Y. Vardi. Basically, the article says that in Computer Science, Journal publications are SECONDARY, and that the primary means for publishing research results in in CONFERENCE PROCEEDINGS.
So, I was like WHAT?? Am I missing out on some totally huge world of computing research right now because I only have journal pubs in my RSS reader?
And that is where I'm at right now. Free time over the next few days shall be dedicated to figuring out how to get me some conference proceedings.
UPDATE (July 2009) Okay, I learned about the existence of IJCAI. This makes TWO conferences I know about, so I will start this bulleted list and will continue to add conferences here as I learn about them.
February 26, 2009
I stumbled upon another site that lets you subscribe to RSS feeds of academic journal publications, so I gobbled up a dozen or so more feeds in my Google Reader.
I also thought that if there's anyone else out there interested in the same or similar stuff as me, they might appreciate knowing about some of the journal articles that are influencing the consciousness of the author of this blog. :) I plan to put stars next to the journals that are particularly close to my field. (Haven't done this yet, at the time of this writing. My five-month-old is currently wailing at me... I better go, heh.)
Advances in Information Retrieval
AI & Society
AI EDAM - Current Issue
Annals of Mathematics and Artificial Intelligence
Artificial Intelligence Review
Artificial Life and Robotics
Automated Software Engineering
Autonomous Agents and Multi-Agent Systems
Cognition, Technology & Work
Combinatorics, Probability and Computing - Current Issue
Data Mining and Knowledge Discovery
Educational Psychology Review
Intelligent Data Analysis
Intelligent Decision Technologies
* International Journal of Artificial Intelligence in Education
International Journal of Hybrid Intelligent Systems
International Journal of Knowledge-Based and Intelligent Engineering Systems
Journal of Ambient Intelligence and Smart Environments
Journal of Artificial Intelligence Research
Journal of Automated Reasoning
Journal of Classification
Journal of Heuristics
Journal of Intelligent and Fuzzy Systems
Journal of Intelligent and Robotic Systems
Journal of Intelligent Information Systems
Journal on Data Semantics X
Knowledge and Information Systems
Mathematical Structures in Computer Science - Current Issue
Mind & Society
Minds and Machines
Probability in the Engineering and Informational Sciences - Current Issue
The Knowledge Engineering Review - Current Issue
User Modeling and User-Adapted Interaction
Visual Data Mining
Web Intelligence and Agent Systems
February 07, 2009
I recently learned that what I do -- actually putting my unfinished meanderings about my research up on the web -- is called "research blogging", via Daniel Lemire's blog entry, about Seb Paquet. (A couple of Montréal profs, I'm so proud! Yours truly was born in Montréal. Mais je pense que j'ai oublié comment parler en français.)
I'm a little freaked out that one of my future advisors will come to this blog and figure out that I have no idea what I'm talking about, and because of that they won't take me as a student. And now that I've typed out that thought I realize how silly I am and how I worry too much. *nervously puts on lip balm*
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,