Index - Second Quarter

• System Dynamics & Game Theory (December 03, 2011)
• Knowledge is a resource that does not become depleted (March 14, 2011)
• Instructional Planning 2.0? (February 12, 2011)
• Path simulation from Operations Research in AIED (November 16, 2010)
• Two Frameworks (mathematical, rigorousCultural) (September 19, 2010)
• Payoff matrix (July 21, 2010)
• Partial order plans, Greedy algorithms and Educational Objectives (May 25, 2010)
• It's not Time-Series Data (May 21, 2010)
• Guy Brousseau - Mathematics education (May 20, 2010)
• Full circle (February 13, 2010)
• Minimax for planning (December 27, 2009)
• Theories of learning (October 02, 2009)
• Planning as a projection of the learner's creation (June 03, 2009)
• Action selection (June 03, 2009)
• Following the steps, but not really (June 01, 2009)
• Generating actions and the transition function (May 05, 2009)
• Modelling teaching strategies (February 07, 2009)

December 03, 2011

System Dynamics & Game Theory

For giggles, I Googled "system dynamics" "game theory. I found this cool paper by M. Rasouli from Sharif University of Technology in Tehran, Iran, and was presented at a systemdynamics.org conference. It's called A Game-Theoretic Frame Work for Studying Dynamics of Multi Decision-maker Systems.

Once upon a time, I was attracted to Artificial Intelligence. (still am!) Then I learned about Cooperative Game Theory (recently - circa 2008). Then I learned about System Dynamics (very recently - Jan 2011). I am attracted to all of these things because the answer to my "building adaptive tutoring systems" might rest in here somewhere. Or, at least, I may find the start of a path.

Here's how I tried to find this path in Game Theory but am not quite there yet: One time, (blog entry) I explained how I consider the student to be some kind of agent and I am trying to build an adaptive environment that tries to optimize their learning. I thought I could take the strategies from Game Theory, and have the educational environment use them, as if the environment itself were an agent, too. Then the educational environment would execute its "moves" to change itself to adapt to the student.

However, I remain critical of Game Theory, even Cooperative Game Theory because it seems designed to deal with a different problem than mine. I even blogged one time that I should try to turn my equation inside out, because the equation was built to deal with "uncertainty" coming from a slightly different angle then the one I needed. (I tried to find that old entry, but haven't yet. Closest so far, Plan-space planning and the optimal policy calculation (June, 2009).)

I am enthralled with System Dynamics because even Cooperative Game Theory had this "competition" thing going on. I remember (blog entry) when I was looking for "details of cooperation" but in the text I only found "groups teaming up against other groups". The "cooperation" wasn't paid any explicit detail. It was just competition re-packaged as a more complex thing involving multiple individuals.

But, System Dynamics has the "detailed look at the cooperation" that Game Theory was lacking. And that is why I am so excited.

The reason I am posting this is that I wanted to say that this author's summary of the relationship between System Dynamics and Game Theory has helped me understand. He says that System Dynamics is usually for a single decision-maker who is seeking to construct a policy that will change the system's overall behaviour to match her desire. On the other hand, Game Theory is about multiple decision makers and finding winning strategy.

There's an article in System Dynamics Review 1997 (A system dynamics model for a mixed-strategy game between police and driver by Dong-Hwan Kim and Doa Hoon Kim) about how SD and GT relate and work together. I should read more. (But I probably won't, even though I want to, because I've already picked my research topic and have finished the first round of my lit review. I can only continue working on things directly related to content sequencing in an educational curriculum. But this is good to know about.)

Edit: Jan 2012 - In emailing a fellow student in one of my classes I wrote something that I wanted to copy to this blog entry because it is relevant: "The reason I was so interested in System Dynamics was that I have been looking for a knowledge representation technique for educational systems. I wanted a way to represent the various knowledge levels of each individual, and to use the computer to help suggest which students should work together or independently, over time, to best support each other as they work on both their individual and common goals. I have found that Decision Theory and Game Theory are quite "competitive", but that System Dynamics is much better to represent the whole environment instead of only one perspective. It could show which areas need more cooperation and which areas are "too busy". "

Posted by Frozone Permalink on December 03, 2011 08:01 AM | Comments (0) categorized under Pedagogical modelling
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March 14, 2011

Knowledge is a resource that does not become depleted

I am super excited about this paper, recently published in Applied Intelligence, Vol. 34, Issue 2.

Viara Popova and Alexei Sharpanskykh. Formal analysis of executions of organizational scenarios based on process-oriented specifications.

I liked reading about the kinds of references that are often used in process modelling systems: starting and finishing time points of processes, types and amounts of resources used/ consumed/ produces/ broken, names of actors, who perform processes.

Next, I would like to address a thought that has been jumping up and down, screaming at me for attention for about a week. :-) In my area, many of the "Resources" do not get "consumed" per se. For example, in order to perform a Tutoring act, one of the things that you require is Knowledge. However, making reference to the knowledge alone does not cause it to become depleted. This is different from most of the applications in planning and operations research literature I have been studying.

At the same time, teaching requires a lot of careful planning. So, what is it that we are trying to optimize? What is the scarce resource? I would say: The limited size of the world in the Learner's head. You can only ask them to hold so many brand new facts in their head at one time as they plough through the lesson. The goal is to get the learner to apply the new concepts, to make them stick long-term. But also recognizing that not EVERYONE needs to be learning EVERYTHING deeply. There are some things (in fact, probably most things) that the tutoring system does not need to work hard to make sure it is helping the Learner cultivate that long-term knowledge. The resources that a tutoring system has available are: Learnign objects that the Learner has stumbled across before (perhaps they read something before that did not make sense earlier, but maybe it would now, etc.), Learning objects that were effective for similar learners in similar situations, or, as a last resort, a Web Search performed by the machine on behalf of the learner.

Anyway, I was not able to read the whole paper but am happily keeping a "bookmark" right here and hope to return to this knowledge again in a future cycle!

I find it a little bit shocking that the paper is dated 2009 but didn't show up in the journal until 2011. I don't get it. Well, I sort of do... I understand that before the Internet, it probably DID take that long to publish things. I also understand that it takes time for peer review to occur. So, now that I talk about it out loud, I am no longer shocked by the date discrepancy.

Posted by Frozone Permalink on March 14, 2011 10:58 AM | Comments (0) categorized under Pedagogical modelling
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February 12, 2011

Instructional Planning 2.0?

One of the academic leaders of instructional planning is, of course, my supervisor, Dr. Gord McCalla, as well as one of his students, Dr. Barbara Wasson. Dr. Wasson was one of the first to distinguish Content vs Delivery planning in her PhD thesis.

Today, I was trying to design a simulation model for an eLearning environment, and I have been thinking a lot about content vs. delivery in the context of "individual vs group" learning.

I just wanted to note the observation that Content planning would normally be based on negotiated Content learning goals of both the individual and the group, while Delivery planning would be based more on what currently available (temporally, practically speaking) on the WWW and also on the individual's current preferences.

I guess I just wanted to voice my observation. That is, to distinguish that the "group" part -- i.e. where you have to negotiate the instructional plan to account for other people -- seems more heavily based on Content planning than Delivery planning.

Posted by Frozone Permalink on February 12, 2011 03:47 PM | Comments (0) categorized under Pedagogical modelling
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November 16, 2010

Path simulation from Operations Research in AIED

In Operations Research, you can do path simulations. Path simulations distinguish event nodes that *are* v.s "are not* under the system's control: system logic and system inputs, respectively. From my trusty and as of late frequently quoted book, Stochastic Modeling: Analysis & Simulation by Barry L. Nelson, I understand that a major part of stochastic modelling (at least, the approach to stochastic modelling in the book) is the system-inputs vs system-logic distinction.

The point of this entry is to acknowledge the terminology, "system input" and "system logic" and how they relate to the problem I've been working on. (This previous entry has a not bad description, Sets of relationships over time in multi-agent influence diagrams.) I want to model pedagogy as separate from task domain knowledge. Pedagogy has an important dimension of "time", in that the order in which you present material to learners is significant. This is why I started looking into Operations Research and the reason I bought Nelson's book. Thus, to adopt this new approach from operations research may be to put "patterns of teaching" into the category of "system logic" and to put things like learner actions and currently-available learning objects into the category of "system input". (Remaining is another question that does not fit into this framework: System-generated, customized Learning Objects.)

So, yeah. That is my point. Thanks for listening! :)

By the way, I wrote this entry while wearing blue eyeshadow and orange lipstick. Booyeah!

The reason I share this fact about my choice of cosmetics for today is that I was inspired by the following article by Melissa McEwan, "On shoez and getting personal, a.k.a. How are we supposed to take feminist bloggers seriously if they post about shoes?"

I think that McEwan's article may relate to Open Research Bloggers such as myself who enjoy throwing in stories about parenting or other such things not obviously related to my research topic.

Posted by Frozone Permalink on November 16, 2010 12:42 PM | Comments (0) categorized under Pedagogical modelling
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September 19, 2010

Two Frameworks (mathematical, rigorousCultural)

The textbook for my new class is making me sweat. It's presenting "familiar" information to me (i.e. pretty much the content of this blog -- my passion!) but within a framework I don't necessarily jive with. It's forcing me to struggle with my assumptions. This is very, very good, and is one of the main reasons I'm in grad school. :)

The book is Dr. B. Woolf's Building Intelligent Interactive Tutors(link to a Google Book of this).

Although each chapter screamed relevance "You NEED to know me!", I zeroed in on Chapter 4, Teaching Knowledge.

In my own head, "teaching" is presenting content, invitations, communication channels, tools, context and a presentation of goals based on the machine's knowledge about the student. Lately, I've been trying to articulate teaching mathematically, separately from the task domain ontology, by:

• exploring Bayesian event nodes for the environment and collecting "priors" on the fly as newly applied evidence for a dynamic Bayesian network (i.e. preceptors)
  
• looking at how an expected value calculation or optimum policy calculation might be used to power the adaptiveness of the environment by driving the selection of "next action" (which, in my head was an obvious translation to "adapt the environment in this way by executing the next action 'a'),
  
• and other tricks I'm sure.... like, Game Theory (example entry)
  

Whew! So, given all of this intellectual overhead, now I am trying to absorb a presentation for a different world view and it's making me sweat. The author is presenting a rigorous framework for analysis of the design of such systems. But the approach is not triggering very much of my own past experience, so it's harder to take. The lingo is not employing any formal notation, which makes sense because it's too early to know what we need to build, too early to abstract it back up into the math. But it's a different approach than what I've been taking on my own lately.

Where to next? I'm just going to have to acknowledge the gulf between my previous experience and that which is being presented before me. I'm going to make a commitment to grapple with this new stuff. Maybe my perspective will generate some new ideas. Stay tuned!

Posted by Frozone Permalink on September 19, 2010 01:15 PM | Comments (0) categorized under Pedagogical modelling
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July 21, 2010

Payoff matrix

I don't think I've ever talked about Payoff Matrices before. They are an element from game theory. My advisor suggested they might be an interesting place to put things like

• listeners from the learner model that know about learner motivation.

I was also thinking you could dump in

• negotiated learning goals, pedagogical measures for attainment
• pedagogical rules for changing your "strategy" or "mode of interaction" with the learner -- conditions for switching, payoff...

And how does constraint satisfaction relate to game theory?

And another thing I haven't thought about much in my other work is the "Many Humans (learners, instructors, etc.)" aspect. Mostly I've been working on {IndividualHumanLearner, ArtificialSupportAgents}.

And, in Education, don't forget that the GOAL is to CHANGE the environment, and even the behaviour (or, experience & understanding, I guess) of other agents. Many robotics applications would be just as happy to try and model the universe though (limited) preceptors, while adapting to change in what I would call a "passive" manner, like kind of a defensive approach. The sort of agents I'm interested in need to be much more aggressive, and strategic. Game Theory meets Planning, I guess.

Posted by Frozone Permalink on July 21, 2010 08:01 AM | Comments (0) categorized under Pedagogical modelling
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May 25, 2010

Partial order plans, Greedy algorithms and Educational Objectives

I had a brainwave the other day about partial order plans. POPs are where you identify the "steps-needed-to-take in order to reach the goal", but the steps are not placed in order -- the agent is free to mix and match them according to its current situation.

(Hmm, I wonder if a greedy algorithm could be used to complete a partial plan, gradually taking the current "best value" step.)

Anyway, my brainwave was about a visualization of "My Learning Objectives" in an online course as compared to "The Actual Structure Of the Course". For example, students are accustomed to seeing a course laid out kinda like: Module 1, Module 2, Module 3, etc... However, it would be important for my system to show "My Learning objective 1", "My learning objective 2", "My Learning objective 3", etc.. within the context of the former.

(I used LucidChart.com to make the image above.)

The point of this image is to show the student's learning objectives within the context of the Module hierarchy. I believe I need another dimension in my visualization in order to make this effective and easy to understand at-a-glance.

In this image, I'm showing that my learning objective is something quite specific - tied to a fine-grained item (exercise) in the overall hierarchy. In order to achieve this specific exercise, the student must pass through the overall "lesson", and then have some exercise-specific activity. This "narrow to broad to narrow" is just one type of teaching strategy.

I hope I am remembering this correctly, but I believe that a discussion about computational encodings of "narrow to big" or similar strategies appears in Wasson 1998, Facilitating dynamic pedagogical decision making: PEPE and GTE.

I am also remembering something about "the fontier" here from Etienne Wenger's book. As usual, I am writing this from my iPod at home and am unable to check my paper references at this tme.

Posted by Frozone Permalink on May 25, 2010 10:34 PM | Comments (0) categorized under Pedagogical modelling
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May 21, 2010

It's not Time-Series Data

I love data and I think that visualizations are delicious things. I read SimpleComplexity and I enjoyed the latest ACM Queue article, A Tour through the VIsualization Zoo.

Reading the latter, I loved the description of time-series data. Lately, anything about "Time" and "Data" is piquing my interest. (ex. recent entry, Decision-making over Time)

Today, I just wanted to note firmly that I realized my interest in process involves time, but that it is not time-series data. Time-series data is like an HMM -- one variable whose value changes over time. (see related entry with discussion about Hidden Markov Models, "It's about influencing the process.")

My fascination with process is about CHANGING RELATIONSHIPS over time. Using graphical models, this would be a change in the set of Edges over time, somehow.

So, yes. The point of this post was to clarify something in my mind about time-series data being about the "Nodes" (Events), and distinguishing my work as being moreso about the "Edges" (Relationships over time, strategy...).

WIth this new perspective, I would like to re-visit my work in game theory and planning.

(On Twitter, I summarized this entry as: Contrasting time-series analysis with process/strategy as "changing relationships", not events)

Posted by Frozone Permalink on May 21, 2010 08:40 AM | Comments (0) categorized under Pedagogical modelling
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May 20, 2010

Guy Brousseau - Mathematics education

Thanks to Egan Chernoff for the reference to the work of Guy Brousseau. I found this article that gives a description of Brousseau's work.

According to my own new, and limited, understanding, Brousseau developed a theory about the situation of a teacher and a student interacting to have the student learn math. I am really excited about seeing SPECIFIC and THOROUGH work on teaching as I read into this work. As a computer scientist I want to further identify the dimensions of this interaction (i.e. task domain ontology, story, techniques, strategy, etc.) for the purpose of designing effective technology support for these things.

That's all for now; time to go to work...

Posted by Frozone Permalink on May 20, 2010 07:39 AM | Comments (0) categorized under Pedagogical modelling
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February 13, 2010

Full circle

This is good stuff, here folks. I know it's only been 1 day since I've jumped back into programming, so this is gut reaction reflection. But I am a highly metacognitive, spiritual and introspective individual, so bear with me. ;-D

Over the last day or so, seeing the web-inf directories again, and the conf files, and the business logic objects, and re-living some of my old Model-View-Controller architectures, I'm recalling my headspace when I chose a career change. At the time I had a sense of mastery of web application architectilures, in the Java world, at least, and I wanted to move beyond modelling objects and explicitly building pages to support business processes. Everything had turned cookie-cutter, and I wanted to learn how to make adaptable processes, where the user could have some ownership in how they pivveyd through the data, but that the system could still offer guiding support in a presentation of options.

I learned a lot about AI. And I learned a lot about the delivery and maintenance of programs (I mean programs like "student leaders provide study sessions" not "computer programs".

I got to re-connect with people, to become deeply involved in a team, and to reflect about becoming a mentor myself, what this means to me and how I think I could improve.

What next? I have to choose how I want to grow, to choose how I spend my time.

I love the technology, I love the AI, I love the system design. This is my primary love. (professionally-speaking) I also think that technology design must come from real people to support real situations. That's why my work with people is so important, it keeps my work "real".

I am also remembering that specific, nitty-gritty technology can be a HUGE time sink. I am extremely busy and time is a precious resource. I believe that he "new" Coder-Stephanie will be highly critical about possible paths to follow during problem-solving. I will be using more prediction, foresight and metaevaluation than I had in my younger days. I hope that I do continue to blast into the unknown and continue acquiring the new skills that exploration and experience brings. It's just that I have become calculated about it.

Posted by Frozone Permalink on February 13, 2010 10:53 AM | Comments (0) categorized under Pedagogical modelling
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December 27, 2009

Minimax for planning

'Picking up from my previous entry, Planning as environment adaptation.

A strong theme in my recent work is the attempt to match up tools from AI to apply to my problem. For example, most recently, I tried to adapt Markov Decision Processes.

Mike suggested that I might want to look at my problem as a game. In game theory, an agent's decisions are affected by the decisions of another agent. A cooperative game is when utility goes up as all parties work towards common goals. This fits my problem. So I would like to explore it a little more.

When I think game theory, I think Minimax. And I keep coming back to planning. So I found this cool paper (1978) on Minimax and planning. (Minimax Solutions to Stochastic Programs - An Aid to Planning Under Uncertainty.) I enjoyed this paper because it was helpful for me to see an application of Minimax outside of a checkers game, chess game, etc.. The take-away message I got from skimming the paper is that instead of using Minimax to anticipate the opponent's move, you are anticipating possible "future scenarios" and the good vs. bad futures, and you want to do everything you can to push towards the GOOD possible future.

During my conversation with Gord, he identified the theme of Global Coherence vs Local Adaptivity in Instructional Planning.

In terms of human learning, it is important for a student to project forward their "theories" of how they believe the world works. That is, they form a theory that is some approximation of the actual task domain model. I think that this is where Global Coherence fits in. The learning environment must create the grounds for where the learner can manifest their theories.

Within these grounds, the learning environment must then provide opportunity for the learner to receive confirmation, affirmation, test their theories. This is how learning works. And this is the Local Adaptivity. This finer-grained territory might be where my process modelling fits in.

Blarg, baby's awake. And I was just getting started! :(

Posted by Frozone Permalink on December 27, 2009 01:55 PM | Comments (0) categorized under Pedagogical modelling
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October 02, 2009

Theories of learning

I am creating this entry for the purpose of listing theories of learning. The importance of these is blaringly obvious to me, and, using the search engine on my own blog, I am flabberghasted that I have not written about them before. One of those things that Is late blooming, I guess.

Learning about the existence of these is what lead me to investigate educational psychology, then, cognitive science. These tangents have lasted years, but I always return to computer science as my home field, and "first love". :)

I will begin this list of learning thories from what I can recall off the top of my head, then I will continually return to this list to elaborate as memories return later, or are called up from reading papers, or even as I learn of the existence of new ones.

- behaviourism
- socio-constructivism
- constructivism

I want to identify the components of these thories that are most relevant for building the computational model of different pedagogical strategies, which would be referenced by an instructional planner.

Posted by Frozone Permalink on October 02, 2009 05:14 PM | Comments (0) categorized under Pedagogical modelling
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June 03, 2009

Planning as a projection of the learner's creation

So, picking up my thought from last time: the planning is a projection of what "I" (taking the perspective of the artificial learning-support system) want the student to create.

The basic flow of learning goes like this: Learner is introduced to new material, and it kinda sits in the back of their brain (literally, sensory input fires at the back of your head). Then, to "absorb", the knowledge, the learner has to re-assemble that input so that it fits into their contextual mind, and essentially re-create the knowledge by PRODUCING it in some way. For example, they could build a model of what they just learned, or do some practice questions, or something. This creation, or projecting forward by the learner requires them to "push out", whether it be by speaking or writing or using their motor skills or something like that. Continuing the pretty picture, these "outward" functions such as motor skills, etc. cause neurons in the FRONT of your brain to fire off. Finally, the learner observes their creation (more sensory input at the back of the brain). They can compare their original input to the new input they received about their own creation -- it sort of creates a loop. From what little I understand about learning, it happens here, when they are comparing their own creation to what they were originally taught. Eventually they forget the original lesson, but they retain their "creation" skills, so that the knowledge is truly internalized.

The computer's job is to
1) present the new material to the learner in such a way that it is easy for them to remember/absorb it initially
2) provide some means for the learner to "create" this new knowledge themselves, i.e. by providing activities, tools, support, etc.
3) facilitate the learner's evaluation of their own creation, and to support refinement, questioning, deeper analysis, integration of even more new material, etc..

From an AI planning perspective, then, I guess the machine has to "stay ahead" of the learner so that it can anticipate where they're going so it can provide the proper tools. The machine's projections can be informed by what it believes to be their learning goals. ¹

What else does the machine have to model?
- The set of next possible teaching-actions, which I talked about a few entries ago.
- The "story threads", which I also talked about a while ago.
- I should keep an overlay of the relevant task domain knowlege. "The fringes", to use lingo from the AIED field. :)

I could be suggesting building materials, but, this is part of the subset of teaching actions, I think.

Hmm, so, ya. The journey continues.

--
¹How do these learning goals get entered into the system? Well, I assume that the student tells me their learning goals. But I also know that these goals can change without them telling me right away, or at all, so there will always be some uncertainty here, even about our GOALS!

Posted by Frozone Permalink on June 03, 2009 11:00 AM | Comments (0) categorized under Pedagogical modelling
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Action selection

Yay! Found another paper about action selection:

Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach

I did a quick skim and couldn't find the arg max thinggy in this paper. This makes it a little harder for me to find something common for me to grasp and then be able to compare to the work I've already done.

Once again I'm overwhelmed by the amount of detail that my problem considers. But it is nice to have one more "very relevant" paper to add to the pile.

Posted by Frozone Permalink on June 03, 2009 10:17 AM | Comments (0) categorized under Pedagogical modelling
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June 01, 2009

Following the steps, but not really

Some other random meanderings as I try to find my way again...

It seems so simple -- I just need an engine to "follow the steps", right? But of course the steps are not in a rigid order. There's a basic pattern, and at each step I have a set of choices and I want to compute the best one as I'm following the pattern, but I definitely have to veer off "track" for the sake of flexibility. It's like there's 2 loops going on: one "master pattern", and a smaller, more specific detail-oriented guide that sort of conversationally follows the big loop in a general way but has its own mini-goals to take care of. (watching for motivational cues, hints, etc.)

I feel like I'm way out of my league, here. Sorry mome wraths, I couldn't do it!

Posted by Frozone Permalink on June 01, 2009 01:27 PM | Comments (0) categorized under Pedagogical modelling
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May 05, 2009

Generating actions and the transition function

I was reading this paper (Decision-Theoretic Planning for Playing Table Soccer [Tacke, Weigel & Nebel, 2004]) about an application of decision theory to a robotic planner that played foosball. I thought that the paper did a good job of explaining the "nuts and bolts" I've been looking for.

To put a name to it, I want to look at the transition function. Basically this function takes the current state and an action in as input, and outputs possible new states. Then you pick your action according to the possible state with the highest utility.

In this paper, their system generated a tree of next possible actions, and for each of those, opponent reactions and corresponding consequences, with probabilities of those consequences. Next came another layer of the next possible states. It looks like they used a bit of naive Bayes and some minimax, which was odd because it was a decision-theoretic system, not game-theoretic. This is an art, really -- you have the tools and you can make them do whatever you want to suit your problem!!

I'm starting to get an idea of how planning works, but I need to look at a few more systems to compare and contrast them so I can pick out the bigger patterns.

I think that the teaching strategy will influence the next-possible actions. I don't know how incoming observations about the learner will fit in... maybe the probabilities of consequences? I also have to keep in mind the overall story -- i.e. my utility is "giving the learner an overall sense of a meaningful experience". I really have to define that mathematically. I want to put them through an introduction, a middle, and an end. And you can braid many of these together.

Well, baby's awake. Hope to come back next time with another example of the process of planning.

Posted by Frozone Permalink on May 05, 2009 09:17 AM | Comments (0) categorized under Pedagogical modelling
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February 07, 2009

Modelling teaching strategies

Modeling teaching strategies: this has been a tug at my research interests for a long time, but I don't know if I've ever actually tackled it head on before.

I keep reading about systems that teach *specific topics*, but I want to know if teaching *itself* has ever been modelled in its abstract form, then applied to a task domain ontology for flexible tutoring. Following the hack¹ I developed over the last couple posts, I'll come back and keep updating this entry as I find more. For now I'll sketchy an outline:

• what is a teaching strategy: references from the educational world
• relevance of the student model
• specifically, how teaching appears in "flattened" models
• sweep of approaches

Following this, I'd like to take a look at how learning objects are the output of a teaching strategy plus task domain ontology, and how a machine might reverse-engineer these two factors. The point of this would be to allow you to take a learning object out of context and maybe use a chunk of it, or a dimension of it, in the execution of an instructional plan. The biggest goal is to maintain "themes" throughout your delivery plan.

¹ hack: My desperate attempt to do research in 15 minute intervals while being a good mommy to my five month old. :) Namely, I publish a post one day, and keep editing it and going back and changing it over several days or weeks. I'm sure this is infuriating to a reader, and I feel bad about that, but then again, the point of this blog is to help me develop my ideas. If I ever publish anything academically, I guess those would be "finished" or "polished" pieces of work that would be more appropriate for actually sharing my work. Anywayz.

Posted by Frozone Permalink on February 07, 2009 11:28 PM | Comments (0) categorized under Pedagogical modelling
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