November 18, 2006
Model-Tracing tutoring and Constraint-Based tutoring
This site's stylsheet still needs a lot of work, but I'll have to save that for another day. Time is too fleeting for me to actually be able to do everything I want; instead, I have to "pick my battles". as they say.
I have always been confused between Case-based reasoning and Constraint-based modelling, simply because they both start with the letter C and contain the word "based". I had hoped that reading this paper would help clarify at least "Constraint-based modelling" As a bonus along the way, I also learned about Model-Tracing; I don't recall ever hearing about this particular approach before.
Model-tracing, as I understand it, involves setting up a set of rules about the Task Domain. (I am trying to use terminology from Kurt VanLehn's summary paper, The Behavior of Tutoring Systems.) In Model-tracing you also need to have rules about misconceptions and incorrect ways of doing things. Then, throughout the tutoring process, the system "watches" the student as they are working on a problem at attempts to match up a series of rules that the student seems to be following. Then, once the system has identified the actual "incorrect rule(s)" that the student is "executing", then the system is able to offer focussed, relevant support to help the student get back on track.
Maybe I am totally wrong about that, but, I'll keep reading.
Constraint-based modelling (CBM) was easier for me to understand because I've already read about it somewhat. I'm also seeing some of the same ideas in the earlier 1994 paper Granularity-Based Reasoning and Belief Revision in Student Models [McCalla, Greer].
Anyway, the gist of CBM (to my own limited understanding) is that when given a particular problem that a student may be working through, the system understands a set of constraints that have to be met in order for the solution to be correct. At each step, the system can watch the student's solution and match up which constraints the student may be satisfying and which constraints may be missing. In this way - by examining the unsatisfied constraints - the system knows which areas of knowledge that the student is missing and where the system can focus on helping them.
The main difference, as pointed out in ["Kodaganallur, Weitz & Rosenthal] is that Model-tracing is focused on what the student is *doing* and the processes that they are following as they are working away, while CBM is more focused on what the student is demonstrating that they *know* as they put stuff down on paper. Something is tugging at the back of my head about the differences between Behaviorism and Cognitive perspective approaches to educational psychology.
I wondered if maybe Model-tracing would be the best approach when teaching procedural knowledge. For example, in a pottery class - first you take the clay, then you mould it for a bit, then you add the water, then you push your thumbs in the middle to start to form the bowl shape... Meanwhile, CBM might be better for representing the overall picture, like - "the finished bowl must be shaped like a, well, bowl, and it also must be free of cracks". If the student's bowl is showing evidence of cracks, then the violation of that particular constraint leads the system to suggest to the student that perhaps they should dab a little water on their fingers to moisten the clay, and thereby eliminate the cracks.
Maybe those are crummy examples, I don't know!
Personally, I am thinking that the CBM approach has more potential for "intelligent" tutoring, but I'll keep reading -- this is good stuff!
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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,