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September 23, 2006
Dealing with Ambiguity in Constraint-based modelling of student understanding
As part of building a background for deep modelling of thought, I wanted to read a good paper on current techniques for diagnosis of student misconceptions. I read Constraint-based Modeling and Abiguity [Menzel, 2006] -- discovered because of my new membership in the International Artificial Intelligence in Education Society. The society has been a great resource - I'm so happy!! :-) :-)
The paper gave a good review of Constraint-based Modelling (CBM) in general, but it was intended as more of a review for scientists already familliar with CBM, which I am not. 'Mental note to do further background reading.
After establishing these "basic" concepts of CBM using an example of a student struggling with an addition problem in mathematics, the author examines another problem in second-language learning that introduces ambiguity. Unlike math, which is relatively easy (er, well, easier) for a computer to model, in the second example the student attempts to construct a sentence in the English language but struggles with grammatical agreement, i.e. "These fish stinks". It is much harder for a computer to analize ambiguous things like language-learning than it is to analyse mathematical-concept-learning. So, more advanced techniques are needed and are described in the paper.
Most of it went over my head, but I read through the whole paper anyway, grasping what I could. I remember that the author discussed weighted constraints and also looked at transitive relationships over the conditions that need to be satisfied. I had to pull out my old 2nd-year logic textbook because I forgot what a transitive relationship was. It's pretty easy:
if x is > y
and y > z
Then you can apply the transitive relational property that x > z.
This is roughly applied in the sense that the demonstrative pronoun "These" and the verb "stinks" aren't matching up properly with the transitive relationship that the whole phrase is either talking about a plural set of fish or a single fish -- the items should be consistent but they're not. It's like knowing the rule that x > z but for some reason in the student's work, they indicated that y < z and yet x > y. Or something like that.
I also thought the idea of weighted constraints was interesting, but I don't understand enough about Constraint-based modelling to really appreciate it.
The author's discussion on the role of CBM in a general ITS architecture was also interesting. In my own set of assumptions, I held that CBM was useful as a diagnostic approach for student misconceptions. Apparantly CBM can also be used as a student model and as a domain mode, but l still don't really understand how to best classify the usefulness of CBM in a general ITS architecture.
The next paper that I'm reading takes a more theoretical approach to ITS architectures -- reminded me of Software Patterns in ITS Architectures -- maybe I should have taken more courses in software engineering at the U of S; I only went up to CMPT 370. Oh well. =D
Back to Constraint-based modelling: My old 862 notes indicated that a good introductory paper would be Evaluation of a Constraint-Based Tutor for a Database Language [Mitrovic & Ohlsson, 1999]. 'Quick highlight helped improve my understanding:
- "The purpose of constraint-based modeling (CBM) is to overcome the overspecificity problem via abstraction (Ohlsson, 1992)."
* keeps reading *
Posted by Frozone Permalink on September 23, 2006 08:30 AM
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categorized under Student Modelling
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Hello, my name is Alex, i'm a newbie here. I really do like your resource and really interested in things you discuss here, also would like to enter your community, hope it is possible:-) Cya around, best regards, Alex!
Posted by: Alexulef at February 10, 2007 06:21 PM
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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- 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,
