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February 03, 2009
Bayesian networks / Causal graphs
This is a juicy topic! Lots of work has gone into using Bayesian networks in AIED, I hope to cover a few examples here as well as throw in a couple thoughts of my own.
I'm not happy with the quality of my previous entry, but at least I'm moving forward. What I *did* like about that posting was how I started it, and finished my thoughts over the next few days. It screws up my RSS feeds because I go back and add to old entries and those edits don't get sent out as notifications. I really should be using a wiki. Alas.
The nodes in a Bayesian network are "Events" from probability theory. The edges represent conditional probability relationships. For example, if you have the probability of A given B (denoted P(A|B)) then you would have 2 nodes: one for A and one for B. There would be an edge pointing from B to A. I always get confused about the direction of the edges. I think of B, (the "given") as being the parent because A is kind of inheriting the given knowledge (B) from before. So, the node that we had marked as A is actually more correctly labelled P(A|B) and the node for B is more correctly labelled P(B).
Ugh, I hope I didn't mess that up! Moving on... I intend to flesh out the following headings over the next while, whenever I can escape from my beautiful baby.
February 04, 2009
Bayesian networks for student modeling
In Assessing effective exploration in open learning environments using bayesian networks (Bunt & Conati, 2002), the system applies a Bayesian network to support student exploration in an open learning environment. The network is used to let the system use its observations of student behaviour to guide hints and feedback to support their exploration.
In this system, nodes are organized into 2 types: knowledge nodes & exploration nodes. As best I understood it: Knowledge nodes represent the probability that the student has the knowledge of that topic. Exploration nodes represent the probability that the student has effectively explored an exercise, unit or category.
Edges represent causal links between the nodes (as with all Bayesian networks!). As best I understand it, the edges were manually constructed by the authors who examined what they thought the dependencies should be between knowledge & exploration nodes. Part of their work was to optimize this and make sure they had set it up in the most effective way possible.
As usual, I'm interested in how a platonic/abstract ontology could have been weaved into this system -- probably using an adapter through the exercises/units/categories.
The most interesting part of this system, to me, was that the node "types" allowed the researchers to code in the relationships between activities (of various grain sizes) and belief about students's knowledge of the concept related to that activity.
February 06, 2009
A second application of Bayesian networks can be found in Student Modelling based on Belief Networks (J. Reye, 2004). In this system, there are also several types of nodes: global nodes for modelling the student's overall characteristics, nodes to represent concepts-to-be-learned and the probability that the student knows that concept, and thirdly: nodes that act as indicators for the concept-nodes that allow the system to "watch" for students to demonstrate how to demonstrate their knowledge for the particular topic.
Prior probabilities are the knowledge the student has coming into the activity. Edges, like in the previous system, are causal links. Prerequisite nodes would point towards child nodes because knowing that piece of information woudl "cause" your knowledge of subsequent concepts. (As best I understood!)
I think that the teaching strategies are still kinda embedded / flattened within the network in this case. Still gotta keep reading to figure out how to abstract out that teaching strategy so that it can be applied to a platonic ontology!
Posted by Frozone Permalink on February 03, 2009 12:56 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,
