Index - thesis
- Math Education (April 19, 2010)
- Should I be worried about self-plagiarism? (April 18, 2010)
- Global coherence and the ability to perform a retrieval (January 14, 2010)
April 19, 2010
For my thesis, it is likely that I will write a tool to support the planning/design/continuous-execution-and-redesign of an online math course. I created this entry to help me keep track of the many educational math tools I have worked with, studied, or stumbled upon. This list is in no particular order. I will continue to add to this list as I encounter more.
April 18, 2010
I am working on a blog post whose purpose is to flesh out some of the motivations behind my thesis work. I thought that maybe I could save myself some work in the future, even copy/pasting some of the paragraphs from my own blog into my own thesis. But, would that be self-plaigiarism? For example, isn't it academically dishonest to submit a paper that you wrote for one class again in a second class, if the topics are similar?
To be safe, I thought that I could just put in a citation to my own blog. That way, I'd be doing it "properly".
But, if all the "meat" is on my own blog and my thesis turns out to be a re-packaging of "older" stuff already on my blog, will this devalue my thesis? Should I keep my ideas unpublished, and "save" them for my thesis?
I won't let these worries hold back my creativity. I will continue to blog with all my might. I just hope I am not shooting myself in the foot. What's a young open researcher to do? :)
January 14, 2010
I'm in the middle of my workday but I just had a brainwave and wanted to record here before I forget. I don't know if it's significant, but, meh, record now and analyze later. ;)
The theme of my M.Sc. thesis is going to be Global Coherence and Local Adaptability in Instructional Planning. I'm starting on this work with the mindset of trying to identify the need for global coherence in an online course or lesson plan. As my advisor said, "Why do you need a syllabus?" If an instructor were to start talking on day 1, and continued with another topic on day 2, and just kept going without giving students any plan or forewarning about upcoming topics, many students would be disoriented and might not perform as well. (could i site this somehow?). So it seems that there is some value in providing an overall vision. This is one premise of my thesis work.
So today my brainwave occurred as I was cleaning up icons on my desktop. I thought, "overall vision helps with retrieval". The only way to make sense of this mess was to have some sense of the "higher order of things" that organizes them. So, without the global coherence you lose your ability to recognize the significance of a given detail.
I understand that the sub-field of AIED, Instructional Planning, which applies Planning technology from artificial intelligence (often used in robotics) for sequencing course content. One reason research has halted in this area is researchers found that the ability to sequence content at the large "Course" level (as opposed to sub-topics within a chapter) was not worth doing because things change so much it doesn't make sense to plan too far ahead. At least that is my current understanding. So why bother with planning and course material sequencing at all?
But without a global plan, you lose context required for making sense of new material.
I just started a new category on this blog tagged, "thesis".
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,