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November 16, 2011
Intro, Reason1, Reason2, ..., Conclu
A good template for a work of research is: Intro, Reason1, Reason2, ..., Conclu. This model comes from The Craft of Research.
Research Area
I am trying to follow this model. Recently, I figured out what what my claim is. Or, at least, I have a coherent paragraph explaining the research area. (Roughly: it's that the Ecological Approach can be used for global coherence planning.). You put the claim at the end of the intro.
Claim
I think that it takes a little more work to get the Claim out of your Research Area paragraph. You have to actually start to try to make some progress on your work and go back and forth and figure out what your claim is as you go.
Lit Review Thesis Statement
I even figured out what the "thesis statement" of my literature review is going to be. Here it is. I might change, because you never know when you are going to stumble upon another paper that rocks your world.
Researchers have worked on Instructional Planning in three perspectives:
1) Based on a Curriculum or Plan set by the system and well marked up learning objects and pedagogical rules ("mathematical / logical"), includes "course generation" research.
2) Based on a Recommendation (based on collaborative filtering, the students own behaviour - student modelling - or other similar students).
3) Direct input from the student (ex. student says "Harder", "Easier" etc.).
Most systems are a combination of these. Most work in (2) is about finding the next "immediate" learning object or action to take. Many recommenders do not consider what the student activity should be in a weeek or a few months; it's more about the activity at hand. My work is kind of a deliberate usage of (2) while taking the global, long-term focus of (1).
Reasons
For a while, I was stuck on how to find my Reasons. See the first paragraph of this post? I start with an Introduction and at the end of my Into is a Claim. The bulk of my thesis is a series of Reasons.
But I got totally stumped on how to come up with Reasons. I know a lot about research methodology and collecting data and stuff. But how do your conclusions and analysis drawn from your experiment fit with these "Reasons"?
And then I figured it out. You have to read through all the papers in your lit review, and beyond, and look for stuff that other people have done that support your claim. And then write about it and cite it. This seems really obvious, but I hadn't clicked two and two together. That is how you begin the process of finding your reasons: gather them from other people's stuff and cite them.
I think part of my personal blocker was an aversion for finding research that "supports" my work because that's not "real science". Real science is when you have a falsifiable experiment.
But I was forgetting that there's no point in doing an experiment unless you know how your results would fit in with the rest of human knowledge on the subject.
And here's where your experiment fits in. The point of your Experiment is to create Evidence to support one of your Reasons in your thesis. So, many of your reasons will be because of other people's work, but your main contribution is putting all of this together and then adding your own OOMPH of evidence as well, as one of your Reasons.
Posted by Frozone Permalink on November 16, 2011 09:40 PM
| Comments (2)
categorized under academia & thesis
Comments
I picked up Craft of Research thanks to you. Just had a look at the intro so far, but looking forward to diving in soon.
(Also, I never know if you reply to these comments unfortunately, so I probably miss out on possible conversations - sorry if that happens!)
Posted by: Gail at November 17, 2011 07:55 PM
Hi Gail - Awesome!! Yeah, I would have to say that the book has changed the way I do my own research. It helped me to create and solidify my own perspective. I hope you get something out of it, too!
Yeah, I am not super impressed with the comment system on my blog. Don't worry, if I ever feel there is a "dangling conversation" in a comments thread where I want your response, I know where to find you on Facebook. LOL! ;) ;)
Posted by: Steph (a.k.a. Frozone) at November 20, 2011 12:02 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,
