Making the Web and World More Learning Friendly By Keeping Track of Resources and Actions using Various Types of Tags and Contexts
Two types of Learning Items: A learner uses RESOURCES and performs ACTIONS.
These items are tagged, with the purpose of grouping and finding. There are different types of tags:
Primary Domain Tags: pre-defined, not frequently updated, MUST choose at least one primary domain for every item. Uncategorized items are thus avoided to happen. Not sure about the maximum number of Primary Domain tags per item. Arbitrary max value is 5 for now. This is to avoid too much ambiguity in classifying an atomic learning item. But do atomic learning items really exist??
Secondary Domain Tags: Find synonyms and sub-fields of the specified Primary Domains, and provide them in this tag type.
Learner’s Topic Tags: Free reign to the learner to give any number of tags. Not really necessary if we are able to do statistical analysis of text contained in the learning item e.g. high-frequency occurence of certains terms or their synonyms automatically gives the items those tags.
Flags and Judgment Tags: To mark resource as useful/not useful (judgement) and to flag certain highlights in learning experience, like doubt arose, doubt solved, question in mind, answer found etc.
Learning Items, i.e. Resources and Actions, can be free-floating while in the universe of possibilities; but when the learner sets upon a quest/journey, the learner’s will and wish summons them into getting grouped and structured into a hierarchy or network of learning programs. This is informed by the Learning Contexts, which are a rich topic of exploration in itself.
WHAT, WHY, HOW: I want to learn physics, because I want to become strong in my quantitative skills, and I will use Khan Academy and MIT OCW as the starting guides into this mission.
I need to build autonomous/controlled drones to use for moving cameras to aesthetically videoshoot the dance, drama and documentary projects. {Here’s what I already have}. {Here’s what I need}. Build a learning plan using this.
How do we tackle the Global Climate Change problem? Who all should be involved; do citizens play a major role or should it be more top-down? [This would be more of a collaborative research project in the beginning, growing to be a global, international movement. - the tagging-classifying-stringing system described above would be useful to keep track of all resources and actions and their utility/value in the specific context]
To speak more about the Learning Contexts, what primarily needs to come out is the PURPOSE, PRIOR LEARNING, and TENTATIVE STRATEGY that the learner(s) might already have in their mind. This would then help inform the experts or the learning system to put together the previously free-floating but tagged resources and actions into learning programs, hierarchical arrangements of resources and actions into items, sections, modules, projects etc. optimized to meet the purpose. Such learning programs should be more of a suggestion/framework rather than a strict order.
The PURPOSE or OBJECTIVE of learning thus forms the basis of a learning program. ASSESSMENTS are those entities that check/verify whether (YES/NO) or to what degree (80%) the Objective is met. The “Learning as Problem-Solving” Paradigm for Open-Credentialing would use unambiguously defined problems as objectives, and take solutions found by learners as inputs to check whether the problem is getting solved. Since finding solution is tougher than checking whether a certain solution is correct (read this idea in P vs NP), the Learner who finds correct solution proves their worth of earning a CREDENTIAL, which is simply a trusted, verified SIGN awarded to the learner, signifying that the OBJECTIVE has been met, or problem has been solved, by that learner.
