Learning & Declarative Knowledge
Memorization, or the acquisition of facts may not be the most trendy of topics in todays day and age, but even a radical constructionist (http://en.wikipedia.org/wiki/Constructionist_learning) would not discount the importance of committing certain knowledge to memory.
Whether trying to learn the names/locations of all the countries in the world, learning anatomy or physiology, or acquiring new vocabulary en route to mastering a second (or third) language, there is a need for both representing these knowledge nuggets or facts, as well as for systematically committing them to memory.
The vision behind creating this a collection of microformats for micro-learning is thus an attempt to provide a universal means of representing "memorizable" information across a variety of domains, and in turn, enabling a multitude of applications to leverage this format for the purpose of helping people acquire declarative knowledge.
In the spirit of microformats, rather than trying to (from the top down) create a highly flexible microformat that covers learning across all domains, we are starting from the bottom up, and reigning in scope, so to speak, by focusing specifically on the domain of language learning.
Language Learning and a first cut at the hVocabulary microformat
When learning a new language, one of the core challenges is learning the meanings of many new words and phrases. Although there is more to language learning than pure vocabulary acquisition, a critical mass of vocabulary must be acquired in order to get anywhere.
A fairly modern approach to language learning that is becoming increasingly popular is commonly referred to as the "Lexical Approach":http://www.cal.org/resources/digest/0102lexical.html. The lexical approach is based on the idea that words and word combinations (such as collocations) constitute lexical chunks, which in turn serve as the "the raw data by which learners perceive patterns of language traditionally thought of as grammar (Lewis, 1993, p. 95)."
A classic format for learning vocabulary is the flash card. Essentially, a flashcard consists of a "cue," presented in the target language (language being studied) and a "response," presented in the source language (often, but not necessarily, the native language of the learner). The analog flashcard pre-dated the computer, but with the advent of digital technology, the flash card has been augmented.
There are a variety of online learning systems, ranging from simple Flashcard applications to Computer Assisted Language Learning suites that provide much more than a simple cue-response pair. Some of these applications employ rich media like sounds, images and video clips, as well as provide example sentences, all of which help enhance the memorization process, and moreover, provide knowledge of how to use a given word or phrase.
One of the primary purposes of the hVocabulary microformat is to represent the different content, in the form of simple text as well as rich media, so it can be openly exchanged between different learning systems. For example, if a person is learning a language, say French, online, and they are studying a group of words and phrases, they are essentially building up an online representation of knowledge that (ideally) they have stored somewhere in their brain (if current neuroscience is correct, the hippocampus plays a crucial role).
hVocabulary, hMemorize and Beyond
The hVocabulary microformat is focused on the representation of a specific type of content, but it could be extended with a hMemorize microformat, which would provide a standard way for representing a given users historical interaction with a particular word or phrases, including but not limited to number of times they have seen it, whether they know it or not, etc. This data would serve as a memory profile, enabling a user to track their history with individual "items" (word, phrase, term, etc.)
Wouldn't both the end user and various learning platforms and services benefit tremendously if there were an open, portable format that enabled a user to import/export what they were learning, including the content itself, as well personal data pertaining to their memory profile for that item?
Our vision is to create a series of microformats that enable the web to be slowly annotated (from the bottom-up) with a series of micro-learning micro-formats, transforming what is currently a disparate landscape of (often silo'd) data into a rich resource of "learnable" knowledge nuggets that can be easily created, enhanced, exchanged and acquired.
Describing a Learning Concept
When learning any new concept, whether a toddler learning ABCs or a college student studying quantam physics, there are small tips, "aha" moments, perhaps learning games that can be shared. Also references to resources specifically in the context of learning a given concept. What seems important is the ability to link a small bit of text, rich media, or resource description to a specific learning concept. If this vocabulary describes learning concepts, then we can use other formats to make the link to the media/text/resource.