XplanaZine

This is the second in a multi–part series on the process of developing ontologies in education and why they matter. These articles are a continuation of a series on taxonomies in education also published in XplanaZine (Part 1, Part 2, Part 3, and Part 4). The first article described what ontologies are using the broader contexts of language and language acquisition. This article talks about “fluency” and how we can create that within libraries of objects. It’s purpose is to lay the groundwork for our discussion on complexity tomorrow by identifying the types of “agents” needed to create fluency or intelligence in an information/language system.

I grew up an either-handed, bi-lingual, bi-cultural white kid in the heart of Texas. My skin said I was white and mono-lingual while my head told me I was actually two different people. The whole language/culture conversion process started before I hit adolescence and, to a large extent, has defined me both personally and professionally.

Since I grew up speaking two of them, languages seemed easy. I was also fascinated by the way they worked and by the fact that they are both systematic and illogical at the same time. A perfect field of study for a guy who struggled with his own identity. Once I went to college I started studying other languages and, eventually, became a language teacher. Between research and my own experiences (personal with my many students), I’ve learned a thing or two about language proficiency.

Most people frame questions about proficiency in two ways — “Do you know French?” or “Are you fluent in French?” Fluency, a common way for the average person to think about language proficiency, implies the highest level of proficiency. Essentially, when most people use the word fluent, they mean a native or near-native ability. They mean that a person who is fluent in a language can go anywhere, talk about anything, and generally pass themselves off as someone who belongs (at least in spirit).

As a linguist, I tend to shy away from the word fluent (at least as it pertains to languages). If I were to use it, however, I would mean that I could pick up a book in any of my languages of fluency, and start reading without ever thinking about what language I was reading. I could walk into a wedding reception, strike up various conversations with people there (who have different accents and vocabulary preferences), and have effortless conversations on topics ranging from sports and weather to religion and technology. Fluency, in that context, would mean that the receiver or transmitter of the language symbols has enough proficiency that he or she can do so without effort and with the highest level of understanding possible.

The question is — and now we return to our discussion of taxonomies and ontologies — how do we make a basic information structure fluent? How do we convert basic taxonomies or cataloguing structures into more emergent entities that are fluent in our needs and can actually anticipate what it is we want as it pertains to a particular discipline?

What I really want to know is how I can create a learning object repository that is actually fluent in education (I’ll talk more about what I mean by that in the fourth article in this series).

In order to answer that question, and to explore how we might create a fluent library or repository, we should first break down the basic components of language fluency in general. Here they are:

• Building Blocks — These consist of vocabulary and common phrase. They provide basic communication abilities but cannot, by themselves, lead to higher levels of proficiency (no matter how many of them you know).
• Rules — In language, these are grammar, syntax, and morphology. What are all the ways we can put building blocks together to make more sophisticated communication statements?
• Exposure — Building Blocks and Rules are meaningless without lots of models for the learner to see how they are used and applied in context. This is why it is often said that no one really learns a language in a classroom. A language is only learned within a community where one can experience first-hand the use of language.
• Practice — Practice implies moving from the passive to the active mode of language usage. It is not enough to know building blocks and rules, or to study models of their usage in a community. In order to become fluent a learner has to actually create his/her own models within that community on frequent basis.
• Meaningful Feedback — Fluency implies that a learner can continue to improve his/her abilities by monitoring feedback from the community, comparing that feedback with hr/his models, and then correcting any inconsistencies. Advanced learners or users eventually learn to create new models that are extensions of the rules and building blocks but that do not yet exist or are not common within the community.

Now, we’ll talk more about complexity and how it plays into the various components of this discussion tomorrow. For now, however, what we have outlined are the components required to go from basic information structure in an object library to a self-organizing object library that can actually anticipate needs.

The information building blocks in my library are taxonomies. An example, for Spanish, would look something like this:

This taxonomy is extremely useful and can be built out with amazing complexity and detail so that any possible object will have a specific location or node with which it can be associated. In order to make this taxonomy fluent in the discipline –in order for it to become somewhat intelligent and begin anticipating what I want — it will need its own grammar or syntax (one that it will share with anyone fluent in this discipline). In this way, for example, a Spanish instructor cold present the object library with his/her syllabus and it would automatically know what was needed to create a course based on that syllabus. This grammar or syntax for the taxonomy, of course, is what we call an ontology (with classes, individuals, and properties).

But we will need more than an ontology. We will need to provide a way for our object library to get some experience in the real world. I mean, it’s one thing to learn this education language out of a textbook, but how do people really use it? We will also need to give our library the ability to “practice” or do its own modeling, and to receive, interpret, and apply feedback based on its models.

The ontology will get us part of the way but we’ll also have to apply some specific technology (algorithms) and information architecture (metrics) to reach the final goal.

This discussion, along with the information in our first article, should provide us with an adequate base for jumping into a discussion on complexity tomorrow. From there we will talk specifically about ontologies and who should or gets to define the rules (and how those actually work).