This article provides an introduction to the theory on which the kungkhie learning design platform is based. First a simple predicate calculus of intended learning outcomes (ILOs) is presented. Superstudent is introduced, a perfect reasoner. Then the kungkhie is defined as a bipartite directed graph formed by a set of cognitive hole nodes, activity nodes and connecting arcs. The simplest kungkhie is provided as an example. Aspects of kungkhie validation related to ILOs are discussed. Definitions of distributed knowledge and implicit knowledge best suited for the development of theory related to teaching and learning in this context are given. The relationship between distributed knowledge, implicit knowledge and cognitive holes is briefly considered.
The kungkhie aims were to produce a system of learning design that would be both simple to use from the points of view of teachers and learners, and also amenable to computer modelling and management.
Success in the former aim is helped by the simplicity of the kungkhie schema and the resultant straightforwardness of user interfaces.
Success in the latter aim will follow largely from the implementation of the theory presented in the Kungkhie Logic document.
The kungkhies platform is a meld of formal teaching and social learning. The formality arises from learning design: a set of learning activities along with guidance on the order they should be carried out.
The society arises from the ability of teachers and learners to pick, choose and publish their preferred activities for a particular kungkhie. The data on preferred activities is then used to produce popmax kungkhies, populated with the most popular activities, and recommended kungkhies, produced by The Kungkhommender, based on kungkher preferences.
To intelligently manage a learning design system one must have a handle on intended learning outcomes (ILOs). So how to formalise ILOs? At first reckoning the task might seem too complex, even with the aid of modern natural language processing software. It would take too long to try to create formal structures for each type of ILO - define, describe, explain...
But, someone else has already done the hard yakka.
It is Charles J. Fillmore and his FrameNet team at Berkeley. Their network of semantic frames provides formal structures for the ILOs. The formalisation process is then a combination of using regular expressions, natural language processing and artifical intelligence methods to produce representations of ILOs as semantic frames from an analysis of real world ILOs from specification documents.
Of course, the formalisation will not be perfect, but initial work seems promising. A description of the formalisation of ILOs will be presented in another article. The software behind the formalisation is to be found at Canonilo.
The potential rewards of intelligent ILO recognition are very great indeed.
Kungkhie Logic finishes with some remarks relating kungkhies to ideas from the computer science of knowledge representation. It is probably fair to say that there has not been a lot of linkup in evidence with the theory of human teaching and learning. Kungkhies can contribute here by providing formal representations of such things as perfect reasoners, distributed knowledge and implicit knowledge.
Go kungkhies!
