Screamer them, Nifty!

It is well-known that a profitable strategy for getting funding from JISC is to think up a great title and then build a project round it. My top ten JISC project names countdown.

10.  SKOS-HASSET.  A Swedish processed meat project?

9.  SWORD-ARM. Oh, the silly billies.

8.  Bricolage.  Tres chic; trust the librarians.

7. AstroDAbis. This is a condition you get after staying up all night looking through a telescope.

6. SupOERGlue. And you need superglue to hold this contracted lexical acronym sandwich together.

5. BeRT.  Salt of the earth, they are, at Brockenhurst. (See ADAMS for middle-class version.)

4. Walking Through Time. Good wholesome stuff.

3. Blacklight in Hull.  Very sinister.

2. Bebop. Good oblique referential stuff.

1. Saving Private Data.  A while ago now - simply broke the mold.

And JISC itself gets a special award for naming a programme SWaNI. Guts.

All this tosh was inspired by the fact that I presently have a project to name. The possibilities are just too dire for some witty word play. The baseline is Chemistry Semantic Frame Network. CSFN? Do me a favour. ChemNet? Been done. SemChem? Sounds like an explosive. ChemSem? Naah. NetChem. It's just not there.

How about a bit of Latin? Reticulum for network. RetChem. ReticuChem. Chemiculum...well.. naah.

If shuffling the words around is not doing the trick then perhaps shuffling the letters will do better. So I put Chemistry FrameNet into an anagram machine to produce 3-word candidates. The results were superb.

Here's my top 5 countdown.

5. A Chemistry Ferment. Bit too sensible.

4. Ferryman Chest Mite. Nasty.

3. Amethyst Ferric Man. Very chemistique.

2. Cashmere Ferny Mitt. A bit sado-masochistic.

1. Mean Mythic Ferrets.

Has to be.

Syllabubs and Syllabi

One of the two kungkhies sub-projects is Canonilo, a software application for the production of formalised representations of ILOs from natural language versions in specification documents.

ILO forms are first recognised using regular expressions, then modelled using methods from lexical semantics.

In order to aid the correct determination of the meaning of an ILO, a natural language parser is used to identify constituent phrases and clauses.

Stripped of the introductory clause, like After completing this module a student will be able to..., ILOs take the form of imperatives (orders): Discuss this or Explain that, for example. The verb might be modified by some adverb or adverbial phrase.

It is interesting to note that another domain in which there are similar constructions for conveying information is - kitchen recipes!

State Boyle's Law and use it in simple calculations.
Chop the filet mignon and add it to the skillet.

It is also interesting that there seems to be have been more research into the natural language processing of recipes than of ILOs. Let the cooks show the way.

For example, there is an article that describes the use of semantic role labelling to extract meaning from recipes [1].

There is also a computer cooking contest:

The goal of the CCC is attract new people (e.g., students) to work with AI technologies such as case-based reasoning, semantic technologies, search, and information extraction.

One of the distant aims of the formalisation of ILOs is the comparison of their expression in different languages.  Very Bolognese.  It is a little known fact that the Hungarians do not use the imperative mood when writing recipes. They use the first person plural: We chop the filet mignon and add it to the skillet.

I wonder if the same is true of the Hungarian ILO?

We state Boyle's Law and use it in simple calculations.

We are amused.


1. Agarwal, R. and Miller, K., Information Extraction from Recipes,  http://nlp.stanford.edu/courses/cs224n/2011/reports/rahul1-kjmiller.pdf

The Three Pathological Flaws of the Learning Object

After the establishment of object oriented design in the software realm, the concept of the learning object was inevitable.  The idea of assembling learning objects to produce educational programmes  in the same way that computer programmes are assembled from software objects was irresistable.

However, there are three pathological flaws in the concept of the learning object that pre-ordained its ultimate failure. 

The first is that, unlike software objects, which can call methods on other objects in a standard manner, there is no corresponding architectural assembly mechanism with which to build complex learning objects from simpler ones.

The second arises from the fact that a learning object is static at the highest definitional level (although it might contain inner processes), making it impossible to use to model processes directly. Teaching and learning are processes and have to be modelled using processes.

The third is that the use to which an object can be put is not an intrinsic property of the object itself. It depends on context.

The concept of paradata has been introduced to solve this third problem.  If one takes the view that this can save the patient then the flaw might be non-pathological.  However, if the cure is to introduce a whole new descriptive data layer, then the patient might live, but with what quality of life? (This grants that the patient might be able to die with the other two other conditions rather than die of them.)

Kungkhies are based on intended learning outcomes and activities.  In this formulation, paradata becomes redundant. The functionality of paradata is assumed by the intended learning outcomes of each activity.

Of course, in formal education intended learning outcomes already occupy a central position. Despite this, there has been little theoretical development of the concept.  The theory behind the kungkhie platform offers several developments in this area: a canonical formulation of intended learning outcomes, a propositional logic of intended learning outcomes, and the idea of pedagogically complete sets of intended learning outcomes.  These developments form the basis on which machine learning techniques can be used to analyse and organise intended learning outcomes.

Ah, you say, an intended learning outcome is heavily dependent on context and thus suffers from the same pathological flaw as the learning object.

Ah, I say, the intended learning outcome is presented within its own process-centred standard wrapper context - the kungkhie.

The logic and canonicalisation of intended learning outcomes will be presented later.

Introducing kungkhies

This is a companion blog to the open source kungkhies project on Google Code.

The Hollywood billing for kungkhies is a new way to teach, a new way to learn and learning design meets the wisdom of crowds.  

In a nutshell, kungkhies starts from learning design pared down to the bare minimum.  A kungkhie document defines a course of instruction essentially in terms of intended learning outcomes.  So far, so ho hum.

The empty kungkhie, however, contains no teaching activities.  These are to be supplied by the kungkhers, those who kungkhie (verb intransitive).

Each kungkhie activity is identified and located by a URL.

Once an empty kungkhie has been supplied with activities it becomes a preference kungkhie which is then uploaded to cloud kungkhie-base.

The kungkhie platform then analyses the preference kungkhies to produce information on the most favoured activities and recommendations for future kungkhers.

More information can be obtained from the Google Code kungkhies wiki.

Just for completeness, or redundancy, there is a web site, too.

There are various flavours brewing of this type of approach to social media learning and time will tell if the kungkhie can occupy a unique place in the canon.

However, kungkhies might sink, or kungkhies might swim, but there's one thing for sure - no other project will have a better name.