ADE is a delivery platform for adaptive educational hypermedia (adaptive e-learning) which I’ve been developing as part of my PhD at the Intelligent and Adaptive Systems group at the University of Warwick.

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Basic strategy that makes all concepts visible.

LAG File | CAF File | ADE Demo

RollOut

The roll-out strategy does two things: the more times you visit a (sub)lesson, the more of its contents (attributes) become visible. At the same time the opposite is also possible: the more times you visit a (sub)lesson, the more of its contents become invisible. This is governed by weight (number of visits) and label (either showafter or showatmost for content that needs to appear or disappear respectively). The keywords strategy is a special version of the same principle, which only uses labels (number of visits is always 1, and the content is always revealed, never hidden)

LAG File | CAF File | ADE Demo

Visual / Verbal

Concepts are either unlabeled (general) or have label ‘visverb’ with a weight indicating where on the vis/verb axis they lie. Visual concepts have weight nearer 0 and verbal concepts have weight nearer 100. The user has a stereotype ‘visverb’ which is also a number between 0 and 100, with the same meaning (0=visual, 100=verbal user). The user selects a stereotype by visiting special ‘settings’ concepts; the weight of the concept becomes their new visverb number.

A variable in the UM defines a threshold. visverb concepts are visible if and only if abs(weight-stereotype)<=threshold. So it’s like a band filter (this makes it more appropriate to knowledge levels, I guess). General concepts are always visible and have no effect on the adaptation.

LAG File | CAF File | AHA! Demo

Beginner, Intermediate, Advanced

This is an attempt at combining all four of the above strategies, without resorting to labels that are cartesian products. Using Cartesian products a label A-B-C is treated as three labels A, B and C respectively… which one the weight applies to is harder to define, although I guess one could split the weight into component numbers (like units vs tens) that can be separated out again. I don’t really think that’s very elegant or robust though.

So the idea was to have labels/weights be inherited by child lessons unless explicitly overruled by their own label/weight. (This way a concept can indirectly get multiple labels). These labels are all stored in an overlay that is then used instead of the GM labels for the adaptation rules. This is basically a workaround for a MOT shortcoming. Unfortunately the inheritance idea doesn’t work because of the way group lessons are handled by LAG (their labels and weights can’t be inspected). The strategy is included for ‘educational’ purposes, since the principle itself is sound.

LAG File | CAF File | ADE Demo

Type Based

This strategy shows concepts based on their type. This particular implementation only shows concepts of the type text and doesn’t show the other concepts.

LAG File | CAF File | ADE Demo

Relation Based

After seeing all beginner concepts sets the user to intermediate and shows the related concepts

LAG File | CAF File | AHA! Demo

Depth First

This strategy shows the concept labelled as start first, then the rest of the content in a depth first manner using the child-parent relations

LAG File | CAF File | ADE Demo

Breadth First

This strategy shows the concepts in a breadth first manner. It uses a user model variable called level which determines which level in the hierarchy gets shown.

LAG File | CAF File | ADE Demo

That’s the last of the University Society websites I’ve been working on this summer.

Slides

Source

This one lasted about 10 minutes and Jonny got stuck in the lift for the duration!

The picture is of my first shot from the 13th hole at Brailsford on the 23rd. Unfortunately my birdie putt stopped short so I could only manage par, however as that’s a rare occurrance at the moment I’m pretty happy with it!

Here’s the abstract in full:

Adaptive Educational Hypermedia offers a more intelligent and customised approach to the field of e-learning then the traditional linear methods. In an Adaptive Educational Hypermedia course a student’s learning experiences can be personalised using a user model which could include such information as the student’s knowledge level or learning style.
Adaptive strategies are sets of adaptive rules and once the learning material to be used in a course has been created, adaptive strategies can be applied to allow the personalisation of the course to students. This talk will describe the initial development of a software platform which imports learning content and adaptive strategies and then can display the resulting Adaptive Educational Hypermedia course to students. The software platform, called an adaptation engine, will be able to implement different types of adaptive strategies and test their suitability.