ADAPTIVITY IN MOODLE BEYOND THE LIMITS

OF ADAPTIVITY IN MOODLE

Klaus P. Jantke and Andr´e Schulz

Fraunhofer IDMT, Childrens Media Dept., Hirschlachufer 7, 99084 Erfurt, Germany

Keywords:

Technology enhanced learning, e-Learning, Didactics, Learning management systems, Moodle, Adaptivity.

Abstract:

Learning management systems (LMS, for short) have meanwhile gained an enormous practical relevance.

Nevertheless, the quality of service offered is, at least in some respect, astonishingly behind the state of the

art. Adaptivity of system behavior is such a ﬁeld in which we conﬁne ourselves to much less than we can

afford. The authors develop an original approach, demonstrate an implementation, and discuss the beneﬁts

for learning of an adaptivity approach which is integrated into the LMS moodle although it apparently goes

beyond the limits of moodle. The approach is sufﬁciently generic to be carried over to other LMSs.

1 INTRODUCTION

As early as in 1957, Cronbach has directed the atten-

tion of educators to adaptivity in setting the goal “not

to ﬁt the average person, but to ﬁt groups of students

with particular aptitude patterns” (Cronbach, 1957).

In harsh contrast, half a century later the state of

affair in technology enhanced learning is still rather

unsatisfactory.

Beyond the horizons of contemporary computer-

supported education, the personalization of products

and services is a quite obvious tendency permeating

nearly all domains of the modern industrial society.

For already a few decades we all are used to the

conﬁgurability of products and services. When you

buy a new car, for instance, you are facing almost un-

countably many decisions about details you can con-

ﬁgure according to your wishes and desires. When

you book your holiday trip, e.g., you can change and

expand the program in numerous ways. This type of

conﬁguration is usually decided about quite a long

time before you get your product or service. As soon

as the product or service, respectively, relies on some

computerized system at your ﬁngertips, usually, you

can set up details immediately prior to usage. Setting

the level of difﬁculty when playing a digital game or

choosing your seat when checking in online or at a

self-checkin terminal in the airport are just two almost

trivial examples of adaptability. But we all know that

we can go even further–computerized systems bear

the potentials of doing the adaptation for us. Instead

of tuning a system according to our needs and desires,

a computerized systemcan tune itself, i.e. be adaptive.

Adaptivity is an established ﬁeld of research and

applications in Artiﬁcial intelligence (AI).

So far, what the authors have been summarizing

above applies to products and to services, in general.

And what about technology enhanced learning ... ?

In proprietary e-learning systems such as the data

mining tutor DaMiT which has been implemented and

used around the turn of the millennium–adaptivity is

well established and has been proven to be effective

(Jantke et al., 2004a; Jantke et al., 2004b).

But as soon as we must rely on standardized

and widely used platforms such as the LMS moodle,

e.g., our dreams about adaptivity and personalization

rarely come true. Those systems are much too rigid.

There are a few obvious reasons for the current

state of affair. Adaptivity requires a ﬁner granularity

of learning objects, a larger number of variants, and

suitable object annotations (Memmel et al., 2007).

To say it brieﬂy, adaptivity is demanding and costly.

These difﬁculties have been recognized and, in re-

sponse, elaborate endeavors such as the GRAPPLE

project

have been launched. In contrast, the present

paper is aiming at a generic light-weight approach.

http://www.grapple-project.org

418

P. Jantke K. and Schulz A..

ADAPTIVITY IN MOODLE BEYOND THE LIMITS OF ADAPTIVITY IN MOODLE.

DOI: 10.5220/0003341904180421

In Proceedings of the 3rd International Conference on Computer Supported Education (CSEDU-2011), pages 418-421

ISBN: 978-989-8425-49-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

2 EXPECTATIONS OF ADAPTIVE

SYSTEM BEHAVIOR

The authors expect the whole CSEDU audience to be

knowledgeable in the problem area of adaptivity and

personalization. Consequently, the introduction may

be kept short.

Figure 1: Two alternative variants of a deﬁnition as they are

in use in the data mining tutor DaMiT (Jantke et al., 2004a).

The two frames on display in ﬁgure 1 show two

variants of deﬁning the quite involved concept of a

“decision tree on regular patterns” within the data

mining tutoring system DaMiT–a proprietary system

for technology enhanced learning that does not rely

on any prefabricated platform software.

The upper variant of the deﬁnition is seen as the

informal one which is quite wordy and contains only

those formal symbols that are inevitable.

The other variant is seen as the more formal one

which offers more concise deﬁnitions and abandons

the appearance of continual text.

The expectation of an adaptive system in the ﬁeld

of technology enhanced learning is to present the one

or the other variant autonomously in accordance to a

human learner’s needs, desires, and wishes.

An implementation of adaptivity requires, at least,

three things:

• the acquisition of knowledge, i.e. modeling

about the pecularities of an individual human

learner (see (Brusilovsky, 2001), in particular),

• content which is prepared for varying system be-

havior as described in (Memmel et al., 2007), e.g.,

• functionalities implementing adaptivity.

The authors support the trend from proprietary

systems such as DaMiT (see above) toward standard-

ized systems used world-wide such as moodle, e.g.

This perspective has been motivating their research

and development reported in this paper.

There is a whole journal named “User Modeling and

User-Adapted Interaction” providing paramount sources:

http://www.springer.com/computer/hci/journal/11257

3 NEEDS IN THE LMS MOODLE

The authors are employing the LMS moodle for

courses on Theoretical Computer Science (TCS) and

on Artiﬁcial Intelligence (AI).

These particular courses have the peculiarity of

incorporating rather new and advanced technologies

such as so-called Webbles as described and discussed

in recent publications such as (Fujima et al., 2010)

and (Jantke and Fujima, 2010).

In the TCS course, webbles are used to model

rules of formal language grammars (“rubble” is short

for rule webble). The derivations of formal language

expressions are realized by plugging webbles together

as shown in ﬁgure 2. When webbles are plugged to-

gether, they perform the derivation by themselves.

Figure 2: Screenshot of moodle at work; rubbles are used as

building blocks for formal grammars `a la Noam Chomsky.

Figure 3: Cutout of a particular moodle group database.

To novice learners, Webble technology, in general,

and rubbles, in particular, are unfamiliar. There is a

need to enrich moodle content by guiding texts which

explain how to operate the objects on the screen.

However, guiding text next to the work space has

also several, at least, potential disadvantages such as

• occupying space which is always scarce,

• extending the cognitive load on the learner,

• diverting the learner’s attention, and

ADAPTIVITY IN MOODLE BEYOND THE LIMITS OF ADAPTIVITY IN MOODLE

419

Figure 4: Change of the visibility attribute (see last row in every table) in response to a learner’s interaction with the system.

• hindering the learners to memorize essentials.

It is ultimately desirable to interact with a work

space as in ﬁgure 2 where text is suppressed as much

as possible (for a debate, see (Kirschner et al., 2006)

and (Kuhn, 2007)).

The two authors’ didactic approach is as follows.

Guiding text shall be available as long as necessary

and appreciated by the learner. As soon as the learners

can manage to complete their tasks without looking

for and at the guiding text, the text shall be dropped.

If necessary lateron, learners shall get guidance back.

But unfortunately, such a dynamic, i.e. adaptive,

behavior is far beyond the limits of moodle.

4 ADAPTIVITY IN MOODLE

The authors have been pondering ways of going with

moodle beyond the limits of moodle. The title of the

paper shall shade some light at the intended quality:

to provide adaptivity by means of moodle which goes

beyond what moodle has been designed for.

Different from the ambitious GRAPPLE approach

(Abel et al., 2009; van der Sluijs and H¨over, 2009),

the authors aim at a light-weight solution which may

be realized by means of a few lines of code.

A ﬁrst idea is to use the grouping functionalities

of the LMS moodle to implement adaptivity (ﬁg. 3).

In detail, this works as follows. In dependence

on a learner’s activities when using the system, a

script writes into the group database and changes the

learner’s group membership dynamically.

The underlying didactic principle is quite obvious.

One assumes that, at least for the concrete course and

for the audience under consideration, it makes sense

to determine learner stereotypes. One may follow

David A. Kolb’s learner types, for instance, (consult

(Kolb and Fry, 1975) and (Kolb, 1984)).

Under the assumption that some learner model has

been chosen, one may group the content variants for

every stereotype appropriately. Learners who belong

to a particular group get content provided in a related

form assumed to be suitable for this learner type.

The art of adaptivity is now to reassign learners

to groups in response to their individual behavior and

success in the course of learning. This is the authors’

ﬁrst approach to adaptivity introduced in the present

paper.

The second idea is to change the content available

to particular learners or learner groups dynamically.

For this purpose, visibility attributes are set as shown

in ﬁgure 4.

Seen from a more general point of view, the two

authors’ idea is to slightly expand the operational

interpretation of some learner activities in different

ways. Basically, particular activities of the human

learner trigger scripts that write into the one or the

other databases of the moodle system as visualized in

the ﬁgures 3 and 4. PHP scripts such as the one on

display in ﬁgure 5 are invoked to update database en-

tries such as group membership attributes or visibility

attributes of learning objects, e.g.

Figure 5: Cutout of a PHP script to implement adaptivity.

The reader may imagine some link which in re-

sponse to a learner’s click does not only, as before,

opens a new page or loads some ﬁle, but sets some

attribute in a certain database, additionally. The script

CSEDU 2011 - 3rd International Conference on Computer Supported Education

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syntax is quite lucid and uniform, thus, being generic.

Based on those simple ideas, even the extremely

rigid moodle system can be made to appear adaptive.

5 SUMMARY & CONCLUSIONS

According to Cronbach (Cronbach, 1957), it is old

hat that good didactics necessarily include efforts of

adaptation to the human learners’ peculiarities (see

also (Salomon, 1972)). This is sound with numer-

ous contemporary insights into technology enhanced

learning and its foundations (see, e.g., (Davis et al.,

2000), (Flechsig, 1996), and (Jank and Meyer, 2002)).

But the opinions about the appropriate degree of

learner guidance–and, thus, of adaptation–are divided

(Kirschner et al., 2006; Sweller et al., 2006).

ACKNOWLEDGEMENTS

The Webbles in use within the authors’ courses on

TCS and AI, respectively, have been provided by their

colleague and friend Jun Fujima.

The underlying Webble technology is a certain

contemporary version of Meme Media as introduced

and developed by Yuzuru Tanaka (Tanaka, 2003).

This work has been supported by the Thuringian

Ministry for Education, Science, and Culture within

the project iCycle under contract PE-004-2-1.

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