BALANCING ADAPTIVE CONTENT WITH AGENTS

Modeling and Reproducing Group Behavior as Computational System

Harri Ketamo

Satakunta University of Applied Sciences, Pori, Finland

Keywords: Adaptive systems, User modelling, Software agents, Artificial intelligence, Social behaviour.

Abstract: To ensure the quality of adaptive contents, there should be continuous testing during the development phase.

One of the most important reasons to empirically test the content during the development phase is the

balance of the adaptive framework. Empirical testing is time-consuming and in many cases several iterative

cycles are needed. In 2007 we started to develop methods of testing in a computational test bench. The idea

to speed up the production process was based on software agents that could behave like real user

community. The study shows that we can construct very reliable artificial behaviour when comparing it to

human behaviour in group level. On design phase’s usability tests, we are especially interested in group

behaviour, not on single action etc., which means that the method suits for it’s purposes.

1 INTRODUCTION

Adaptation can be seen as being high end

personalization: In adaptation, the system optimizes

the output with technologies that, in general, can be

divided into two main groups: static (indirect)

adaptation and dynamic (direct) adaptation. In static

adaptation the rules are fixed beforehand by

developers. In dynamic adaptation the system tracks

the user and optimizes the navigation paths

according to the user's behaviour. Dynamic

adaptation of system requires at the least a user

model, a context model and artificial intelligence.

(Kinshuk, Patel & Russel 2002; Brusilovsky, 2001;

Manslow 2002).

Because the idea of adaptive educational systems

is to produce individual and optimized learning

experiences the high end user models as well as

methods are relatively complex (e.g. Raye, 2004;

Lucas, 2005). Furthermore, the social dimension

should not be forgotten: In very large samples, the

most successful outcomes in group level may

contain valuable guidelines for adaptation.

Before we can ensure the quality of the

adaptation, at the least we have to know if 1) the

adaptive framework is in tune, 2) is there out layered

content and 3) are the learning paths really unique?

It is possible to theoretically check the first three

questions by constructing an algorithm that

computes all the possible combinations, but there

will be no such system that can compute the problem

in a reasonable amount of time within large

domains.

Another solution to this is to model human

behaviour as a system and then run the learning

material using these artificial users. In our study the

behaviour of software agents are constructed

according to social behaviour: We have constructed

several archetypes of a user by clustering the

behaviour of human users.

2 RESEARCH TASK AND

PROCEDURE

2.1 Research Task

In this study, the adaptation is studied in terms of

Complex Adaptive Systems: self-organization,

entropy and emergence. These concepts describe and

define the high level system properties. In a

computational system that aims at simulating group

behaviour, focusing on high level phenomena might

incur the best outcome.

Earlier results, received from preliminary studies

of the application (e.g. Ketamo, 2008) are referenced

in this study. The computational core is based on the

author's previous work that had been applied, for

example, as the background of an educational game

291

Ketamo H.

BALANCING ADAPTIVE CONTENT WITH AGENTS - Modeling and Reproducing Group Behavior as Computational System.

DOI: 10.5220/0002781502910296

In Proceedings of the 6th International Conference on Web Information Systems and Technology (WEBIST 2010), page

ISBN: 978-989-674-025-2

series (e.g. Ketamo & Suominen 2008; Ketamo

2009).

2.2 Material in Testing

Mathematics Navigator is a product family

distributed by Otava Publishing Company Ltd..

Mathematics Navigator is based on an adaptive

content management system (Figure 1) and content

objects. Mathematics Navigator is a high-end

platform designed to produce dynamically adaptive

and self-organizing content. Dynamic adaptation is

done by varying the sequences of course elements

(bits of theory, examples, exercises) and supplying

individual learning paths for the students. In this

study Mathematics Navigator is used as a test and

evaluation platform.

Adaptation of content is done according to a

user's / student's learning and studying behaviour.

This kind of modelling requires at the least 1) a user

model that records the skills and learning of each

user and 2) a domain or context model on the

relations between the bits of information of learning

content. Mathematics Navigator gathers information

on a student's actions and, based on this information,

creates and modifies an understanding of his/her

mathematical competence. Mathematics Navigator

adjusts the exercises and content to support the

development of students. On the user interface

(Figure 2), a student has a graphical representation

of the development of his/her learning profile.

Figure 1: The high level architecture of Navigator.

The user interface of Mathematics Navigator is

based on a menu-bar and three main areas (Figure

3). On the left side of the interface is a table of

contents and a content-related competence profile of

the user. The table of contents presents two different

views of the content: 1) a traditional book-like table

of contents and 2) an exercise-adapted table of

contents.

The exercises are presented one at a time in the

bottom-right corner of the interface. The user can't

proceed to a new exercise before the current one has

been answered by picking an answer from a total of

4 alternative answers. The exercises are selected to

support an individual user's learning needs. There

are no fixed paths for learning: everything is based

on the student's competence profile and estimated

need for practice and content. Guiding factors in

exercise selection are: 1) course structure (a

traditional table of contents), 2) the measured and

estimated learning abilities and areas of weaknesses,

and 3) critical points, derived from the learning

community's actions.

The competence profile values are indicated by

colours that vary from red (insufficient skills or

skills not yet estimated) via yellow to green (good

skills). Those skills mastered and measured within a

certain theme will be transferred with certain

estimates to other themes requiring similar (by

proximity or by hierarchy) skills.

Figure 2: The user interface of Mathematics Navigator and

a basic mathematics course (in Finnish).

The entire content - theory, examples and exercises -

is based on the idea of content objects. A content

object is a unit containing the smallest possible bit of

content that can be used independently without the

support of other content objects. Naturally, products

are based on content objects that strongly support

one another. The content objects are described by 1)

detailed rank ordered keywords (tags) that define the

structure of content and 2) single relations between

objects in a preferred order. The preferred order is

calculated as a state of semantic network, formed

according to tags. The method can be described in

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general in terms of weighted proximity between

objects (Figure 3).

Figure 3: Defining proximity between the content objects.

The calculated proximities between the content

objects can be visualized as weighted graph (Figure

4) which basically visualizes the whole idea behind

the content model.

Figure 4: Calculated proximities between content objects

visualized as weighted graph.

This kind structure and content model offers

numerous possible paths through the material. A

special feature of the modelling is the capability of

self-organization: The clusters inside the material

were self-organized according to mathematical

definitions and context. This self-organization is a

key feature of Mathematics Navigator and it

remarkably reduces the costs of producing adaptive

content. In this study the aim is to reduce the costs

of empirical testing, but we can find similarities in

information modelling between Mathematics

Navigator and the test method.

In Mathematics Navigator, a learner's

performance can be estimated in relatively complex

domains with very detailed and extensible user

models, combined with a learnable system. Of

course, this kind of modelling takes some time: the

system must learn the performance level of the user.

Empirically formed decision trees were used when

the system had not yet constructed a detailed enough

profile about the learner in order to dynamically

adapt the content and exercises to his/her learning

needs.

Empirical testing of such systems takes a lot of

time and in many cases iterative cycles are needed

that incur new time costs on development.

2.3 Procedure

This study is a design study with actions like the

design of the system, implementation of the system,

empirical testing and evaluation. The design and

implementation of the system was done in the

autumn 2007-winter 2008 and the design was

revised during the summer 2008 as a consequence of

pre-tests.

Empirical testing (n=447) was done in two

phases: the data from human users was collected

during the content development projects of

Mathematics Navigator in 2005-2008. The data

collected during those studies was meant to support

the development of those specific projects.

However, the data was found to be complete enough

to serve as empirical data for this study. This

existing data makes it possible to run tests while the

work was in progress.

The empirical testing reported in this study is

based on the autumn 2008 -version of the system.

Tests were run for up to 1000 artificial workers, but

in most cases the system found its balance using less

than 100 artificial workers. The limit of the test

sample size is defined by an estimator meant to

measure the possibility of changing the visualization

structures (Figures 6-8). In fact, the testing costs

would not be higher even if we run the tests using

millions of artificial workers. Naturally, the need for

computing time would have increased.

The system is implemented with software agents

using the same application interface as the host

system, which means that they can act as human

users. The host system that they are logged in does

not recognize which connections are from artificial

users and which are from human users. This method

requires that the user interface should be described

in detail. This description focuses only on

interactions, not on visualisation. Because

Mathematic Navigator is a Web Service, the user

interface was defined with SOAP and WSDL and no

further definition for this study was needed.

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3 RESULTS

In this study, artificial users represent archetypes of

human users. Artificial users are based on complex

modelling which means, that there are several

archetypes of users as well as variance inside the

archetypes. The common denominator for all is that

everything is based on human user- and group

behaviour.

In the first step, data collected from human users

is used to construct a model about behaviour in

Mathematics Navigator. This model is formed from

a group behaviour point of view. The key elements

in the model were patterns of UI actions and

particularly the probabilities and uncertainties of

following action according to observed patterns. In

the model the averages and variances were not the

key point. In fact the modelling is not statistical at

all: The model aims to predict behaviour, not to test

any hypothesis. The patterns of behaviour were

clustered in order to build the archetypes of users,

which increases the similarity of artificial users to

human users.

One interesting find during the pre-tests was that,

when increasing uncertainty in decision-making

(increasing variance in decision in statistical terms)

up to specified limits, the system could point out

possible problems in a shorter time. This finding is

relatively straightforward: Because we are not

looking for statistical evidence, we can focus on

pointing out problems without proving them

statistically. This helps us in two ways. Firstly, the

computational requirements were decreased.

Secondly, the estimator, calculated to detect when

the simulation is ready, could be used in a more

reliable way. The challenges of avoiding behaviour

that never happens with human users are great. On

the other hand, we never can be sure how users

behave in digital environments. At this point, we

have to accept the fact that we might construct

agents that fail when compared to human users'

behaviour.

As a result of modelling, a finite state system

with a structure equation about action probabilities,

uncertainty and disorder was formed. In Figure 5

one part of the model behind the decision-making

system (probability network) of the artificial user is

visualized. User and context modelling was solved

technically by constructing a dynamically extensible

Semantic Networks. The user model could be

exported e.g. as an XML Topic Map and could be

manipulated by xPath.

Figure 5: Adaptation schema as a system model.

The three key elements of Complex Adaptive

Systems are self-organization, entropy and

emergence. All of these can be found in the

visualized learning paths of Navigator's users

(Figure 6). Similar results could be achieved when

the artificial user's behaviour is visualized (Figure

7). In the figures the paths are formed by connecting

content objects browsed during use of Mathematics

Navigator. In the figures the nodes are essential

pieces of content that can be defined as being 'the

backbone of the domain'. The connectors show the

paths that users had passed through. When reading

the figures, the assumption is that there were

numerous connectors between nodes, but some

directions or patterns are more frequent than others.

In terms of Complex Adaptive Systems, this

systematic organization of connectors pointed out

from disordered can be called emergence. In Figures

6 and 7 only connectors that are stronger than

average are presented. Learning paths and

progression can be read from left to right. In Figures

6-8, only the most significant paths are visualized. If

all paths were to be visualized, the figures would not

be readable at this scale.

The most important finding is that the

visualizations remain the same when comparing a

human user's visualization and visualizations

constructed according to the behaviour of artificial

users.

Naturally there are differences: artificial user's

visualization (Figure 7) is rougher than a human

user's visualization (Figure 6), because it is based on

archetypes.

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Figure 6: Emergence formed by a human user's learning

paths.

Figure 7: Emergence formed by artificial user's learning

paths – current version (partial visualization in order to

show similarities to visualization in Figure 6).

However, during the development of the

algorithm, the roughness of the group behaviour of

artificial users has decreased (compare to figure 8).

When in pre-test version of artificial users (figure 8)

there were relatively few strong navigation paths, in

current version (figure 7) the visualization of

behaviour remains more versatile.

The visualizations indicate that, at a general

level, human users and artificial users cause similar

emergences and as a group they behave in a similar

way. However, this similar behaviour cannot be seen

at an individual level. We have to focus on group

behaviour at a very general level.

Figure 8: Emergence formed by artificial user's learning

paths – pre-test version (partial visualization).

From the product development point of view, the

question is about how modelling and visualizations

can be used is to check if the framework is in tune.

One common problem caused by the complexity of a

system is visualized in Figure 9: The definitions of

the content objects construct a framework that can

point out a 'local minimum' on competence for an

unexpectedly long time. In visualization this is seen

as a connector back to the object itself.

Figure 9: Problems with content: the framework is not in

tune.

Figure 10: Unexpected, but correct re-organization of the

content.

Another example that could sound like a problem

is visualized in figure 10, where we can see a strong

parallel navigation patterns. At first it seems an

unexpected behaviour of the system. In fact, in the

visualization we can see a refresh path, organized for

those of the artificial users whose mathematical

skills were not developed enough during the course.

The refresh path was launched by 2-5 last exercises

in the course.

Artificial users cannot correct such problems but

authors of the content can check and improve the

definitions of content objects. In most cases such an

event is fixed by adding relevant keywords into the

material.

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4 CONCLUSIONS

The aim of the study was to construct a method of

reducing time and workload costs when developing

new courses in the Mathematics Navigator platform.

The main questions to be studied computationally

were the following: 1) Is the adaptive framework in

tune? 2) Is there out layered content? and 3) Are the

learning paths really unique?

One of the efficient solutions was artificial users

that represents archetypes of human users. The

artificial users was implemented as software agents

using the same application interface as the host

system, which means that they can act as human

users. The host system that they are logged in does

not recognize which connections are from artificial

users and which are from human users.

Questions 2 and 3 could be studied in detail:

Learning paths are relatively unique but not

randomized. There is clear emergence as well as

remarkable entropy in paths formed by human users

and artificial users. Also, out layered content can be

noticed easily. However, Question 1 (Is the

framework in tune?) was challenging: Being in tune

is not only a computational task, it also deals with

curriculum. In the current system, the curriculum

was not in focus and therefore we could not say that

the framework is in tune according to the

curriculum. At best we can say that the framework is

very probably in tune in a solution specific

mathematical context.

In general, the studies show that we can

construct similar group behaviour between human

and artificial users and the saved resources in

development can be significant. In terms of

resources, the development project's savings are

estimated to be as high as 2-4 months of a

developers work and 2-3 months of total time in

project schedule.

The next phase of the study is to apply the

method to other relevant contexts, for example in

game development or in applications of social

media. The game development applications could

use relatively similar mechanics described in this

study. However, social media has brought an

increasing need for intelligent agents, that could

search and pick the most important pieces of content

out of the enormous information overload.

Teachable software agents could be used as

personal media readers that could pick up those

personally meaningful messages. Furthermore,

behaviour on reading messages is relatively close to

navigation behaviour in general level: In both cases

there are individual behaviour patterns and what is

different between individual behaviour and

normal/average behaviour explains something from

the goals of the behaviour.

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