USING CASUAL GAMES AND ONTOLOGIES TO TRAIN

UNSKILLED WORKERS

An Experience with the FoodWeb2.0 Platform

Nils Malzahn, Sabrina Ziebarth and H. Ulrich Hoppe

Department of Computer Science and Applied Cognitive Science, University Duisburg-Essen, 47048 Duisburg,Germany

Keywords: Web2.0, Collaborative Learning, Games with a Purpose (GWAP), Ontology Enhancement.

Abstract: Training and continuing education in and for the food industry face the challenge of reaching and

motivating workers without or with only a low level of formal qualification. In the FoodWeb2.0 project a

platform with specific support for training opportunities and selected Web2.0 enabled courses has been

established. This paper reports on first results of a Web2.0 course conducted on the platform and elaborates

on the usage of a casual game for training knowledge on food safety and hazardous material regulations.

Since the tentative results show that the students are motivated to influence the content of a course by

commenting provided material, we designed the casual game in such a way that it can be used in two

modes: as a learning game and as an interactive mechanism for ontology enrichment. Our evaluation at a

private professional training academy has shown promising results.

1 INTRODUCTION

The food industry in Germany is characterized by a

high amount of workers without or with only a low

level of formal qualification. While these workers

can be easily recruited and trained to perform simple

and often physically exhaustive tasks, there is a lack

of skilled workers who are able to use and control

the complex machines and processes of the food

production industry. Thus, human resource

managers try to develop some of the unskilled

workers to a higher qualification level to close this

gap. Typical obstacles are language problems (often

German is not the first language) and a lack of

motivation and confidence to learn because of

various reasons, e.g., education is not seen as an

asset or their current work is so exhausting that they

are not ready to learn. The project FoodWeb2.0

(funded by the German Ministry of Research and

Education) aims at training the employees of the

German food industry using two basic strategies:

motivating employees for vocational training and

performing education in collaborative, blended

learning scenarios using Web2.0 technologies. One

of the applications combining both strategies is the

use of a collaborative casual game called

“Matchballs”.

In the subsequent sections of this paper we

present the general approach of the FoodWeb2.0

project, the tools set up to achieve the project goals

as well as first results of on-going courses using this

platform from a qualitative study conducted with 40

students. The paper continues with a description of

the Matchballs game, its underlying architecture and

ontology enrichment mechanisms. Afterwards we

present the results of an evaluation of the game

conducted with 18 students of a vocational training

school specialized in sweets production. The paper

concludes with a discussion of the results and an

outlook on future work.

2 THE FOODWEB2.0 APPROACH

The FoodWeb2.0 project focuses on professional

training in the food industry. These trainings are

characterized by students of heterogeneous

background and knowledge. Furthermore, there are

only a few training facilities in Germany offering

specialized training for the food industry. At the

moment, these courses are conducted at the training

institutions. This implies that students from all over

Germany have to travel to the training facilities. To

reduce travel costs most of the courses are quite

531

Malzahn N., Ziebarth S. and Hoppe H..

USING CASUAL GAMES AND ONTOLOGIES TO TRAIN UNSKILLED WORKERS - An Experience with the FoodWeb2.0 Platform.

DOI: 10.5220/0003914605310537

In Proceedings of the 4th International Conference on Computer Supported Education (SGoCSL-2012), pages 531-537

ISBN: 978-989-8565-07-5

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

intensive, i.e. much information is covered in a very

short time. The courses are often organized in phases

of training at a training facility and phases in which

the students have to apply their newly acquired

knowledge at their particular enterprise setting. In

the latter phase they are often not supported by

training facilities, since they are not in touch with

their trainers. This leads to a difficult learning and

transfer situation.

The FoodWeb2.0 project aims at improving this

situation by providing an online platform where

students and trainers can be in touch before, during

and after a face-to-face training block. We use a

Liferay portal as basis for our platform. It offers

basic tools like wikis, blogs, forums, document

libraries etc., and a sophisticated role/permission

system that allows to offer courses of several

training facilities on a single platform without

compromising their security and data privacy.

The most challenging part of further education

and learning at work is the application of “book

knowledge” to the specific work situation (cf.

Baldwin & Ford, 1988; Burke & Hutchins, 2007).

Web2.0 provides adequate ways of supporting this

kind of learning. Instead of just presenting

knowledge like in web based trainings or (video)

pod casts explaining the content with a most often

artificial case study (at best), the students are

instructed to share their experience regarding the

lessons learned with the other students by providing

blog entries, wiki based learning or in the case of the

more craft-oriented courses by recording videos of

their own performance.

In addition to providing learners with the

opportunity to communicate with their trainers

during phases of work at their specific enterprise the

platform is also used to provide preparatory courses.

Especially in the area of qualification courses from

unlearned workers to skilled workers, there is a

diversity of age and educational background

implying a wide variety of learning skills and

background knowledge. To harmonize the

knowledge levels with respect to specific courses,

the involved training facilities offer preparatory

courses, where the students may refresh their

knowledge on topics that are considered pre-

requisites for a particular course. Due to the Web2.0

spirit of the platform the students may always

discuss the subject matter in forums and present

their solutions in form of blog entries or videos,

which can be uploaded into the platform. Other

students are asked to peer-review these solutions.

We think that the Web2.0 spirit of the platform

helps to overcome the motivational issues of the

target group, because by providing user generated

content the students are allowed to shape the course

content by providing learning materials for other

students and getting feedback on their own real-

world solutions (for real-world problems) from their

colleagues. The basic pedagogical approach is

borrowed from collaborative learning designs like

jig-saw or gallery methods, but furthermore the

learners are always asked to comment on the

learning material provided by the trainers for further

improvement of the course content.

A first qualitative study during the testing phase

of a preparatory course (see Figure 1) with 40

students from a class of food technicians at a private

training facility was promising. The students were

excited by the possibility to give feedback on the

presented learning material and the comments of

their co-students. There was only one drawback. The

teachers had turned off the possibility to rate a

comment, because this did not seem necessary to

them. However, some of the students did not

comment on any resource, because they agreed to a

previously written comment. That explains why we

had only 28 comments from 40 students. The

follow-up group discussion confirmed this

hypothesis.

Figure 1: Glimpse of a preparatory course conducted on

the FoodWeb2.0 platform.

The students liked the possibility to repeat

lessons from school and prior courses by conducting

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532

quizzes with immediate feedback. Whenever the

students were not able to solve an issue – regardless

if it was a technical issue or a task provided by the

trainer – they tried to solve it by requesting help

from peer students.

As the students liked the quizzes so much we

thought of using their motivation effect to pursue

three goals: 1) help the students to learn, 2) help the

trainers to detect misconceptions to improve their

teaching 3) provide a means to collect and formalize

knowledge about a specific domain. To achieve

these goals as sub goals of FoodWeb2.0 we

developed a framework for casual games.

3 MATCHBALLS

Matchballs is designed as a simple allocation game,

in which the player creates statements by linking

(“matching”) concepts displayed as balls (see Figure

2). A statement consists of two concepts linked by

one of four predetermined relation types. The game

can be played either as two player game or as single

player game with a bot. Each pair of players sees the

same game field (concepts) and the goal is to agree

with the teammate on as many relations as possible

in a given time. To agree on a relation both players

have to create it. If they agree on a relation, they

score points and get time bonuses. Players may see

the relations of their teammates, but not the relation

types. The connecting symbols representing the

relation change according to the state of agreement.

There are different symbols for proposals,

agreements and disagreements of the players.

Figure 2: Matchballs user interface.

As knowledge domain we use the domain of

food safety and hazardous material regulations,

which is an important topic of further education in

the German food industry. The considered concepts

are specific situations, actions, dangerous substances

and edibles, which can be linked by using the four

semantic relations “is similar to”, “is more general

than”, “results in” and “then you may not”. Such a

statement might be:

To be flexible concerning the learning domain we

decided to use an ontology-based approach. The

ontology may be easily exchanged to adapt the game

to another domain. Furthermore, the ontology

principally offers the opportunity to encode specific

feedback for common misconceptions like it is often

done in intelligent tutoring systems. However,

ontologies are usually incomplete, since it is nearly

impossible to represent even a limited domain in

exhaustive detail. Accordingly, relations created by

the players that do not occur in the knowledge base

are not necessarily wrong, but possibly just missing,

especially if a significant amount of players creates

them. Thus, it is possible to use the so called

“wisdom of the crowd” of the game players to enrich

a pre-built ontology like it is done in games with a

purpose (gwap) for semantic applications (cf.

Siorpaes and Hepp, 2008).

The game has been designed as casual game: The

rules of the game are very easy to learn, the controls

are simple, single play sessions are short and

agreements are instantly rewarded. Casual games are

considered as “games for all”, which not only appeal

to gamers but to the mass audiences irrespective of

their age, gender or background (Kuittinen et al.,

2007). They are not very time consuming and can be

played occasionally. Thus, this game genre seems

appropriate to our divergent target group.

There are several different incentives for

different types of players: For competitive players

there are high scores and time bonuses, which are a

well-known and often used incentive since early

arcade games. Furthermore, players can collect

“achievements”, which are trophies for solving

certain predefined tasks (e.g. for team play with

another player or for scoring many points).

While playing the game the players have to

remember facts and rules in the context of food

safety and hazardous material regulations. Thus, it

can be used in a corresponding course for training

and recapitulation, not only in the class room, but

also online at home.

4 ARCHITECTURE

The central game server is based on a tuple space

middleware called SQLSpaces (Weinbrenner, et al.,

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2007). Software agents communicate by writing and

reading messages on and from the tuplespace. These

messages consist of tuples made of primitive data

types (integer, characters, booleans) and strings. A

single tuplespace server may contain several

tuplespaces used to divide the data stored in the

server into logic or semantic units.

The Matchballs architecture distinguishes four

different categories of tuplespaces: the Coordination

Space, the Game Spaces, the Intermediate Space,

and the Ontology Space (see Figure 3). The

Coordination Space is used to conduct the

matchmaking between two human players or to start

a single player game. The GameClients, which

reside in a Liferay portal, register at the

Coordination space to announce their availability for

a new game session and retrieve the information

about the Game Space they have to connect to. The

Game Client is implemented using HTML5 and

JavaScript to cover a wide amount of browsers and

operating systems.

Figure 3: Architecture of Matchballs.

Each game session has its own Game Space, to

which either two human players (multiplayer game)

or a human player and a GameBot (single player

game) are connected. The Game Space holds all

necessary information for a Matchballs game. That

means the Game Space consists of an excerpt of the

ontology space, the current timer as set by the Timer

agent, the current score, and the links made by the

players as well as its assessment by the Session

Manager. The GameBot has access to the whole

information stored in the ontology excerpt, i.e. it is

aware of the complete knowledge that is represented

in that ontology excerpt. Thus, all associations made

by the GameBot are correct assuming that the

ontology is adequately modeled. The excerpt from

the ontology is created by the Session Manager

agent. It takes care that there is always a minimum

of possible relations between balls in the beginning.

It also detects concordances of the two players with

respect to the links between balls made by each

player. If an agreement on an association is detected

by the Session Manager, i.e. both players’

GameClients wrote exactly the same relation tuple

into the Game Space. A game ends if the time is up.

Afterwards the links made by the players are

collected by the Collector and put into the

Intermediate Space for further inspection. At the

moment the Collector just counts the occurrence of

the specific relations made by the players and stores

or updates the amount in the Intermediate Space.

The Intermediate Space is used for analyses.

There is a threshold of at least five different players

linking two concepts with the same association type

(not present in the ontology yet) to propose this

association for inclusion into the ontology. Another

basic analysis looks for common misconceptions by

the players, i.e. the players frequently contradict an

association in the ontology by using another

association type than the one represented in the

ontology. Both analysis results are specifically

marked in the Ontology Space.

The Ontology Space holds a tuple

representation of the ontology. Every concept and

relation is represented by a tuple. The ontology

design is based on SKOS (Simple Knowledge

Organization System) (Miles and Bechofer, 2009).

We distinguish only four association types, easily

derived from SKOS-relations and we have few

concepts/classes and many individuals/instances,

which can be categorized in SKOS’s concept

schemes. The restrictions on the ontology are caused

by the spirit of the game. Since it shall be a casual

game for unskilled people a huge, sophisticated

system of associations and concepts/classes is

misleading. Furthermore, SKOS is suitable for

teachers to express their domain knowledge without

much help from ontology engineers. Last but not

least there is a plugin for Protégé for SKOS. Thus,

we can use a popular editor for ontology creation,

inspection and refinement.

The markers of the analyses agents are

translated into respective concept schemes:

misconception and new. Accordingly, the specific

individuals may easily be found by teachers and

ontology engineers. The ontology used in our

evaluation (see below) consists of 191 individuals

connected with 91 associations distributed on the

different association types.

5 EVALUATION

The Matchballs game has been evaluated with a

class of 18 students at the Academy of Sweets in

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Solingen, Germany. Since there were only six

laptops available for conducting the experiment,

after a short plenary introduction of the game, the

class was divided into three groups of six students.

The students of each group had a timeslot of five

minutes for playing as many games as possible and

were encouraged to start with a single-player game

for learning the controls and then perform at least

one game with a human partner. After playing the

game they had to complete a questionnaire and at the

end there was a short plenary discussion.

The subjects consisted of five females and

thirteen males, sixteen were German native

speakers. They rated themselves to have high

knowledge in the fields of safety at work and danger

symbols (median of five on a scale from one to six)

and also some knowledge of hazardous materials

(median of four on a scale from one to six). Thus,

the participants are considered to have at least basic

knowledge of food safety and hazardous materials

and to represent a group recapitulating their

knowledge on these topics. Together the subjects

created 155 different relations, most were only

created by one user, but there were also relations

created by up to seven different participants.

Table 1: Comparison of minimal support for considered

relations, precision of relations and percentage of errors

based on wrong direction of the relation.

minimal support precision

errors based on wrong

direction

4 1.00 none

3 0.85 100,00 %

2 0.68 58,33 %

1 0.40 25,80 %

The higher the amount of users supporting a

relation, the higher is the probability that the relation

is correct (precision) (see Table 1). A frequent error

lies in choosing the wrong direction of the relation,

around 26% of the overall wrong relations are

correct except for the direction; if not all relations

but the ones with a minimal support higher than one

are considered, the percentage is even higher (see

Table 1). These errors can be considered as careless

mistakes instead of real misconceptions.

All frequently occurring, correct relations which

have a support of at least three are already in the

ontology. This is due to the initial composition of

the playing field containing two-thirds of concepts

being linked in the ontology and only one-third of

concepts being not linked with the other selected

concepts. But these relations only cover 17.7% of

the correct relations created by the users. Thus, the

relations with high support have a high precision,

but a low recall. 75.8% of the correct relations

already are in the ontology, 4.8% of the correct new

ones are supported by two and 19.4% by only one

user. The low support of the correct new concepts on

the one hand can also be explained by the initial

composition of the playing field and on the other

hand by the limited number of games played in the

experiment.

The “wisdom of the crowds” approach is often

criticized arguing that expert contributions would be

enough. In our case, the four “best” students (22.2%)

who created the biggest amount of correct new

relations could only provide 46.67% of the overall

number of correct new relations.

Table 2: Cluster centers.

Cluster 1

(14 students)

Cluster 2

(4 students)

Distinct Relations 14.07 5.25

Precision 0.49 0.51

Innovativeness 0.07 0.07

Sloppiness 0.14 0.00

The users show different profiles in creating

relations in terms of the number of different

relations created, their precision, the number of

correct relations missing in the ontology and the

number of errors based on direction issues. Each

user averagely created twelve relations, of which six

(0.51 %) were correct, one (8.3 %) was new and

correct and approximately two (15 %) were wrong

because of direction problems. There is no

correlation between these variables, but partitioning

the students into two clusters using the kMeans

algorithm (see Table 2), reveals one small group of

students (22.2 %), who create a number of distinct

relations far below average, are a bit more precise

and make no direction mistakes and a larger group,

who creates slightly more distinct relations than

average, are a bit less precise than the other group

and make much more direction errors.

On the whole the students had fun playing the

game and experienced it as motivating (each

variable had a median of four on a scale from one to

six). 77.8 % stated they would like to play the game

again and averagely stated they would play it once to

several times a month. The complexity is perceived

as medium (median of 3.5 on a scale from one to

six), which is an indicator that the game is neither

overstraining nor boring and hence appropriate.

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6 DISCUSSION

The evaluation results show that the Matchballs

game was perceived as a casual game. Based on the

data generated during the game sessions we were

able to identify 17 relevant associations or relations

previously not present in the ontology. These

relations were integrated by knowledge engineers. In

this sense, our learning game can also be seen as a

“game with a purpose” to enrich an initial ontology

is feasible.

Thus, the game may be used as an interactive

mechanism for closing gaps in the. At the moment,

the game can only be used for adding new relations

to the ontology. In the future, we plan to use the

game to acquire knowledge from new fields in class

sessions. The teacher may add an originally

disconnected set of new concepts to an existing

ontology. The students are then asked to play the

game by connecting these concepts with each other

as well as with the old ones, thus integrating them

into the existing ontology. This activity may be

viewed as a multi-player concept map creation game

in which the players create a shared concept map.

Concept maps have been successfully used as

learning tool for linking existing and new knowledge

as well as for evaluation and identifying valid and

invalid ideas of students (Novak & Canãs, 2006). If

the game is played in single player mode, the game

may still be used as an advanced vocabulary trainer.

Even when playing the game individually the

students still collaborate indirectly. Teachers can use

the game to extract information about typical

misconceptions of the group but also of individual

students.

In the context of the FoodWeb2.0 project there

have already been several requests by teachers and

students for transferring the game to further

knowledge domains. We will try to incorporate these

domains and enhance these ontologies with specific

feedback on the newly introduced relations. For the

multi-player scenario, feedback will be given about

the existence of these relations in the ontology. In

single-player scenarios the feedback will identify

possible misconceptions automatically based on

information on particular error types explicitly

represented in the ontology. Similar to intelligent

tutoring systems the semantic ontology structure will

be used for the generation of generic feedback.

Evaluation results concerning the FoodWeb2.0

platform in general (and not only the game lements)

are currently Janus-faced: On the one hand, the

perceived usefulness of the platform rated by the

students in general is very high. They like to be able

to look up subject matters on the internet, especially

if the trainer or peer students provide additional

information for further learning. On the other hand,

the trainers are either very enthusiastic or quite

reluctant to use the platform. Those of the trainers

that are enthusiastic often underestimate the time

needed for transforming their material and lesson

planning to an online supported course. The

reluctant ones overestimate the needed effort and

underestimate their students’ skill with Web2.0

tools. The trainers usually have a professional

background in one of the disciplines related to the

food industry (like veterinaries, food engineers or

biologists) but not in pedagogy. This may explain

why some of them are hesitant to employ

collaborative learning strategies in their courses and

consequently have issues with Web2.0 learning.

They use a “traditional” line of argumentation: loss

of control, quality control of the results, perceived

inefficiency of group work. Thus, they are surprised

by their students’ enthusiasm to work with the

platform and by the positive results that are

achieved. We plan to conduct an elaborate study

concerning the perceived usefulness of our platform

differentiated by roles and the changes to the course

content and pedagogical design that have happened

as a consequence of student feedback and the

projects’ “train-the-trainers” program.

ACKNOWLEDGEMENTS

We would like to thank Christopher Charles, Peter

Horster, Dominik Kloke, Carolin Pohl and Carsten

Wieringer, who implemented the Matchballs game

during a student project.

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