An Adaptive, Competence based, Approach to Serious Games

Sequencing in Technology Enhanced Learning

Luca Cuoco

, Andrea Sterbini

and Marco Temperini

Department of Computer, Control and Management Engineering, Sapienza University, Via Ariosto 25, Roma, Italy

Department of Computer Science, Sapienza University, Via Salaria 113, Roma, Italy

Keywords: Personalized e-Learning, Game based e-Learning, Adaptivity, Student Model.

Abstract: We present a platform for Technology Enhanced Learning, allowing the learner to follow a path of game

applications towards a learning objective. The path is determined by the learner, by selecting each time the

next game of her choosing. The games are defined by teachers and experts, or even imported as web

resources: they are associated to the system through suitable metadata, that express their pedagogical

meaning. The system’s interface allows the student to navigate the repository of learning games (organized

as a graph) and see among them those that she can select to undertake, and those that are not yet affordable.

Whether a game is affordable, at a given moment, is determined by comparing the Student Model with the

game’s specification. In conclusion, the path of learning activities followed by the learner is built

interactively, by the learner, according to learner’s choice and the system’s pedagogical guidance. The

system has not yet been experimented in a real class: we report about its design and implementation, and

provide the reader with some simulated applications showing the system’s behaviour.

1 INTRODUCTION

In the area of Technology Enhanced Learning (TEL)

game based learning is founded on the use of serious

games, i.e. (digital) games designed and

implemented in order to allow teaching/learning

rather than (pure, exclusive) entertainment. Rather

than discarding or dulling entertainment, the serious

game aims to couple "meaningful play" (Salen and

Zimmermann 2004) with a learning outcome.

Certain characteristics of games (such as the

need to make choices, and perform an action, rather

than explaining what to do) can facilitate learning

and increase learning performance in applicative

fields (Coller and Scott, 2009; Pasin and Giroux,

2011). In time the use of games and simulations has

become one of the most significant approaches to

assisted learning (Wu et al. 2012). There are studies

that might reduce the enthusiasm (Kebritchi et al.

2012), yet this is one of the hot topics for research in

TEL (Van Eck, 2006; Metawaa and Berkling, 2016).

Game Based Learning can also be accompanied

by (other) playful aspects of the learning

environment, referred to by the term "gamification"

(Deterding et al., 2011; Vassileva, 2012). They are

in general incorporating game-inspired elements in a

non-game environment; examples are the support to

leaderboard and badges.

Similarly to what happens in traditional adaptive

learning systems (Liang et al., 2012; De Marsico et

al., 2013), in an adaptive game based learning

system the personalization of the learning offer is

based on a Student Model, i.e. a representation of a

set of personal traits of the individual student.

Examples of such personal traits are, among others,

the competence (that is the amount of knowledge

possessed at that time by the learner, in relation to

the subject matter at hand), or the student's learning

style. In such an adaptive system, the student is

proposed with different games, or with different

(adapted) versions of the same game, according to

the current state of her student model.

In this paper we present an approach to

personalized game based learning, by the web

system DEV, in which the learner is offered to build

her personal learning (gaming) path, by selecting

games among those available, provided that they are

“affordable” according to her Student Model. The

system provides an interface to allow teachers to

build their course’s game repository; the inclusion of

a game is implemented by means of a “specification

step”, in which the teacher defines a “learning

Cuoco, L., Sterbini, A. and Temperini, M.

An Adaptive, Competence based, Approach to Serious Games Sequencing in Technology Enhanced Learning.

DOI: 10.5220/0006338305890596

In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 589-596

ISBN: 978-989-758-239-4

589

(gameful) experience”, by selecting the game from a

general repository and assigning to it the

pedagogical characteristics the game is going to

have in the course, namely the competence needed

in order to play the game fruitfully (i.e. with

possibilities of success), and the competence that a

success in the game witnesses. The student interface

allows the learner to select a game for play. After

“game over”, the learner’s Student Model is

updated, hopefully by adding in it the competencies

gained/witnessed through the learning game

experience completion.

2 RELATED WORK

Hays (2005) reviewed 48 empirical studies on the

effectiveness of instructional games published

between 1973 and 2005. Results revealed that K-12

learners might find games useful for learning math,

social sciences, vocabulary, but that no information

existed about game-related learning for other

disciplines (e.g., health and geography). In addition,

games were found to be useful in teaching social

sciences, physics, electronics and engineering

principles to college students and, in the workplace,

games showed positive learning effects on teaching

attention, periscope skills, technical skills and so on.

However, no evidence indicates that games are the

preferred instructional method in all situations.

Based on the meta-analysis approach, Vogel et

al. (2006) investigated the relative strengths of

games and interactive simulations against traditional

teaching methods. Results indicated that, across

populations and situations, games and interactive

simulations produce better cognitive gain outcomes.

Applicative fields in which Game based learning

is found are many. In (Morelli et al. 2011; MacLean

and Robertson, 2012) serious games are applied to

provide guide and support to physical exercise. In

(de Jong and van Joolingen, 1998) the development

of a game simulation deemed “to teach about

collisions in physics” is presented.

In the system proposed by the present paper there

are two significant aspects that characterize the

approach to game based learning: the adaptive

student interface, and the possible function of games

as assessment means; the former characteristics

allows for the construction of a personalized

learning (gaming) path, while the second support the

updating of the student model after every gaming

experience. So in the following we recall some

research related to the above mentioned aspects.

Magerko et al. (2008) tackles the problem of

delivering “adaptive games”, i.e. game applications

that can present the learner with differentiated

interface and diverse basic game mechanics,

depending on the player's gaming type. Significant

factors for the personalization are the structure of the

interface, and the way the learner is presented with

knowledge (such as giving importance to high score,

or visualizing texts about the subject matter, or

having a time limit to frame the gaming activity). So

a single game might come to represent a “space of

possible games”. Gamer types are taken from an

analysis of literature; they are intrinsically motivated

Explorers, extrinsically motivated Achievers, and

extrinsically motivated “Winners”. A mini-game

prototype (S.C.R.U.B.) is presented, ranging over

microbiology concepts.

In the system presented in this paper, the

approach is cruder with respect to the games (we

have different games helping pursuing different

skills, yet only one version of each game), On the

other hand, the personalization is obtained by

helping the learner to build her own path of games,

by choosing at each time the preferred game among

those that are pedagogically affordable at the

moment. By "affordable" we mean that a game can

be met by the learner, according to the current state

of her student model. In (Metawaa et al. 2016) an

approach to personalization in game based learning

is shown, for students with little or no access to

teachers, whereas games (the learning activities

available in the platform) are suggested basing on a

lightweight modelization of the learner. The student

model is focused on previous choices of the learner,

and on a game rating derived by the preference

accorded by learners to the game.

Another experience possibly related to the

personalization in a game based learning

environment is in (Lindberg and Laine, 2016), where

foundational blocks for the provision of adaptivity in

a game based learning system are studied: learner’s

learning style, and gaming style. Results shed some

light on the play and learning styles among South

Korean elementary school students. In this

experience the provision for adaptation allowed to

change the learning activity settings, according to

the personally preferable style of the moment, and

provided for an important degree of versatility in the

experimented framework.

Regarding the nature of games as assessment

means, Loh (2007) treats the problem of designing

assessment in a game and uses the concept of

"information trail" as a means to track, from a

pedagogical point of view, the learner's in-game

behaviour ("avatar tracking system") and assess her

SGoCSL 2017 - Special Session on Serious Games on Computer Science Learning

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accomplishments.

3 THE DEV SYSTEM

The DEV system presents the learner with an

environment in which several games are available

and the learner student model is managed. In the

present version of the system the games are all

related to topics in Basic Physics, and are designed

to allow the learner for experimenting with concepts

(such as principles, equations, and computation of

the motion of a body in two and three dimensions)

and build the answer to questions (such as

composing the right equation to use, or computing

some values). At the end of each game it is possible

to appreciate whether the play has been successful

and to what extent (and the system is passed

information suitable to update the student model).

We have designed the DEV system around the

main goal of allowing the learner to follow a

personal path of experiences to reach a goal

knowledge (a set of target skills defined by the

teacher). The definition and management of the

learner’s Student Model is material to such aim: the

system points out for the learner, among the whole

set of available games, those that are at the moment

“affordable” for her, so the learner can choose her

path, although according to a pedagogical guidance.

In this we have taken inspiration by the concept of

“zone of proximal development” known in the

educational theory of Vygotsky (Chaiklin, 2003;

Vygotskij, 1981).

DEV is comprised of a repository where all the

available games are collected. Different courses can

be built by associating games from the repository to

the course area, according to the pedagogical

specifications described in the next section.

4 GAME DEFINITION AND

STUDENT MODEL

The games presently available in DEV are

implemented by different technologies: so far they

are either interactive questionnaires or game

applications built through the Unity3D framework

(Unity3D, 2016). Besides its implementation, each

game, say G, is specified by a set of metadata,

declaring the skills that are necessary in order to

play the game (Required Skills – G.RS) and those

that can be considered owned by the learner once

she has been successful in the game (Acquired Skills

– G.AS).

A skill is defined as a pair <k, c>. In it k is a

“Knowledge Item” (KI), that is an identifier (a

name) for a concept or ability (which is further

described, in the DEV system with a Glossary

topic), while c is the “certainty” that is associated to

the possession of k. The certainty factor is a real

value in [0,1]. The skill’s certainty factor, basically

describes how much we can be sure that the student

does actually possess the related KI. The Student

Model (SM) contains the current list of skills

possessed by the learner. When a game, G, has been

experienced by a learner, l, then she is supposed to

have acquired the skills in G.AS, and her student

model, l.SM, can be updated accordingly. In

particular, if G is specified as follows, where the r

and a

are KIs

 G.RS={<r

,rc

>, …, r

,rc

 G.AS={<a

,ac

>, …, r

,ac

The basic updating rule for the SM is as follows:

for all <k,c> in G.AS, <k,c> is added to l.SM

In other words, each skill acquired by the game, is

added to the learner’s SM, with a certainty equal to

that specified in the game definition.

The basic SM updating rule, though, has to be

enhanced: it is apparent that, for any given skill, the

system could provide (or actually must provide)

several games acknowledging it. Moreover, it is

possible that a learner will select and complete the

same game several times. This, from the teacher’s

perspective, can be interpreted as useful practice:

repeating a game, or, possibly, playing a game that

awards skills that are already in one’s student model,

is a strengthening practice for such skills. On the

other hand, while the certainty assigned to a skill

that is just being added to the Student Model after a

game (a newly acquired skill) is a remarkable

fraction of 1 (such as 0.6, the default we are

currently using in fact), the increase in certainty

awarded to a skill already in l.SM ought to be quite

lower. This is to avoid reaching a certainty of 1

without a considerable effort, and yet allows for a

perceivable increase (fostering a sense of

accomplishment in the learner).

For a game specified as above, the cases to be

managed during the post-game student model update

are as follows (with l a learner, and G a game)

 for all <k,c>G.AS, with <k,c>l.SM, <k,c>

is added to l.SM

 for all <k,c>G.AS, with <k,C>l.SM, i.e.

with the skill already present in the student

model, with an associated certainty C, the

skill’s certainty is updated by just a fraction of

c: C





(C, c, n

), where  computes the new

An Adaptive, Competence based, Approach to Serious Games Sequencing in Technology Enhanced Learning

591

value for C, according to the value c and to the

number of times the game G has been already

played n

So the basic updating rule, valid only for the

acquired skills <k,*>



l.SM, is enhanced by

applying the principle that we cannot grant the

whole amount of certainty that would be granted the

first time the game is solved, or the first time the

skill is placed in l.SM. This is done by applying an

algorithm that reduces c according to the cases (a

repeated game will acknowledge just a small

increase of certainty, progressively decreasing to

none_at_all; on the other hand, already possessed

skills have a similar, yet less drastic, treatment

granting that strengthening competence through

different games is more valuable than in the case of

game replay.

5 PEDAGOGICAL ASPECTS OF

THE SYSTEM

The games occur in a general learning activity: a

course, or a cycle of (game-)experiences flanking a

course. The objectives of such an activity (Target

Skills - TS) are defined as a set of skills as well. The

abovementioned general learning activity can be

considered completed once all the elements of TS

are “covered” by the learner’s model: Namely, when

the TS is satisfied by the student's SM.

Definition 1. (Skill coverage)

Given two skills, <k

> and <k

> we say that

> covers <k

>, and write <k

> → <k

iff k

AND c

c

Definition 2. (Skill set Satisfaction)

Given a leaner l, and a set of skills,

S = {<K

>, <K

>, ... , <K

> },

we say that the learner’s student model

l.SM ={<k

>, …, k

satisfies S, and write S

⊆

iff  i[1….,q],  j[1,…,p]

such that <k

> covers <K

So, the course, or cycle of experiences, with

target skills TS, is considered concluded by a learner

l, once TS

⊆

SM.

In DEV, while all games can be inspected by

learners at will (to see their definitions), they are not

always available for playing (accessible henceforth).

In particular, only the games, G, whose requirement

set, G.RS, is empty, are accessible by everybody.

Otherwise, G is accessible for l, if and only if l.SM

satisfies G.RS: G.RS

⊆

l.SM.

Definition 3. (Game accessibility)

Given a learner l, and a game G, we say G is

accessible by l, and write G  ZPD(l), iff

G.RS

⊆

Namely, the system’s interface shows to the

learner all the available games, let her inspect them,

yet let her selects for use only those that are

accessible to that particular learner.

The interface is shown in Fig.1; further

description of the interface is in the next section.

The AS/RS annotation and the current student

model SM cooperate to keep the student inside the

set of activities that she is able to try without

frustration. In this we get inspiration from the

Vygotsky's Zone of Proximal Development

(Chaiklin, 2003; Vygotskij, 1981). Notice that the

certainty factors are not to be interpreted as a

description of knowledge levels 'a la Bloom (Bloom,

1964; Anderson and Krathwohl, 2000), because

cognitive levels are not additive. To represent

different knowledge levels, we use different KIs.

Finally, we describe an additional feature of the

system, although it is not exclusively connected to

game experience. As it is imaginable, the games in

the system are to be implemented in such a way to

offer both gaming activity and communication with

the system itself. The communication part is crucial,

as it is providing the system with evidences to use

for the personal student model updates. We call such

a game a formal resource. Also a questionnaire (as

the system allows the construction of multiple

choice questionnaires) can function as a formal

resource in the above “communication” sense

(although it is not properly a game).

On the other hand, the possibility to join into the

system “informal resources” is valuable, and very

likely to be requested.

Such informal resources are all those activities

that 1) can be easily retrieved on the web, and 2) are

(by consequence) not specifically programmed to

communicate with the system, and can hardly

provide the system with feedback: A YouTube clip,

an swf game, taken “as is”, and actually any other

resource that is available on the web (through a

uniform resource indicator, for instance).

So the system provides for the possibility to

import an informal resource, by associating to it a

questionnaire, so to make of it an overall formal

resource.

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6 SYSTEM’S FEATURES AND

FUNCTIONALITIES

From the point of view of a course designer, the

system provides a dedicated area to upload, add and

modify the contents which compose the experiences,

an editor for their distribution on the navigation

graph, and an area for learners’ management.

From the point of view of the student, the system

provides the environment to explore the course

graph and to face the experiences that are accessible,

depending on her SM. In addition, a glossary is

provided to explain the meaning of the Knowledge

Items. Fig. 1 shows a sample student’s view of the

course repository: games accessible to the learner

are circled in yellow; games already played (still

accessible) are circled in violet, and the lock

represents non-accessibility. Placing the mouse

arrow on a node-game highlights the connections of

the selected node. A connection is represented by a

directed arc in the graph: an arc from G1 to G2 says

that part of, or whole, G1.AS could be used to cover

(part of) G2.RS, towards making G2 accessible.

Figure 1: Navigation Graph Tool. Initial state of the games

when SM = {} – only some initial games are accessible.

So the student can plan her next experience also

basing of what it would unlock (let acquire). This

allows, in turn, to build one’s own path towards the

TS satisfaction. Fig. 2 shows a visualization of the

learner’s Student Model.

The system provides a questionnaire editor to

compose questions and evaluate answers. The

questions can be enhanced with Javascript code (e.g.

to generate different random parametric instances of

the question each time the experience is visited)

through an integrated scripting environment.

On the side of permissions management, the

system provides ways to freely associate privileges

to the users. One user can view a specific area of the

system in read-only mode, while another can have

more privileges to edit and delete contents. In a

second time, the first user can obtain the access to

modification. This way, work-flows can be defined

to manage different developers working on the same

activities (e.g. learning designer, web designer,

multimedia/graphic designer, Javascript developer).

Figure 2: Visualization of the student model. For each

element in TS, possession and certainty are shown. The

upper bar gives a comparative degree of completion for

the whole course (i.e. a degree of current coverage for

TS).

6.1 The System in Action

In the following we show a very simple example of

how the content definition produces the student view

of the course.

The first step is the identification of the

Knowledge Items to match the learning needs. We

suppose to build a virtual physics laboratory where

students can interact with the laws of physics in an

immersive environment that simulates everyday life

problems. The first KIs defined are: Vectors, Force,

Velocity, Acceleration, Linear motion, Uniformly

accelerated linear motion, Projectile motion. For

each KI a description of the meaning is added to the

glossary. Having KIs, we can then define the course

learning objectives, TS.

As previously described, each skill obtained by a

student has a certainty value between 0.0 and 1.0.

We want the student to let develop her Student

Model to reach TS satisfaction. Suppose that

TS = {<Vectors, 0.6>, <Velocity, 0.6>,

<Acceleration, 0.6>, <Linear motion, 0.8>,

<Uniformly accelerated linear motion, 0.8>,

<Projectile motion, 0.9>}

To cover TS the student needs to enhance her SM,

by executing some of the available experiences.

Supposing that SM is empty at start, the course must

contain a group of initial experiences with empty

requirements. When new skills are acquired, the SM

grows, new experiences become accessible, and the

An Adaptive, Competence based, Approach to Serious Games Sequencing in Technology Enhanced Learning

593

course can proceed. An example of starting

experience, from Fig. 1, follows:

Name: The Velocity Definition

Informal resource: A video with real life

examples and formulas definition

Formal resource: A game application that let

the learner do experience with the concept of

velocity and also verifies the comprehension of

the velocity concept

Req. Skills: { } empty set

Acq. Skills:{<Vectors, 0.6>, <Velocity, 0.6> }

Let’s assume that the learner undertakes this first

experience, successfully; now Fig. 3 shows the

outcome: the experience becomes “played” in the

interface, and new experiences are unlocked

(become accessible).

Figure 3: State after completion of the first game. Now

SM = { <Vectors, 0.6>, <Velocity, 0.6> } and the

experiences “The Acceleration Definition” and “Speed of

Sound” became accessible.

Let’s also assume that the student undertook the

following experience:

Name: The Acceleration Definition

Informal resource: An interactive animation

that presents the concept and the formulas

Formal resource: A intelligent questionnaire

Req. Skills:{ <Vector, 0.6>, <Velocity, 0.6> }

Acq. Skills: { <Acceleration, 0.4> }

Once the above experience has been completed, the

student still needs another experience providing at

least other 0.2 certainty value of Acceleration to

fulfil the course Target Skills (where a 0.6 certainty

is required for the Acceleration KI). Well, the

following experience is available, and now

accessible, to help covering the gap (Fig. 4 shows

the consequences on the student model of

undertaking this experience):

Name: Gravitational acceleration

Informal resource: An animation of the

newton's apple with some mathematical

reflections

Formal resource: A 3D application with inputs

for the replication of the falling apple

Req. Skills: { <Acceleration, 0.4> }

Acq. Skills: { <Acceleration, 0.6> }

Figure 4. State of the Navigation Graph Tool after

completion of two games. Now SM = { <Vectors, 0.6>,

<Velocity, 0.6>, <Acceleration, 0.4> } and the experience

“Gravitational acceleration” became accessible.

The idea of this example is to show how the course

designer could make a whole graph of experiences

available (games, closed answer questionnaires,

informal resources made formal by questionnaires),

and how the learner can select a personal path to let

her SM grow towards TS satisfaction. A course

would have to provide several alternative starting

nodes; moreover, several different resources ought

to have intersecting AS, and grant the same skills. It

is this redundancy the feature that would provide

learners with a true opportunity to build her own

personal learning path.

An example of DEV physics games is shown in

Fig. 5, where a 3D physics soccer simulator asks the

student to solve the problem of passing the ball to a

team-mate.

Figure 5: Soccer simulator.

The solution needs stating 1) the right trajectory, as

resulting from a correct definition of the motion

equations (as shown in Fig. 6), and 2) a correct

shooting angle.

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594

Figure 6: Soccer simulator. Construction of the motion

equation, through a puzzle game.

7 CONCLUSIONS

DEV is not yet being experimented in a real class,

mainly due to the necessity of providing the system

with a wealth of good games, to obtain the

aforementioned redundancy. So the main activity

related to DEV in this time regards the development

of games, and make them available into the system.

We are currently working of the definition of an API

allowing to produce different scenarios of a same

game. Moreover, editors for the rapid production of

small games, such as puzzle games, ruzzle based

games and Tower games, are under design. This

should allow us to nourish the system and proceed

beyond the simulation we have presented in this

paper.

When the presented approach will be more

mature, we plan to extend it towards collaborative

(game based) learning, by means of multi-player

game activities (Sterbini and Temperini, 2009;

Nebel et al. 2016). A parallel line of development is

towards peer-based assessment and learning (Cho

and MacArthur, 2010; Sterbini and Temperini, 2012;

Sterbini and Temperini, 2013; Isabwe, 2013;

Tenorio et al. 2016). By involving new teachers, we

also plan to extend the learning domain to

mathematics and formal languages (Hancock, 1995;

Formisano et al. 2000; Formisano et al. 2001;

Isabwe, 2013). In a near future we want to enhance

DEV by gamification applied to the overall system

interface. Examples of interventions are as follows:

 making the games more immersive, by

showing a personal Avatar actively interacting

with the graph of experiences and within the

experiences themselves;

 engaging learners in competitions, as it can be

supported by showing a leaderboard or

awarding badges;

 allowing a different visualization of the student

model, under the form of the avatar inventory

of abilities, with “power-ups” related to the

measure of certainty. In some sense the Student

Model already can be thought as an inventory

of items, yet the games should be modified to

behave differently depending on the “items”

carried by the player.

 collecting an overall measure of experience

(XP points, which could be thought as a

measure of reputation or of learning

involvement) describing how well the student

has played the whole course.

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