Mapping Task Types and Gameplay Categories in the Context of

Declarative Knowledge Training

enice Lemoine

, Pierre Laforcade

and S

ebastien George

LIUM Computer Science Laboratory, Le Mans Universit

e, Laval, France

Keywords:

Serious Game, Didactic, Design, Training, Declarative Knowledge, Gameplay.

Abstract:

Learning games for the training of declarative knowledge must offer learners a wide variety of game situ-

ations in order to keep them engaged. Designing such situations remains a challenge due to the inherent

entanglement of didactical objectives and gaming implementations. This article proposes to tackle the need

for mapping different training tasks to different gameplays in order to help the design of relevant gameplay-

oriented training situations. We identiﬁed an approach during the design of a Roguelite-oriented training game

for multiplication tables. This approach has been intentionally speciﬁed towards a genericity purpose by using

domain-independent task types and abstract gameplays. This article details the method we followed to identify

this approach and presents the resulting mappings when applied to our speciﬁc application context.

1 INTRODUCTION

This last decade, the design and use of learning games

has become a common practice (Codish and Ravid,

2015). However, most learning games fail to be seen

as real video games mostly because of their lack of

gameplay (i.e., fun elements that can be controlled,

decided and done by the players) (Prensky, 2005). Al-

though combining the fun of real game and educa-

tional content is not easy (Prensky, 2005), it is a key

component of a good learning game design. Com-

bining the two dimensions (i.e., game and education)

is difﬁcult, primarily because there are multiple vari-

ables to consider. Moreover, these variables often de-

pend on the knowledge to be learned and the targeted

game genre.

Learning games targeting declarative knowledge

training require a deeper commitment from learners

as they are used regularly for repetitive training ses-

sions. This implies that the design of such learning

games must offer a wide variety of situations, game

mechanisms or gameplays. Designers then have to

deal with various stakes such as the dynamic genera-

tion of training sessions or the adaptation of these ses-

sions to take into account the different teachers and/or

learners’ needs or preferences. This also includes de-

signing how the variety of situations will be tackled.

https://orcid.org/0000-0002-7608-3223

https://orcid.org/0000-0001-8498-2731

https://orcid.org/0000-0003-0812-0712

Our research interest is about that challenge. We

focus on it by considering the design of varied game-

plays. These gameplays involve that the player-

learner makes the avatar, which he/she controls, in-

teract with the environments and objects in the game

to perform contextualized actions to answer the ques-

tion being asked. Because training games include

several domain-speciﬁc parameterized training tasks,

many gameplays have to be identiﬁed for each one of

them. Identifying how different training tasks can be

implemented using these different gameplay concepts

requires conceptualizing and addressing a transdisci-

plinary TEL (Technology Enhanced Learning) prob-

lem: how can didactic knowledge be mapped to dif-

ferent gameplays?

We encountered this challenge during the design

of a Roguelite oriented learning game to train the re-

tention of the multiplication tables. First, we iden-

tiﬁed an approach that involves addressing the chal-

lenge at a higher level of abstraction (task types in-

stead of domain-speciﬁc tasks and game categories

instead of practical gameplays). This allows for

a more generic and domain-independent approach.

Secondly, the main thrust of the approach is to use a

dedicated pivot to help identify the source (task types)

and target (gameplay categories) parameters whose

values will guide the elicitation of practical mappings.

This paper explains how we identiﬁed this approach

(method), what this approach consists in (proposal),

and which mappings are obtained in our context (ap-

264

Lemoine, B., Laforcade, P. and George, S.

Mapping Task Types and Gameplay Categories in the Context of Declarative Knowledge Training.

DOI: 10.5220/0011840100003470

In Proceedings of the 15th International Conference on Computer Supported Education (CSEDU 2023) - Volume 2, pages 264-275

ISBN: 978-989-758-641-5; ISSN: 2184-5026

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

plication). We assume that this approach is sufﬁ-

ciently generic and reusable to contribute to help-

ing multidisciplinary design teams identify and map

gameplays for their domain-speciﬁc tasks (contribu-

tion).

In cognitive psychology, test-based learning rep-

resents the idea that the process of retrieving (i.e. re-

membering) concepts or facts increases their long-

term retention. While tests are mainly used as sum-

mative assessment tools, they can also be forma-

tive. Repeated retrieval (i.e., retrieval practice) is

one way to implement test-based learning, which has

been shown to improve long-term retention in re-

search (Roediger and Pyc, 2012; Brame and Biel,

2015). In addition, research also suggests that various

test formats enhance learning (i.e. the beneﬁts are not

linked to a speciﬁc type of retrieval practice) (Brame

and Biel, 2015). In our work, training means pro-

viding the learner with different forms of questions

repeatedly, which is a form of retrieval practice.

The structure of this paper is as follows: Section 2

presents our research context including our gaming

(Roguelite genre) and learning (training of multiplica-

tion facts) contexts as well as the mapping issue; Sec-

tion 3 draws current close research studies for tackling

this challenge; Section 4 details how the identiﬁcation

of the mapping approach we propose has been con-

ducted; Section 5 presents the resulting approach and

its application in our speciﬁc context; Section 6 dis-

cusses the next challenges to overcome, mainly about

formalizing and exploiting the resulting mappings to

drive the generation of adapted training sessions.

2 RESEARCH CONTEXT

Our research interest is about generating effective and

engaging training sessions for the retention of declar-

ative knowledge. Declarative knowledge (i.e. knowl-

edge about facts, laws, statements, etc.) are known

to require repetition for encouraging their memoriza-

tion, generalization, and retention (Kim et al., 2013;

Roediger and Pyc, 2012). Our concern is about identi-

fying approaches, models, and processes helping mul-

tidisciplinary teams in designing learning games tar-

geting such training. Nevertheless, creating activities

combining fun and educational content is not an easy

job (Prensky, 2005). To that extent, Prensky pro-

posed a three steps process to create digital game-

based learning: “(1) Find or create a game with great

gameplay that will engage our audience, (2) Find the

learning activities and techniques that will teach what

is required (doing each with the other in mind), and

then (3) successfully blend the two”.

2.1 A RogueLite for Multiplication

Tables Training

As a ﬁrst application domain, our research takes place

into the context of the AdapTABLES project

which

aims at designing and developing a learning game for

the training of multiplication tables. It implies the

design of a generator for providing game levels dur-

ing training sessions. It is worth notice that practic-

ing multiplication tables will complement classroom

learning (from the teacher’s point of view): learning

the tables, application in problem-solving, and gener-

alization are outside the scope of the learning game.

This project involves mathematical teachers and di-

dactic experts, as well as game designers and serious

game experts.

In addition, we studied different game genres in

order to identify those capable of keeping players en-

gaged while providing repetitive but various game-

plays. The Roguelite game genre meets these needs.

It is mainly characterized by procedural dungeon gen-

eration with randomized content, permanent death

(each avatar’s death implies for the player to start a

new playthrough), and limited retention of unlockable

game elements (characters, items, powerups, ...).

Had

es, Rogue Legacy, Binding of Isaac are well

known Roguelite video games. They also share a

dungeon-crawler approach: the player has to explore

dungeons made up of interconnected rooms where

actions take place. Therefore, Roguelite combines

all necessary prerequisites for training declarative

knowledge (i.e., generation → variety, repetition, and

it is a well-known and liked game genre). Brieﬂy,

learners-players will get across successive generated

dungeon levels, wherein most of the rooms will chal-

lenge them to answer task-oriented questions.

2.2 Abstract Training Task Types

In an exploratory research, partially presented

in (Laforcade et al., 2022), a user group composed

of 2nd to 6th grade teachers and mathematics experts

has identiﬁed several tasks for training multiplication

tables, such as: complete a fact where the result is

missing, complete a fact where the operand is miss-

ing, decide if a fact is correct, identify the results of a

table. These tasks also embeds dedicated parameters

in order to drive which facts to consider, how to build

them and how to answer them.

In a perspective of genericity with other domain-

related declarative knowledge, we abstracted them

into 5 task types:

https://projets-lium.univ-lemans.fr/adaptables/

Mapping Task Types and Gameplay Categories in the Context of Declarative Knowledge Training

265

1. Completion 1: complete an incomplete fact that

has one missing element (e.g., 3×? = 15, 15 =

? × 5, 3 × 5 = ?);

2. Completion 2: complete an incomplete fact that

has two missing elements (e.g., ?×? = 15 with

a set of given choices [3, 6, 5, 10], ? × 5 = ? or

3×? = ? also with sets of given choices);

3. Reconstruction: replace, in the correct order, all

important elements of a fact (e.g., ?×? = ? with a

set of given choices [3, 6, 5, 10, 15]);

4. Identiﬁcation: identify the correctness or incor-

rectness of one or several facts (e.g., 3 × 5 = 15,

true or false?);

5. (Non-)Membership Identiﬁcation: identify the

elements that share or do not share a given prop-

erty (e.g., [3, 5, 9, 12, 14, 21] which are results of

the table 3?);

These task types do not claim to be exhaustive. They

only cover the speciﬁc multiplication tables tasks and

are expressed at a higher level of abstraction.

2.3 Abstract Gameplay Types

As mentioned by (Prensky, 2005), the main reason

for learning game failure is their lack of gameplay.

Therefore, we intend to provide a variety of possi-

ble gameplays. To that extent, informal interviews

were conducted with game designers to gather possi-

ble ideas which lead to the design of gameplay mock-

ups. A game prototype with a few gameplays was

created to test some ideas and collect some feedback.

One observation was made after the design of the

mock-ups: some gameplays seemed to belong to the

same category (e.g. breaking the pot wearing the an-

swer and opening the chest wearing the answer are

two similar ways of selecting an object). This ob-

servation followed the idea of the game classiﬁcation

proposed by (Djaouti et al., 2008) which consists in

describing games through gameplay bricks (i.e., cat-

egories of actions that can be performed within the

games). Consequently, further reﬂexion led to the

deﬁnition of 5 categories of gameplay in our context

(as for the task types, these categories do not claim to

be exhaustive):

1. SELECT: select (e.g., touch, kill, break, open)

objects wearing the correct answers, through

avatar actions;

2. MOVE: correctly place objects at speciﬁc loca-

tions through avatar actions;

3. ORIENT: orient objects (e.g., rotate), through

avatar actions, towards the correct answer;

4. POSITION: move the avatar to the necessary po-

sitions for choosing or typing the correct answers;

5. DIRECT RESPONSE: no action is required

through the avatar, learners can directly type down

their answer by using an input device (e.g., enter

the correct answer through a keyboard).

Figure 1 presents a gameplay mock-up for each cate-

gory.

(a) SELECT example (b) MOVE example

(e) DIRECT RESPONSE

Figure 1: Example of mock-ups by gameplay categories.

2.4 Research Questions

Following Prensky’s process, our main question now

is: how to determine and specify the relationships be-

tween task types and game categories necessary to the

design of learning game activities? Indeed, know-

ing these relationships is important at design-time to

guide the identiﬁcation of practical gameplays for ev-

ery speciﬁc tasks, and at run-time to drive the genera-

tion process. We assume that answering this question

at a higher level of abstraction (task types and abstract

gameplays) will allow these relationships to be reused

in different declarative knowledge contexts.

This research question (i.e., illustrated in Figure 2)

consists of answering more precisely to the follow-

ing questions: Which kinds of abstract gameplays are

suitable for which task types? Is the mapping system-

atic or conditional? If conditional, how to ﬁnd these

conditions?

According to (Tchounikine et al., 2009), this is

typically a problem of research in TEL engineering

CSEDU 2023 - 15th International Conference on Computer Supported Education

266

Figure 2: Our Research Question.

falling into the “elaborating powerful abstractions”

case where the problem must be addressed from a

transdisciplinary perspective.

Previous works have addressed the problem of re-

lationships identiﬁcation between the educational and

game dimensions. The following section aims to po-

sition our work in relation to these existing works.

3 RELATED WORK &

POSITIONNING

Several existing works have focused on determining

the relationships between educational elements and

game elements. A pioneer is (Prensky, 2005), who

proposed compatible game genres (e.g., action, role-

play, adventure) with knowledge to be learned (e.g.,

facts, skills, judgement, behavior) and learning activ-

ities (e.g., questions, experiments, observation). An

extension to this work, consisting of adding a relation

to the learning styles (i.e., activists, reﬂectors, theo-

rists, and pragmatists) of (Chong et al., 2005) has

been proposed by (Rapeepisarn et al., 2008). Like

wise, (Sherry, 2010) deﬁned relationships between

some game genres and the 6 levels of Bloom’s tax-

onomy (Bloom, 1956). Similarly, (Gosper and Mc-

Neill, 2012) proposed a framework to support the in-

tegration of technology in education. This framework

deﬁnes relationships between learning outcomes (e.g.

acquisition of basic facts, automation of skills and

concepts), learning processes (e.g. memorization,

analogical reasoning, proceduralization), assessment

(e.g. self-assessment, peer assessment) and game

genres. Although these works are very interesting for

the general design of learning games, the identiﬁed re-

lationships are not usable at a speciﬁcation stage for

guiding the deﬁnition of practical gameplays.

Some work attempts to provide relationships at a

speciﬁcation level. (Dondi and Moretti, 2007) linked

learning objectives (e.g., memorization/repetition/ re-

tention), knowledge types (e.g., factual knowledge),

and game genres to high-level features that games

should possess (e.g., presence of content engine, as-

sessment engine). However, these high-level features

do not describe how the relationships are to be imple-

mented in practice.

Other works offer a framework to specify rela-

tionships (i.e., either for analyzing existing games

or conceiving one). The LM-GM framework (Lim

et al., 2013) supports the transition from learning ob-

jectives/practices to game elements through a con-

cept called Serious Game Mechanic (SGM). It deﬁnes

learning mechanics and game mechanics and uses

SGM to associate both concepts. However, the pre-

sented mechanics are high-level ones (e.g., guidance,

collaboration, explore) and the relationships are not

meant to be implemented as such. Furthermore, (Hall

et al., 2014) proposed a framework to guide the de-

signer in specifying the transition from learning con-

tent to core-gameplay. It is composed of 5 categories

(i.e., goal, choice, action, rules, feedback) in which a

series of questions need to be answered from a real-

world and a game-world perspective. However, the

framework is more oriented towards the general de-

sign of the game rather than its implementation.

In conclusion, existing approaches are more ori-

ented towards deﬁning relationships for analysis pur-

poses or to assist in the high-level design of games

rather than specifying relationships for low-level de-

sign purposes. Indeed, they address speciﬁc learning

targets or the contexts of speciﬁc game genres, as we

do. In our context, we seek to propose an approach

to specify relationships between declarative knowl-

edge training tasks and gameplays from the Roguelite

genre. These relations need to respect one condition:

their speciﬁcation must allow their implementation.

It is well known that the training and evaluation

of declarative knowledge is done through question-

naires/quizzes. Moreover, numerical quizzes, com-

pared to paper ones, allow for user interactions that

are closer to the ones we can found into basic train-

ing games (for example a multiplication table training

games where correct answers make the avatar run-

ning faster or jumping to higher platforms). There-

fore, using exercises types from quizzes formats as a

pivot (i.e., a way to close the gap between task types

for declarative knowledge training and gameplay cat-

egories) seemed interesting in particular because the

use of existing content is a way to reduce subjectivity.

Therefore, our work intends to propose a systematic

mapping approach based on the use of quizzes exer-

cises types as a pivot. The next sections present the

development of our approach, followed by the pro-

posed approach and an application example.

Mapping Task Types and Gameplay Categories in the Context of Declarative Knowledge Training

267

4 APPROACH DESIGN

Several steps were necessary to the elaboration of our

mapping approach. At ﬁrst, we conducted an anal-

ysis of quizzes design formats to deﬁne the types of

existing exercises (i.e., our pivot), after which the fol-

lowing questions appeared: (1) How can we draw a

parallel between the types of tasks and the exercises

identiﬁed? (2) How can we draw a parallel between

the gameplay categories and the exercises identiﬁed?

As previously mentioned, the interactions offered by

each quiz exercise are closer to games’ interactions.

Moreover, every concept (i.e., task types, gameplay

categories, and exercises) are characterized by their

possible response modalities (e.g., enter one answer,

choose between X propositions). Therefore, our sec-

ond step consisted in using the exercises types to iden-

tify possible criteria and parameters in order to spec-

ify the task types and gameplay categories and ease

the identiﬁcation of the mappings. The last step con-

sisted in using these parameters values (from both

task types and gameplay categories perspectives) to

compare and identify matches.

4.1 Identiﬁcation of the Pivot

Foremost, we analyzed the proposed exercises types

of 6 formats, mostly extracted from Learning Man-

agement Systems (LMS), allowing the creation of nu-

merical questionnaires/quizzes:

• the eponymous and proprietary format from the

itsLearning (#1) LMS;

• GIFT (#2) a markup language for describing tests

that is associated to the Moodle LMS;

• Performance Matters Assessment and Analytics

(#3) format associated to the PowerSchool LMS;

• NetQuizzPro (#4) a software allowing the creation

of questionnaire;

• QTI (Question & Test Interoperability speciﬁca-

tion) (#5) from the IMS global learning consor-

tium that deﬁnes a standard format to exchange

and store assessment content;

• Tactileo - Maskott (#6) format associated to the

French pedagogical platform of the same name.

This analysis lead us to the deﬁnition of 12 dif-

ferent types of exercises useful for declarative knowl-

edge (i.e., only exercises for which the veriﬁcation of

the results can be automatized). Mainly the analysis

consisted of a comparison of the exercises in terms

of what they allow. Exercises (of different format)

that shared the same type of statement, the same num-

ber of desired answers and for which the interaction

of answers was similar have been merged to com-

pose a single exercise type. Furthermore, some for-

mats combine several exercises into one (i.e. mul-

tiple choice and answers were merged in the format

of itsLearning). In these cases, we considered them

to be two independent exercises. Moreover, the pos-

sibility of having intruders (i.e., elements not to be

associated) was something asked by domain experts,

however, none of the “Associate” type of exercise an-

alyzed from the formats offer that possibility. There-

fore, it was considered as a possibility in our type of

this exercise deﬁnition. The ﬁnal types of exercises

deﬁned are:

• Alternative: choosing one answer between 2 op-

tions;

• Multiple choice: choosing one answer between X

(i.e., X ⩾ 2) options;

• Multiple responses: choosing Y (i.e., zero or

more) answers between X (i.e., X ⩾ 2) options;

• Short answer: enter the correct answer. Multiple

form of answers can be accepted, e.g., for exam-

ple, How much is 3 times 5? as two possible an-

swers, which are 15 and ﬁfteen;

• Fill-in-the-blanks: enter for each gap of a text the

wanted “short” answer;

• Fill-in-the-blanks choices: choose for each gap of

a text the correct answer from a list. Each gap can

have an associated list of options, or one list can

be associated to all gaps;

• Reconstruction: reassemble, in the correct order,

each signiﬁcant element of an information;

• Associate—Group: associate elements from a list

or multiple lists together. The association can be

done by pairs, or not. The elements can be asso-

ciated with zero to several other ones;

• Order: replace a set of information in the correct

order;

• Graphic choice: point or locate X (i.e., X ⩾ 1)

elements on a picture.

• Graphic identiﬁcation: write the correct label for

each area-to-complete of a picture;

• Graphic association: associate the correct labels

to X areas of a picture.

As a reminder, these types of exercises aim at dealing

with declarative knowledge in general, not just mul-

tiplication tables. Therefore, some exercises offer a

more visual approach that could be useful, for exam-

ple, in the context of geographical facts. An overview

of which format allows for which exercises is pre-

sented in Table 1.

CSEDU 2023 - 15th International Conference on Computer Supported Education

268

Table 1: Exercises by quiz format (✓ present; ✗ absent;

— present but incomplete).

#1 #2 #3 #4 #5 #6

Alternative ✓ — — ✗ ✗ ✗

Multiple Choice ✓ ✓ ✓ ✓ ✓ ✓

Multiple Resp. ✓ ✓ ✓ ✓ ✓ ✓

Short Answer ✗ ✓ ✓ ✗ ✓ ✓

Fill-in ✓ — ✓ ✓ ✗ ✓

Fill-in Choice ✓ — ✗ ✓ ✓ ✓

Reconstruction ✗ ✗ ✗ ✓ ✗ ✗

Association — — — — — —

Order ✓ ✗ ✓ ✓ ✓ ✓

G. Choice — ✗ — ✗ ✓ —

G. Identiﬁcation ✗ ✗ ✗ ✓ ✗ ✗

G. Association ✗ ✗ ✗ — ✓ ✓

These exercises are characterized by several pa-

rameters, such as: their interactions, their response

modality (i.e., input or choice), their statement type

(i.e., format of the question asked), the number of an-

swers desired, and the number of propositions pre-

sented (i.e. if the response modality of a concrete

task of this type is “Choice”). Through our analy-

sis, 6 types of interactions were identiﬁed: Select Y

From X (i.e., the learner must select Y answers from

a set of X values); Y (Select 1 from X

to X

) (i.e,

the learner must make a selection of one answer from

each set of proposals); a variant is Y (Select 1 from X)

(i.e, the learner must select Y answers, one by one,

from a set of proposals.); Write X (i.e., the learner as

to enter X answers); Order X (i.e., the learner must

order X elements correctly); Point X or Locate X (i.e.,

the learner must point X elements on a picture or lo-

cate them); Match Y with X 1-to-1 or Match Y with X

(i.e., the learner must associate elements from Y with

those from X by pairs or not). In addition, 3 types

of statement were found: 1) classic statement (i.e.,

simple text question), 2) graphic statement (i.e., text

question accompanied by an image), and 3) To ﬁll-in

statement (i.e., question separating textual elements

with other types of elements such as answer boxes).

Table 2 presents the exercises through their character-

ization. It is important to note that none of the formats

allows for all possible forms of exercise.

4.2 Mapping Task Types to Game

Categories

Now that the pivot is speciﬁed, the remaining ques-

tions are: How to match (1) task types to exercises,

and (2) game categories to exercises? The main idea

is to use some of the parameters that characterize each

concept (i.e. task types, gameplay categories and ex-

ercises) to map them.

Task Type to Exercises

Task types are characterized by several parameters,

which are: the number of facts targeted by the task,

the types of statements allowed for such a task, the re-

sponse modalities, the number of desired responses,

and the number of propositions presented (i.e., if the

response modality of a concrete task of this type is

“Choice”). For example, the Completion 1 type is

characterized as follows: one fact is targeted, all state-

ment types are allowed (i.e., classic, graphic and To

ﬁll-in), both response modalities can be used (i.e., in-

put and choice), only one response is desired and at

least 2 propositions must be presented if the modality

equals Choice.

Nevertheless, the assignment of parameter values

is not an easy task. At ﬁrst sight, it could seem

possible to carry out a task of the Completion 2

type through the response modality Input. However,

presenting a statement in the context of declarative

knowledge, such as “?×? = 12”, does not give enough

information about the fact to work with (i.e., is it 3×4

or 6 × 2). As another example, for a Completion 2

task, it is possible to choose one or two answers. This

depends on how the choices are presented. If the set

of propositions represents numbers, such as [3, 5, 7,

4], two answers must be chosen. However, if each

proposition is presented as a multiplication (without

the result), such as [3 × 4; 4 × 5; 6 × 3], then only one

answer is required. Table 3 presents the task types

through their characterization. Thus, except for the

interactions, task types and exercises are character-

ized by the same parameters.

Consequently, the mapping consists in comparing

the shared parameter’s values between task types and

exercises. For examples, Completion 1 corresponds to

Short answer because the speciﬁcation of Short an-

swer, i.e., {number of facts = 1; type of statement

= classic; modality = input; number of desired an-

swers = 1}, is a possible conﬁguration of a concrete

task of the type Completion 1 (i.e., the parameter val-

ues are included into those of the type Completion 1).

This gives questions such as “What times 5 equals

15?” and “What does 3 times 5 equal?”. Comple-

tion 1 also corresponds to Fill-in-the-blanks choices

exercise speciﬁed as {number of facts = 1; type of

statement = to ﬁll-in; modality = choice; number

of desired answers = [1 − ∞]; number of choices =

[2 − ∞]}. This gives questions such as “ times 5

equals 15”.

Thanks to these mappings, each task type can also

be described by its possible interactions (i.e., the in-

teractions of the exercises corresponding to the task

type considering the number of requested answers).

As an example, the Short answer exercise type asks

Mapping Task Types and Gameplay Categories in the Context of Declarative Knowledge Training

269

Table 2: Characterization of the exercises.

Number Statement Response Number Number of

of Facts Types Modality Answers Choices

Interactions

Alternative 1 Classic Choice 1 2 Select 1 from 2

Multiple Choice 1 Classic Choice 1 2 to ∞ Select 1 from X

Multiple Resp. 1 Classic Choice 0 to ∞ 2 to ∞ Select Y from X

Short Answer 1 Classic Input 1 0 Write 1

Fill-in 1 To ﬁll-in Input 1 to ∞ 0 Write Y

Fill-in Choice 1 To ﬁll-in Choice 1 to ∞ 2 to ∞

Y (Select 1 from X)

Y (Select 1 from X

to X

)

Reconstruction 1 To ﬁll-in Choice 2 to ∞ 2 to ∞ Match Y with X 1-to-1

Association 2 to ∞ Classic Choice 2 to ∞ 4 to ∞

Match Y with X 1-to-1

Match Y with X

Order 2 to ∞

Classic

Choice 1 to ∞ 2 to ∞ Order Y

Graphic

G. Choice 1 to ∞ Graphic Choice 1 to ∞ 2 to ∞ Point Y or Locate Y

G. Identiﬁcation 1 to ∞ Graphic Input 1 to ∞ 0 Write Y

G. Association 1 to ∞ Graphic Choice 1 to ∞ 1 to ∞ Match Y with X 1-to-1

Table 3: Characterization of the task types.

Number Statement Response Number Number of

of Facts Types Modality Answers Choices

Completion 1 1

Classic

1Graphic

Input 0

To ﬁll-in

Choice 2 to ∞

Completion 2 1

Classic

Choice 1 or 2 2 to ∞Graphic

To ﬁll-in

Reconstruction 1

Graphic

Choice 2 to ∞ 2 to ∞

To ﬁll-in

Identiﬁcation 1 to ∞ Classic

Input

1 to ∞

Choice 2 to ∞

(Non-)Membership

1 to ∞

Classic Input

2 to ∞

Identiﬁcation Graphic Choice 2 to ∞

the learner to write down one short answer to a tex-

tual question. Thus, its interaction parameter as for

value Write 1. A task of the Completion 1 type could

ask questions such as “What does 3 times 5 equal?”,

leaving the learner to write down 15. Therefore, Com-

pletion 1 has Write 1 for possible interaction. As a

result of all mappings, Completion 1 can be achieved

through the following interactions: Select 1 from X

(i.e., X ∈ [2, ∞]), Write Y (i.e., Y is the number of

wanted answer therefore Y = 1), Y (Select 1 from X),

Point Y or Locate Y, Match Y with X 1-to-1.

Gameplay Categories to Exercises

Each game category represents gameplays that are

similar in terms of the actions to be performed, such

as opening the right chest, choosing the right pot,

passing through the right bridge which belong to the

SELECT category. Therefore, the common parame-

ters of these gameplays (e.g., number of facts interro-

gated, number of possible answers) represent those of

the category itself.

After analysis, we characterized these categories

by the following parameters: the interactions, the re-

sponse modality (i.e., input or choice), the statement

type (i.e., format of the question asked), the number of

facts targeted, the number of answers desired, and the

number of propositions presented (i.e., if the response

modality of a concrete task of this type is “Choice”).

These parameters are quite similar to those used for

the exercises. They represent a minimal and relevant

set of parameters that allow us to discriminate the dif-

ferent categories and gameplays. As an example, the

SELECT category is characterized as follows: 1 to

many facts can be targeted, both classic and to ﬁll-in

statement types are allowed, choice is the only possi-

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270

ble response modality, 1 to many answers can be de-

sired, and two interactions (i.e., Select Y from X, and

Y (Select 1 from X

to X

)) are possible.

However, during the characterization phase, we

realized that the possible interactions and the type

of statement changed depending on whether one or

more responses were desired. Therefore, in order to

simplify the mappings, each category allowing one to

multiple possible responses was divided into two sub-

categories: single (i.e., only one possible response)

and multiple (i.e., 2 to many possible responses).

As a result, our 5 categories turned into 9. Ta-

ble 4 presents the gameplay categories through their

characterization. Afterwards, the mapping consisted

in directly comparing the parameter’s values.

Task Type to Gameplay Categories

From there, we disposed of all the necessary informa-

tion to answer our main question: Which task type are

suitable for which gameplay categories? What are the

conditions?

The ﬁnal step consisted in comparing the task

types and categories on the basis of their parameter

values (i.e. comparing Table 3 with Table 4). During

that step, we observed that 2 parameters represented

mapping conditions based on their values: the type

of statement and the response modality. Therefore,

the obtained relationships are quadruplet composed

as follows: (<task type>, <statement type>,

<response modality>, (<category1>, <category2>,

. . . )). Each task type can have from 1 to 6 associated

relationships.

In conclusion, this section has presented the pro-

cess followed to map the task types for declarative

knowledge training to gameplay categories for the

Roguelite video game genre. The next section will

present the results obtained.

5 RESULTS

The presented work led to two contributions: (1) an

approach to map designers’ own task types to their

own categories of gameplays, and (2) mappings be-

tween our own task types with our gameplay cate-

gories.

5.1 A Mapping Approach

Our proposal consists of a two to ﬁve-steps approach,

illustrated in Figure 3. The initial steps are to:

Figure 3: Proposed Mapping Approach.

1. abstract the concrete tasks by using the types of

tasks presented (e.g. a task “associate the right

date with the historical event” becomes complete

a fact with a missing element) or by creating new

task types;

2. associate the gameplay to one of the categories

presented or to a new category.

From here, four states are possible: new task types

and categories have been created, only new task types

have been created, only new gameplay categories

have been created, or nothing has been created. De-

pending on the condition, the instructions below must

be followed:

1. If new task types and new gameplay categories

were created:

(a) The ﬁrst step is to characterize the task types

using the six parameters deﬁned above (i.e.,

number of facts, types of statements, response

modalities, number of desired responses, num-

ber of propositions, and interactions). In a sub-

step, the mapping between task types and quiz

exercises (see Table 2) must be done to deﬁne

the values of the interactions parameter.

(b) The second step is to characterize the gameplay

categories using the same parameters.

(i.e., characterization) through their values. As

Mapping Task Types and Gameplay Categories in the Context of Declarative Knowledge Training

271

Table 4: Characterization of the gameplay categories ((S) = Single; (M) = Multiple).

Number Statement Response Number Number of

of Facts Types Modality Answers Choices

Interactions

SELECT (S) 1

Classic

Choice 1 2 to ∞ Select 1 from X

To ﬁll-in

SELECT (M) 1 to ∞

Classic

Choice 2 to ∞ 2 to ∞

Select Y from X

To ﬁll-in Y (Select 1 from X

to X

)

MOVE (S) 1

Classic

Choice 1 2 to ∞

Select 1 from X

Graphic Point 1 or Locate 1

To ﬁll-in Match 1 with 1

MOVE (M) 1 to ∞ Choice 2 to ∞ 2 to ∞

Match Y with X

Classic

Point Y or Locate Y

Graphic

Select Y from X

To ﬁll-in

Y (Select 1 from X

to X

)

ORIENT (S) 1

Classic

Choice 1 2 to ∞ Select 1 from X

To ﬁll-in

ORIENT (M) 1 to ∞

Classic

Choice 2 to ∞ 2 to ∞

Y (Select 1 from X

to X

)

To ﬁll-in Y (Select 1 from X)

POSITION (S) 1

To ﬁll-in

Input 0

Classic

Select 1 from X

Graphic

Choice 2 to ∞ Point 1 or Locate 1

POSITION (M) 1 Graphic Input 2 to ∞ 0 Write Y

DIRECT RESP. 1 Classic Input 1 0 Write 1

a reminder, the values of the Statement Type

and the Response Modality parameters are pos-

sible conditions of the relations.

2. If only new task types were created, then realize

step 1a and step 1c.

3. If only new gameplay categories were created,

then realize step 1b and step 1c.

4. If no new elements have been created, the work is

already done, cf. Figure 4.

Let’s take as example a task type T1 characterizes

as such {number of facts = 1; type of statement =

classic; modality = input or choice; number of de-

sired answers = 1}, and a gameplay category C1 =

{number of facts = [1 − ∞]; type of statement = clas-

sic or to ﬁll-in; modality = choice; number of desired

answers = [1 − ∞]}. In this case, only one relation-

ship would result: (T1, Classic, Choice, C1).

5.2 Resulting Mappings

As a result of the process presented in section 3, sev-

eral conditional relationships were identiﬁed between

each task type and the gameplay categories.

These relationships are presented in Figure 4. If

the categories do not have (S) or (M), it means that

both are present.

For example, the task type Identiﬁcation has two

relationships: (Identiﬁcation, Classic, Input, (Position

(S), DIRECT RESPONSE)) and (Identiﬁcation, Clas-

sic, Choice, (SELECTION (S,M), MOVE (S,M), ORI-

ENT (S,M)).

6 DISCUSSION & FURTHER

WORK

This section discusses the limitations of our work,

presents an experiment to validate some mappings,

and outlines a way to model the identiﬁed relation-

ships.

6.1 Extension of the Resulting

Mappings and Genericity

Our approach revealed some limits. First, due to the

non-exhaustiveness of our task types, all possible ex-

ercise types are not covered (i.e., this is the case of

Order). It is mainly due to our method: we intended

to start with a very ﬁrst domain context while ab-

stracting tasks types and gameplay categories. It is a

very ﬁrst step towards proposing a complete domain-

independent and generic approach.

Consequently, other domains than multiplication

tables have to be considered to cover, and reﬁne if

necessary, our proposal. It is also possible that the

current task types have to be modiﬁed or extended

to cover all exercise types. In particular, we are cur-

CSEDU 2023 - 15th International Conference on Computer Supported Education

272

Figure 4: Conditional relations between task types and gameplay categories.

rently studying a second domain, consisting in the his-

tory and geography declarative knowledge necessary

to obtain the French diploma “Dipl

ome National du

Brevet” (present in the 9th grade).

Figure 5: Second solution example.

6.2 Validation of the Mappings

In order to gather feedbacks on the gameplay mock-

ups (i.e., to identify relevant gameplays and game el-

ements), we proposed to the members (≈ 10 persons)

of the user group (of the project AdapTABLES) a

survey presenting possible gameplays for each type

of task. This experimentation was also an opportu-

nity to validate some mappings (i.e., the relations for

which the categories have existing gameplay mock-

ups). As the experiment is situated in the context

of multiplication tables, none of the mappings with

the condition “Graphic” are evaluated here. How-

ever, since mock-ups of each compatible categories

for each task types were created, other mappings are

evaluated here. Let’s take the example of a gameplay

consisting in selecting the right pot among several

pots bearing propositions (i.e., SELECT with Choice)

to answer a textual question of the type “3×? = 15”

(i.e., Completion 1). If this gameplay is validated by

the survey, then so is the relationship (Completion 1,

Classic, Choice, SELECT).

According to the ﬁrst results, the mappings pre-

sented seem relevant. Negative comments are gener-

ally related to the lack of precision of some informa-

Mapping Task Types and Gameplay Categories in the Context of Declarative Knowledge Training

273

Figure 6: Example of EMF Ecore Modelling for relationships, task types and gameplays.

tion or to didactic problems. For example, MOVE-

type gameplays requiring objects to be placed in the

correct answer areas were rejected because the ob-

jects would hide the answers, thus impacting learn-

ers’ thinking. The issue is not related to the mechan-

ics (i.e., moving objects to predeﬁned areas), but a

cognitive one that can be corrected. A basic solution

would be to display the value above the selected ob-

ject. An alternative would be to display the chosen

value in the statement at the right position using a dif-

ferent color. Figure 5 illustrates the latter solution:

the statue pushed on the left tile hides the associated

’5’ value, but this value appears now in purple in the

room’s statement.

Furthermore, the mock-ups of the ORIENT game

have received mixed reviews. This is due to the fact

that the object chosen to be oriented (i.e., directing the

light of a streetlamp) lacks cognitive meaning. There-

fore, other mock-ups should be proposed to properly

assess the relationships associated with the ORIENT

category.

6.3 Modelling the Mappings

As mentioned earlier, our overall goal is to build a

generator of Roguelite oriented learning game activi-

ties for declarative knowledge training.

The originality of our overall work is the use of

Model-Driven Engineering (MDE) (Kent, 2002) tools

and principles to design the generator. The idea is to

specify all the information (domain-dependent tasks,

task types, game categories, gameplays, game ele-

ments and relationships) in different, interconnected

models that are consistent with a dedicated meta-

model that we are also specifying. Therefore, each

generation could be considered as a model transfor-

mation according to MDE.

Figure 6 proposes a ﬁrst insight of the part of this

metamodel focusing on the mapping structure deﬁned

in this article. The resulting mappings will be speci-

ﬁed as a model in conformance with this metamodel

extract. The domain-dependant tasks, as well as the

speciﬁcation of the concrete gameplays, and their im-

plementations into game elements, are not depicted

in the ﬁgure. As an overview of the mapping process

followed at runtime, here are the main steps starting

from a given identiﬁed domain-dependent task:

1. get the associated task type;

2. collect all relations from the mapping model that

are related to this task type;

3. restrict the collected relations to those whose as-

sociated condition is satisﬁed by comparing the

statement and modality values of the condition to

those of the original task;

4. collect the gameplay categories of the remaining

relations.

The generation algorithm will then handle fur-

ther steps such as selecting the gameplays implement-

ing these game categories, ﬁltering them according

to other information (e.g. which gameplays are un-

locked and available for this learner-player), instanti-

ating the game elements composing the chosen game-

play, etc.

7 CONCLUSION

To conclude, this paper introduced an approach to

map task types for declarative knowledge training to

CSEDU 2023 - 15th International Conference on Computer Supported Education

274

Roguelite oriented gameplay categories. The origi-

nality of this work lies on two points. Firstly, the pro-

posed approach is based on the use of a pivot (i.e.,

exercises in the form of questionnaires). Secondly, it

is oriented towards automating the design of learning

game activities (i.e., generation) and therefore speci-

ﬁes ﬁne-grained relationships.

In the future, we plan to: (1) continue the analy-

sis of the second application domain in order to have

generic task types with declarative knowledge; (2) fo-

cus on specifying gameplays in terms of game ele-

ments; and (3) model relationships (and other con-

cepts) to implement a ﬁrst version of the generator.

ACKNOWLEDGEMENTS

We thank all the members of the user group (teachers

and didactics experts) as well as the game designers

without whom our research would not be successful.

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