Proposal for a metamodel of studied domain & automatic re­contextualization of learning scenarios

Abdelwahab Naji IT Department

FST Mohammedia Moahammedia, Morocco

ab _ naji@yahoo.fr

Abstract- this paper is focused on the automatic re­

contextualization, and the conception and operationalization

upgrade of learning scenarios in environment for human

learning. On the one hand, the learners' levels knowledge allow

designers and teachers to discover: blocking situations and steps

leading students to fail. On the other hand, they are used to

calculate a new scenario from the set of scenarios describing the

same pedagogical objective. For this purpose, the choice of a

modelling language and its infrastructure is relevant. The study

of different modelling languages allows us to find that the

adaptation of learning is far than the sought outcomes. In this

paper, our choice is particularly fixed on the LDL model in which

we provide some extensions to promote the adaptation and

regulation of learning.

Keywords- Learning activity, LDL-LDI, pedagogical scenario, learning adaptation.

I. INTRODUCTION

In the framework of the learning personalization, BLOOM [1] pointed out that learning occurs more effectively when a teacher provides each learner with the appropriate learning conditions. In the nineties, many studies have indicated that the educational online content must meet the following criteria: interoperability, adaptability, sharing and reuse. Nowadays, personalization of learning in the human learning environments remains a major challenge. Some studies [2] [3] allow us to discover that the production of an adaptive learning content is not a simple task to perform, but a whole procedure to be followed. The production of the educational adaptive content meets further challenges in two phases: conception and contextualization. These phases need special attention in the production process. We focus in this paper on the automatic re­contextualization and upgrade the conception of learning scenarios in human learning environments. On the one hand, the results that reflect the learners' levels knowledge enable designers and teachers to discover the blocking situations and the steps which lead students to fail. On the other hand, they are used to calculate a new scenario from the set of scenarios describing the same pedagogical objective.

This paper is organised on four sections. The first presents the introduction. The second section is devoted to the art state of the educational modelling focused on activities. A study of the language LDL and its infrastructure LDI is presented in the third section. In this study, we raise some limitations and improvements we make to this model and to the life cycle of a

978-1-4799-0299-6/13/$31.00 ©2013 IEEE

Mohamed ramdani IT Department

FST Mohammedia Moahammedia, Morocco

ramdani@fstm.ac.ma

learning scenario. The fourth and fmal section is reserved to the conclusion and perspectives.

II. MODELING OF EDUCATIONAL CONTENT

A. Emergence of educational modeling languages (EML)

Gagne [4] announced that the research for efficiency in the learning process is to combine learning objectives for teaching methods. In the early 2000s, educational modelling languages are subject to more detailed studies. These studies show real progress in placing the activity in the center of the learning process.

Thus, the progress of work on educational modelling focusing in the approach centred on the activities, has led to the emergence of the first educational modelling language called EML (Educational Modelling Language). Koper says in his study [5] that educational resources are not the core of a learning process but the activities that are associated to these resources. Learning situations are described by using the educational modeling language basing on a meta-model. Thus, relations between educational objectives, actors (trainer, tutor, learner, etc.) involved in the learning unit, learning activities, the environment and the resources to ensure the success of a learning situation were introduced [6] [7].

Following these studies, the concept of the learning scenario was introduced. This concept represents « a description of how a learning situation occurs in terms of roles, activities and necessary environment for its implementation status and also in terms of manipulated knowledge» [8]. The pedagogical scenario or learning scenario is a central component in the modeling process of educational resources that took a lot of interest in the pedagogical engineering [9] [10].

There are currently a number of LMP (educational modeling language), the best known are IMS-LD and LDL, which allow describing the learning activities scenario.

B. IM S Learning D esign(IM S-LD )

IMS-LD is established in 2003 by the American consortium IMS, it's largely based on the work of Koper modeling languages [Koper 2001] particularly on EML (Educational Modeling Langage). IMS-LD [11] proposes a model allowing to include the diversity of pedagogical approaches while providing indexing, sharing and interoperability of resources that put them in scene. Historically, IMS-LD is the best known

and most publicized. It can represent a meta-model, as it does not impose a particular pedagogical model and it knows flexibility resulting from its use with different pedagogical models.

However, there are researches that showed some difficulties encountered during the implementation of collaborative learning situations using the language IMS-LD [12] [13].

0 . . 1 Regule

Observable

C. The LDL language and its infrastructure LDI

To respond to the shortcomings of IMS-LD, LDL [14] [12] is intended as an alternative to IMS-LD. This language allows the modeling of collaborative learning situations. Martel [12] presented a simplified version of LDL. This version is based on seven concepts (figure 1) to model a collaborative learning unit.

Joue

Pnsepar Porte sur

Teste

Assocfee a

Fig. 1. The meta-model of LDL language (version presented in [12])

III. LEARNING ADAPT A nON

The study of approaches and pedagogical modelling languages allow to describe and implement learning activities, allows us to find that the adaptation of learning is differentiated depending on modelling languages and methods of implementing. As Koper stated [5], an educational modelling language should allow to adapt the content and activities of each learner in terms of preferences, prior knowledges or learning needs.

In this paper, we are interested in LDL language and its infrastructure LDI by raising the limits of this model and making improvements in the context of adaptation of the collaborative learning. Moreover, we propose an improved life cycle of a learning scenario to promote the adaptation of learning.

A. Learning adaptation with LDL-LDI : Limits and proposed extensions

1) Limits ofLDL-LDI

Following an analysis of the XML schema of the language previously studied (LDL). Especially rules enceinte, position and observable concepts (Figure 1). It appears that they allow to specify the candidate's answers and its scores. However, these concepts do not specify how to calculate these scores and do not give an explicit relationship between the issues of an assessment and pedagogical resources. This leads to not having enough details that allow to locate the gaps that led the learner to fail. The absence of learner's knowledge model based on educational objectives predetermined by the teacher in the LDL language has a major handicap for a controlled learning.

2) Extensions of LDL-LDI Following the study of the limits of LDL-LDI, this section

presents the extensions that we bring to this model (Figure 2), for the establishment of a generic model which allow the development of adaptive learning environments.

-

I 0 .. 1 0 .. " 0.\) 0."

Scenario � Rule global pedagogical

objective 1 " �"--

0.16( < > " " Learner Teacher Designer

0." � It II ., � 0 .. 1 " Questionnaire 1." Intermediate Step

�

pedagogical " �

" 1 -1 objective

1&,7 --

() t n III t" Question 1." Elementary pedagogical E I e me nta ryAtiviti e "-

Role - Actor

objective �" - -1 �"

" "-1." 'I'

1

Evnvironnement

--

II Learning ressource SeNice Tool

" " "

"I " " " Position

I ObseNable ObseNed Declared " -

1

Fig. 2. A domain metamodel : LDL evolved

As Pernin stated [10], it's important to divide the educational content into elementary fragments to ensure appropriate concatenation of educational activities. For a purpose of locating the learner's gaps and the blocking situations of learning, we proceed to divide a learning unit into three levels. Each level defmes the objective type implemented (elementary pedagogical objective, intermediate pedagogical objective and global pedagogical objective). Then, describe the entity type (elementary activity, step, scenario) that allows the progress of this objective type:

• Level I: This is the lowest pedagogical concepts level. It is used to perform one or more elementary activities allowing learner to acquire a specific elementary pedagogical objective.

• Level 2: This level represents an intermediate pedagogical objective. It includes several elementary pedagogical objectives. In this level, the conduct of the learning is described in an entity called step (activities structure by LDL).

• Level 3: This is the highest level. It corresponds to a global pedagogical objective and includes several intermediate pedagogical objectives. In this level, it is the pedagogical scenario (set of structures) which allows to describe the sequence of learning.

For example, a pedagogical scenario is divided into several steps. Each step contains a set of elementary activities referring to learning resources, services and tools.

3) Learner knowledge model In order to improve their learning, learners need an

interactive system informing them of their proficiency level for each pedagogical objective in the structure of the learning unit. So, we are supposed to create a leamer's knowledge model dice choice of learning unit [15] [16]. This model should include the entire structure of pedagogical objectives of the pedagogical designer.

Global pedagogical Intermediate pedagogical Elementary pedagogical

I nteractive assessment system

objectives objectives objectives c=o :> C = -= >---....

r--Elementary pedagogical -.J) total Mastery

objective 1

,----- Intermediate pedagogical Elementary pedagogical -+0 Partiel Mastery objective 1 objective 2

L-- Elementary pedagogical �O Total Ignorance objective 3

Global pedagogical

objective

- Elementary pedagogical il Total ignorance Intermediate pedagogical

objective 1 '--

objective 2 Elementary pedagogical il Partiel Ignorance

objective 2

- Elementary pedagogical il Total i!nOrance ob' ive3

I Scenario I Steps I Activities

Dl�astery

Fig. 3. Control of the learners' knowledge

In a controlled learning situation, it seems important to regularly update the learner's knowledge model. According to Bloom [17], the evaluation has two fonns: fonnative and summative. The evaluation is called summative when it controls the learner's progress at the system output. It's formative when it provides information allowing an adaptation of learning. In our paper, we rely on the formative evaluation to control the learner's knowledge level and we propose the better steps that lead to improve his level (Figure 3).

For this purpose, it's essential to develop indicators to measure the achievement level of educational objectives through the acquisition level of the elementary pedagogical objective. To achieve this, three indicators expressed as % will be adopted:

• The fIrst one is DM(OE(i)): represents the degree of

mastery of tme elementary pedagogical objective OE(i)calculated as follows:

DM(OE(i» = Iscore(question(i,j)) * WQ(i,j) (1)

n

Where:

score(question(i.;;J: score obtained in question numberjrelated to "OE(i)" ;

WQ( i,j) the weight of question(ij) in "OE(i)" ;

n: number of questions related to the elementary pedagogical objective "OE(i)".

The second is D M ( 0 I (k) ): represents the degree of

mastery of keme intermediate pedagogical objective "OI(k)" calculated as follows:

DM( OI(k)) = If;,l DM( OE(k, l)) * WEek, I) (2)

DM( OE(k, l)): degree of mastery of the

elementary pedagogical objective "OE(I)" related to "OI(k)";

WE(k.l) the weight of "OE(I)" in "OI(k)" ;

m: number of elementary pedagogical objectives in "OI(k)" ;.

• The last one is DM( OG(u)): represents the degree of

mastery of each global pedagogical objective "OG(u)" calculated as follows:

DM(OG(u)) = I;=lDM(OI(u,v)) * WI(u,v) (3) Where:

DM( OI(u, v)): degree of mastery

intermediate pedagogical objective related to "OG(u)";

of the

"OI(v) "

WI(u, v) the weight of"OI(v)" in "OG(u)";

p: number of intermediate pedagogical objectives.

For this purpose, we added to our domain metamodel an association between the "learner" entity and the "elementary pedagogical objective" entity (Figure 2). The value of these indicators is updated after each formative assessment by using a questionnaire. The formative assessment is an activity that takes place at the end of each step.

B. Improvement of the life cycle of a LDL pedagogical scenario

Adaptation realised with LDL and its LDI infrastructure is fIxed at the conception stage of the learning scenario by defming rules. This type of adaptation treats only the provided situations. For example, it's possible to use the rules and the observables to generate other alternatives by using the structure:

"if condition then actionI else action 2". However, for a pedagogical objective, new situations may

appear conducting to disruption of learning: LDL-LDI does not provide alternatives to blocking situations that are not mentioned in the rules base. For example, it's not possible to

identify the resources that are not tailored to learners during the execution of the scenario in order to replace them with others more efficient.

The multiplicity of designers, the variety of editors and the variety of difficulty levels encountered during the development of learning activities lead to many learning scenarios dealing with the same educational objective. These scenarios are thus seen in different ways.

In a teaching/learning situation, the interactions between the different actors of the system on one hand, and the interaction between the actors and the system on the other hand allow to create a space of a gradual transformation of the learners' cognitive skills. This space is represented by a graph whose nodes represent steps and arcs represent the links between steps. Learners are represented by virtual agents that perform the learning activities. In the case of a free navigation, learners execute scenarios while seeking steps that best meet their needs. Certainly, in front of this situation, learners take different paths (Figure 4), find blocking situations in the different steps, spend a lot of time to find the scenario

: scenario calculated by system

satisfying their needs in terms of learning. Then they have different results may be low.

Following these observations, it is useful to perform some stages of the life cycle of a pedagogical scenario in order to promote the adaptation and the regulation of learning. For this purpose, we proceed to calculate the best scenario (we can fmd several) based on the best steps of learning scenario (Figure 4). Thus, we found it useful to introduce a measure called the effectiveness of a step in a scenario.

effectiveness(step(DI(k), t)) = IDM(OI(k») (4) n

step(DI(k), t): step operationalized by the teacher "t" and that describe activities of the intermediate pedagogical objective DI(k);

DM( DI(k)): is defined in (1);

n: the number of learners who visited the step "step(DI(k), tr.

calculating a best-case

scenario

Step « k »

teacher «t » having problems

Fig. 4. Example of calculating a best-case scenario

Identify a calculated scenario from the different steps of multiple path scenarios, minimizing blocking situations and allowing to have good scores is the main objective of our work. Therefore, giving a particular interest in the design phase and the contextualization phase is an important element to achieve our goal.

As described in Figure 5, we propose to improve the life cycle of the learning scenario by two processes:

The first one is the calculating of the best-case scenario: This process is presented as a method to calculate the pedagogical scenario representing the best steps that led learners to get good scores in the associated objectives. This process is performed as long as learners continue to have unacceptable results. To avoid an infinite loop, the number of

attempts (a) to calculate the best-case scenario should not exceed a threshold (11).

For example, for a global pedagogical objective DG(u) the best steps are defined according to the method:

While (learners have(DM( OG(u))< "partial mastery'')

And (a< 11) do find_best_stepsO.

find_best_stepsO:a method that allows us to find the best steps.

To avoid an infinite loop, if the nwnber (a) of attempts for the calculation of the best scenario exceeds a threshold (11), the instructional designer is advised (automatically) for a review of the instructional conception.

Revision of the instructional conception: if the number of attempts (a > /-l), the designer is informed to review the scenario models. Learners' results and traces are an important element to locate gaps and the lack which can have the scenario models. In this process, the designer can add concepts, change prerequisites, add rules or create other educational objectives to facilitate the learning process.

For example, for a global pedagogical objective OG(u) the scenario review is defined according to the method:

Oesien review

PO &

Concepts

Rules &

constraints

a>= 1-1

Actors Pedac:oC:ical & R.ssourc.s I'"oles

jf(learners have(DM( OG(u))< "partial mastery")

And (a>= Il) then review _scenarioO.

review scenarioO: the method in which the designer revises the

-scenario. after accomplishing this method the value

of a is reset to O.

Calculating a best-case scenario

E/� efficiency indicator PO: Pedagogical Objective

Fig. 5. life cycle of a learning scenariovie

IV. CONCLUSION

In this paper we are focused on the automatic re­contextualization and the upgrade of the design and operationalization of learning scenarios in computing environment for human learning. Based on the results reflecting the learners' level of knowledge, we calculated a new scenario from the set of scenarios describing the same pedagogical objective. The use of a next experience with data from actual assessments for a set of learners is an important step in the validation of our approach. However, the accuracy of the learner's knowledge model depends on effective evaluation. Thus, the establishment of a procedure for an advanced evaluation is the aim of our next work.

REFERENCES

[I] BLOOM, Learning For Mastery, London, 1968.

[2] Buitrago, Simulations et contr6le pedagogique architectures logicielles Reutilisables, Grenoble I: These de doctorat, Octobre 1999.

[3] C.Martel et ai, «Scenariser les 4 piliers de la pedagogie,» chez EIAH 07, 2007.

[4] Gagne, «Conditions of learning: Holt, Rinehart and Winston,» New York, 1985.

[5] R.Koper, «Modeling units of study from a pedagogical perspective. The pedagogical meta-model behind EML,» chez Rapport de recherche, Open University of the Netherlands,juin 2001.

[6] R.Koper, Combining re-usable learning, resources and services to pedagogical purposeful units of learning. In A. Littlejohn (Ed.), Reusing Online Resources: A Sustainable Approach to eLearning, London: Kogan, 2003, pp. 46-59.

[7] P.Tchounikine, «Environnements informatiques pour I'apprentissage humain,» chez Cognition et traitement de I'information, Hermes Lavoisier, ISBN 2-7462-1171-8, Chapitre 6, Mars, 2006.

[8] Pernin, «Objets pedagogiques : unites d'apprentissage, activites ou ressources ?,» chez Sciences et Techniques Educatives, Numero special Ressources numeriques, XML et education, 2003.

[9] G.Paquette, «L'ingenierie du teleapprentissage, pour construire l'apprentissage en reseaux, Sainte-Foy,» chez Presses de l'Universite du Quebec, Quebec, 2002.

[10] Pernin et ai, «Dispositifs d'apprentissage instrumentes par les technologies : vers une ingenierie centree sur les scenarios,» 2004.

[II] «IMS-LD(2003),» [En ligne]. Available: http://www.imsglobal.orgllearningdesignl. [Acces Ie 5 12 2012].

[12] C.Martel et ai, «A language to model collaborative learning activities,» chez EDMEDlA: World Conference on Educational Multimedia, Hypermedia and Telecommunications, 2006.

[13] NODENOT, «Etude du potentiel du langage IMS-LD pour scenariser des situations d'apprentissage : resultats et propositions,» chez in Pernin J. -P., Godinet H., actes du colloque Scenariser I'enseignement et I'apprentissage : une nouvelle competence, 2006.

[14] C.Ferraris et ai, «Modelisation de scenarios d'apprentissage collaboratif pour la classe : vers une operationnalisation au sein d'un ENT,» TNRP, Institut Montpellier II, 2005.

[15] C.Piombo, Modelisation probabiliste du style d'apprentissage et application :'t I'adaptation de contenus pedagogiques indexes par une ontologie, 2007, pp. 46-50.

[16] H.Bozic, «AHyCo: a Web-Based Adaptive Hypermedia Courseware System,» Journal of Computing and Information Technology -JeIT, vol. 3, n° 113, p. 165-176,2005.

[17] BLOOM, Handbook on Formative and Summative Evaluation of Student Learning, New York: McGraw-Hill Book Co, 1971.

[18] C.Martel et ai, «Une Ingenierie des Environnements Informatiques pour I'Apprentissage Humain basee sur un modele de I'activite - de la conception :'t l'operationnalisation et I'execution des scenarios d'apprentissage,» chez Actes du colloque IDM, 2007.

[19] C.Piombo, Modelisation probabiliste du style d'apprentissage et application :'t I'adaptation de contenus pedagogiques indexes par une ontologie, these de doctorat obtennu en 2007, 2007

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