A Design Framework For Smart City Learning Scenarios

Jihen MALEK, Mona LAROUSSI, Henda BEN GHEZALA

RIADI Laboratory School of Engineering in Computing Science (ENSI)

La Manouba, Tunisia mjihene@yahoo.fr; Mona.laroussi@univ-lille1.fr; henda.bg@cck.rnu.tn

Abstract— In this paper we present a design Framework for Smart City Learning scenarios based on three main aspects Learner, Contextualized Activity and Space. The strengths of our Design framework lie in the fact that, in a formal manner through friendly graphical interfaces, it allows pedagogical designers and teachers:(1) to specify, model, generate and simulate different types of context-aware and adaptive learning activities (e-learning, M-learning and P-learning) and their related contexts. (2) to design indoor and outdoor spaces within smart cities to enable pupils or students to learn through factual cases and to experiment various learning scenarios.(3) To model and simulate interactions and co-adaptivity rules between Learner, Contextualized Activity and Space.

Keywords-component; Pervasive learning; Context-aware; Aadaptation; Smart City Learning; MDD: Model driven development; EML:Educational Modeling Language; Technology-enhanced Learning Design.

I. INTRODUCTION The main thrust of our work presented here evolves

around the modeler Contact-Me we have developed from previous work in pervasive learning [1].

Our survey of related research and practice shows that the notions of learner and activity are not sufficient for describing Smart City Learning Systems. In fact, there is a high interest on Space dimension expressed by the pervasive computing community, which considers Space as a key to design pervasive applications [2] [3] [4]. Due to this interest, a huge amount of contributions have been made in this field. However, a review in existant Educational modeling approaches and their Authoring tool, shows that none of them supports pervasive computing related concepts and especially space [5 ][6 ][7 ] [8 ][ 9][10] [12] [13] [14].

This paper aims at presenting an innovative approach for modeling, emulating and simulating interactions and co-adaptivity between Space, Learner and Activity (and their related contexts). Co-adaptivity approach (or bijective adaptation) [15], proposed in previous works, defines two classes of “adaptivity”: adaptivity of context to activity and activity to context. Such a bijective adaptation aims to facilitate the learner’s life and to create an adequate learning

environment which helps him/her to concentrate better on her/his learning tasks.

This paper is organized as follows. Section 2 describes key dimensions of our Framework while its extended CAAML language is presented in section 3. Section 4 presents the conceptual architecture of our Framework while its supporting tool and modules tests are described in section 5. Section 6 includes evaluation of our work while Section 7 concludes the paper and suggests future research directions.

II. KEY DIMENSIONS OF OUR FRAMEWORK

Fig 1. Key dimensions of Smart city learning design

Our starting point has been a previous pervasive learning design position that there are two dimensions to take into account in modeling pervasive learning scenarios: Learner and activity and their contexts [1]. We propose in this work to widen the initial approach and to take into account Space as a key dimension in the smart city learning design, and not only as a contextual element related to learner (See Fig1). Our Framework leads us to consider Smart city learning systems in terms of three key dimensions: Learner, Activity and Space.

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The essence of our approach to the Learning design is the effective integration of space, by allowing teachers and pedagogical designers to design physical space and to embed digital and smart components within it.

Our approach has the potential of being a scenario driven design framework for implementing Smart city learning systems.

This framework models all possible interactions between Learner, Activity and Space because they influence each others in learning processes. Three types of technology-driven interaction are modeled:

- Learning- Space Interaction (LSI) - Activity-Space Interaction (ASI) - Activity-Learner Interaction (ALI)

III. AN EXTENDED CAAML LANGUAGE The CAAML Language has been introduced in a

previous work as a visual and educational modeling language specific to mobile and pervasive learning [16]. Some extensions of this language are proposed in this paper. Those extensions of the CAAML language lead the introduction of an extended CAAML Meta-Model which integrates the key dimensions presented in section II. The extended CAAML meta-model describes a learning scenario as being a composition of several phases. Each phase includes contextualized activities as shown in Fig 2. A space is composed of many zones and each of them includes technological tools: context and action providers. The extended CAAML Meta-model defines co-adaptivity rules of each phase and relates them to context to trigger the adequate co-adaptivity actions on Space, activities or learner. Context is acquired by context providers (sensors or mobile Smart device) and it is related to learner or activity.

IV. CONCEPTUAL ARCHITECTURE OF OUR FRAMEWORK Based on key dimensions described in section II and according to Fig 3, our Framework includes the following modules: The graphical modeler: through this module based on the extended version of the CAAML language, the teacher or the graphical designer can: (1) Create the Learning Design (specifies the title, learning objectives, prerequisites and outcomes of learning scenario); (2) Model graphically different levels of the extended CAAML Language (resources, components, scenario structure, co-adaptivity rules and Space); (3) Generate a contextualized Scenario Model. The generator and emulator of Mobile applications and The module of transformation into IMS-LD Model were described in a previous work [1].

The Space Designer: based on the contextualized scenario Model generated by the graphical modeler, this module allows the pedagogical designer to design space into zones and to place on them technology entities (sensors, smart objects, actuators).

The context Manager: Realizes the context acquisition from different sources or context providers (Sensors, mobile smart devices and mobile learning application). It filters, interprets and aggregates the acquired contextual data to make them amenable to the controller needs. The co-adaptivity controller : receives contextual data from context manager, and based on co-adaptivity rules, it determines the actions to be triggered on Action providers (Actuators, mobile learning application and Smart mobile device).And those latter act on key dimensions of environment described in section II (space, Learner and activities). Simulation Renderer: the environment Simulator passes the simulation data to the simulation renderer that graphically renders the simulated scenario and shows Learner-Space-activity interactions and co-adaptivity between the generated mobile learning application and context.

Fig 2. The Extended CAAML Meta-Model Structure

class Extended CAAML Meta-Model

Activity

Space

Zone

Context

Learner

Smart Object

Sensor

Actuator

Mobile Smart Device

Action provider

Learning Scenario

Phase

Contextualized Activity

- Role: int

Co-adaptivity Rule

Context Provider

Technological Tool

1..*0..*

1..*

0..*

0..*

1

10..*0..*

10..*

1

1..*

0..*

1..*

1

0..*

1

1..*

1

1..*

1

0..*

0..*

1..*

0..*

1..*

1

0..*

1..*

1..*

0..*

1010

Fig 3. Conceptual Architecture of our Framework

V. FROM FRAMEWORK TO DESIGN TOOL

A. Presentation Our framework leads us to consider Smart City learning scenarios in terms of three key elements: Learner, Contextualized activity and space. To go beyond the theoretical base and to operationalize the framework in a form that designers can readily use, we extend The CONTACT-Me (the Model-Driven Design tool) based on the extended CAAML Meta-model.

To implement those extensions proposed to ContAct-Me, we used:

� Domain-specific modeling Eclipse Tools (EMF and GMF) for developing the graphical modeler;

� XSLT and HTML5 to generate mobile user interfaces;

� The context Simulator DIASIM and the DiaSpec language to simulate the execution of the modeled malleable learning scenario: we integrated this simulator in Contact-me.

Our Design tool, in a formal manner through friendly graphical interfaces, allows pedagogical designers and teachers:

(1) to specify, model, generate and simulate different types of context-aware and adaptive learning activities (e-learning, M-learning and P-learning) and their related contexts.

(2) to design indoor and outdoor spaces within smart cities to enable pupils or students to learn through factual cases and to experiment various learning scenarios.

(3) To model and simulate interactions and co-adaptivity rules between Learner, Contextualized Activity and Space.

B. Test 1) Case Study:Smart City Learning scenario

In order to illustrate how pervasive learning can be applied in a realistic scenario, we will start by introducing a case study. A French high school decides to raise awareness of pupils on the effects of pollution on the environment by organizing a trial allowing the follow-up of the pupil’s educational curriculum in the field of “Education about the environment”. This trial enables pupils to learn through factual cases and to experiment various scenarios using pervasive and mobile technologies.

The physical settings of this trial, where activities take place, are the laboratory of the school and an ecological zone near by an industrial area.

In order to boost intra-group competition, students were divided in three groups under the supervision of their coach and each group consisted of six pupils. Additionally, each group was divided in two subgroups of three students each, where one subgroup was working indoors in the laboratory of the school while the other group was outdoors in the field. The ultimate goal behind this clustering is to reinforce teamwork and collaboration within the individual subgroups. Only one group is conducting this activity at a time, which makes it a collaborative and challenging game that takes place in different locations and four stages as follows:

� Plant sample picking and identification � Water treatment (the measure of the pH and

conductivity rates and the collection of water samples in appropriate recipients).

� Soil analysis (soil sample picking and nature identification)

� Plant characteristics analysis (verification of presence of toxic gases such as co and co4) and plant preservation in the laboratory.

The outdoor subgroup is equipped with iPhone with a 3G connection. The indoor subgroup was equipped with a laptop computer and a Wi-Fi connection.

At the beginning of each of the first three stages, the outdoor subgroup must identify and take a photo of the QR-code stuck to a tree. Instantly, a text adapted to the pupils’ level and pictures that visualize and describe the activities to accomplish in the current stage is displayed on the iPhone. To accomplish those activities, indoor and outdoor subgroups should collaborate together. For example, in the first stage, the outdoor subgroup can take a photo of a plant and send it to the indoor group for identification through internet research.

After a pre-defined time of each stage, the subgroup will receive a stage-adapted quiz via automatic text message. Pupils need to write an answer using their iPhone and submit it. If the answer is correct, the system sends the instructions describing how to reach and identify the next QR-code of the next stage. Else, if the answer submitted by the group is not correct, the system sends an alert to the

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coach informing him/her that pupils need some support. The coach should send to them some hints.

Having completed the aforementioned stages, the indoor subgroups will receive the list of activities of the fourth stage and will get joined by their corresponding outdoor subgroups that will hand over the picked plant samples. The indoor team should run experiments on those samples in view of investigating the presence of toxic gases such as CO and CO4. They should also preserve those samples at an adequate temperature conditions. If the temperature is not appropriate the system will interact with the smart object in which the plant sample will be preserved in order to adjust it to the required experimental temperature.

At the end, each of the three groups, should draw their own conclusions and present the outcome of their study about effects of pollution on environment.

2) Test of the graphical modeler Through this module based on the extended CAAML

meta-model, the teacher or the pedagogical designer can: (1) Create the Learning Design (specifies the title, learning objectives, prerequisites and outcomes of learning scenario); (2) Model different levels of the extended CAAML Language (resources, components, scenario structure, co-adaptivity rules and Space); (3) Generate a contextualized Scenario Model. Fig 4 shows a snapshot of the tool concerning the edition of a co-adaptivity rule. (4) Generate an extended CAAML model in XMI format. Fig 5a shows an example concerning the resources section for the case-study.

Fig 4. Edition of a co-adaptivity rule with Contact-Me

3) Test of the module of CAAML/IMS-LD Transformation

This module transforms models represented in CAAML language into executable models represented in IMS-LD. This is done in a way the IMS-LD complexity is hidden by the use of concepts related to context-awareness.

IMS Learning Design (IMS LD) is a specification for a meta-language which enables the modelling of learning processes. The specification is maintained by IMS Global Learning Consortium [7].

Regarding the technical details, in ContAct-Me the CAAML meta-model is represented in ECore, while the transformation from CAAML to IMS-LD is encoded in the ATL transformation language. In this way, the transformation module is composed by a set of ATL rules (see Fig 5b). Each rule defines the way an input element (that is a given type of entity of the input model) is transformed into a target element (that is a given type of entity of the output model).

Fig 5. (a) Excerpt of the XML encoding concerning the resources; (b)

some rules of the CAAML/IMS-LD ATL transformation

4) Test of the Simulator of pervasive learning scenarios module

Based on the extended CAAML models generated by the graphical modeler, this module allows the teacher to perform the following tasks:

(1) Generate automatically mobile interface for each phase of the pervasive learning scenario based on the generated CAAML model or the IMS-LD model;

(2) Model the execution environment of the simulation; (3) Place and put smart objects and sensors (defined in

the CAAML model) in the adequate zones of the pervasive learning environment;

(4) Launch the simulation of the execution of the pervasive learning scenario in run time thanks to Context manager and co-adaptivity cotroller and

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Simulation renderer. The simulation shows interactions and co-adaptivity between the generated mobile interfaces and the context (the surrounding pervasive learning environment).

Fig 6 shows a snapshot of a simulation scenario

concerning the case-study. In order to enable such a simulation, we generated corresponding mobile interfaces for each phase of the Pervasive scenario. Then we defined the parameters of the simulation, which finally made it possible to execute such a simulation.

Fig 6. Simulation of the case-study scenario

VI. EVALUATION As the pervasive learning is a very innovative field and

most of the pedagogical designers and teachers ignore its key concepts and utilities and in view of assessing the system CONT-me, we had chosen teachers with extensive experience in using E-learning platforms and in creating learning activities in compliance with IMS-LD standard through different authoring tools: 10 designers from computer science field and 10 from different fields (Economics, medicine and sociology). At the beginning, we had briefed the pedagogical designers and teachers on pervasive learning and its basic concepts such sensors, smart objects, QR code, context and co-adaptivity and described the proposed scenario. They had been allowed then to explore different modules of the ContAct-me system while providing them with assistance. The goal is to evaluate the usability of the system from the graphical, ergonomic and functional aspects, by means of questionnaire (Cf. TABLEI). The questionnaire consists of 15 questions divided into 7 groups (HCI, System structure and organization, Consistency and relevance of the CAAML language, Transparency support, Learnability, effectiveness and

satisfaction, technical support). Each group has several close ended questions which covers different aspects of usability. Five levels have been adjusted for agree or disagree with the questions.

Based on the results of the evaluation, the teachers had appreciated:

� The pleasant and friendly graphical interfaces as well as the workflow of the different steps starting from Resource creation up to CAAML model generation.

� The creation of context-aware and adaptive activities unlike the other authoring tools which are not based on context-awareness.

� The structure of the scenario creation being based on phases allowing the creation of game-oriented learning scenarios.

� Their involvement in the specification of adaptivity in design time as it allows them to participate in the development of adaptive mobile and pervasive learning applications. Designers, who disapprove of this, expressed a fear that the design should become more complicated.

� The possibility to export the CAAML model into IMS-LD format in a very smooth manner.

� The model-driven automatic generation of mobile interfaces that corresponds to different phases without forcefully having the knowledge and skills of IT programmers.

� The outputs of the simulation run of their proper designed works.

� The adding of some gaming aspects through the CAAML Language elements.

Along this experience, the teachers from different fields

(Economics, medicine and sociology) did not feel comfortable with the high number of new concepts that are not in common use and they expressed a highly strong agreement with the necessity to get assisted to run the system.

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TABLE I. Results of the Evaluation

N° Questions

Strongly

agree

5

Agree

4

Undecided

3

Disagree

2

Strongly

disagree

1

Response

Average

HCI of the system

1 The interface is pleasant and attractive 12 (60%) 7(35%) 0 (0%) 1(5%) 0(0%) 4.5

2 ContAct-Me is intuitive and easy to understand with

friendly graphical interfaces

10

(50%)

6

(30%)

2

(10%)

2

(10%)

0

(0%) 4.2

System Structure and organization

3 I approve the workflow of the different steps starting

from resource creation up to CAAML model

generation and simulation.

13

(65%)

4

(20%)

2

(10%)

1

(5%)

0

(0%) 4.45

4 I approve the structure of the scenario creation being

based on phases

12

(60%)

4

(20%)

2

(10%)

1

(5%)

1

(5%) 4.25

Consistency and relevance of the CAAML language

5 The same words, concepts and symbols refer to the

same thing in different modules (modeler, mobile

application generator and simulator)

14

(70%)

4

(20%)

2

(10%)

0

(0%)

0

(0%) 4.6

6 I appreciate the model transformation to generate

activities conforming to IMS-LD. 12 (60%)

2

(10%)

3

(15%)

2

(10%)

1

(5%) 4.1

7 I appreciate the adding of some gaming aspects for

attracting and motivating learners 15 (75%)

4

(15%)

1

(5%)

0

(0%)

0

(0%) 4.7

8 Space-human-activity interaction is relevant 12 (60%) 3(15%) 3 (15%) 2 (10%) 0 (0%) 4.25

9 The specification of adaptivity in design time is

relevant

16

(80%)

3

(15%)

1

(5%)

0

(0%)

0

(0%) 4.75

Transparency support

10 I value the model-driven automatic generation of

mobile interfaces based on the CAAML model

13

(65%)

4

(20%)

2

(10%)

1

(15%)

0

(0%) 4.45

11 It is not a necessity to get assisted to run the system 12(60%) 1(5%) 0(0%) 7 (35%) 0 (0%) 3.9

Learnability

12

The new concepts needed to use the system are easy to

understand 11

(55%)

2

(10%)

3

(15%)

3

(15%)

1

(5%) 3.95

Effectiveness and satisfaction

13 I appreciate the involvement of designers within

adaptivity specification and modeling

19

(95%)

0

(0%)

0

(0%)

1

(5%)

0

(0%) 4.85

14 I enjoy the output of the simulation run of my proper

designed work

13

(65%)

4

(20%)

2

(10%)

1

(5%)

0

(0%) 4.45

Technical aspects

15 System speed is good 12 (60%) 2(10%) 3 (15%) 2(10%) 1 (5%) 4.0

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VII. CONCLUSION In this paper we presented a Framework for Smart city

Learning scenarios based on three main dimensions: Learner, Contextualized Activity and Space. This Proposed Framework is an extended version of a previous authoring tool (ContAct-Me). The strengths of our Design framework lie in the fact that, in a formal manner through friendly graphical interfaces, it allows pedagogical designers and teachers:

(1) to specify, model, generate and simulate different types of context-aware and adaptive learning activities (e-learning, M-learning and P-learning) and their related contexts. This step is based on an extended CAAML Meta-Model.

(2) to design indoor and outdoor spaces within smart cities to enable pupils or students to learn through factual cases and to experiment various learning scenarios.

(3) To model and simulate interactions and co-adaptivity rules between Learner, Contextualized Activity and Space.

The proposed Framework is based on an MDD (Model-

driven development) approach and situated in the design time step of the development cycle of a pervasive learning system. Finally, we will attempt in future works to: (1) Boost the game approach in learning as recommended

by the teachers and designers that assessed the Contact-Me system.

(2) Take into account an hybrid simulation that combines simulated and real technological tools. An Hybrid simulation is a key feature to successfully transition to a real environment: it allows real components to be added incrementally in the simulation, as the implementation and deployment progress.

REFERENCES

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[2] KOSTAKOS, V., O'NEILL, E. AND PENN, A. (2006). Architectural Space, Interaction Space and Information Spheres: Designing Urban Pervasive Systems. IEEE Computer, 39(9):52-59.

[3] DAVID RADCLIFFE, HAMILTON WILSON, DEREK POWELL, BELINDA TIBBETTS , « Designing next generation places of learning: collaboration at the Pedagogy-Space-Technology nexus”,The University of Queensland , 2008

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[6] Burgos D., et al. 'IMS Learning Design : la flexibilité pédagogique au service des besoins de l'e-formation.' Revue de l'EPI, 2005.

[7] IMS Learning Design Specification. Retrieved July 03, 2003

[8] Paquette.G, Introduction à la spécification IMS-LD D’une perspective d’ingénierie pédagogique, Centre de recherche LICEF, Canada, 2006.

[9] Pernin J-P., Organisation et animation d'une table ronde, Scénarisation pédagogique : que faire de la proposition IMS Learning Design ? , Choquet C., Martel C., Nodenot T., Conférence EIAH'2005 Environnements Informatiques pour l'Apprentissage Humain, Mai 2005, Montpellier

[10] Koper, R., et al. (2004). ' Representing the Learning Design of Units of Learning.' Educational Technology & Society 7 (3),: 97-111.

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[12] From the website of Mot-LD http://www.cogigraph.com/Produits/IMSLDScenarioEditors/tabid/1099/language/fr-FR/Default.aspx, 2008.

[13] Griffiths, D., Beauvoir, P., Barret-Baxendale, M., Hazlewood, P., & Oddie, A.(2007, November 19). Development and evaluation of the Reload Learning Design Editor. Retrieved January 21, 2008, from Paper presented at TENCompetence Open Workshop on Current research on IMS Learning Design and Lifelong Competence Development Infrastructures:http://hdl.handle.net/1820/1135

[14] LAMS. Retrieved January 16, 2008, from website of LAMS

[15] Malek J., Laroussi M., Deryicke A., Ben Ghezala H., "A Multi-Layer Ubiquitous Middleware for Bijective Adaptation between Context and Activity in a Mobile and Collaborative learning", International Conference on Systems and Networks Communications (ICSNC '06), IEEE Computer Society, Tahiti -French Polynesia, 1-3 November 2006.

[16] Malek J., Laroussi M., Deryicke A., Ben Ghezala H., " Model-Driven Development of Context-aware adaptive learning systems, the IEEE International Conference on Advanced Learning Technology (ICALT’10), Sousse/Tunisia , July 05 – 7, 2010.

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