A Virtual Environment Design Methodology for Educational and/or Training Purposes

Edgar Luis da Silva, Marcelo da Silva Hounsell, Avanilde KemczinskiLARVA – LAboratório de Realidade Virtual Aplicada

DCC – Departamento de Ciência da ComputaçãoUDESC – Universidade do Estado de Santa Cataring

{dcc6eda, marcelo, avanilde}@joinville.udesc.br

Abstract

It is becoming ever more frequent to find 3D Virtual Environments (VEs) being used as part of the teaching-learning process (TLP). These systems can show emphases on the educational and/or training aspect of the TLP which were found to be close but different concepts. So far there have been no guidelines for conceiving them in such different perspectives. This paper presents a design approach for VEs aimed at educational and/or training purposes. It starts by showing aspects of such systems, all gathered from the literature, in order to differentiate them from other kind of VE systems and between themselves. Then, an evaluation on how important these features are is presented and turned into a quantifying measurement which is thereafter incorporated in a methodology that drives the design in an easy, questioning-like and interactive process without losing the perspective of a VE and the TLP.

1. Introduction

The application of Virtual Reality techniques as an educational aid produce 3D Virtual Environments (VE) that facilitate the Teaching-Learning Process (TLP) by offering a new media for experimentation as well as a new way to present contents to the apprentice. It can create alternative realities, in the same way it calls for previous user experiences engaging the apprentice in an interactive and responsive world where learning cam be structured and controllable [7].

In addition, other benefits of using VE in education include [4]: repeating procedures in a oriented way; safety, both for the equipment and for the apprentice, and; cost, if compared to the difficulty of preparing a simulation involving all the real elements.

Among the applications of VEs aimed at the TLP, two different emphases can be identified: the emphasis on education and, on training.

It is not so usual to have an explicit definition of the VE emphasis and there still exists a lack of consensus in the debate about the differences. However, the recognition of the distinction and the identification of aspects of each emphasis can guide the use of techniques and resources while conceiving and developing VEs in such a way that it would facilitate achieving the educators goals (which are different for each emphasis).

This paper presents a research on the differences between educational and/or training VEs and how these guidelines were incorporated into a design methodology so developers can extract the best of the Virtual Reality technology in a quantitative and qualitative approach.

The text is presented as follows: The terminology will be explained in section 2 altogether with a detailed presentation of the differentiating aspects; Section 3 explores the importance of the aspects in face of already existing VEs and argues that the importance can be turned into a quantifying measure in order to be able to calculate an index which are then detailed in section 4 resulting in the identification of four different types of VEs aimed at the TLP; Section 5 presents the designing methodology that was taken as a reference to include the proposed differentiation strategy which is then detailed in section 6; Section 7 presents how the differentiation analysis was incorporated in a methodology decision flow and how it was made a milestone to a given project, and; Section 8 discusses the benefits of the approach and concludes this text.

2. Education and Training

Education and training are two different concepts but somehow close and vague that are difficult to gather if not by close attention and proper comparison. They are distinct, complementary and not competing approaches of the same issue: learning. Nevertheless, it is quite usual that the education and training terms are confused, and in many cases are used as synonyms which they are not.

The differences can be subtle as subtle can be the underlying goals of each emphasis. Making them explicit also highlights completely different perspectives of the TLP.

Conceptually, education is defined as an intellectual challenge that increases the ability to behave and think as opposed to training which improves skills and specific capabilities or procedures for a particular task [3].

It is expected that educational-oriented VEs would allow the apprentice to analyze and ponder on the focus of the study, and all that it encompasses [5]. In these types of VEs time and reflex (for motor actions) would be of minor importance. As for training VEs, they must farm a specific skill over a limited time span, which should work as an innate one while dealing with some task [5]. They also work out memory such as sequences of objects to be manipulated.

To design a VE aimed at the TLP, one must have clear understanding of the characteristics that distinguish those emphases which, in turn, should be taken as requirements. Categories of aspects that differentiate educational and training emphasis have been recently established ([9], [20]) and are summarized below.

The aspects taken into account were selected because they have support in the literature and are supposed to be technology-independent and time disconnected. These aspects were obtained by analyzing various VEs found in the literature and the resulting set focus exclusively on the concepts of education and training.

The categories are (see Figure 1): Content; Pedagogical Model; Communication Model, and; Assessment.

In the Content category of aspects, “what” the VE is presenting to the apprentice is categorized more clearly. The Pedagogical Model and Communication Model categories of aspects highlights “how” and “when” the VE deals with pedagogical and communication issues, respectively. The Assessment category of aspects infers

“why” the environment exists as an educational tool at all.

Figure 1: Structured aspects for pedagogical characterization

All 4 categories are divided into 16 aspects that unfold to a total of 27 different features. For the context of this paper, an aspect is a conceptual characteristic of the VE while a feature is an atomic subdivision of an aspect that have some antagonistic options of realization.

For instance, consider that in the category “contents” there is an aspect of “form” which unfolds onto 3 different antagonistic features related to the ways the content can be presented. Therefore, the “form” of the “content” can be realized as (the following features) “theory or practice”; “descriptions or instructions” or; “metaphors or sequence of information”. Content is the category, form is the aspect and theory/practice (to take just one example) are the features.

Following, all the categories of aspects will be detailed. For every single differentiating aspect of each Educational-oriented (EVE) and Training-oriented (TVE) virtual environments a most commonly found feature realizations will be made explicit. However, they should be seen as guidelines to get the feeling and help distinguish the approaches as other similar features can also be found. In addition, the references given ahead back up the concept by either discussing the issue or exemplifying it in the form of an existing VE.

2.1 ContentRegarding the focus, according to [19], EVEs have

used abstractions as an important features and value comprehension. TVEs focus mainly on the use of instructions and operations for the acquisition of skills.

As for the form of the contents presented to the apprentice, a TVE normally follows the “learning by watching” format ([11], where learning is performed through observation) or “learning by doing” ([11], where learning is performed through the execution of procedures/practices). On the other hand, a VE is governed by mental processes, through the application of techniques such as “learning by comparing” (through definitions/comparisons) and “learning by thinking” through pondering on theories and concepts).

As for the knowledge presented in the content, it is observed that most EVEs are focused on formal content and curricula. Whereas most TVEs deals with contents related to industrial and operational experiences (non formal, neither curricular, unstructured knowledge).

2.2 Pedagogical ModelIn EVEs the pedagogical objectives are the

understanding and perception of values and perspectives while in TVEs the objectives are the acquisition of specific skills and dexterity for technical end.

It was observed that the promotion of learning in the EVE is obtained through mental models construction and decision-making thinking, normally associated to Constructivism [17] or Socio-interactionism [6]. TVEs are focused on actions and technical procedures which are usually associated to Instructional or Behaviorist approaches [12].

EVEs use non-exhaustive (varied) pedagogical procedures through explanations and visualizations [16]. And, TVEs use repetitive drills, with information/data given by commands and orders.

2.3 Communication ModelIn the communication process, EVE presents a

comprehensive and discursive feedbacks whilst TVEs use specific and direct ones (for instance: scores, training status, etc.) [5].

Regarding the collaboration, it is expected that EVEs are multiuser, provide more interactions among apprentices, thus creating learning situations. TVEs are usually single-user, because the acquisition of the training skills (normally physical and motor skills) are acquired through individual processes, although in some cases it may also require other apprentices for experience exchange.

On the navigation aspect which communicates how the apprentice wants to move around, it was observed that for most EVEs the user freely explores the environment, leading to unrestricted movements [16]. In the TVE there are predefined steps (direction and orientation). In these environments the apprentice needs to respect a sequence of steps.

The behavior of the objects is a fundamental element in the communication of the EVE with the apprentice. In several situations it suffices to EVEs to have approximate actions of the real thing but need to cover a wide range of situations for variety sake. TVE emphasize a detailed behavior of some specific (set of) object(s), in search for phenomena fidelity and control.

Regarding graphics, it was observed that the EVE normally use mockery objects, because it is not necessary to visualize the target object with high visual accuracy to understand it [7]. And in the TVE, it is important for the training that objects are represented with realism, once the details are of great importance for the acquisition of specific skills [7].

In the EVE, the greater number of human senses are preferred for the perception of the content, helping the knowledge acquisition to become more meaningful, engaging and therefore better. In the TVE, there is a tendency to concentrate efforts in a minimum set of senses, but with a more faithful representation. This tendency reflects the kinesthetic and/or visual/motor coordination, since the communication is established according to the desired dexterity.

As for the pace of communication between the VE and the apprentice, it was observed that the EVE allows the control of the communication progress, because it aims at the learning/understanding efficiency. And, the TVE presents the events and situations simulated in real and actual time, without the user being able to freeze or interrupt the communication, which emphasizes reflex development and the effectiveness in the performance of tasks, because the actions must occur at the right time.

In EVEs the computational challenge has been the learning model, because in these environments object models and behaviors are simpler. And, in TVE the computational challenge resides fundamentally in the modeling and simulation of the target phenomenon. A clarity over the challenges highlights the attention that must be paid to the communication metaphor. For instance, what is more difficult to model usually is more difficult to get across.

2.4 Assessment

Regarding the assessment strategy, it has been noticed that EVEs focus on a continuous evaluation of the apprentice or, level by level judgment allowing a formative assessment and mainly evaluating mental decision-making process. In TVEs a definitive and final assessment is more commonly used, mainly related to manual process, that is, whether the task was successfully completed, closer to a summative assessment strategy.

As a result of the leaning process an EVE seeks the apprentice´s understanding of concepts and a TVE seeks more concrete results, usually related to conditioning (action) [13].

3. Aspects Dissemination and Pondering

The instances (features) of the aspects over 37 VEs have already been surveyed [10] where 16 of them claimed to have emphasized training while 21 claimed to have emphasized education. Table 1 shows the amount of VEs that implement a feature realization for every aspect. Column "qtd” shows the number of VEs where a particular feature of the environment were found to agree with the alleged emphasis. Column “%” shows the same information considering the total of VEs of that particular emphasis.

It can be seen that even though all aspects have been backed up by the literature, there are some of them which are more usually realized than others. This observation can be used to relate how important a sound concept has been considered by developers so that it becomes implemented in their VEs.

In order to understand the meaning of that distribution a qualitative significance has been assigned, as follows (scale adapted from [18]):

Those aspects that were ever so frequently implemented on the related emphasis, and are above 90% of frequency, were defined as "necessary" to it (shown in green on Table 1);

Aspects with frequency of implementation between 75% and 90%, were defined as "desirable" to that specific emphasis (shown in yellow);

Aspects which frequency vary between 50% and 75%, were defined as "usual" (shown in orange), and;

Other aspects less than 50% frequent were defined as "optional" for defining that specific emphasis (shown in white).

It should be noticed that even “optional” aspects are valued and were not discarded because they have sound theoretical background to help distinguish between emphasis even though they have been scarcely found over existing implementations so far. Therefore, at the end, they add just a little to the differentiation but they do contribute.

Table 1: Frequency of aspects on various VEs

Educational Aspects

Training Aspects

Aspect Qtd % Qtd %

Focus 21 100,0% 15 93,8%

Form (T/P) 20 95,2% 14 87,5%

Form (D/I) 20 95,2% 14 87,5%

Form (C/S) 20 95,2% 14 87,5%

Knowledge (F/E) 15 71,4% 16 100,0%

Knowledge (C/T) 21 100,0% 15 93,8%

Objective (P/D) 21 100,0% 15 93,8%

Objective (Pc/Pd) 21 100,0% 16 100,0%

Objective (F/C) 21 100,0% 16 100,0%

Objective (C/H) 21 100,0% 16 100,0%

Learning (R/A) 21 100,0% 16 100,0%

Learning (C/I) 21 100,0% 14 87,5%

Procedures (E/O) 21 100,0% 14 87,5%

Procedures (V/I) 21 100,0% 16 100,0%

Procedures (V/R) 21 100,0% 16 100,0%

Feedback 21 100,0% 9 56,3%

Collaboration 10 47,6% 13 81,3%

Navigation (L/D) 19 90,5% 9 56,3%

Navigation (E/O) 19 90,5% 12 75,0%

Behavior 20 95,2% 10 62,5%

Graphics 18 85,7% 14 87,5%

Perception 20 95,2% 10 62,5%

Pace 17 81,0% 9 56,3%

Challenge 19 90,5% 13 81,3%

Strategy (C/F) 7 33,3% 7 43,8%

Strategy (Me/Ma) 20 95,2% 15 93,8%

Result 21 100,0% 16 100,0%

From the total of 27 aspects taken into consideration most of the aspects (92.59% for education and 77.77% for training) have been found to be “necessary' or “desirable” to characterize their focus and thus, indeed contribute to distinguish between emphases. This means that the whole set of aspects taken in consideration in Table 1 is, in fact, a meaningful one.

It is possible to establish a weighting (numerical value) that reflects the importance of each aspect which would turn quantifiable the qualitative assignment made before. A straightforward weighting, from 1 to 4, was established for the aspects ranging from “optional” to the most important “necessary” ones, respectively.

It was selected “1” to start the weighting scale instead of “0” because each specific aspect has a sound reason to be included (was based on the specific literature on education and training) and do contribute to the emphasis although it could be minor. Thus, an aspect of conceptual origin, can contribute more or less depending on their weight (which reflects its importance).

The assigned weight shows how the aspect influence and contribute to that emphasis in face of all aspects that contribute to it. Therefore, it should be noticed that the weighting can be different for education and training features (notice that the colors in Table 1 vary from left to right columns).

4. Index Calculation

Once all aspects have been observed, or not, on a particular VE, the total count of existing and pondered aspects can be obtained. This count can then be compared to a maximum possible account in order to see how "educational" or "training" a VE is. This account can be thought of as an index that reflects both the theoretical and conceptual issues of an emphasis (since their origin was derived from the literature) as well as their practical realization (since their importance came from a survey of existing and already implemented VEs).

An index can be calculated through dividing the contribution of each observed aspect against all the "valid aspect", for that emphasis. A “valid aspect” is the one that the evaluator was able to effectively perceive their features to affirm its presence, or absence, in a given VE. An "invalid aspect" is the one in which the VE evaluator is not sure or did not want to comment on the presence (or not) of the realization of that aspect in that VE.

As an example, an index can be calculated based on only 7 “valid aspects” because they were the only ones possible to be evaluated in order to assert whether or

not they are present in the VE. The 20 remaining aspects are identified as invalid. In other words, they could not be assessed and therefore can not influence the index of VE. This means that the index takes into account only what was actually observed, considering the influence of every aspect and ignoring others whose presence was not confirmed or discarded.

Equations 1 and 2 was established to calculate the indexes as stated before.

I ED=[i=1

i=27 weight−ed i ∗ed−asw−prst ] i=1

i=27 weight i∗valid−aspects i Equation 1: Educational emphasis index

I TR=[i=1

i=27weight−tr i ∗tr−asw− prst ]i=1

i=27 weight i ∗valid −aspects i Equation 2: Training emphasis index

Where: IED measures how many weighted aspects of the

educational emphasis the VE shows; ITR measures how many weighted aspects of the

training emphasis the VE shows; i indicates the ith aspect among the 27 existing in

Table 1; weight-ed(i) indicates the importance (from 1 to 4)

of the ith aspect for an educational emphasis; weight-tr(i) indicates the importance (from 1 to 4)

of the ith aspect for a training emphasis; ed-asw-prst is set to 1 if the ith aspect presents

educational features, and 0 otherwise, and;tr-asw-prst is set to 1 if the ith aspect presents

training features, and 0 otherwise; valid-aspects(i) is 1 for every ith response actually

asserted (either for the educational or training emphasis) and 0 for those that have not been defined.

The possibility of not being sure of the presence or absence, in a sense, brings the need to also calculate a "degree of confidence" (DC) on the indexes

DC can be calculated through the Equation 3:

Equation 3: Degree of confidence calculation

Where: DC is the degree of confidence on the indexes on

the basis of valid aspects;

DC= NRA27

∗100%

NRA is the number of valid aspects, that is, aspects effectively indicated as present or absent, and;

27 is the number of aspects that can be taken into account for total confidence.

Thus, as the DC calculates how many aspects were actually observed, its ratings can vary from 0% to 100%, depending on what was possible to be observed.

As an example, a VE which has the IED of 100% but GC = 55%, indicates that only 15 (15 = 55% of 27) aspects were actually inspected but all of them presented features related to the educational emphasis. Alternatively, a VE with ITR of 44% and DC 100% indicates that all aspects were inspected, but only some of them contributed to the emphasis on training.

As ITR and IED do not interfere in each other (once a VE can present both features of a particular aspect, one for training purposes and another for education), it creates a space of combinations which, in turn, helps to classify VEs.

As outlined in Figure 2, after calculating and plotting IED and ITR, a VE would fall into one of four types, as follows:

Educational and Training Virtual Environment (AVET), if there is a strong presence of both educational and training aspects realization, i. e. more 50% of IED and more than 50% of ITR. This VE type contains elements that allow its use both as an educational as well as a training tool;

Educational Virtual Environment (AVEd), if more than 50% of IED and less than 50% of ITR

was found. This kind of VE has a majority of educational aspects implemented, but may offer some resources for training purposes;

Training Virtual Environment (AVTr), if there is more than 50% of ITR and less than 50% of IED. This kind of VE emphasizes training aspects but also offer educational reseources on some aspects ;

Informational Virtual Environment (AVIn), if it does not fit the criteria above (when IED and ITR are both below 50%). These VE do not offer a minimal amount of resources to be fully considered as a TLP helping tool.

Figure 2 help understand the source of many confusion on the subject. Education and training applications are note competing implementations and a bit of some can appear on a VE. Therefore, it is difficult to tell apart each bit and stand for one (only) emphasis.

Figure 2: Types of virtual environments

5. Maieutics Methodology (M²)

M² is a methodology which aims to help the conception, design and development of VEs and its process is driven by questions (mostly of narrative answers, but also of objective multi alternative straight options). Its questioning-like approach helps developers thoroughly think over what is being conceived and is done in order to provide a better use and awareness of Virtual Reality (VR) technology potentials [1]. M² is a wide coverage methodology but still an evolving one where several features have been added for detailing aspects of the VR software design, such as the design of auditory sense [8]; the need and use of complete texts [21]; strategies for usability and 3D interaction [2] and, selection of interaction input devices [15].

M² has a web version [14] and can be used for any designer who has an interest in design / build a new VE. M² is a methodology to aid the project and therefore, is a technical tool, which appears divided into different levels of detail and complexity. It is not a tool for the end-user but rather a tool for engineering the VR software.

M² is divided into two main steps: the Conceptual Design step and, the Detailing step. The latter is composed of three other independent sub projects, which could be developed at the same time, allowing a concurrent engineering teamwork. Therefore, M² is composed of the following subproject: Conceptual Design (at the Conceptual step), Communication Design, Structural Design and Construction Design (all last three at the Detailing step).

At the Conceptual Design step the functional, educational and informational requirements/capabilities of the VE are defined in a broad sense. The objective of Conceptual Design step is to identify what the purpose (goal) of the VE is and to gather information, make explicit the VE added

value, define target audience, identify problems, milestones, functional, technical and technological development requirements and to assess the systems feasibility and risks.

This project consists of both objective questions (of multiple alternative choices) and subjective questions (of narrative answers). It is responsible for setting the Basic Objective Profile (POB), the Advanced Objective Profile (POA, with aspects of interaction), the Conceptual Description (DC, a textual and/or graphical documentation of the VE); and the Interaction Device Objective Profile (POD).

The POB questions are mandatory because they define the profile of the system, the "DNA" of the VE, and altogether are used as database primary key for other M² features.

6. Education and Training in M²

M² was chosen to have education and training analysis added to it because it constitute one of the few methodology available in the literature that have specific concern on environments with the use of technology for Virtual Reality (various questions throughout M² relates to VR technology and goals such as immersion, navigation, exploration, sensory interface, object´s behavior, and so on).

If the VE type (from Figure 2: AVET, AVEd, AVTr and, AVIn) is taken as a design objective all educational or training aspects presented before become requirements. Therefore this specification has been incorporated into M² Conceptual Design step and implemented in the form of questions, called Objective Educational Profile (POE), one for each of the 27 aspects. POEs, like all M² objective questions, are numbered, bring the subject in parentheses, the question in full and offers an explanation if required. Five alternative answers are presented for every aspect:

(i) the particular resource for educational emphasis;

(ii) the particular resource for training emphasis;(iii) "both", an option to inform that the VE

presents/should offer resources related to either education and training;

(iv) "not applicable" (or none) indicates that the evaluator (or designer) confirms that there are (will be) no features related to neither emphases;

(v) "I don't know" indicates that there is a lack of confidence (there is no clear understanding about the presence or absence of any particular feature of that kind of aspect in the system) or

one simply decided not to respond to this aspect (possibly to decide about latter).

In order to register the desired VE type (among the options presented in Figure 2) a new and specific question has been included in M² as part of the system DNA, a POB-6, that establishes the VE "pedagogical gene".

Once the complete VE DNA has been established by answering all POBs the user can choose to answer the Conceptual Description (DC), the Objective Advanced Profile (POA) or, from now on, the Objective Educational Profile (POE). Figure 3 details the flow of sub projects and decisions for the new Conceptual Design step.

Figure 3: M² Design flowchart

If the designer chooses to specify the Objective Advanced Profile (POA), all the VE 3D usability issues are specified. After that, a 3D usability checklist is adaptively generated in agreement with the usability requirement set for the VE. By answering the checklist the developer would identify conforming and non

conforming issues to decide if that diagnosis lead to an accepting, or not, VE. Details of the POA can be found in [2].

At this stage (shown in Figure 3 by the "(*)" symbol), the designer altogether with the educator, are those who decide whether the project is looking good, or not, because the diagnosis is multifaceted (too many “conformances” to achieve) and is not "decisive".

Alternatively to answering the POA or, immediately after that, the designer can answer the Conceptual Description (DC) questions, which will account for more clear definition for the VE sequencing, decisions, aesthetics, mission and goals. The completion of all DC questions generates an additional documentation and help answer POEs but are not a prerequisite to them.

PODs must be answered after POA completion because M² can only suggest on input devices after a complete specification of the interaction.

After answering POAs and DCs, the designer can move to answer POEs which will be a decisive point on the project, as explained below.

7. M² Decisions Flow with POEs

POEs questions the resources that will be/are available in the VE for every one of the 27 already mentioned aspects. Once they are all properly answered, M² system generates a pedagogical diagnosis, as shown in Figure 4.

Figure 4 shows the actual design step (as indicated by a small circle with the number 1), the pedagogical diagnosis, the desired “pedagogical gene” chosen while answering POB-6 (see circle number 2), a set of three bars (see circle number 3), the resulting VE type according to the evaluated indexes (see circle number 4) and, the final agreement status between what was desired and what was found from the POE answers (see circle number 5).

The set of three bars indicate the educational index (IED), the training index (ITR) and the “degree of confidence” (DC), respectively, from the left to the right. The values are shown in numbers as well as a level for each bar.

If the final status shows a red bar, no agreement was found and thus the system does not allow the designer to go forward on the design specification. Otherwise, a green bar would allow the designer to continue on the following steps of M².

So, if the diagnosis indicates agreement to the VE type (pedagogical category) previously specified it means that the characteristics defined at the beginning of the project were consistently detailed in POEs. On the other hand, if the VE specification ended up

containing features that do not coincide with what was expected changes somewhere are in order.

Figure 4: Pedagogical diagnosis

This pedagogical diagnosis step is shown in Figure 3 by the "(**)" symbol and it is quantifiable, objective and "decisive", and is done automatically by the M² system without the designer intervention. That´s quite different from the other “(*)” decision point presented before where continuing with the design was left for the designer/educator to decide.

When no agreement is found a red bar appears indicating that the designer (and educator) must return to the previous steps to change design decisions. One or more of the revisions below must be done in order to achieve agreement (they are shown on the left hand side of Figure 3):

Reviewing the VE “pedagogical gene”, when it is found that the POB-6 has been improperly answered in face of all other POEs, DCs and POAs. It could be said that the right emphasis was misinterpreted at the beginning according to Figure 3;

Reviewing POE answers, because they do not agree with what is really sought. This could have happened due to misinterpreting DCs or due to misunderstanding the meaning of POEs alternatives, and;

Reviewing DCs because the VE functionalities and behavior must be revisited. Although DCs are not POEs prerequisites and could have been answered regardless POEs they could have been used as reference to them and therefore if any functional divergence between DC and the

pedagogical gene exists, it could have influenced POEs.

After performing one or more of the decision changes, the indexes are recalculated and a new pedagogical diagnosis is generated. If agreement is found, the “pedagogical gene”, DCs, POEs are all coherent among each other and therefore, M² would allow the designer to go forward.

In the case the VE is not of educational purpose it should be characterized as an AVIn in POB-6 and therefore, any value given for DC, IED and ITR would make M² free the designing process.

It should be noticed that because DC exists, M² is able to present the pedagogical diagnosis at any time, even after answering every single POE and thus, the designer/educator can gather the sense of how the answers reflect on IED and ITR.

In addition, because DCs and POAs are not used to compute the “pedagogical gene” by answering only POEs one could have a pedagogical diagnosis for any existing system which means that this approach can be used solely as an analysis tool. All of this is presented in a very visual and easy way to understand.

8. Conclusion

Knowing the aspects that lead to an educational or training emphasis are key to a pedagogical Virtual Environment (VE) project that better enjoy the benefits that VR can bring to this niche of applications. Although important, these aspects were scattered all over the literature, disorganized for the VR context and, often hidden in the VEs. Now they were made explicit, listed, pondered and related to each emphasis thanks to the structured approach presented in this paper.

The identified list of aspects may not be the only one and no definite list was intended to be presented, but instead they are clear indicators for the two existing emphasis. The aspects were obtained from sound theoretical and conceptual research and proved pragmatic on a survey carried out with various VE focused on the Teaching-Learning Process (TLP).

It was found that all 27 aspects can be realized both for educational and for training purposes because they are not exclusive but complementary and still, plainly distinguishable from each another.

Once the characterizing aspects were identified, they were incorporated into a methodology that helps to conceive new VEs with pedagogical focus. For this aim, it was necessary to establish a quantifiable measurement (calculated as indexes) for each emphasis that, along with a “degree of confidence”, is now a checkpoint for

the Maieutics Methodology (M²). M² was chosen because it shows particular interest and support of the use of VR technology on its core. The result is a new methodological decisive milestone approach for the comprehensive and innovative design of educational and or training VEs.

The way the POEs were incorporated in M² allows them to be used not only for designing but also (and only) for diagnosis of any VE to establish or confirm the emphasis it presents. It is worth mentioning that the analysis approach presented can help bring some uniformity of evaluation to avoid cases (found during the research) in which the author claims his/her VE as being of one emphasis but, in fact, it presents aspects of the other.

As stated before, there is no definite list of aspects and there is the need to extensively use M² new approach beyond the former 37 VEs taken as a reference in order to fine tune it regarding emphasis differentiation.

Nevertheless, the list of aspects appears to be good enough even for characterizing (differentiating) any educational and/or training multimedia systems (once few of the presented aspects are intimate to VR technology). On the other hand, because no such differentiation on multimedia systems was found and the systems taken into consideration to this research were all VR related, further research would be required to confirm broadening the usage of the list of aspects.

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