**3. Heuristics for user interface design in the context of cognitive styles of learning and attention deficit disorder**

The user population is not a system composed of only one type of user. In general, there is a mixture of multiple profiles of users who need to somehow get their needs met [15].

Speaking of users interacting with computers, we refer to the user's knowledge that should be taken into account in the design of an IHC. Below are some features that must be ob‐ served during the interface design [12],[13],[20]:

**•** The presence of an internationalized system or used in more than one country or region. Each country or region has its own peculiarities. Dialects, cultures, ethnicities, races, etc. All these elements end up generating needs that to be satisfied;

Relational-Synthetic style users "tend to have ease of mind to work with images and appre‐ ciate the use of charts, diagrams and demonstrations. They are profecient in working with

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Synthetic-Evaluative style was not evaluated because "the subject of this class carryies fea‐

**•** Analogue-Analytical style (AA): the user cares about the information in blocks, prefers everything organized. Regarding to the interfaces, it is more preferred that objective, or‐ ganized, with infomation presented clearly, without many shades. These users prefer the

**•** Concrete-Generic style (CG): colors should be worked upon the variations of the same color, ie, linearly, less elaborate, meeting the expectations of those who receive informa‐

**•** Deductive-Evaluative style (DE): the information may be indirect, can cause the user hav‐

**•** Relational-Synthetic style (RS): users better understand the information presented through images, different colors, diagrams, etc. The own style is defined with the use of

**•** Synthetic-Evaluative style (SE): the presentation is not what counts most, but the content. A presentation with plenty of written information is of utmost importance. Coherence

From the CLSs (Cognitive Learning Styles) studied, we found that it is extremely important to take into account the characteristics of each user in the construction of new interfaces for the computer. We must take into consideration the colors used in the construction of the interface, the way that the information must be displayed, directly or indirectly, if the interface must be objective or not, using figures. It was necessary to relate the CLSs with users suffering from At‐ tention Deficit Disorder (ADD) or Attention Deficit Disorder with Hyperactivity (ADHD) be‐

The people with ADD and ADHD can not develop the scholar knowledge as expected for their ages. The diagnostic in the scholar age is common because in this period can be found the difficulties of attention and remain silent as the studies of Siqueira and Gusgel Giannetti

This research contributed to the GuideExpert tool incorporating new knowledge items to it, enabling the groups of users with different learning styles (CLSs) and users suffering from

and justification of the data determine how the user will engage with the text.

cause they are related to learning style and how each acquires knowledge.

attention deficit disorder (ADD and ADHD) to obtain special guidelines.

basic colors, or colors worked according to the information and its importance.

charts and mind maps" [21].

tures from the analytics, synthetics, and evaluative" [21].

tion and belongs to that kind of style.

ing to think to acquire information.

[22], Rosa Neto and Poeta [18].

characters, colors and pictoric examples.

In analysis of the cognitive styles of learning, this study concluded that:


People with special needs are another installment of the user community of the system and need adjustments in the system to operate in the environment without difficulty.

For the ECAs we used the basis of the research article: Project Tapejara from Souto [21]. The ECAs refer the subject's characteristic way of learning new concepts or even to generate elaborations of prior knowledge. According to Madeira, et al. (Bica et al., 2001), the ECAs are: Analogue-Analytical (AA), Concrete-Generic (CG), Deductive-Evaluative (DA), Rela‐ tional-Synthetic (RS) and Synthetic-Evaluative (SA).


Souto [21] states that users having Analytic-Analog style "may require more time for learn‐ ing, because when confronted with new information, tend to get a considerable depth on the subject, through intense".

Users having Concrete-Generic style, Souto [21] says that they "tend to be pragmatic and careful in their learning situation. The learning objectives, evaluation criteria and feedback must be clear to this style, because then he can work towards the goals".

Users having Deductive-Evaluative style "may come to disregard large number of concrete examples, when they believe they have already understood the logical pattern underlying the new information" [21].

Relational-Synthetic style users "tend to have ease of mind to work with images and appre‐ ciate the use of charts, diagrams and demonstrations. They are profecient in working with charts and mind maps" [21].

Synthetic-Evaluative style was not evaluated because "the subject of this class carryies fea‐ tures from the analytics, synthetics, and evaluative" [21].

In analysis of the cognitive styles of learning, this study concluded that:

**•** The presence of an internationalized system or used in more than one country or region. Each country or region has its own peculiarities. Dialects, cultures, ethnicities, races, etc.

**•** These characteristics are considered common in a traditional interface, may not corre‐ spond to those made for children. They have unique needs for their age. Beyond these specific needs for the children users the designers need to deal with the dangers that are usually present in a web environment, such as pornography and violent or racist content;

**•** The existence of elderly among the users should be checked for these and needs met;

need adjustments in the system to operate in the environment without difficulty.

People with special needs are another installment of the user community of the system and

For the ECAs we used the basis of the research article: Project Tapejara from Souto [21]. The ECAs refer the subject's characteristic way of learning new concepts or even to generate elaborations of prior knowledge. According to Madeira, et al. (Bica et al., 2001), the ECAs are: Analogue-Analytical (AA), Concrete-Generic (CG), Deductive-Evaluative (DA), Rela‐

**•** Analogue-Analytical Style: using prior knowledge, seek information using standards of comparison. The information is analyzed in blocks. Performs elaborations relating the

**•** Concrete-Generic Style: trying to understand the contents in a linear and sequential way; works with memorization through systematic exemplification. The individual is pragmat‐

**•** Deductive-Evaluative Style: is systematic and critical, making analysis of the information.

**•** Relational-Synthetic Style: the individual better understands the information through pic‐

**•** Synthetic-Evaluative Style: by intercalation between the global view of data and its evalu‐ ation, seek to learn new information, analyzing them as a whole. They organize the study

Souto [21] states that users having Analytic-Analog style "may require more time for learn‐ ing, because when confronted with new information, tend to get a considerable depth on the

Users having Concrete-Generic style, Souto [21] says that they "tend to be pragmatic and careful in their learning situation. The learning objectives, evaluation criteria and feedback

Users having Deductive-Evaluative style "may come to disregard large number of concrete examples, when they believe they have already understood the logical pattern underlying

Does not consider concrtet examples. His work and attention are high.

tures, colors, diagrams, etc. Has the ability to abstract hypotheses.

must be clear to this style, because then he can work towards the goals".

material, preferring theoretical material; they are systematic.

All these elements end up generating needs that to be satisfied;

tional-Synthetic (RS) and Synthetic-Evaluative (SA).

previously acquired knowledge and the new.

ic and careful.

92 Advances in Expert Systems

subject, through intense".

the new information" [21].


From the CLSs (Cognitive Learning Styles) studied, we found that it is extremely important to take into account the characteristics of each user in the construction of new interfaces for the computer. We must take into consideration the colors used in the construction of the interface, the way that the information must be displayed, directly or indirectly, if the interface must be objective or not, using figures. It was necessary to relate the CLSs with users suffering from At‐ tention Deficit Disorder (ADD) or Attention Deficit Disorder with Hyperactivity (ADHD) be‐ cause they are related to learning style and how each acquires knowledge.

The people with ADD and ADHD can not develop the scholar knowledge as expected for their ages. The diagnostic in the scholar age is common because in this period can be found the difficulties of attention and remain silent as the studies of Siqueira and Gusgel Giannetti [22], Rosa Neto and Poeta [18].

This research contributed to the GuideExpert tool incorporating new knowledge items to it, enabling the groups of users with different learning styles (CLSs) and users suffering from attention deficit disorder (ADD and ADHD) to obtain special guidelines.


**R1: When carriers \_ADD == child**

R4: When carriers \_ADHD == elderly

Then meta-guideline = help\_adhd; user\_child

Then meta-guideline = help\_adhd; user\_elderly R5: When carriers \_ colorblindness== child

R6: When carriers \_ colorblindness == elderly

R7: When carriers\_visual\_impairment == child

R8: When carriers \_ visual\_impairment == elderly

Then meta-guideline = help\_ special\_need; user\_child

Then meta-guideline = help\_special\_need; user\_elderly

R9: When carriers\_special\_need == child

R10: When carriers\_ special\_need == elderly

Then meta-guideline = eca\_aa; user\_child

Then meta-guideline = eca\_aa; user\_elderly

Then meta-guideline = eca\_cg; user\_child

Then meta-guideline = eca\_cg; user\_elderly

Then meta-guideline = eca\_da; user\_child

Then meta-guideline = eca\_da; user\_elderly

Then meta-guideline = eca\_rs; user\_child

Then meta-guideline = eca\_rs; user\_elderly

R11: When eca\_aa == child

R12: When eca\_aa == elderly

R13: When eca\_cg == child

R14: When eca\_cg == elderly

R15: When eca\_da == child

R16: When eca\_da == elderly

R17: When eca\_rs == child

R18: When eca\_rs == elderly

**Table 3.** Rule Selection

Then meta-guideline = help\_ colorblindness; user\_child

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Then meta-guideline = help\_ colorblindness; user\_elderly

Then meta-guideline = help\_ visual\_impairment; user\_child

Then meta-guideline = help\_ visual\_impairment; user\_elderly

**Table 2.** Augmented expert system taxonomy

**Figure 5.** Rule Knowledge Base - People with ADD - Children – Elderly

Eighteen new classes were created and added to the taxonomy, according to the surveys [3], [22] and [18]. The newly added metaguidelines are listed at Table 2



**Table 3.** Rule Selection

**Meta***GUIDELINES*

94 Advances in Expert Systems

*9.13* **Volume/sound design**

*9.19* **Figure/image design**

*11* **User Types** *11.1* **Elderly Users** *11.2* **Children Users**

**Table 2.** Augmented expert system taxonomy

*10* **Assistance to people with disabilities** *10.3* **Asssitance to people having ADHD**

*12* **CLS – Cognitive Learning Style** *12.1* **AA – (Analogue-Analytical)** *12.2* **DE – (Deductive-Evaluative)** *12.3* **RS – (Relational-Synthetic)** *12.4* **CG – (Concrete-Generic)**

**Figure 5.** Rule Knowledge Base - People with ADD - Children – Elderly

**R1: When carriers \_ADD == child**

R2: When carriers \_ADD == elderly

R3: When carriers \_ADHD == child

Then meta-guideline = help\_add; user\_child

Then meta-guideline = help\_add; user\_elderly

[22] and [18]. The newly added metaguidelines are listed at Table 2

Eighteen new classes were created and added to the taxonomy, according to the surveys [3],

*10.4* **Assistance to people having visual disabilities**

*9.14* **Mouse design** *9.15* **Keyboard design** *9.16* **Source design** *9.17* **Help design** *9.18* **Link design**

The knowledge basis of GuideExpert consist in the "WHEN-THEN" rules. This study adds the 18 selected rules that were created according to the preview research to the base already built, and follow the same syntax as shown in Table 3.

For the construction of the selection rules we cross information of users of ECAs with ADD and ADHD disorders and other characteristics, we used the parameter age (child and adult). The resulting guidelines for the rule R1, for example, is shown in the Figure 6, which was

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We recommend changes to the GuideExpert interfaces because of the addition of new taxono‐ mies. The changes were suggested in the items: task analysis (because it does not allow the user to choose the user "child" and also to choose the needs of the users); context analysis (new

selected set of guidelines for users with ADD and set of guidelines for user-child.

**Figure 8.** User Profile Analysis

**Figure 9.** HCI evaluation interface


**Figure 6.** Guidelines - People with ADD - Children


**Figure 7.** Cognitive Learling Style elicitation.

As example is shown in Figure 5, the rule knowledge base for people with ADD related to children and elderly people. It was increased to the knowledge base tool.

For the construction of the selection rules we cross information of users of ECAs with ADD and ADHD disorders and other characteristics, we used the parameter age (child and adult). The resulting guidelines for the rule R1, for example, is shown in the Figure 6, which was selected set of guidelines for users with ADD and set of guidelines for user-child.


**Figure 8.** User Profile Analysis

The knowledge basis of GuideExpert consist in the "WHEN-THEN" rules. This study adds the 18 selected rules that were created according to the preview research to the base already

As example is shown in Figure 5, the rule knowledge base for people with ADD related to

children and elderly people. It was increased to the knowledge base tool.

built, and follow the same syntax as shown in Table 3.

96 Advances in Expert Systems

**Figure 6.** Guidelines - People with ADD - Children

**Figure 7.** Cognitive Learling Style elicitation.


**Figure 9.** HCI evaluation interface

We recommend changes to the GuideExpert interfaces because of the addition of new taxono‐ mies. The changes were suggested in the items: task analysis (because it does not allow the user to choose the user "child" and also to choose the needs of the users); context analysis (new items of graphical user interface were added); evaluation of interface design (new items of choices were added for the visual deficient, special needs, ADD, ADHD and others).

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[2] Angele, J., Fensel, D., Landes, D., & Studer, R. (1998). Developing Knowledge-Based Systems with MIKE. *Journal Automated Software Engineering.*, Kluwer Academic Pub‐

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[8] Gennari, J., Musen, M., Fergerson, R., Grosso, W., Crubezy, M., Eriksson, H., Noy, N., & Tu, S. (2002). The evolution of Protégé: an environment for Knowledge-Based Systems Development. *International Journal of Human-Computer Studies*, 58, 89-123.

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GuideExpert user interface was updated with new questions related to the cognitive learn‐ ing style, as shown in Figure 7.

Figure 8 shows the elicitation of the difficulties. The question is if the user has some kind of need such as: ADD, ADHD, color blindness, visual impairment, other disabilities. The sys‐ tem also questions about the user's age (Figure 8, highlighted in red).

The other GuideExpert user interfaces that have changed refer to task analysis, where new items were added to the description of the GUI and interface evaluation. Options have been added to select among different profiles: child users, visually impaired, special needs, ADD, ADHD (Figure 9, highlighted in red). The screen shown in Figure 9 allows the Guide Expert system to select a series of recommendations to evaluate an interface. In order to allow this, the user must highlight the main features of the interface.

By extending GuideExpert it will be possible to specialize more and more recommendations; it will help the designer to automate a way of selecting guidelines that will guide the design or evaluation of interfaces.

## **4. Conclusions**

It was observed during this study through the references related to the proposed theme, au‐ thors are conceptualized as Nielsen, Shneiderman and Plaisant, making several recommen‐ dations for building interfaces for children, elderly, etc. However, most of the recommendations deals with isolated aspects of the characteristics of users. It was noticed a large gap in this area in order to relate more than one feature. Given this problem, this study examined the learning styles and attentional deficits, allowing to generate a series of recom‐ mendations, guidelines, that fit the specific characteristics of the users profile. At the stage of acquisition of this new knowledge it was used as basis the class ontological description re‐ lated to the content of the knowledge of GuideExpert. Following the methodology for ontol‐ ogy construction, it was made the acquisition of knowledge and conceptualization of new classes. Thus, a new taxonomy was added to the GuideExpert system together with the guidelines. The use of these recommendations helps the designer to interface with more knowledge giving the possibility to access them in an automated fashion and with various features, resulting in better recommendations and with best models specified by users.

## **Author details**

Sandra Rodrigues Sarro Boarati, Cecilia Sosa Arias Peixoto and Cleberson Eugenio Forte

Methodist University of Piracicaba – UNIMEP, Brazil
