Enhancing Users’ Interactions in Mobile Augmented Reality Systems

Through Fuzzy Logic-Based Modelling of Computer Skills

Christos Troussas

, Christos Papakostas

and Cleo Sgouropoulou

Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece

Keywords: Fuzzy Logic, Mobile Augmented Reality, Spatial Ability Training, Intelligent Tutoring System, Computer

Skills, Feedback, Assistive Messages, Personalization.

Abstract: Mobile augmented reality (AR) systems offer exciting opportunities for blending digital content with the real

world. However, engagement in mobile AR environments mainly relies on users’ computer skills, which vary

among users and impact their ability to utilize this technology. This research addresses this gap in

understanding the influence of users’ computer skills on interactions in mobile AR. In view of the above, this

paper presents a fuzzy logic-based model to assess and refine users’ computer skills in the context of mobile

AR systems. Modelling the users' computer skills, the system is responsible for providing personalized

assistive messages and feedback. These messages are designed to align with the fuzzy weights that have been

established, enhancing users’ interactions within the mobile AR environment. The presented approach is

integrated in a personalized mobile AR system for spatial ability training. The evaluation results demonstrate

a highly positive outlook. A major conclusion of this work is that fuzzy logic modelling has significant

potential to enhance user experiences and drive advancements in mobile augmented reality technology.

1 INTRODUCTION

Current research advancements in the field of

augmented reality (AR) technology, including mobile

AR, have revealed important opportunities for

different applications, either for entertainment

purposes (e.g., gaming) or education and professional

training purposes (Papakostas et al., 2021). Mobile

AR offers to users an experience blending digital and

physical world, where virtual elements are integrated

into the real environment through the use of mobile

devices (White et al., 2019). However, despite the

potential benefits that mobile AR may offer, there are

several challenges for the users, interacting with these

systems. These challenges can arise from the lack of

familiarity with the technology, limited computer

skills, or lack of knowledge regarding the capabilities

and functionalities of such systems (Verma et al.,

2022).

Indeed, computer skills and knowledge of users

can play a crucial role in determining their ability to

effectively interact with mobile AR systems. Users

https://orcid.org/0000-0002-9604-2015

https://orcid.org/0000-0002-5157-347X

https://orcid.org/0000-0001-8173-2622

who have a higher level of computer skills and

knowledge are more likely to easily navigate and

utilize the features and functionalities of mobile AR

applications. On the other hand, users with limited

computer skills may face difficulties in understanding

and utilizing the various interaction types or

functionalities offered by mobile AR. Therefore, it

becomes imperative to model users’ computer skills

and knowledge (Irshad et al., 2021; Virvou et al.,

2012; Virvou & Troussas, 2011) in order to enhance

the way of interaction and overall experience with

mobile AR systems.

There are various techniques available to model

users' computer skills and knowledge in the context of

mobile AR systems. One such technique is fuzzy logic

(Campanella, 2021; Krouska et al., 2019; Troussas et

al., 2020), which allows for the representation and

analysis of imprecise and uncertain information. Fuzzy

logic provides a flexible framework for capturing and

reasoning about the vagueness and uncertainty

associated with users' computer skills. By employing

fuzzy logic-based models, it becomes possible to

Troussas, C., Papakostas, C. and Sgouropoulou, C.

Enhancing Users’ Interactions in Mobile Augmented Reality Systems Through Fuzzy Logic-Based Modelling of Computer Skills.

DOI: 10.5220/0012204300003584

In Proceedings of the 19th International Conference on Web Information Systems and Technologies (WEBIST 2023), pages 381-390

ISBN: 978-989-758-672-9; ISSN: 2184-3252

381

assess users' computer skills on a continuum rather

than a binary classification. This enables a more

nuanced understanding of users' proficiency levels and

allows for personalized interaction experiences in

mobile AR systems.

Enhancing user interaction in mobile AR systems

can be achieved through the implementation of

various techniques, such as providing feedback and

delivering informative messages (Kassim et al., 2017;

Troussas et al., 2021). Feedback mechanisms can

guide users, provide assistance, and offer real-time

suggestions to enhance their understanding and

utilization of mobile AR features. Messages can be

delivered to inform users about specific

functionalities, tips, or updates related to the mobile

AR application they are using. These enhancements

aim to improve the user experience, increase

engagement, and facilitate effective interactions in

mobile AR environments.

Analysing the literature on mobile AR systems, it

is obvious that it has witnessed significant growth

throughout the last years. The studies mainly focus on

ameliorating the user experience, rendering the

interface user-friendly, providing new forms of

interaction (Han et al., 2022; Ito & Nakajima, 2021;

Kim et al., 2022; Lee et al., 2020; Linowes, 2021; Lo

& Lai, 2021; Petrovic et al., 2021; Qiao et al., 2019;

Vardhan et al., 2022; C. Wang et al., 2019; Y. Wang

et al., 2021; Xu et al., 2022; Zhao & Guo, 2022; Zhou

et al., 2020) as well as injecting intelligence in such

systems for the purposes of modeling users,

knowledge, etc. (Leon Garza et al., 2020; Papakostas

et al., 2022, 2023; Peña-Rios et al., 2016, 2017;

Strousopoulos et al., 2023). The intelligence in these

systems can be achieved using various techniques,

including fuzzy logic.

This paper focuses on the investigation of users’

computer skills and their impact on interactions

within mobile AR systems. Towards this direction, a

fuzzy logic-based model is presented to assess and

refine users’ computer skills in the context of mobile

AR. The assessment of computer knowledge in

relation to users’ interaction with the mobile AR

environment was conducted using a questionnaire,

being developed by experts in the field and

specifically targeted the various aspects of mobile AR

interaction. The presented approach is integrated into

a personalized mobile AR system for spatial ability

training, where assistive messages and feedback are

delivered based on the established fuzzy weights.

Evaluation results demonstrate a highly positive

outlook, highlighting the potential of fuzzy logic

modeling to enhance user experiences and drive

advancements in mobile AR technology.

2 FUZZY WEIGHTS

Providing assistance to users of mobile augmented

reality systems involves considering their computer

skills, which is a complex task accompanied by

uncertainty. For our study, we utilized a questionnaire

developed by a panel of 15 informatics faculty

members from public universities. The questionnaire

consisted of 10 questions; each assigned a score

ranging from lower to higher proficiency. Participants

were instructed to select one of the given options for

each question, with each option corresponding to a

specific grade (e.g., one grade for option A, two

grades for option B, etc.); the maximum grading for

each participant was set at 40. The questionnaire was

designed to assess participants’ familiarity and

proficiency in various aspects of mobile augmented

reality systems. Its questions explored their previous

experiences, comfort levels, and knowledge related to

AR technology and its integration with other digital

tools or platforms.

For instance, it is challenging to definitively

classify a user with a computer knowledge test score

of 7.5/10 as either good or very good, as both

classifications have some level of truth. To address

this challenge, fuzzy logic offers a suitable solution.

In this approach, learners' computer skills are

represented by four fuzzy weights: Novice (N), Basic

(B), Advanced (A), and Proficient (P), each

characterized by trapezoidal membership functions

(Table 1, Figure 1). These functions are defined by

four boundary values (a1, a2, a3, a4), where the

degree of membership gradually increases from 0 to

1 between a1 and a2, remains constant at 1 between

a2 and a3, and decreases from 1 to 0 between a3 and

a4. Trapezoidal membership functions were chosen

because they accurately capture the interval where

students' scores fully belong to a specific knowledge

category.

As previously mentioned, the current level of

computer knowledge of a user in AR interactions is

represented using the membership functions

discussed earlier. These membership functions define

the values of the fuzzy weights, ranging from 0 to 1.

A value of 1 for the knowledge level indicates that the

user has achieved mastery in the domain and

possesses comprehensive knowledge. Consequently,

the total value of each divided fuzzy set represents the

knowledge level of a domain learning unit and sums

up to 1, as shown by the equation 𝜇





𝑥



+𝜇





𝑥



𝜇





𝑥



+𝜇





𝑥



=1.

WEBIST 2023 - 19th International Conference on Web Information Systems and Technologies

382

Table 1: Membership functions.

Computer Knowledge Level

Membership Function

Novice (N)

𝜇





𝑥





1𝑥10

1

𝑥10

10𝑥15

0𝑥15

Basic (B)

𝜇





𝑥



⎩

⎪

⎨

⎪

⎧

𝑥10

10 𝑥15

1 15𝑥20

1

𝑥20

20𝑥25

0 𝑥10 𝑜𝑟 𝑥25

Advanced (A)

𝜇





𝑥



⎩

⎪

⎨

⎪

⎧

𝑥20

20𝑥25

1 25𝑥35

1

𝑥35

35𝑥37

0 𝑥20 𝑜𝑟 𝑥37

Proficient (P)

𝜇





𝑥





𝑥35

35𝑥37

137𝑥40

0𝑥40

Figure 1: Schemes of Fuzzy Weights.

The determination of the fuzzy weights and the

thresholds for their membership functions was carried

out by 15 faculty members from public universities

specializing in informatics. These faculty members

were asked to provide descriptive definitions of

learners' computer knowledge levels throughout the

learning process, as well as the ranges of achievement

describing each knowledge level. With over 12 years

of experience in university contexts, the faculty

members possess the expertise necessary to

accurately depict users' computer knowledge skills.

3 ASSISTIVE MESSAGES

DELIVERY

In this section, we provide a comprehensive overview

of the assistive messages and feedback that are

specifically customized to align with the previously

established fuzzy weights. These personalized

messages are delivered to users at different stages of

their interaction with the mobile AR environment,

aiming to enhance their overall experience and

facilitate their engagement with the technology.

Each fuzzy weight encompasses several

categories of assistive messages (see subsections

3.1.1, 3.1.2, 3.1.3, 3.2.1, 3.2.2, 3.2.3, 3.3.1, 3.3.2,

3.3.3, 3.4.1, 3.4.2, 3.4.3, 3.4.4) that can be provided

to the user in a random way. For example, under the

novice user category, there exist message categories

such as "Getting Started", "Navigation and

Exploration", and "Help and Guidance". Once the

system determines the user’s fuzzy weight, it is

responsible for selecting the most suitable category

for delivery on each occasion. To accomplish this, the

system utilizes an if-then algorithmic approach that

takes into account the user’s interaction history, as

well as the current task or context. Within each

category, the system randomly selects a message to

deliver to the specific user, ensuring a personalized

and tailored experience.

3.1 Feedback to Novice Users

Novice users, characterized by their limited or no

prior experience with AR systems, necessitate

comprehensive guidance and step-by-step

instructions. The personalized feedback messages

designed for this user group fall into three primary

categories.

3.1.1 Category: Getting Started

Novices are welcomed to the world of augmented

reality and introduced to basic features through step-

by-step instructions. Instructions include tapping on

virtual objects for interaction and maintaining an

appropriate distance for optimal engagement. An

illustration of a message is: “To interact with virtual

objects, simply tap on them. Try tapping on the

floating cube to see it respond!”.

3.1.2 Category: Navigation and Exploration

Novices are guided on navigation within the AR

environment, encouraging them to explore and

discover virtual content. An emphasis is placed on the

Enhancing Users’ Interactions in Mobile Augmented Reality Systems Through Fuzzy Logic-Based Modelling of Computer Skills

383

use of the on-screen guide and user manual as sources

of assistance when needed. An illustration of a

message is: “To navigate through the AR

environment, swipe left, right, up, or down to move

around and discover more virtual content”.

3.1.3 Category: Help and Guidance

Novices are reassured that they can refer to the user

guide for detailed explanations and helpful tips. The

availability of in-app support is highlighted,

promoting self-paced learning and confidence

building. An illustration of a message is: “If you need

assistance at any point, tap on the help icon in the

menu to access the FAQ section or get in-app support

from our team”.

3.2 Feedback to Basic Users

Basic users have acquired some familiarity with AR

systems and mobile devices, allowing them to

perform common tasks. Nevertheless, they may

require occasional assistance or reference materials

for more advanced features. The personalized

feedback messages tailored to this user group

encompass three primary categories.

3.2.1 Category: Enhancing Interaction

Basic users are congratulated for their familiarity with

augmented reality and encouraged to enhance their

interaction skills. They are guided to experiment with

different gestures and tapping techniques for various

virtual object interactions. An illustration of a

message is: “To interact with virtual objects, tap on

them, and observe how they respond. You can also try

using long presses or double taps for additional

actions”.

3.2.2 Category: Exploring Advanced

Features

Basic users are prompted to explore the settings menu

to customize preferences, thus delving into more

advanced features. Recommendations include

adjusting sensitivity settings and experimenting with

additional features like voice commands. An

illustration of a message is: “Adjust the sensitivity of

your device’s motion tracking to ensure a smoother

and more immersive augmented reality experience”.

3.2.3 Category: Assistance and Resources

Basic users are advised to refer to the user guide for

detailed explanations and troubleshooting tips,

particularly when encountering challenges. The

availability of a help section within the app,

comprising FAQs and video tutorials, is emphasized

to provide valuable insights and guidance. An

illustration of a message is: “If you encounter any

challenges or have questions about specific features,

refer to the user guide for detailed explanations and

troubleshooting tips”.

3.3 Feedback to Advanced Users

Advanced users demonstrate a comprehensive

understanding of AR systems and are proficient in

utilizing their features. These users can navigate

complex interfaces, customize settings, interact

seamlessly with virtual objects, and troubleshoot

common issues. The personalized feedback messages

for advanced users span three primary categories.

3.3.1 Category: Mastering Advanced

Interactions

Advanced users are acknowledged for their mastery

of the basics and encouraged to explore complex

interactions confidently. Suggestions include

experimenting with advanced gestures and exploring

hand or body tracking options for more natural

interactions. An illustration of a message is:

“Consider exploring hand tracking or body tracking

options for a more immersive and natural interaction

with the augmented reality environment”.

3.3.2 Category: Customization and

Optimization

Advanced users are guided toward customizing and

optimizing their AR experience.

They are encouraged to explore advanced settings,

including shaders, level of detail (LoD), and

occlusion culling, to maximize performance and

visual quality. An illustration of a message is:

“Explore the advanced settings to enable features like

occlusion, physics simulations, or real-time

reflections. Push the boundaries of realism and create

captivating AR scenes”.

3.3.3 Category: Troubleshooting and

Support

Advanced users are recognized for their

troubleshooting skills and encouraged to share their

knowledge with the community. Recommendations

include staying updated with software releases and

reaching out to dedicated support teams for more

complex issues. An illustration of a message is: “Stay

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384

updated with the latest software updates and firmware

releases to ensure compatibility, stability, and access

to new features and improvements”.

3.4 Feedback to Proficient Users

Proficient users are highly skilled and experienced,

possessing in-depth knowledge of advanced features

and the ability to optimize system performance. They

may even have the capacity to develop or customize

their own augmented reality experiences. The

personalized feedback messages tailored to this user

group encompass four primary categories.

3.4.1 Category: Unleashing Creative

Potential

Proficient users are welcomed as AR experts, with an

emphasis on their valuable knowledge and skills.

They are encouraged to leverage their expertise to

push the boundaries of augmented reality and develop

unique, interactive narratives or games. An

illustration of a message is: “Welcome, AR expert!

Your knowledge and skills are highly valuable. Use

your expertise to push the boundaries of augmented

reality and unleash your creative potential”.

3.4.2 Category: Performance Optimization

and Customization

Proficient users are prompted to optimize system

performance and visual quality. Suggestions include

fine-tuning advanced settings, experimenting with

lighting and shadow techniques, and implementing

optimization strategies. An illustration of a message

is: “Experiment with advanced lighting and shadow

techniques to add depth and realism to your AR

scenes. Leverage the full potential of the rendering

engine to create visually stunning experiences”.

3.4.3 Category: Collaboration and Sharing

Proficient users are encouraged to share their

expertise with the AR community through forums,

online communities, or developer conferences. They

are advised to consider publishing their AR

experiences or apps on dedicated platforms to reach a

wider audience. An illustration of a message is:

“Share your expertise with the AR community!

Engage in forums, online communities, or developer

conferences to exchange knowledge, collaborate on

projects, and inspire others with your creations.”.

3.4.4 Category: Cutting-Edge Research and

Innovation

Proficient users are recognized as pioneers of AR

innovation. They are encouraged to stay informed

about the latest advancements, explore cutting-edge

areas, and contribute to the field through research,

development, and mentoring. An illustration of a

message is: “You are at the forefront of AR

innovation! Stay informed about the latest

advancements, research papers, and emerging

technologies to continue expanding your expertise”.

4 EVALUATION

In this section, we present the evaluation of the

system’s performance and effectiveness.

4.1 Descriptive Analysis

The evaluation process spanned an entire academic

semester and involved the concept of spatial ability

training tutoring in the context of the undergraduate

course "Educational Technologies and IT Didactics"

of a public university located in the capital city of the

country. The evaluation included the participation of

80 undergraduate students. It is worth noting that the

measurements of gender and age were obtained from

a randomly selected sample and did not influence the

research findings.

Table 2: Questionnaire.

Constructs Question

Personalization Rate the effectiveness of the

feedback messages you received

regarding your AR usage and

roficienc

How well did the personalized

feedback messages address your

specific areas of improvement and

help you enhance your understanding

and skills in AR?

Design Rate the user interface of the AR

lication

ou used.

How intuitive and user-friendly was

the user interface of the AR

application in terms of navigation,

interactions, and accessing features?

User

experience

How would you rate your overall

user experience with the AR

application?

How likely are you to recommend it

to others who are interested in

lorin

AR technolo

Enhancing Users’ Interactions in Mobile Augmented Reality Systems Through Fuzzy Logic-Based Modelling of Computer Skills

385

The questionnaire (Table 2) aims to assess

participants' knowledge, experience, and comfort

level with augmented reality (AR) technology. The

questionnaire consists of four questions that cover

various aspects of AR, including knowledge, usage,

proficiency, familiarity with virtual objects, comfort

with mobile devices, experience in creating AR

experiences, familiarity with different AR

technologies, troubleshooting skills, and integration

of AR with digital tools. The responses will provide

valuable insights into the participants' understanding

and proficiency in AR, which can help in identifying

areas for improvement and tailoring future AR-

related initiatives to their needs.

To conduct a descriptive analysis of the

questionnaire responses, the authors summarized the

data by calculating the frequency for each question

(Figure 2).

Based on the responses from the participants, the

descriptive analysis reveals interesting insights about

their knowledge, experience, and comfort level with

AR technology.

The descriptive analysis of the effectiveness of

feedback messages regarding AR usage and

proficiency reveals that the majority of participants

found the feedback to be helpful. Combining the

“extremely helpful” and “very helpful” categories,

53.75% of participants rated the feedback as highly

beneficial. This indicates that the feedback messages

provided valuable insights and guidance for

improving participants' understanding and skills in

AR. However, there is room for improvement, as

18.75% of participants expressed lower levels of

effectiveness. This highlights the importance of

tailoring feedback to address individual areas of

improvement and accommodating varying levels of

proficiency.

Analyzing the effectiveness of personalized

feedback messages in addressing specific areas of

improvement, the results indicate that a considerable

number of participants (62.5%) felt that the feedback

addressed their needs either “very well” or “well”.

This demonstrates that the personalized feedback

messages were effective in targeting specific areas of

improvement and enhancing participants'

understanding and skills in AR. However, it is

essential to address the concerns of the participants

who found the feedback to be less effective (15%) to

ensure a comprehensive and tailored approach.

Regarding the user interface of the AR

application, the analysis shows that the majority of

participants (71.25%) rated it as either “excellent” or

“good”. This indicates a positive evaluation of the

application's design and usability, suggesting that the

user interface provided a satisfactory experience for

most participants. However, the ratings for the user

interface were not uniformly positive, with 10%

expressing lower satisfaction levels. This highlights

the importance of continuous improvement and

refining the user interface to cater to a wide range of

user preferences and needs.

Overall, the participants' ratings for their overall

user experience with the AR application are positive.

The majority (68.75%) rated their experience as

either “excellent” or “good”. This suggests that the

AR application provided a positive user experience

Figure 2: Frequency of the answers in stacked-bar mode.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

Rate the effectiveness of the feedback messages

you received regarding your AR usage and

proficiency.

How well did the personalized feedback messages

address your specific areas of improvement and

help you enhance your understanding and skills…

Rate the user interface of the AR application you

used.

How intuitive and user-friendly was the user

interface of the AR application in terms of

navigation, interactions, and accessing features?

How would you rate your overall user experience

with the AR application?

How likely are you to recommend it to others who

are interested in exploring AR technology?

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386

for most participants, indicating that it met their

expectations in terms of performance, usability, and

enjoyment. However, a small percentage of

participants (12.5%) rated their experience as average

or poor, indicating areas where the application could

be improved to enhance user satisfaction.

Regarding the likelihood of recommending the

AR application to others interested in exploring AR

technology, the analysis reveals a positive inclination

among participants. A significant majority (81.25%)

expressed a likelihood of recommending the

application, either “very likely” or “likely”. This high

recommendation potential indicates that participants

perceived the AR application as valuable and

beneficial, indicating a positive overall impression.

However, it is crucial to address the concerns of

participants who expressed reluctance or neutrality

(6.25%) to maximize the application's potential reach

and impact.

In conclusion, the descriptive analysis provides

valuable insights into participants' perceptions and

experiences with AR technology. It highlights the

effectiveness of feedback messages, the usability of

the application's user interface, the overall user

experience, and the likelihood of recommendation.

These insights can be used to inform further

improvements and refinements to the feedback

process, user interface design, and overall AR

experience, ultimately enhancing participants’

engagement, satisfaction, and proficiency in AR

technology.

4.2 Statistical Analysis

The instructors divided the population into two

groups, each consisting of 40 students. One group,

known as the experimental group, was instructed to

utilize an AR application that included feedback

messaging. In contrast, the second group, referred to

as the control group, did not have access to the

feedback generation module.

Adaptive feedback messaging plays a significant

role in evaluating the effectiveness of an intelligent

tutoring system that incorporates personalized

feedback through fuzzy logic. In this regard, a

questionnaire was administered to assess users'

perceptions of the system's ability to personalize the

feedback.

To determine whether there is a statistically

significant difference in personalized messages

between students who used the AR application with

personalized feedback and those who did not, a t-test

analysis was conducted. The t-test analysis involved

the following steps:

 Definition of the null hypothesis (H0) and

alternative hypothesis (H1) based on the

research question and objectives.

 Selection of the significance level (alpha) to

determine the threshold for accepting or

rejecting the null hypothesis. We used .05 value

for alpha.

 Data collection from both the experimental

group and the control group.

 Calculation of the mean scores of the two

groups separately, representing the perceptions

of personalized messages.

 Calculation of the variance for each group,

which provides an indication of the spread or

variability in the data.

 Calculation of the t-test, which compares the

means of the two groups and assesses whether

the difference is statistically significant.

 Determination of the p-value associated with

the calculated t-statistic. The critical value is

compared to the significance level (alpha) to

determine if the null hypothesis should be

rejected or not.

 Interpretation of the results: If the p-value is

less than the significance level, the null

hypothesis is rejected, indicating a statistically

significant difference in personalized messages

between the two groups. On the other hand, if

the p-value is greater than the significance

level, the null hypothesis is not rejected,

suggesting no statistically significant

difference between the groups.

At the end of the semester, both the experimental

and control groups were given a questionnaire to

complete. The questionnaire utilized a 5-point Likert

scale, where participants could indicate their

agreement or disagreement on a scale from 1

(strongly disagree) to 5 (strongly agree).

To conduct a t-test analysis between the

experimental group and the control group regarding

the effectiveness of the feedback messages received

regarding AR usage and proficiency, we compared

the ratings provided by the two groups. The t-test

determined if there is a significant difference between

the means of the two groups.

First, we set up the null hypothesis (H0) and the

alternative hypothesis (H1):

H0: There is no significant difference in the

effectiveness of feedback messages between the

experimental and control groups.

H1: There is a significant difference in the

effectiveness of feedback messages between the

experimental and control groups.

Enhancing Users’ Interactions in Mobile Augmented Reality Systems Through Fuzzy Logic-Based Modelling of Computer Skills

387

Next, we performed a t-test analysis using the

ratings provided by the two groups, calculating the t-

value and p-value (Table 3).

Table 3: t-test results.

Grou

A Grou

Mean 4.129 3.083

Variance 0.558 0.742

Observations 40 40

Hypothesized Mean

Difference

df 186

t Stat 11.400

P (T ≤ t) one-tail < .001

t Critical one-tail 1.582

(

T ≤ t

)

two-tail < .001

t Critical two-tail 1.861

Based on the t-value and degrees of freedom, we

can determine the statistical significance of the

results. The p-value is below the predetermined

significance level of .05, so we reject the null

hypothesis and conclude that there is a significant

difference in the effectiveness of feedback messages

between the experimental and control groups.

The t-test analysis reveals a t-statistic of 11.400.

With 186 degrees of freedom, the p-value for a one-

tailed test is less than 0.001. This indicates that the

observed difference in the effectiveness of feedback

messages between the two groups is statistically

significant.

The null hypothesis (H0) assumes no significant

difference between the two groups, while the

alternative hypothesis (H1) suggests a significant

difference. Given that the p-value is less than the

chosen significance level, we reject the null

hypothesis and conclude that there is a significant

difference in the effectiveness of feedback messages

between the experimental and control groups.

Furthermore, the t-test results provide evidence

that the mean effectiveness rating for the

experimental group (M = 4.129) is significantly

higher than the mean effectiveness rating for the

control group (M = 3.083). This suggests that the

feedback messages received by the participants in the

experimental group were more effective in enhancing

their understanding and skills in AR compared to the

control group.

5 CONCLUSIONS

This paper presents a novel approach for enhancing

users’ interactions in mobile augmented reality

systems through the application of fuzzy logic-based

modelling of computer skills. The proposed fuzzy

logic model allows for personalized delivery of

assistive messages and feedback tailored to individual

users’ computer skills, enabling them to navigate and

utilize mobile AR environments effectively.

The evaluation results demonstrate the effectiveness

of the approach, emphasizing the potential of fuzzy

logic modelling to refine users’ interactions and pave

the way for future advancements in mobile AR

technology.

Future plans involve the development of a hybrid

algorithmic approach that combines fuzzy logic with

machine learning techniques. We will explore how

this approach will affect accuracy and adaptability in

tailoring messages to individual users’ needs and

preferences.

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