Implementation of the augmented reality to electronic practice

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Comput Appl Eng Educ. 2020;28:420–434. wileyonlinelibrary.com/journal/cae

420

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R E S E A R C H A R T I C L E

Implementation of the augmented reality to

electronic practice

Murat Selek

1

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Yunus E. Kıymaz

2

1

Vocational School of Technical Sciences, Konya Technical University, Konya, Turkey

2

Department of Information Technology Engineering, Selçuk University, Konya, Turkey

Correspondence

Yunus E. Kıymaz, Department of Information Technology Engineering, Selçuk University, Konya 42150, Turkey. Email:yunusemrekiymaz89@hotmail.com

Abstract

This study focuses on the application of augmented reality (AR) to electronic practice and its impact on the success of student. The developed application in this study has a recognition‐based system structure. The Unity3D platform and the Vuforia AR software development kit are used to perform the application. Flowcharts have been prepared for each electronic experiment and the audio files and the texts to be displayed on the screen have been used to improve interaction with the user. To test the effect of AR application on student success, an experimental study was also conducted on two groups of students (control group [non‐using AR] and test group [using AR]). The data obtained from the experimental study have been analyzed using independent sample ttest. According to t‐test result at 95% confidence level, the probability value p= .002113432 < .05 for the use of AR is found. These results show that the use of AR in electronic practice makes a significant difference in student success.

K E Y W O R D S

augmented reality, basic electronics, independent sample t test, Vuforia

1 |

I N T R O D U C T I O N

Technological innovations, which are advancing faster every day, change the learning methods and improve learning skills. One of these methods is augmented rea-lity (AR), which has been extensively used in many areas in recent years. AR allows offering a virtual object in a real environment.

Expressions that are used to describe virtual and real environments made it difficult to understand the concept of the AR. Based on the results of the study conducted by Milgram in 1994, Milgram suggested that the interval between virtual and real environments can be expressed as a mixed reality (MR) environment that would in-corporate the AR, as well as the augmented virtuality (AV) [18]. When a point of junction in the range of the MR is selected for two different environments, this point is called AR if it is close to the real environment whereas

it is called AV if it is close to the virtual environment (Figure1).

1.1 |

The significance of AR

As a result of these pioneering studies performed by Milgram [18,19], AR and AV have become attractive study subjects for researchers. Nowadays, we observe many AR and AV applications in various fields. It is possible to classify these studies based on the fields in which they are applied like engineering, geographic in-formation systems, medicine, management and organi-zation, human behavior, and education.

Up to the present, many different AR studies have been conducted to improve active learning in the field of education. Some of these studies are presented in this section.

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experiential learning theory. In the study, it is empha-sized that the use of such interesting technologies in-creases the students' desire to learn more about the environment, as well as their desire to develop more positive emotional bonds [10].

The AR application developed in another study is de-signed to provide mobile learning (m‐learning) process for subjects taught on cultural heritage. The developed soft-ware is designed for m‐learning in the educational context and applied by using the mixed method study analysis on subjects consisting of elementary school students in Chile. The quantitative and qualitative results obtained show that in the context of electronic learning, AR can be more ef-fective than using similar technologies [12].

The basic approach in the study of Wei et al. is to introduce a motivational design, social psychology, and computational creativity model. Many high schools are aiming to improve students' creativity and learning pro-ductivity as well as learning motivation including AR technology in creative design courses in China [25].

In the study of Yılmaz, educational magical toys (EMT) have been developed with AR technology. EMT includes puzzles, flashcards, and match cards to teach students aged 5–6 years animals, fruits, vegetables, vehicles, objects, occupations, colors, numbers, and shapes in their early childhood education. This study has revealed that teachers and children like EMT activity. It is also suggested that this study will fill a gap in the field of educational tech-nology by introducing a new AR application [27].

The study by Hasan et al. describes the application of AR in primary education by using “individual subject cards” for different subjects of the primary school curri-culum. The review of the results of the research shows that, in general, AR technologies have a favorable out-come and potential to be adapted to education [8].

In the study of Da Silva et al., the use of AR and interactive design as a tool to teach anatomy and knowledge discovery is discussed. It is stated that inter-active visualization techniques such as AR and VR can work together with knowledge discovery process in medical and biomedical databases [6].

In Lu and Liu's study, an innovative marine learning program has been designed to integrate digital game‐ based learning concepts into AR technology for

Salmi et al. examine learning and motivation and cognitive aspects related to informal learning by using AR technology. The results show that the AR‐technology experience is beneficial for everyone and it is a promising method for learning abstract phenomena in a concrete way [23].

In this study, Martín‐Gutiérrez et al. have presented an AR application to improve the spatial capabilities of engineering students. To encourage the development of students' spatial abilities, an augmented book called AR‐ Dehaes was designed to provide 3D virtual models that help students in performing their visualization tasks. A validation study with 24 first‐year mechanical engineer-ing students at the University of La Laguna showed that education had a measurable and positive impact on stu-dents' spatial capabilities. At the same time, a survey shows that AR‐Dehaes is accepted as an easy‐to‐use, at-tractive, and very useful technique for students [17].

In this study, Andújar et al. proposed a new design called augmented remote laboratory (ARL) for virtual and remote laboratories. ARL has been tested on In-dustrial Engineering and Computer engineering students at the Huelva Engineering Faculty in Huelva, Spain. Thanks to AR techniques, it is emphasized that ARL gives students more practical and learning experience than traditional laboratory classes. The effectiveness of using this method as a remote laboratory has been eval-uated comparatively on the same group of students in practical laboratory courses at the university. The stu-dents completed a questionnaire after experiencing both types of practice, and the results showed that the use of ARL improves student outcomes [2].

In this study, Borrero and Márquez present new learning concepts for laboratory applications in en-gineering. Their AR‐based laboratory application (ARBL) enables teachers and students to remotely use the facil-ities of the real laboratory environment (Internet/in-tranet). To evaluate the developed ARBL, an educational experience was realized with the participation of a group of 10 teachers and a group of 20 students. Both groups completed their laboratory applications in the fields of Digital Systems, Robotics, and Industrial Automation in the field of Electronic Engineering. Laboratory applica-tions were carried out in three different ways as

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classroom, virtual, and ARBL. After the applications were completed, both groups completed some questionnaires to measure the contribution of ARBL to classroom lab and virtual lab (VL). The obtained results suggest that ARBL significantly increases the existing VL and remote lab possibilities [3].

In the study of Cerqueira et al., the development of an AR application to visualize five convex regular poly-hedrons known as platonic solids has been discussed. The pilot study has shown that the students enjoy the application and that it facilitates the understanding of the study of the solids [5].

In another study, an application supporting m‐learning was designed using geolocation‐based AR, which is thought to be useful in directing students to laboratories or sites in clinical areas. It is stated that mobile teaching strategies such as AR in learning clinical skills have the ability to facilitate learning in a low‐cost way [7].

Different studies such as use of AR technology as an alternative to the traditional education approach, which is based on books, as an application that combines learning processes based on the cooperative learning technique [11,16]; investigation of the effects of the use of AR technologies on university students' attitudes about laboratory skills and laboratories [1]; using the AR in power engineering education [20]; proposing a mobile AR system to assist in the understanding of resistive electrical circuits for undergraduate engineering students [20] can be given as example.

1.2 |

The aim of the proposed AR‐based

electronic practice

In the experimental setups used in electronic education, many circuit elements, circuit type, measuring device, and measurement type can be used. Therefore, it may be difficult for the student to fully understand and success-fully complete the application at first sight [21]. The best way to resolve this problem may be to ensure that stu-dents are prepared for electronic applications in advance. Participating in the electronic practice with this pre-paration, students will be able to notice and correct the mistakes that they can make while performing the practice, to practice having a better understanding of it, and to be able to manage the time more effectively [4].

Our study is intended to demonstrate its contribution to electronic education and the design of the application of an innovative engineering which is teaching assistant. For this purpose, the article emphasizes both the en-gineering side and the contribution of the study to edu-cation. In this study, the AR‐based electronic practice approach that enables the student to prepare in advance

for the practical work which will be carried out in the laboratory after the lesson has been developed. Thus, it is ensured that the electronic practices to be performed on the basic electronic training set are simulated as AR in mobile devices and in different environments. For this purpose, in our study, the model and the practice of an AR application which will serve as an example for the use of innovative technologies in electronic education has been studied. At the same time, an experimental study has been also included in our study to determine the effect of the developed AR application on learning success.

In this article, Section 2 consists of Materials and Methods used in AR‐based electronic practice applica-tion, while Section3 reveals the Results, Section4 and Section5consists of the Conclusion and Discussion.

2 |

M A T E R I A L S A N D M E T H O D S

In this section, necessary software for performing AR application, training set used for the application of AR to electronic practice, the AR‐based electronic practice ap-plication, its use and the experimental study are described.

2.1 |

The materials of the proposed

AR

‐based electronic practice

Some developer tools were required for AR im-plementation, and the Unity3D platform is one of them [14]. Unity3D is a development environment with vari-able contents that are suitvari-able for creating 3D content. By using the ready‐to‐use sets and intuitive workspace, it allows application developers to save time [14]. One of the reasons why Unity3D is preferred is that it includes an intuitive graphical environment [28].

Libraries were also needed to develop the applica-tion. It is essential to select the appropriate library, which is suitable to the base where the application will be created. Apple launched iOS (iPhone/iPad operating system) for smartphones and tablets, while Google re-leased Android OS (operating system) and application programming interface as an alternative. Android and iOS are the most commonly used OSs for smartphones and tablets.

For Android and iOS, it is necessary to download the files from relevant websites and make the required ad-justments before launching the compiler and the soft-ware development kit (SDK) installation.

Vuforia is an SDK for developing AR applications as well. With iOS, Android, and Unity3D support, the

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Vuforia platform enables us to create a single native ap-plication which can reach numerous users of smartphone and tablet at the same time [26].

The Vuforia SDK uses advanced algorithms to capture and monitor the features that are inherent in the image. The Vuforia SDK recognizes the target in the image by comparing these features with a known target resource database [24].

The Vuforia SDK is not an optical character recognition system but it works on the basis of pattern matching. An application developer uploads the“trackable” images on the Vuforia website. The image‐processing algorithm extracts property points after converting each image to the grayscale. The property point data are then used in the application. Thanks to these points, the prepared applica-tion can recognize and track the objects displayed in the image in the database [13]. The Unity3D program is downloaded and installed from the relevant site and then the project is initiated.

The original state of the circuit board, circuit elements, and measuring probes of the training set to be used in the proposed AR‐based electronic practice application are presented in Figure 2. The basic electronic training set which is manufactured by LabVolt was developed for students to practice in circuit analysis, analogue electro-nics, and electronics measurement lessons.

2.2 |

The method of the proposed

AR

‐based electronic practice

The design of the AR application for the basic electronic training set consists of four stages: design of AR circuit board, the design of AR circuit elements, the design of AR measuring probes and virtual buttons, and finally, the design of AR application user interface (UI).

In the first stage of the method, the ground image that is used for virtual simulation of the board is likened to the circuit board by adjusting the color, light, and placing the pins (Figure3a,b).

If the ground image consists of irregular and non-repeating patterns, the property points obtained from the image increase. The high number of these property points makes it easier to match the ground and the object. In light of this information, the ground image in Figure3bhas been selected and a conformity test has been performed. Since the evaluation score is high, the image is added to the project database in our developer account on Vuforia webpage.

The evaluation which is obtained at the end of the conformity test for the ground image is displayed in Figure 4. At the end of the process, it is comprehended that the matching ratio between the pins on the circuit board and the points obtained from the ground image is considerably high.

F I G U R E 2 (a) Original circuit board; (b) circuit elements; (c) measuring probes

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When we compare the original image of the board used in the LabVolt basic electronic training set (Figure 4a) and the AR circuit board image (Figure 4b) created in the image editor, it can be seen that the placement of the pins in the original image and the placement of the pins in the virtual image are very similar. After determining the suitability of the ground image that we have designed, the second design stage is initiated.

For designing the AR circuit elements and transfer-ring them to the Unity3D program, 3D Builder, and Paint 3D programs are used, respectively. A particular atten-tion is attached to design the circuit elements displayed in Figure5a, in accordance with the original elements.

In the third stage, virtual buttons are hidden elements that users will use to perform operations in the AR ap-plication. Probes are the contact tips used for measure-ment when using the meter (such as the voltmeter, ampere meter). Actual probes are presented in Figure2c. Designed probes are displayed in Figure5b.

The virtual buttons mentioned in Figure6aare invisible during operation. However, to indicate the location of the “reset” button, a reset button design in Figure6bis added to the lower right side.

In the final stage, a simple structure is used in the UI design as observed in Figure7. In the introduction of UI, the experiment numbers and the details of the experi-ment are found when one of the experiexperi-ments is selected.

Unity3D is a program that supports running the AR application on a cross‐platform. The Vuforia library supports Android, iOS, and Universal Win-dows Platforms as well as some of the smart glasses. By benefiting from these characteristics, it will be possible to run our AR application on cross‐platforms. The appearance of the application on the Android platform is shown in Figure8a, while the appearance on the iPod platform is presented in Figure 8b. The implementation of the application on cross‐platforms can be carried out in a short time, in case the libraries of related platforms are installed.

2.3 |

Implementation of the proposed

AR

‐based electronic practice

In this section, one of the experiments that can be per-formed in AR application and the measurements to be performed on it are mentioned. In addition, the process steps of this experiment, the flow diagram used to per-form them, the test applied to the actual circuit board, and the image of the experiment in AR application are presented.

Our experiment aims to improve the student's ability to measure voltage and current and to use the multi-meter. In the experiment, it is needed to carry out the

F I G U R E 4 (a) Original image; (b) virtual image of the circuit board

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measurement procedures listed below. The circuit dia-gram, the installation of the experiment on the experi-ment board, and the image of the AR application are presented in Figures9a,9b, and9c, respectively.

When using AR‐based electronic practice application, the performance of voltage measurement with a volt-meter is shown in Figure 10.

In the experiment, it is needed to carry out the measurement procedures listed below.

(1) Measuring of the voltages that drop on R1, R2, R3, R4, R5, and R6.

(2) Measuring of the currents that flow through the R1, R2, R3, R4, R5, and R6 elements.

While these operations are performed on the AR ap-plication, the flowchart given in Figure11is used. When the AR code that executes the given flowchart runs, the steps and images that are formed during the execution of the experiment are presented below.

• The selecting of the experiment, the forming of the experiment circuit on the ground, and displaying of the first operation are given in Figure12.

• The steps to be performed to complete the operations are indicated as text messages at the bottom of the screen (Figure12b).

• The measurement in text message is performed (Figure13).

F I G U R E 6 (a) Layout of the virtual buttons in the circuit (blue rectangles); (b) design of the reset button

F I G U R E 7 (a) Application experiment list screen; (b) experiment detail

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• If the measurement is wrong, the user is warned with sound and text messages (Figure14a).

• If the measurement is carried out using the wrong measuring instrument, the user is also warned by sound and text message (Figure14b).

• When the correct measurement is performed, the next step is initiated (Figure15a).

• When the experiment is completed successfully, it is indicated by sound and text message (Figure15b).

When using the AR application first, the relevant measuring points are marked, secondly, the measuring device is displayed between the marked points, and finally, the result is displayed on the screen depending on whether the measurement is true or false.

2.4 |

Experimental study

In vocational and technical education, it should be supported with laboratory practices to better under-stand the education given in the classroom [4]. For this purpose, vocational lessons are generally made as two separate sections in the form of classroom lectures and laboratory practices. At the end of the practice to measure the contribution of practice to the knowledge

level of the student, different evaluation methods are used such as preparation of an experiment report, making a small written or oral exam [4]. According to the results of this evaluation, the level of under-standing of the subject by the student is tried to be determined.

After the developed AR‐based electronic practice was completed, to see its contribution to electronic practice, an experimental study was conducted on students in the same way as in the education process.

For this purpose, six control groups and six test groups consisting of students of the Electronics and Au-tomation Department, Vocational School of Technical Sciences, Konya Technical University were formed.

The experiments are conducted as groups of four in the classes. To create a natural distribution, each of the control and test groups was arranged as a four‐ person group, similar to the one in the training process.

To evaluate the results of the electronic practice ap-plied in groups in terms of student success, the apap-plied lesson grades and the academic averages at the end of first and second semesters of 48 students were taken into consideration.

The lessons with in‐class experiment practice were selected as the applied lessons. These lessons consist of

F I G U R E 9 (a) Circuit to be installed; (b) installation on the circuit board; (c) view of the experiment in augmented reality‐based electronic practice application

F I G U R E 1 0 Steps of a measurement process in an augmented reality‐based electronic practice application (a) marking of the 1st measurement point; (b) marking of the 2nd measurement point; (c) connecting the measuring instrument to selected points; (d) implementation of operations on the proposed AR application

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F I G U R E 1 1 Flowchart of augmented reality application

F I G U R E 1 2 (a) Selection of the experiment; (b) finding the ground and displaying the operation

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AC circuit analysis (ACCA), DC circuit analysis (DCCA), digital electronics (DE), digital design (DD), analog electronics 1 (AE1), and analog electronics 2 (AE2).

All of the control and the test groups have performed the electronic practices on the same basic electronic training set. The six test groups have made their electronic applications after using the AR‐based electronic application. On the contrary, the six control groups have made their electronic applications without using the AR‐based electronic application.

According to the given answers in the experiment reports of the students that constitute the control and the test groups, the successes of the control and the test groups were evaluated.

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R E S U L T S

To evaluate the effect of the AR‐based electronic practice in our study on the success of the students receiving the electronic training, the experimental study given in Sec-tion2.4was carried out on 12 groups (6 control and 6 test groups) each consists of 4 students. A few images of the electronic practice in the experimental study are given in Figure16.

The academic data used in the experimental study for evaluating the control and the test groups consist of the applied lessons grades and the academic averages of 48 students for 2016–2017 academic year. The grade averages in applied lessons of the students in each group

F I G U R E 1 4 (a) Incorrect measurement status; (b) use of incorrect measuring instrument

F I G U R E 1 5 (a) True measurement status; (b) the successful completion status of the experiment

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that constitutes the control and the test groups are given Tables1and2, respectively.

The academic averages of each group that constitutes the control and test groups are given Table3 along with their overall academic averages.

As can be seen from Tables 1, 2, and3, the control groups are more successful than the test groups in both the overall grade average and the overall academic average.

As a result of the electronic practices performed, ac-cording to the given answers of each group that con-stitutes the control and test groups, the percentages of success of groups are given in Table4.

To enable comparison with each other of different data given in different scaling systems such as grade

average, success percentage, and academic average, these data are given in Tables5 and 6as normalized data for the control and the test groups, respectively.

The normalized grade averages, the normalized suc-cess percentages, and the normalized academic averages of the control and test groups are shown in Figures 17

and18, respectively.

As it can be observed in Figures17and18, the overall academic average (2.35) and the overall grade average of the applied lessons (50.1) of the control groups, are higher than the overall academic average (1.97) and the overall grade average of the applied lessons (41.96) of the test groups. However, the overall success average on the practice of the control groups (66.64%) is lower than

6 52.5 38.75 37.75 37.25 38.5 22.5 37.87

Abbreviations: ACCA, AC circuit analysis; AE1, analog electronics 1; AE2, analog electronics 2; DCCA, DC circuit analysis; DD, digital design; DE, digital electronics.

T A B L E 2 The grade averages in the applied lessons of the test groups

Groups ACCA DCCA DE DD AE1 AE2

Grade average of groups Overall grade average 1 64.5 50.75 51.75 60 45.5 76.5 58.16 41.96 2 58.25 37.25 50.5 57 36.5 58.75 49.7 3 53.25 37.5 41.25 41 31.25 38.5 40.45 4 53 42.75 36.75 28.25 28.75 29 36.41 5 35.75 43 39.25 34.75 22.5 35 35.04 6 41 28 31.75 32 23.75 35.5 32

Abbreviations: ACCA, AC circuit analysis; AE1, analog electronics 1; AE2, analog electronics 2; DCCA, DC circuit analysis; DD, digital design; DE, digital electronics.

T A B L E 3 The comparison of the academic averages of the control and the test groups

Groups 1 2 3 4 5 6 Overall academic average Control 3.67 2.67 2.39 2.08 1.87 1.47 2.35 Test 2.97 2.41 1.95 1.75 1.6 1.18 1.97

T A B L E 4 The percentages of success of the groups for electronic practices performed

Groups 1 2 3 4 5 6 Overall success average Control 83.33 74.97 74.97 58.31 58.31 50 66.64 Test 100 100 91.63 91.63 83.33 83.33 91.65

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the overall success average on the practice of test groups (91.65%).

In addition to these evaluations, independent samples t test is used to reveal statistically the results of the ex-perimental study. t Test is the most widely used test among hypothesis tests [9]. It can be defined as an approach that tries to determine statistically whether there is a significant difference between the averages of the two groups of data [9]. t Test is also called Student's

ttest. Small samples are also known as testing technique. That is known in this way because it is a statistical technique that can be applied when n < 30 or when there is doubt that the main mass average is normal [22]. With this technique, it can be said whether there is a re-lationship between the two data groups [9], but the de-gree of the relationship is not mentioned. In this test, whether a parameter or its interaction is effective on the response is determined by p (significance/probability). Considering the 95% confidence interval, it is concluded that the parameter is effective on the response when p< .05 (5% significance value).

The distributions of the grade averages and the aca-demic averages of the control and test groups are shown in Figures19 and20, respectively. Based on these data, t‐test results of the practical lessons grade averages and the academic grade averages are given in Tables7and8, respectively.

t‐Test results of the control and test groups based on the applied lesson grade averages and the academic averages gave the results of p = .246953967 > .05 and p= .36926391 > .05, respectively. In terms of the grade averages, t‐test results at 95% confidence level have not revealed a significant difference for the control and test groups.

Likewise, the success percentages achieved at the end of electronic practices (Figure 21) are also analyzed. According to t‐test result at 95% confidence level, the probability value p = .002113432 < .05 for the success percentages (Table9) is found.

This probability value (p) means that the use of the AR application in electronic practice provided a sig-nificant difference in the successes of the test groups. As can be seen from these results, the practical successes of the test groups which use AR application developed in the study have yielded better results.

T A B L E 5 Normalized data for the control groups

Groups Normalized grade average Normalized success percentage Normalized academic average 1 0.7158 0.8333 0.9175 2 0.5579 0.7497 0.6675 3 0.5254 0.7497 0.5975 4 0.4379 0.5831 0.52 5 0.3904 0.5831 0.4675 6 0.3787 0.5 0.3675

T A B L E 6 Normalized data for the test groups

Groups Normalized grade average Normalized success percentage Normalized academic average 1 0.5816 1 0.7425 2 0.497 1 0.6025 3 0.4045 0.9163 0.4875 4 0.3641 0.9163 0.4375 5 0.3504 0.8333 0.4 6 0.32 0.8333 0.295

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4 |

C O N C L U S I O N

The proposed approach has been developed to enable the student to prepare in advance for the practical work which will be carried out in the laboratory after the

lecture. The use of AR for this purpose has given a better result in the practical success due to the excessive desire of young people to use and learn the technological de-vices and applications.

As a result of the experimental study, when the overall success average for the control and test groups are compared, it seems that the test groups are ap-proximately 25% more successful than the control groups.

In independent samples, t test performed using the same data at 95% confidence level, the significance value for the success percentages of the control and test groups is p = .002113432 < .05. Therefore, it can be said that the use of AR application in electronic practice makes a significant difference for application success. As can be seen from these results, the application successes of the test groups used AR‐based electronic practice developed in the study yield better results.

As can be understood from the overall success aver-age and p value, the test groups realized their electronic practice with a better understanding.

AR application developed in our study was designed on the basis of LabVolt basic electronic training set, therefore, it includes the circuit elements and applications which are present in this set. Therefore, the applications that can be performed are limited. This application can be also developed and expanded for different training sets.

Although the subject teaching and preparation information were given in advance and their academic data were higher (Figures17and18), the control groups were not as successful as the test groups for electronic practice (Figure 19). This shows that the willingness of the student to perform the practical preparation process has a positive effect on the success. Using AR in appli-cation has contributed greatly to the motivation of the test groups for the preparation process.

F I G U R E 1 8 Normalized grade average, normalized success percentages, normalized academic average for test groups

F I G U R E 1 9 The distribution of the grade averages of the applied lessons for the control and the test groups

F I G U R E 2 0 The distribution of the academic averages for the control and the test groups

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5 |

D I S C U S S I O N

In this study, it has been observed that the success of students with low academic achievements has been increased when innovative technological applications are included in the classical education methods. This shows that enriching electronics education using novel technological applications will draw attention from larger groups of students, and will enable

them to participate more intensely in the training process.

In addition, thanks to object images designed in ac-cordance with the original, students are provided with the opportunity to learn and recognize electronic circuit elements and other equipment that they will use in real experiment before their electronic practice. By this way, the students' perception that electronic training which includes many circuit elements, measuring instruments, and circuit types, is difficult, can be beaten with simple and practical AR applications.

At the same time, the audio and visual interactive warnings inside the application are provided to enable the users to see the errors and mistakes made by them-selves, correct them, and prepare them for electronic practice with more knowledge. Depending on these warnings, it has been observed that the error rates of the students decrease in the experiment.

With this simple and easily applicable AR approach, for all practical courses, it is possible to create more in-teractive AR systems that would work in harmony with the web environment by increasing the diversity of ex-periments. It is seen that the application of this method to the vocational courses will increase the training success.

T A B L E 7 t‐Test results of the grade averages of the applied lessons for the control and the test groups

Group n Var m SD t df p

Test 6 100.548206 41.96527778 10.02737284 −1.229705177 10 .246953967

Control 6 162.284549 50.10416667 12.73909528

Abbreviations: df, degrees of freedom; m, mean of the group; n, sample size of the group; p, probability value; SD, standard deviation of the group; t, value of ttest; Var, variance of the group.

T A B L E 8 t‐Test results of the academic averages for the control and the test groups

Test 6 0.3990075 1.9825 0.631670405 −0.940213917 10 .369263913

Control 6 0.583231 2.362916667 0.763695647

F I G U R E 2 1 The distribution of the success percentages for the control and the test groups

T A B L E 9 t‐Test results of the success percentages for the control and the test groups

Test 6 55.57810667 91.65333333 7.455072546 4.109147026 10 .002113432

Control 6 166.6001767 66.64833333 12.90736908

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A U T H O R B I O G R A P H I E S

Murat Selek received the MSc degree in electronics engineering from the Selcuk University, Turkey, in 1997 and the PhD degree from the Selcuk University, Turkey, in 2007. He is head of the Department of Electronics and Auto-mation, Vocational School of Technical Sciences, Konya Technical University. His research interests

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are in the fields of image processing, infrared thermography, autofocusing, remote control and telecommunications.

Yunus E. Kıymaz received the MSc degree in information technology en-gineering from Selcuk University, in 2018. Currently, MSc student in en-ergy systems engineering from Nec-mettin Erbakan University. He works as a software developer in a private company. His research

interests are augmented reality, image processing, artificial intelligence and renewable energy systems.

How to cite this article: Selek M, Kıymaz YE. Implementation of the augmented reality to electronic practice. Comput Appl Eng Educ. 2020;28:420–434. https://doi.org/10.1002/cae.22204