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Article

The Perception Scale for the 7E Model-Based Augmented Reality Enriched Computer Course (7EMAGBAÖ): Validity and Reliability Study

by
Erinç Erçağ
1,* and
Aykut Yasakcı
2,*
1
Department of Computer and Instructional Technology, University of Kyrenia, North Cyprus via Mersin 10, Girne 99320, Türkiye
2
Department of Computer and Instructional Technology, Near East University, North Cyprus via Mersin 10, Nicosia 99138, Türkiye
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(19), 12037; https://doi.org/10.3390/su141912037
Submission received: 30 June 2022 / Revised: 10 September 2022 / Accepted: 16 September 2022 / Published: 23 September 2022
(This article belongs to the Special Issue Future Online Learning for Education Resilience – Humanized, Flexible and Smart)
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Abstract

:
Education, teaching, and learning topics, known to have gained an international dimension with technological developments, are still seen as the most discussed themes and subject to change. It is clear that in the 21st century, the increasing information density, the means of transfer, and the technological adaptation skills of the teacher and the learner are at the forefront, and more efforts are required to develop them. The integration of technologies in education and training is related to the necessity of preparing learners in the most suitable way for future work and lifelong learning within the information society. For this reason, for the COVID-19 pandemic process and thereafter, starting with known education models makes it necessary to enable the development of education, teaching, and learning under better conditions and situations by blending them with technological developments. Everyone has understood the ever-changing and developing universal digital world much better during this pandemic. The 7E model of the Constructivist Learning Theory (CLT), known as the student-centered model based on distance education, has been mandatory for the entire education community during the first global pandemic of the digital age. Augmented Reality (AR) is another web-based technological development that can work in harmony with the 7E model. In the 7E model, the teaching of the lessons was at the forefront since the learners learn by doing, experiencing, and applying, directly participating in the lesson, and sharing opinions. For the present study, a scale was developed to determine the perceptions of the learners about the 7E model-based AR-enriched computer lesson. Validity and reliability studies were also conducted on the data obtained from the developed scale. The scale, which was prepared using a five-point Likert scale, was applied to 400 students who fit the profile of the sample group. A statistical analysis of the results concluded that 26 low-factor loading items should be removed from the questionnaire, and the final version of the 28-item scale was a six-factor structure. The statistical analysis concluded that the scale was suitable for all criteria in terms of validity and reliability. Considering the values revealed in the study, it was concluded that the overall scale (α = 0.932) was highly reliable.

1. Introduction

Human beings learn something new every day, and they do this through education [1]. The concept of teaching emerged to make education more meaningful and understandable [2]. There are two elements to education. These are the ones who teach and those who learn [3]. For a teacher to teach effectively, they must also have qualifications and experience in instructional design [4]. The learner’s success is achieved once they transfer the updated and correct information to the new generation [5].
Educational scientists have researched how new technologies could be integrated into education and how knowledge could be transferred more successfully to new generations [6]. They have stated in their studies that the structuring of learning-teaching environments is insufficient in terms of meeting the requirements foreseen in the 21st century [7]. Educational needs, which are thought to be insufficient [8], appear as inevitable parts of learning [9] (pp. 6–7) within the scope of contemporary education understanding. It is necessary to design and develop the physical condition of the teaching environment, a democratic climate, the seating arrangement, and the teaching tools to be used, taking into account the individual differences of the students to motivate them and ensure their participation in the activities to be conducted in the contemporary teaching approach [10]. Based on this, there is a strong hypothesis that the Behavioral Learning Theory (BLT) is insufficient to meet the educational needs of today’s generation, which is a versatile one [11].
The technological developments mentioned above highlight the need to change the existing BLT education system [11] to meet the educational needs of new generations. There has been a gradual distancing from the BLT-based education system in the 21st century. Educators who search for new systems prefer Constructivist Learning Theory (CLT), which represents an educational approach related to student-centered knowledge and learning [12,13,14].
CLT aims to encourage students to use and reflect on thoughts they produce in their daily lives in the classroom environment [6,15,16]. Kanlı [17] states that the concrete foundations of CLT are in the Science Curriculum Improvement Study (SCIS) and that some models emerged during the implementation of the theory, which contributed to its development. These models are the 3E, 5E, and 7E. The 3E model consists of three steps, the 5E model consists of five steps, and the 7E model consists of seven steps. In the 1960s, the “Learning Cycle” model emerged during the primary education curriculum project supported by the National Science Foundation (NSF) [18]. The stages of exploration, concept introduction (explanation), and concept application (expansion) constitute the learning cycle [19]. The most basic learning circle of CLT is 3E, where the learners’ conceptual development is based on the outcome of their acquisition of knowledge through classroom discussions [20,21].
As a result of their efforts to make the 3E model more efficient, the researchers developed the 4E model, which has four steps: explore, explain, expansion, and evaluation [22]. After the 4E model, educational scientists developed the 5E model. For the development of the 5E model, Kanlı [17] stated that “the model which is generally accepted and widely applied by science educators divides the exploration phase in the 3E model into curiosity/participation and discovery, expresses the term introduction phase as the explanation phase, changes the concept application phase to expansion, and additionally expresses the last phase as evaluation”. In the 5E model, which is shaped by the CLT like the 3E model, each E symbolizes a stage in the model, experiential learning is deliberately encouraged, and learners are motivated and engaged [23].
The 5E model was regarded as inadequate by Eisenkraft [24], so they developed the 7E model, a higher education version with more training steps. Şahin [25] stated that although the educator needs to exert a little more effort during the preparation stage for the lesson in the 7E model, the model responds to all phases of contemporary education. In addition, Eisenkraft [24] and Şahin [25] stated that this effort and time can be used correctly thanks to the fact that all learners in the classroom can follow the class equally, participate, discuss in groups, and produce new information based on their existing knowledge. Furthermore, educators have adopted the view that learners are aware that they can access information at any time and place outside the classroom-school and learn it effortlessly without needing to memorize it and that the learning process is an enjoyable one during which they can participate and express their opinions [25,26]. The Flipped Classroom Model (FCM), which resources the Internet, stands out as another educational model that fully overlaps with the aforementioned issues [27]. The pedagogical foundations of the FCM are based on CLT and were developed by trainers [28]. In the FCM, each student in the classroom has the opportunity to participate in the practices equally since the learners can rewatch the lesson as many times as they want in a comfortable environment at home or in any environment suitable for them, with interactive video, and come to the class having learned the subject [26]. On the other hand, in traditional education, some maintain that learners do not receive equal educational opportunities in the classroom [25].
The course content prepared with the FCM is presented to the students using an easy-to-access web-based Learning Management System (LMS). In addition, an LMS is also known as a virtual learning application [29,30]. However, Web 2.0 tools are used as add-ons to assist training on LMSs [31]. Web 2.0 tools emerged as a result of web-based technological developments [32]. Another innovation that is one of the web-based technological developments is Augmented Reality (AR) [33]. Ibáñez and Kloos [34] defined the concept of AR, which is becoming more common every day, as follows: “It is a 3D technology that improves the user’s sensory perception of the real world with a contextual information layer.” At this point, it is worth noting that the idea of using AR in education continues to gain popularity with new trials every day. In fact, educational scientists who support this idea agree that AR has been the most popular and promising subject in educational research in the last decade [34,35,36]. AR applications attract student attention, especially when studying at home. They appeal to the learners visually and audibly; therefore, they are much more engaging than the traditional educational methods [37], hence why AR and FCM videos are associated. In addition, students experience psychological anxiety in the computer lab just like they do in other labs. [38] The reason for this is that the laboratory is an unfamiliar environment. They think that these tools and equipment will harm them, so they refrain from using the equipment [38]. However, AR effectively provides a friendly atmosphere [39] in the labs; therefore, the learners adapt much more easily and faster. In light of what has been said, many maintain the belief that AR-supported CLT, which can best integrate with present-day technological developments in education and training, can meet the educational needs of the present-day generation [11].
COVID-19, which bears the title of the first global pandemic of the digital age, has made itself fully felt in the global context since March 2020 and has also highlighted the adoption of technology in terms of Digital Learning (DL) [40]. In line with all the information mentioned above, including AR, both during COVID-19 and thereafter, it is foreseen that DL and its varieties will be an indispensable part of education as a support to education but will not completely replace traditional education [41,42]. In the first global pandemic of the digital age, COVID-19, the negative effects on stress, anxiety, and learner ability [43] associated with the pandemic show that DL alone cannot be sufficient [44,45]. However, a study by Zawacki-Richter [46] found that learners who have some of their courses online are more likely to prefer blended environments and less likely to elect solely face-to-face courses. In this respect, we can clearly state that the necessity of using face-to-face education along with DL has become a governing choice in education. In addition, blended learning has various benefits such as “pedagogical richness, ease of access to information, social interaction, cost-effectiveness, reorganization, and flexibility” [47]. It is possible to maintain that DL, which has come to the fore with technology acceptance, has gained popularity with the necessity of using it in conjunction with face-to-face education. In other words, under the current pandemic conjuncture, DL will be used together with face-to-face education and will take its place as blended learning shortly [47,48].
Researchers have carried out separate studies that deal with each of the definitions and concepts mentioned above. However, a study in which all of the “7E Model” consisting of “Augmented Reality”, “Flipped Classroom Model”, and “Learning Management System” blended was not found in the literature to date. This study found that it is necessary to determine the extent of the required perception levels of the learners within the scope of the 7E model-based AR-enriched computer course.
The literature review proved that studies related to AR, the 7E model related to FCM, and LMS-related studies were reached separately. This scale development study aims to fill a gap in the existing literature, arouse curiosity in the field of education, and help future similar studies by creating a scale on which all the concepts of the 7E Model, AR, FCM, and LMS are used together for a computer course. We believe the present study will contribute to the literature in this context.

Purpose of the Research

The purpose of this research is to develop the scale to determine the students perceptions of the 7E model-based AR enriched computer course. For this purpose, validity and reliability studies were conducted on the data obtained from the developed scale.

2. Materials and Methods

2.1. Study Group

The research involved 400 students attending the 2019–2020 academic year at Near East University in the Turkish Republic of Northern Cyprus (TRNC). The Simple Random Sampling Method (SRSM) was implemented during the selection process [49]. Taherdoost [49] defines SRSM as an impartial selection method in which each member of a population has an equal chance of being a participant. Accordingly, 40 students were selected from each faculty to reach the total number of 400 students. In the first week, the students received the necessary information about the technologies they were going to use in the course and the method that was going to be applied. After obtaining all the required permissions, we completed the study in 7 weeks.
The study aimed to provide an equal educational opportunity to all participants (students) who participated in the study voluntarily, regardless of whether they had previous experience, so each student had a good command of the subject. In the presentation, the students learned about which tools they were to use, how to access the system, how to be active in the system, how to use the applications, and how they could find answers to many questions as such. This orientation period was carried out face-to-face and even one-on-one at times. In addition, all questions were answered one by one to eliminate any doubts the participants might have.
After the participants were oriented, they attended the course as part of the research for 7 weeks. Before coming to the lesson, the students were required to follow the subjects prepared with FCM, which were uploaded on the LMS by the teacher. Since the students were familiar with the subject of the day, the 7E steps were applied one by one. This helped to attract the student’s attention, and the learning phase was reinforced with group work and activities.
Regarding the gender ratio, 51.2% (205) female students and 48.8% (195) male students participated. Of the 400 students, 35.7% (143) were in their sophomore year, 35.5% (142) were in their junior year, and 28.8% (115) were in their senior year. Furthermore, we determined that 97.5% (390) of the students participating in the study used computers, 2.5% (10) did not, 95.5% (382) had a personal computer at home, and 4.5% (18) did not.

2.2. The Process of Creating the Scale

The scale was developed to determine the university students’ perceptions of the 7E model-based AR-enriched computer course. During the scale development stage, a literature review helped create a pool of 59 items. Two different linguistic experts examined the drafted items for expression, spelling, and punctuation errors, and the necessary corrections were made in line with their feedback. In addition, the scale item was measured for content validity [50]. A study should include sufficient expert opinions to determine the intended feature’s coverage power to measure the scale item [51,52,53,54,55,56]. The reason is that if the number of experts (between 5–40) is limited to a sufficient number, the scale’s validity will be high [51,57,58]. Therefore, we consulted five instructors from the Department of Computer Education and Instructional Technologies for content and coverage validity and made the necessary corrections in line with their feedback. We removed five statements from the scale and finalized the data collection tool with 54 items (see Table A1).
The 400 students who fit the criteria of the population were subjected to the scale to test its validity and reliability. Expressions unrelated to the subject should be removed, and those reflecting the essence of the subject should be included to determine the exact content and scope of the scale [54,55,57,58]. As a result of the statistical analysis and in light of the data obtained, 26 more items with low-factor loading were removed from the questionnaire. The remaining 28 items made up the final version of the scale (see Table A2), which had 6 factors.
The 6 factors created are as follows:
  • Emotional Attitudes Towards Computer Courses (Factor 1): This sub-dimension aims to measure whether students thought the computer course is fun, interesting, straightforward, motivating, and important;
  • Flipped Classroom Videos (Factor 2): This sub-dimension aims to assess whether the teacher’s explanation of the subject increased student participation, whether it was clear and informative, and whether the lecturer supported their statements with examples;
  • Computer Assisted Education Applications (Factor 3): This factor aims to assess whether applications in computer lessons increase teacher-student interaction, increase student-student interaction, help students understand better, and make learning more enjoyable and efficient;
  • Laboratory Assisted Computer Course (Factor 4): To assess the development of empathy skills, the increase of communication skills, the development of new perspectives, the encouragement of working with computers, and the contribution of the practices in the development of thinking skills, and the improvement of these practices in the computer lab;
  • AR Activities (Factor 5): To assess whether AR activities in the computer courses arouse curiosity, provide a sense of reality to the atmosphere, and help learn three-dimensional technology;
  • Computer Lab Anxiety (Factor 6): To assess why learners do not wish to participate in the activities in the computer lab.
The following paragraph was prepared as a cover page of the questionnaire. It aimed to familiarize the participants with the nature of the questionnaire and how to respond to the questions.
“Dear students; This scale aims to measure student perception toward the computer course enriched with Augmented Reality, designed with the 7E Model. The opinions you express here will be used for research purposes only. This information will not be shared with any organization or third parties. Be sure that your identity and answers will be kept strictly confidential. Your answers must be sincere to ensure that the results will truly reflect your perception. Please answer each question thoughtfully and honestly. Please put an “X” in the relative space when giving your responses. The information you provide will shed light on my research. Thank you for your participation.”
They were also given a certain period to express their thoughts and opinions objectively. The questionnaires were collected after the allocated time had expired.

2.3. Data Analysis

The data collection tool, “7E Model-Based Perception Scale for Computer Courses Enriched with AR (7EMAGBAÖ),” implemented a 5-point Likert scale [59] to determine the degree of agreement with statements about learner perceptions. Its range was from “Strongly Agree” (5 points) to “Strongly Disagree” (1 point), and the statistical analyses were carried out based on the answers the participants gave. A Factor Analysis (FA) was performed to explain the large-scale variables with the smaller number of variables called “Factor” to determine the relationship between the developed scale and the variables [60]. To perform the FA, the Kaiser–Meyer–Olkin Test (KMO) [61] was applied to determine sample adequacy. The Bartlett’s test of sphericity [61] was also implemented to ensure that the data conformed to the multivariate normal distribution [62]. Exploratory Factor Analysis (EFA) [63] was applied to the acquired data to obtain information about the nature and structure of the factors measured with the developed scale. In FA, Principal Components Analysis (PCA) [64] was used at the stage of separating the factors into new factors to understand and increase the ability to interpret them. In parallel, the Varimax rotation technique [65] was used, and the factor loading matrix [66] was calculated by Kaiser normalization [65]. The Measures of Sampling Adequacy (MSA) [67] values of the items were calculated to obtain an idea of the items that make up the scale, and the Cronbach’s alpha value was calculated to determine the internal consistency of the scale.

2.4. Validity Study

During the development of the scale, data obtained from 400 scale forms with 54 items, developed as a draft, were used. The SPSS-24 package program was used in the analysis of the collected data and EFA [63] was applied first. PCA [64] and the Varimax rotation method [65] were implemented during this process. In addition, as a result of these procedures, items with item loading values below 0.40 and items with a load value less than 0.10 differences across multiple factors were excluded from the analysis, and the FA continued through repetition. As a result of EFA [63], we obtained a six-factor structure consisting of 28 items. To determine the conformity of this structure, Confirmatory Factor Analysis (CFA) was utilized [68], by using the AMOS-24 program, and for testing the CFA [68], we implemented the maximum likelihood method [69]. A Pearson correlation analysis [70] was applied to determine the concordance validity between the factors. To determine the reliability of the measurement tool obtained from the FA, the Cronbach’s alpha value of the whole scale was determined by calculating the total from each sub-dimension.

3. Results

3.1. Findings Related to EFA

According to the findings obtained from the KMO and Bartlett’s sphericity tests (Table 1), which were conducted to determine whether the obtained data were suitable for FA, the KMO value was found to be 0.921. This value ranges from 0 to 1 and is considered perfect when it is above 0.90 [71,72,73]. Bartlett’s test of sphericity was also statistically significant (χ2 = 6079.890, p < 0.001). According to the results obtained from these values, we can say that the sample size is sufficient and the dataset meets the necessary criteria to continue the FA.
In Figure 1, the eigenvalue scree plot of the 7EMAGBAÖ scale is examined, and six breakpoints can be identified with an eigenvalue of over 1. When these breakpoints are considered, it is visible that the scale has a factor structure consisting of six dimensions.
When we examined the eigenvalue and explained variance ratios of each factor in Table 2, we found that it has a six-factor structure with an eigenvalue above 1, the variance ratio of Factor 1, which consists of seven items, has an eigenvalue of 10.224, and factor loads ranging from 0.533 to 0.829, was 36.51%. The variance rate explained by Factor 2, which consists of 5 items with an eigenvalue of 2.564 and factor loads ranging from 0.620 to 0.751, is 9.15%. The variance rate explained by Factor 3, which consists of 5 items with an eigenvalue of 1.545 and factor loading values between 0.566 and 0.0754, is 5.51%. The variance ratio explained by Factor 5, which consists of three items, has an eigenvalue of 1.215, and factor loads ranging from 0.814 to 0.826, was 4.34%. The variance rate of Factor 6, which consists of two items, has an eigenvalue of 1.169 and factor loadings of 0.744 and 0.851, which was the variance ratio explained. It was found that the rate was 4.17%. In other words, the factor load values of the 7EMAGBAÖ scale have no value below 0.40. These values consist of 28 items varying between 0.455 and 0.851. According to Tabachnick and Fidel [74], the minimum value of factor load values should be 0.32. The analysis shows that common variance values vary between 0.444 and 0.823. According to the literature, a total variance value between 40 and 60% is considered an adequate criterion. The findings found that the total variance value of this scale is 65.036%.

3.2. Correlation Analysis for Concordance Validity

When we examined the values of the 7EMAGBAÖ scale given in Table 3, we found that the relationship between the first factor and the other factors varies between 0.267 and 0.620, and these relationships are positively significant (p < 0.05). While there is a high level (r = 0.620) relationship between the 1st factor and the 3rd factor (p < 0.05), there is a significant moderate positive relationship (r = 0.588) between the 1st factor and the 4th factor. There is a weak and positive significant relationship between the 1st factor and the 2nd factor (r = 0.395), the 5th factor (r = 0.310), and the 6th factor (r = 0.267) (p < 0.05). When the relationship between the 2nd factor and other factors was examined, the results showed that the highest correlation with the 4th factor (r = 0.527) is moderate and positive, and the lowest relationship with the 6th factor (r = 0.267) is weak and positive. When the relationship between the 3rd factor and the other factors was examined, we found that the highest correlation with the 4th factor (r = 0.639) is highly positive, and the lowest relationship with the 6th factor (r = 0.331) is weakly positive.

3.3. Model Fit Criteria of CFA

When we examined the values of the 7EMAGBAÖ scale in Table 4, we found the chi-square test to be 1.957. This value (X² = 649.615, sd = 332, p = 0.00, X²/sd = 1.957) shows a perfect fit. When the Root Mean Square Error of Approximation (RMSEA) value was assessed, this value was found to be 0.049 (RMSEA = 0.049 ≤ 0.05). According to this result, the RMSEA value shows a perfect fit. When the Goodness-of-Fit Index (GFI) value was analyzed, we found it was slightly below the acceptable fit value of 0.899 (0.90 ≤ GFI ≤ 0.95). When the findings of the Normed Fit Index (NFI) value were examined, the findings showed that this value is slightly below the 0.896 acceptable fit. When the incremental fit index (IFI) value was examined, it was concluded that it had an acceptable standardized fit value of 0.946, the CFI had an acceptable standardised fit value between 0.946, the AGFI value was 0.876 acceptable standardized fit value, and an acceptable standardized RMR model fit value of 0.056. According to these results, it was concluded that χ2/sd, RMSEA values were perfectly compatible, IFI, CFI, AGFI, and RMR values were within acceptable standards, and it can be said that the model has a compatible structure based on these values.
The factor loading values according to the CFA results in the path diagram of the 7EMAGBAÖ scale are shown in Figure 2.

3.4. Findings on Reliability

We analyzed the Cronbach’s alpha values of the 7EMAGBAÖ scale in Table 5 and found them to be 0.878 for the “Emotional Attitudes Towards Computer Courses” factor, 0.820 for the “Flipped Classroom Videos” factor, 0.864 for the “Computer-Assisted Education Applications” factor, 0.848 for the “Laboratory-Assisted Computer Course” factor, 0.893 for the “AR Activities” factor, 0.639 for the “Computer Lab Anxiety” factor, and 0.932 for the overall scale. This value is considered “good” when the Cronbach’s alpha value is between 0.9 > α ≥ 0.8. These values prove that the first, second, third, fourth, and fifth factors have good reliability, whereas the sixth factor is within the acceptable limits. The overall scale (α = 0.932) is highly reliable.

4. Conclusions, Discussion and Suggestions

The results indicate an increase in student attitudes towards the computer courses in line with their proficiency. A study conducted by Gündüzalp and Yıldız [80] had similar findings concerning computer proficiency. The results of the present study also found an increase in the participation and learning skills of the students with the FCM videos used in the computer courses. Yılmaz et al. [81] reached similar conclusions about students’ performance, participation, and learning skills in their study. The results of this study further found that students gained positive attitudes and their self-efficacy perceptions increased with the computer-aided education applications used in the course. Polat and Karakuş [82] reached similar results in their research on the effects of computer-aided education applications. This study found that the students were more successful with the AR activities used in the computer courses compared to the classes conducted using traditional methods. Alınlı and Yazıcı [83] reached similar results in their study on the implementation of AR activities in the course.
In the light of the developments in technology in recent years, delivering better education to learners, trying to find the best among the existing education models, ensuring the active participation of the learners in the class, researching this subject, and following the latest research have become a necessity for all educators who wish to improve themselves [84]. We found that a positive attitude toward the use of technology in education [84,85], self-efficacy [84,85,86], and perceived usefulness [84] were strong predictors [84,85,87]. Accordingly, the educator should carefully select appropriate auxiliary resources (such as the Internet, LMS, and Web 2.0 tools) to increase the quality of education in computer courses [4]. Learners should be able to receive education in the best conditions in the computer laboratories and benefit from them [88]. Therefore, it is necessary to determine learners’ perceptions of computer courses.
Creating the course plan based on the 7E Model of CLT with all its phases [89], uploading the class videos created via FCM [90] on the LMS and presenting them to the learners, planning the activities using the LMS, planning the laboratory environment and activities, and using AR for activities and applications constitute the infrastructure of the computer course. However, reintroducing mobile devices, which have been included in this infrastructure and have started to become a problem both inside and outside the classroom, into education [91] is necessary. In this context, it is important that learners access AR via mobile devices to use classroom applications [92]. A scale is needed to determine the learners’ perceptions of their proficiency in the course. However, the literature review proved that an adequate and qualified scale for scientific studies in this area does not exist. Consequently, it becomes clear how important the study is. The scale for the 7E Model-Based AR Enriched Computer Course developed in the study proved an empirically valuable scale that will enable national and international researchers to conduct further research.
Educational scientists are searching for new education models and systems not only for the new generation but also for all future generations due to the conditions of the first global pandemic of the Digital Age, COVID-19. For education to continue under all circumstances, the emphasis is on a student-centered education model in which technology acceptance and DL are part of the education. The most recent technology and tools should be included and utilized to the maximum extent to implement the training model. Once these conditions have been met, a DL model can be created. It will be possible for the learners to use their existing knowledge without getting bored, to add their comments and opinions, and to understand the subjects more consciously by actively participating and applying. As a result, since learners will be conscious at every stage of education, the effects of the COVID-19 pandemic and the disruption of education will not be experienced.
At this stage, it is necessary to determine how effective the education is. For this, it is imperative to determine the self-efficacy perceptions of the learners towards the course content. For this purpose, the 7EMAGBAÖ scale was confirmed in six dimensions through a 28-item CFA. The developed scale is the first of its kind in developing countries such as TRNC. The results concluded that the scale was suitable for all criteria in terms of validity and reliability. Thus, a scale developed within the scope of DL can be implemented to determine learners’ self-efficacy in future scientific studies conducted in TRNC and other countries to identify the “Perceptions of the 7E Model-Based AR Enriched Computer Course” (Appendix A).

Author Contributions

Supervision, E.E.; methodology, E.E. and A.Y.; writing-review and editing, E.E. and A.Y.; conceptualization, A.Y.; investigation, A.Y.; resources, A.Y.; writing—original draft, A.Y. We have read and agreed to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Near East University Educational Sciences Ethics Committee of EDUCATIONAL SCIENCES INSTITUTE (protocol code YDU/EB/2018 and date of approval 07/06/2018).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Except for our own data, no external data has been used.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. The first version of the scale before the application (54 items).
Table A1. The first version of the scale before the application (54 items).
Item No Strongly DisagreeDisagreeNeutralAgreeStrongly Agree
1.Sub-dimension (Emotional Attitudes towards Computer Courses)
1Computer course is my best course
2Computer course is fun
3Computer course is interesting
4Computer course is important
5Computer course is enjoyable
6Computer course is easy
7Computer course is motivating
8Computer course is useful
9Computer course helps me to increase my motivation
10The success that increases with the computer course also increases my success in other courses.
11Computer course is a waste of time
12Computer course makes me feel that I do not have enough knowledge in computers.
2. Sub-dimension (Flipped Classroom Videos)
13Computer course lecture videos are well prepared.
14Computer course lecture videos increase my participation.
15Computer course lecture videos are sufficient because they are informative
16Computer course lecture videos should be supported by examples.
17Computer course lecture videos allow careful monitoring of the topics the teacher covers.
18Computer course lecture videos should be efficient so that the course can be understood better
19Computer course lecture videos allow me to have control of my learning.
20Computer course lecture videos are difficult because they contain few examples.
21Computer course lecture videos are a waste of time for me.
22Computer course lecture videos are not suitable for practice in activities.
3. Sub-dimension (Computer Assisted Education Applications)
23Applications in the computer course allow me to learn.
24Applications in the computer course increase my performance.
25Applications in the computer course are as valuable as the grade I received from this course.
26Applications in the computer course allow me to learn to use the computer.
27Applications in the computer course contribute to the development of thinking skills.
28Applications in the computer course make it easier to develop new perspectives.
29Applications in the computer course require using the relevant technology appropriately.
30Applications in the computer course improve my problem-solving skills.
31Applications in the computer course allow me to take more responsibility in the learning process.
32Applications in the computer course make it easier for me to learn the basic concepts in the course.
33Applications in the computer course allow me to participate actively in the lesson.
34Applications in the computer course help me to understand the lecture better
35Applications made in the computer course make learning more enjoyable
36Applications in the computer courses increase teacher-student interaction
37Applications in the computer courses increase student-student interaction
38Applications in the computer course allow the course to be processed in a student-centered manner.
39Applications in the computer course allow effective group work.
40Applications in the computer course make it easier to access information resources.
41Applications in the computer course make the course more efficient
42Applications in the computer class reduce my self-confidence.
4. Sub-dimension (Laboratory Assisted Computer Course)
43Learning the course in the computer laboratory increases my communication skills.
44Working with computers in the computer lab is enjoyable.
45Working with computers in the computer lab is encourages me to learn more
46Learning in the computer lab improves my empathy skills
47Being in the computer lab is a big nuisance.
48Failure to attend activities performed in the computer lab causes me to have problems.
5. Sub-dimension (Augmented Reality Activities)
49AR activities in the computer courses make me curious
50AR activities in the computer courses reduce class participation.
51AR activities in the computer courses give a sense of reality to the atmosphere
52AR activities in the computer course do not reinforce the subject.
53When AR activities are carried out with a mobile phone in the computer courses, it increases the desire to learn.
54AR activities in the computer courses help to learn 3D technology
Table
Table A2. The final version of the scale after the application (28 items, 6 factors).
Table A2. The final version of the scale after the application (28 items, 6 factors).
Item No Strongly DisagreeDisagreeNeutralAgreeStrongly Agree
1.Sub-dimension (Emotional Attitudes towards Computer Courses)
1Computer course is fun
2Computer course is enjoyable
3Computer course is interesting
4Computer course is easy
5Computer course is my best course
6Computer course is motivating
7Computer course is important
2. Sub-dimension (Flipped Classroom Videos)
8Computer course lecture videos allow careful monitoring of the topics the teacher covers.
9Computer course lecture videos increase my participation
10Computer course lecture videos should be efficient so that the course can be understood better
11Computer course lecture videos are sufficient because they are informative
12Computer course lecture videos should be supported by examples
3. Sub-dimension (Computer Assisted Education Applications)
13Applications in the computer courses increase teacher-student interaction
14Applications in the computer courses increase student-student interaction
15Applications in the computer course help me to understand the lecture better
16Applications made in the computer course make learning more enjoyable
17Applications made in the computer course make the course more efficient
4. Sub-dimension (Laboratory Assisted Computer Course)
18Learning in the computer lab improves my empathy skills
19Learning the course in the computer laboratory increases my communication skills.
20Applications in the computer course make it easier to develop new perspectives
21Working with computers in the computer lab encourages me to learn more
22Applications in the computer course contribute to the development of thinking skills
23Applications in computer courses are as valuable as the grades I take in this course
5. Sub-dimension (Augmented Reality Activities)
24AR activities in computer courses make me curious
25AR activities in computer courses give a sense of reality to the atmosphere
26AR activities in computer courses help to learn 3D technology
6. Sub-dimension (Computer Lab Anxiety)
27Failure to attend activities performed in the computer lab causes me to have problems
28Being in the computer lab is a big nuisance
Table
Table A3. The final version of the scale after the application (28 items, 6 factors—Turkish version).
Table A3. The final version of the scale after the application (28 items, 6 factors—Turkish version).
Madde NoMADDELERKesinlikle KatılıyorumKatılıyorumKararsızımKatılmıyorumKesinlikle Katılmıyorum
1.Altboyut (Bilgisayar Dersine Yönelik Duygusal Tutumlar)
1Bilgisayar dersi eğlencelidir
2Bilgisayar dersi zevklidir
3Bilgisayar dersi ilgi çekicidir
4Bilgisayar dersi kolaydır
5Bilgisayar dersi en iyi dersimdir
6Bilgisayar dersi güdüleyicidir
7Bilgisayar dersi önemlidir
2. Altboyut (Ters-Yüz Edilmiş Sınıf Videoları)
8Bilgisayar dersi konu anlatım videoları öğretmenin anlattığı konuların dikkatle izlenmesini sağlar
9Bilgisayar dersi konu anlatım videoları derse katılımımı artırır
10Bilgisayar dersi konu anlatım videoları yeterli olmalıdır, böylelikle ders daha iyi anlaşılır
11Bilgisayar dersi konu anlatım videoları bilgi verici olduğu için yeterlidir
12Bilgisayar dersi konu anlatım videoları örneklerle desteklenmelidir
3. Altboyut (Bilgisayar Destekli Eğitim Uygulamaları)
13Bilgisayar derslerinde yapılan uygulamalar öğretmen-öğrenci etkileşimini artırır
14Bilgisayar derslerinde yapılan uygulamalar öğrenci-öğrenci etkileşimini artırır
15Bilgisayar dersinde yapılan uygulamalar dersi daha iyi anlamama yardımcı olur
16Bilgisayar dersinde yapılan uygulamalar öğretimi daha zevkli hale getirir
17Bilgisayar dersinde yapılan uygulamalar dersin daha verimli olmasını sağlar
4. Altboyut (Laboratuvar Destekli Bilgisayar Dersi)
18Bilgisayar laboratuvarında ders yapılması empati yeteneğimi geliştirir
19Bilgisayar laboratuvarında dersin işlenmesi iletişim becerimi artırır
20Bilgisayar dersinde yapılan uygulamalar yeni bakış açıları geliştirmeyi kolaylaştırır
21Bilgisayar laboratuvarında bilgisayarlarla çalışmak teşvik edicidir
22Bilgisayar dersinde yapılan uygulamalar düşünme becerilerinin gelişmesine katkı sağlar
23Bilgisayar dersinde yapılan uygulamalar bu dersten aldığım not kadar değerlidir
5. Altboyut (Artırılmış Gerçeklik Etkinlikleri)
24Bilgisayar dersinde Artırılmış Gerçeklik etkinlikleri merak uyandırır
25Bilgisayar dersinde Artırılmış Gerçeklik etkinlikleri ortama gerçeklik hissi verir
26Bilgisayar dersinde Artırılmış Gerçeklik etkinlikleri 3 Boyutlu teknolojiyi öğrenmeye yardımcı olur
6. Altboyut (Bilgisayar Laboratuvarı Kaygısı)
27Bilgisayar laboratuvarında yapılan etkinliklere katılmamam, problem yaşamama neden olur
28Bilgisayar laboratuvarında bulunmak büyük bir sorundur

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Figure 1. Eigenvalue scree plot of the 7EMAGBAÖ scale.
Figure 1. Eigenvalue scree plot of the 7EMAGBAÖ scale.
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Figure 2. The factor loading values of the 7EMAGBAÖ scale are shown according to the CFA results in the path diagram.
Figure 2. The factor loading values of the 7EMAGBAÖ scale are shown according to the CFA results in the path diagram.
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Table
Table 1. KMO and Bartlett’s sphericity tests of the 7EMAGBAÖ Scale.
Table 1. KMO and Bartlett’s sphericity tests of the 7EMAGBAÖ Scale.
KMO Sample Adequacy Measure 0.921
χ26079.890
Bartlett’s sphericity testdf378
P0.000
Table
Table 2. Distribution of the 7EMAGBAÖ scale by factors, item factor loads, and factor variances.
Table 2. Distribution of the 7EMAGBAÖ scale by factors, item factor loads, and factor variances.
New Item NoItem NoRotated Components Factor Load Values
Factor
Common
Variance		Factor 1Factor 2Factor 3Factor 4Factor 5Factor 6
120.7680.829
250.7790.823
330.7350.774
460.4990.673
510.530.634
670.5280.612
740.5290.533
8170.668 0.751
9140.629 0.715
10180.598 0.714
11150.585 0.696
12160.489 0.620
13360.737 0.754
14370.614 0.723
15340.686 0.716
16350.708 0.652
17410.61 0.566
18460.615 0.756
19430.678 0.752
20280.647 0.642
21450.593 0.628
22270.654 0.609
23250.444 0.455
24490.818 0.826
25510.823 0.823
26540.80 0.814
27480.749 0.851
28470.699 0.744
Factor 1Factor 2Factor 3Factor 4Factor 5Factor 6
Eigenvalue10.2242.5641.5451.4921.2151.169
Explained variance %36.519.155.515.334.344.17
Total variance65.036%
Table
Table 3. Pearson correlation results to determine the relationship between the sub-dimensions of the 7EMAGBAÖ scale.
Table 3. Pearson correlation results to determine the relationship between the sub-dimensions of the 7EMAGBAÖ scale.
VariablesFactor 1Factor 2Factor 3Factor 4Factor 5Factor 6P
Factor 110.3950.6200.5880.3100.2670.000
Factor 20.39510.4790.5110.5270.2520.000
Factor 3 0.6200.47910.6390.4620.3310.000
Factor 40.5880.5110.63910.4440.2680.000
Factor 50.3100.5270.4620.44410.3160.000
Factor 60.2670.2520.3310.2680.31610.000
Table
Table 4. Excellent and acceptable fit values with the findings of CFA [74,75,76,77,78,79].
Table 4. Excellent and acceptable fit values with the findings of CFA [74,75,76,77,78,79].
Model Fit Indexes7EMAGBAÖPerfect Fit
Criteria		Acceptable
Fit Criteria
χ2/sd1.957≤ 3≤4–5
RMSEA0.049≤ 0.050.06–0.08
NFI0.896≥ 0.950.94–0.90
IFI0.946≥ 0.950.94–0.90
CFI0.946≥ 0.970.90–0.95
GFI0.899≥ 0.950.90–0.95
AGFI0.876≥ 0.900.85–0.95
RMR0.056≤ 0.050.05–0.10
Table
Table 5. Results of the Cronbach’s alpha reliability analysis.
Table 5. Results of the Cronbach’s alpha reliability analysis.
Sub-DimensionsCronbach’s Alpha
Internal Consistency Coefficient (α)
Emotional attitudes towards computer Courses flipped classroom videos
Computer-assisted education Applications
Laboratory-assisted computer course
AR activities
Computer lab anxiety		0.878
0.820
0.864
0.848
0.893
0.639
0.932
Scale in general
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Erçağ, E.; Yasakcı, A. The Perception Scale for the 7E Model-Based Augmented Reality Enriched Computer Course (7EMAGBAÖ): Validity and Reliability Study. Sustainability 2022, 14, 12037. https://doi.org/10.3390/su141912037

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Erçağ E, Yasakcı A. The Perception Scale for the 7E Model-Based Augmented Reality Enriched Computer Course (7EMAGBAÖ): Validity and Reliability Study. Sustainability. 2022; 14(19):12037. https://doi.org/10.3390/su141912037

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Erçağ, Erinç, and Aykut Yasakcı. 2022. "The Perception Scale for the 7E Model-Based Augmented Reality Enriched Computer Course (7EMAGBAÖ): Validity and Reliability Study" Sustainability 14, no. 19: 12037. https://doi.org/10.3390/su141912037

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