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Developing and Testing Psychometric Properties of “readiness for e-learning scale” among a Sample of Egyptian University Students

Abdul-Naser El-sayed Aamer, Shewikar Farrag*

Professor of Pediatric Nursing, Former Acting Vice-Dean of Postgraduate Studies and Scientific Research, Faculty of Nursing, Umm Al-Qura University, Saudi Arabia

Abstract: Students’ readiness for distance learning is used as a main factor to evaluate electronic /distance education. Therefore, the purpose of this study was to develop and validate an instrument to measure students ‘readiness for distance education, and to assess the level / dimensions of student readiness for its utilization. An electronic survey was developed and administered to 544 students who were enrolled in Suez Canal University, Egypt. The results demonstrated that age (M=19.35, SD=2.86). Data has been analysed using descriptive statistics, exploratory and confirmatory factor analysis via the use of SPSS (28) and MPLUS (7). The results showed that the correlated five factors model of e- learning readiness was the best fit model with the sample data, moderate levels of self-directed learning, learner control, motivation, online interaction and self-efficacy during communication, while a high level of self-efficacy concerning the use of internet to access distance education. Male students had more readiness for distance learning than females in the study group. The study recommended raising awareness and developing programs to enhance distance education and increase students ‘readiness toward its application.

Keywords: electronic / distance learning readiness, learning motivation, online learning environment, factor analysis, psychometric properties

Received February 02, 2023; Revised March 04, 2023; Accepted March 10, 2023

Citation: Abdul-Naser El-sayed Aamer, Shewikar Farrag, Developing and Testing Psychometric Properties of “readiness for e-learning scale” among a Sample of Egyptian University Students. American Journal of Educational Research, 11 (3). 116-124. January 2023. doi:10.12691/education-11-3-3

1. Introduction

Recently many countries have relied on distance learning, using information technology and the Internet in the teaching and learning process to deliver the content of the courses to students and researchers. This change occurred without prior preparation, the lack of the necessary infrastructure for the requirements of distance learning, and also without adequate preparation for the required skills of e-learning, and dealing with distance education methods that depend on delivering lectures through platforms and programs such as Blackboard, Microsoft teams, Zoom, Webex, and others. Over time, students became enrolled in an educational system that relied on delivering some lectures electronically and some through traditional face-to-face education or what is known as hybrid learning, in which many students expressed academic and psychological pressure, and dissatisfaction with it as a result of the lack of technological capabilities, poor Internet services, especially in remote areas, poor interaction with their faculty staff, lack of teaching and technological skills among some faculty members, and their inability to manage time efficiently [1,2,3].

E-learning is the ability of the learner to use the computer and the Internet to obtain courses from anywhere and at any time, meaning converting traditional educational activities into a digital form, and delivering them to the learner through electronic applications and platforms. Although most of the studies focused on teaching strategies and the effects or psychological consequences in the e-learning environment, they ignored two variables that are extremely important to its success and quality, which are readiness and satisfaction in the e-learning environment [4], that was the reason behind conducting the current study, in which authors tried to focus on exploring the missing variables.

Therefore, measuring students' readiness and motivation to participate in e-learning contributes to alleviating difficulties and pressure that make them reluctant to continue to this type of learning developed in our Arab educational institutions. In the Egyptian society in particular and the Arab society in general, where Allem [5] indicated that the measures available within the research heritage suffered from the absence of standardized instrument, reliability and validity required for their use.

1.1. Readiness for E-Learning / Distance Education

The concept of readiness for e-learning was developed by Warner et al. [6], and defined its structure in the light of three dimensions: preference for e-learning, efficiency and confidence in dealing with computers and the Internet, and the ability of the learner to integrate and organize the learning process. The readiness was measured and interpreted in light of related dimensions or concepts. For example, McVay (2000) [7] developed a 13-item e-learning readiness scale, while Smith et al. [8] examined the structure of the McVay scale in the light of two factors: the first: students' satisfaction and motivation for the use of e-learning resources, and the second: self-regulation of the learning process, and the development of Bernard et al. [9] A tool for measuring readiness for e-learning consisting of 38 items, including the 13 items in the McVay scale, distributed on four dimensions: belief or perception about distance learning, confidence in e-learning skills, self-regulation of learning, and motivation to interact with professors and colleagues. Kerr et al. [10] A measure of readiness for e-learning consisting of six sub-concepts: computer skills, time management, motivation, academic skills (reading and writing), the need for e-learning, and learning skills. While Liu et al. [11] a measure consisting of the concepts of motivation, learning style, self-efficacy, perseverance, knowledge of computer skills, and communication skills. However, one of the most important and widespread electronic readiness measures in the research heritage is a measure developed by Hung et al. [12] and consists of 18 items distributed on five dimensions affecting the formation of willingness and desire for e-learning, which are computer and Internet competence, communication and interaction competence on the Internet, self-directed learning, learner control, and learning motivation. This scale has proven its validity and stability in multiple cultures. Such as Indonesia, Taiwan, Malaysia, Turkey, and others.

As for the level and degree of readiness for e-learning, in the Egyptian society, Hussein [13] concluded that there were insufficient levels of readiness for e-learning among a sample of tourism and hotel students. Hung et al. [12] that students' readiness for e-learning is high in two dimensions of their confidence to use the computer and the Internet, and communication and interaction on the Internet among university students in Taiwan, and Blayone et al. [14] indicated that a large percentage of students in Ukraine and Georgia are not ready for e-learning activities, but a high percentage are willing to communicate via the Internet and social media. Among the student community in Turkey, Kalkan [15] concluded that the two dimensions of computer and internet self-efficacy, and online communication were more available, while the dimensions of self-regulated learning, learner control, and learning motivation were the least available, and Ates-Cobanoglu & Cobanoglu [16] reached The readiness of student teachers is highly available, and Kibici & Sarikaya [17] found that computer and internet competency is high, while the two dimensions of self-directed learning, learner control and total score are available at a moderate degree, and the two dimensions of motivation to learn are available, and communication competence is low, and there were Statistically significant differences in the two dimensions of readiness, computer and internet competence, and online communication between male and female teachers in favor of male teachers, while there was no statistical significance for the other three dimensions.

In a cross-cultural study of university students in America and Germany, Küsel et al. [18] indicated that the dimensions of readiness for e-learning were available in a moderate to high degree for a sample of German students, while it was available in a high degree for a sample of American students. In a student community in Indonesia, Allam et al. [19] showed that self-computer and internet competency is high, while self-directed learning is low, and Chung et al. [20] indicated that levels of e-learning readiness dimensions were available in a medium degree, where the highest was computer and internet competency, followed by learning motivation, then online communication competency, and the least of them were learner control and self-directed learning. The study concluded that there were no statistically significant differences between males and females. Dimensions of readiness for e-learning. For a sample of university students in Sri Lanka, Akuratiya & Meddage [21] found that readiness for e-learning was moderately available. For a sample of university students in the Philippines, Fearnley & Malay [22] found that there were dimensions of motivation, computer and internet competency, online communication, self-directed learning, and learner control with a high degree. Males and females in readiness dimensions. Among university students in Malaysia researchers [23] found electronic readiness to be moderate. Weary Tang et al. [24] found that the differences in readiness for e-learning in light of gender and educational level were rarely addressed in the research heritage, and they concluded that there were no statistically significant differences between males and females in readiness for e-learning.

1.2. Components of Readiness for e-learning: Addressing Research Heritage Components or Dimensions of Readiness for e-learning are as follows

Competence with the use of computers and the Internet: Competence with computers and the Internet in the e-learning environment plays an important role in shaping readiness for e-learning. The efficiency of the computer is the student's confidence in the extent to which he possesses the basic skills to deal with the computer efficiently, while the efficiency of the Internet reflects the student's self-confidence in using the Internet efficiently to organize more about this source text Source text required for additional translation information.

1.3. Online Communication/Interaction Self-efficacy

Online communication/interaction self-efficacy: considered by Hung et al. [12] an essential part of assessing readiness and motivation for e-learning, and interaction is one of the most important factors influencing satisfaction with e-learning, and in the current study, the same efficiency of communication and interaction in the e-learning environment includes the ability to interact with faculty staff and colleagues, participate in discussions and inquiries, and express their opinions and ideas during e-learning.

1.4. Self-directed Learning

Self-directed learning is a strategy that allows the learner to assume the responsibilities of his learning, directing, evaluating the learning process, defining his priorities and goals, determining appropriate learning strategies for him, organizing his information, managing his time, searching for help and evaluating his performance during the e-learning process [25]. Self-directed learning skills are an important part of students' effective participation in e-course learning, and also an essential determinant of success and satisfaction with it [26,27]. The current study defined self-directed learning as the ability of the learner to prepare his assignments and duties at the specified time, to seek the help of others to face his problems, to set his priorities and goals, to manage his time, and to have expectations about his performance.

2. Learner Control

Chung et al. [20] reported that e-learning is completely different from traditional learning, as it requires that the student be able to direct and control how he learns without attending face to face during lectures, as the student determines where, when, and how he learns in this environment. In the current study, the learner's control includes evaluating his performance in e-learning, not being distracted by any distractions during learning, how to search for additional information whenever he needs it, detecting and correcting errors.

3. Motivation for Learning

Motivation has a major role in the field of achievement and learning, and it also has a positive role in the success of e-learning and integration into it. Many studies have found that motivation is positively related to how the learner perceives and is satisfied with his performance in electronic courses [20,28,29]. In the current study, motivation includes enthusiasm, love of sharing with others, realizing the importance of e-learning, and its role in opening new horizons for learning and acquiring information and skills, and feeling comfortable with its performance in e-learning. Although these are the most important components of readiness for e-learning included in the research heritage, some studies have added a factor of great importance that contributes to the formation of readiness for e-learning, which is the actual or perceived benefit [4,30], defined as to what degree does the student believe that e-learning is beneficial to him and improves his performance and skills [31]. It can be seen as the student’s judgment about his knowledge and understanding and the extent to which he benefits from this type of learning. Chow & Shi [28] concluded that the level of perceptions of University students about e-learning, including perceived benefit.

4. Research Gap and Questions

The study of the culture of e-learning and the factors influencing it gained importance in all cultures, especially in East Asian countries in China, Taiwan, Malaysia, Indonesia, and also in Turkey, but the research heritage in the Arab community did not address it sufficiently in research. With regard to readiness for e-learning, its dimensions and components were numerous, and the current study included most of these dimensions in addition to the perceived benefit dimension that most previous studies ignored. With the exception of a few studies that relied on confirmatory factor analysis within the framework of the entrance to verify a single factor construct and study the extent to which the data matched, for example [4,29,32], but the current study employed the structural equation modeling methodology Using confirmatory factor analysis through the strategy of comparing several factorial models that reflect different visions of the concept of electronic readiness. Accordingly, the current study attempted to bridge a gap in the research heritage by building and developing a measure of readiness for e-learning, and identifying levels of readiness among a sample of students from Suez Canal University, as it is one of the Egyptian universities. In conclusion, the study attempted to answer the following questions:

1- Does the e-learning readiness scale on a sample of university students have good psychometric properties of validity and reliability?

2- To what degree are dimensions of readiness for e-learning available (computer and internet self-efficacy, online communication proficiency, self-directed learning, learner control and learning motivation) among university students?

- What is the effect of gender on the dimensions of readiness for e-learning?

5. Objectives of the Study

The study aimed to develop a scale / an instrument of readiness for e-learning and verify its psychometric properties of validity and reliability, and to determine the degree and level of dimensions or components of readiness in the e-learning environment of university students. As well as studying the differences in the components of readiness for e-learning, which are attributed to gender.

6. The Value and Impact of the Current Study

The importance of the study stems from the following: (1) Providing a reliable and valid measurement tool that researchers and educational institutions depend on, to measure their motivation and willingness to participate in e-learning programs to ensure their effective performance and continuation. (2) Determining levels of readiness for e-learning among university students.

7. Methodology

7.1. Study Design

The study followed the quantitative research design using the cross-sectional type in which study sample used the Arabic version of the scale through an electronic link made accessible for undergraduate university students to assess the level or degree of readiness for distance learning.

7.2. Study Sample

The study population included students of Suez Canal University, which is one of the Egyptian universities located on the course of the Suez Canal in Ismailia Governorate, in which more than 30 thousand male and female students enrolled in 19 colleges.

7.3. Methodological Procedures

The study relied on a snowball sample, where an electronic link with the study scale was sent to an initial sample of students, then they are asked to send it to their peers and colleagues, and this sample is not a guarantee of randomization, but it is classified as an available sample [33], and the electronic link was sent to students who the 1st author taught them the courses of measurement, evaluation and statistics in the faculty of education, arts and humanities, tourism and hotels at the undergraduate and postgraduate levels, where the courses are taught 50% electronically, and 50% traditionally face-to-face, and the sample size was 544 students, and it varied according to gender to 91 males ( 16.7%), and 453 (83.3%) females, with an average of 19.35 years, with a standard deviation of 2.86, and the years of experience in dealing with computers ranged in the range from 0.0 to 30 years, with an average of 5.55 years, with a standard deviation of 5.0.

7.4. Procedures

An electronic link was applied that includes a measure of readiness for e-learning and basic data from 1/12/2021 to 12/25/2021 for undergraduate in the College of Education, and the Faculty of Arts and Humanities, students of the first and third years of population studies, where 50% of the lectures are taught electronically through the Zoom, and Microsoft Teams, and the willingness and motivation of the students to participate for the purposes of research was taken into account and they were informed of the strict confidentiality of the data, and the ethical considerations used in psychological and educational studies were adhered to.

7.5. Measurements

Measure of readiness for e-learning / Distance Learning: In light of the research heritage that was previously dealt with in the theoretical framework, the concept of readiness for e-learning is a multidimensional concept, as the researcher adopted the five dimensions of Hung et al. [12] In addition to the perceived importance or benefit dimension, and by examining the preparedness measures previously presented, most of them neglected to delve deeper into the basic skills of dealing with the Internet, such as the use of smart phones in e-learning, the use of e-mail, and the ability to use e-learning applications / platforms such as Zoom, Microsoft Teams and others, and this is what the current study tried to include.

7.6. Vocabulary Scope

The scale vocabulary for the six dimensions of readiness was formulated after reviewing previous studies, in addition to conducting interviews with many students during teaching to find out their opinions about their readiness, experiences, and their ability to deal with this type of learning. Many students expressed some problems and difficulties such as lack of experience to deal with the Internet, lack of good Internet services, lack of good communication with the professor, and others. Also, interviews were conducted with some professors specialized in the field of information technology to find out their views on the components of readiness for e-learning. In light of this, eight terms were formulated for the efficiency dimension. The same computer and the Internet, and three items for self-communicating efficiency on the Internet, and four items for the self-directed learning dimension, and three items for the learner’s control of his learning dimension, and four items for the perceived benefit dimension, and four items for the motivation dimension for learning, so the initial version reached 26 items.

7.7. Validity of the Content

The scale was presented in its initial form consisting of 26 items to five specialists in educational psychology, psychometrics, and educational technology, to ensure the integrity of the technical and linguistic formulations, and the extent to which the term is suitable for measuring the dimension, and the percentage of agreement was 90% or more on most of the terms, as well as The percentage of content validity was estimated using the Lawshe equation [34], and its value for all items was higher than 0.90, except for two items in the dimension of self-computer and internet competency. Vocabulary was corrected in the light of the five-response Likert scale, which is very much (5), very much (4), medium (3), very little (2), and very little (1). Therefore, the final picture of the scale after analyzing the validity of the content 24 items or phrases.

8. Data Analysis

The data of the study were analyzed using the SPSS (28) and MPLUS (7) programs, but before analyzing the data, the axioms of the analysis were verified such as the moderation distribution of the individual data, linear analysis, and multi-colinearity. The postulate of moderation was verified by calculating the indices of skewness and kurtosis, where if their value ranged from (-2, +2), then the data was described with moderation [35], and the exploratory factor analysis was carried out using the principal components method and Promax oblique rotation to assume the existence of correlations between dimensions to reveal the factorial structure of the scale, and relying on the criterion of the value of the underlying root is greater than the correct one with the logical and theoretical interpretation of the factors resulting from the analysis to determine the number of factors, and the item was considered saturated with the factor if its saturation exceeded 0.32 [36] and the Kaisr-Mayer-Olkin (KMO).

Criterion was relied upon to verify the suitability of the sample correlation coefficients for the exploratory factor analysis, and its value should be at least 0.60. The confirmatory factor analysis was conducted for five global models for the dimensions of readiness for e-learning using the maximum probability method. The indicators of good fit were used, which are chi squared χ^2, the Tucker-Lewis TLI index (NNFI), the comparative fit index (CFI), and the residual index RMSEA. The researchers used the criteria reached by Hu & Bentler [37] to determine the appropriate and good fit, which is for the CFI and TLI indicators if their value is greater than 0.90, and for the RMSEA index, the value from 0.05 to 0.08 is a suitable fit, and less than 0.05 is a good match, and for the chi squared statistic (χ^2) If it is not statistically significant, then the model is identical to the data, and if it is a function, then the model is not identical, and the study relied on the information test of Aikiki (AIC) to compare between models, where the model that has the lowest value for this indicator is more consistent with the data [38].

Averages and standard deviations were estimated to find out the levels of readiness dimensions for e-learning, and the study adopted the criterion adopted by Allam et al. [19], where in the light of the five-point Likert scale, the result of subtracting the response is a degree Very large (5) of the response with a very low degree (1) equal to 4 divided by three, which is the number of classifications (high level, medium level, low level) equal to the value 1.33. Therefore, the criteria are as follows: The value in the range from (1.00-2.33) expresses For a low level, and in the range (2.34 -3.67) a medium level, and in the range (3.68)-(5.00) a high level. A multivariate Analysis of variance (MANOVA) was used to study the effect of gender on the dimensions of readiness for e-learning separately and interaction.

9. Results

Verification of postulates: Before conducting the statistical analyzes of the study variables, the postulate of moderation was verified for the items of readiness dimensions by calculating the indices of kurtosis and skewness, where the skewness values of the scale items ranged in the range from -0.08 to -0.90, and the kurtosis values ranged in the range from 0.021 to -0.97. This confirms the moderation of the data of the readiness scale items in the e-learning environment, and the multiple linear dependence was verified by estimating the Pearson correlation coefficient between the readiness items. The existence of high correlation coefficients between some items, and by examining the correlation coefficients, it became clear that the highest correlations were between the items of the two dimensions of readiness, perceived benefit and learning motivation, and this indicates the existence of a problem of linear correlation between the items of the two dimensions, as the value of the correlation coefficient between the total degree of the dimensions of perceived benefit and motivation was 0.91, It is a very strong correlation coefficient, and therefore the terms of the two dimensions were merged into one dimension called motivation.

9.1. The First Question

What are the psychometric properties of a measure of readiness for e-learning among a sample of university students? The psychometric properties were verified as follows:

Validity of the concept: The method of principal components and oblique rotation of Promax was employed, as the value of KMO = 0.97 and the value of Bartlett's Test of Sphericity = 11328.745, Sig = 0.00, which is statistically significant, and this indicates the validity of the data and correlation coefficients for the analysis, and the analysis produced three factors that exceeded the underlying root of them. The right one.

The following are the results of the factor analysis: More about this source text

Table 1. Results of the exploratory factor analysis of dimensions of readiness for e-learning (N = 544)

It is clear from Table 1 that the vocabulary of the two dimensions Motivation and Perceived Benefit were saturated with the first factor, and this confirms the importance of integrating the two dimensions together, and that the vocabulary of the dimensions of communication on the Internet, self-efficiency of the computer and the Internet was saturated with the second factor, and it can be called technological self-efficiency, while the vocabulary of learning was saturated The learner is self-directed and controlled by the third factor except for the term “I evaluate my performance and my learning in e-learning.” It is noted that the first factor explained most of the variance of the matrix of correlations between the items 56.70%, and this indicates the importance of motivation and perceived benefit as one of the most important components of readiness to learn, while the three factors explained Together they account for 68.36% of the variance of the correlation matrix, and in this regard Meyers et al. [39] indicated that the construct is effective when the amount of variance explained by factors is at least 50% of the variance of the matrix of correlation coefficients.

9.1.1. Confirmatory Factor Analysis

The confirmatory factor analysis was conducted for five factor models that reflect the nature of the relationships between the five dimensions, namely the general factor model in which the variances of the five dimensions are explained by a general factor, the five independent factors model where the correlations between the five dimensions are equal to zero, and the linked five factors model Where the correlations between the factors are free, and the three-factor model resulting from the exploratory factor analysis, and the two-rank model where the variance of the five single-rank factors is explained by a general two-rank factor, the following are the indicators of good fit for the five models:

Table 2. Indices of good fit to the five global models of readiness dimensions for e-learning (N = 544)

Figure 1. The path diagram of the confirmatory factor analysis model of the e-learning readiness scale with its statistically significant standard paths

It is evident from Table 2 the superiority of the correlated five factors model over the rest of the other confirmatory factor models, as it had the lowest value for the AIC index, the chi-squared statistic, and the RMSEA index, while it had the largest values for the TLI and CFI indices, followed by the two-factor analysis model in preference. Rank, and in this context, it is possible to accept the one-dimensional concept of readiness for e-learning. Also, the heuristic three factor model proved to be a good fit with the data. As shown in Figure 1 of the paths of the five correlated factors model, it is clear that all the items were saturated with the specified factor, and the saturation coefficients for most of the items were more than 0.70, which is statistically significant at 0.05, as the T values corresponding to all the saturations increased from 1.96, which confirms the validity The convergence of the scale items, and also the correlation coefficients between the five factors increased by 0.70, which confirms the internal consistency between the components of readiness for distance learning /E-learning. Measurement of scale vocabulary is elevated.

9.1.2. Internal Consistency Reliability of the Dimensions

The stability of internal consistency was estimated using coefficient Omega, and its value was 0.92 for the self-computer and internet competency dimension, 0.88 for the online communication dimension, 0.82 for self-directed learning, 0.80 for learner control, and 0.95 for motivation. This confirms the validity of the data for statistical analysis.

9.2. The Second Question

What is the level or degree of availability of dimensions of readiness for e-learning and satisfaction among university students? To verify this, the percentages, averages, and standard deviations were estimated:

Table 3. Means, standard deviations, and the level of readiness dimensions for E-learning / Distance Learning and satisfaction (N=544)

It is clear from Table 3 that the computer and Internet competency dimension is available to a large extent, while the rest of the readiness dimensions are available in a medium degree, and the least available dimensions are communication and interaction with colleagues and professors. It is clear from the table that 17.3% of the sample showed a low level of communication and interaction in e-learning environment.

9.3. The Third Question

What is the effect of gender on the dimensions of readiness for e-learning? A multivariate analysis of variance was conducted to study the effect of the gender on the dimensions of readiness for e-learning interacting and each separately, where it was found that there were statistically significant differences between males and females in the interaction of the six readiness dimensions Wilks lambda=4.14, p<0.01, µ_p^2=0.044, the following are the results of the analysis of variance test for the effect of gender on the dimensions of readiness for e-learning:

Table 4. A variance analysis of the effect of gender on electronic readiness dimensions

It is clear from Table 4 that there are statistically significant differences at 0.05 between male and female students in all dimensions of readiness for e-learning, where the averages were in favor of male students, and by looking at the value of the effect size in light of the weighted ETA square, it is clear that the gender variable contributed in a very weak degree in explaining the variation in the dimensions of readiness for e-learning, ranging from (1% for motivation) to 3.1% (for computer and Internet competence),and this is a weak effect.

10. Discussion

The study dealt with a variable of great importance in the field of e-learning and represents its most important inputs represented in the components of readiness and desire to participate in the learning process. The study was conducted on undergraduate and postgraduate students in an Egyptian university (Suez Canal University). The study addressed several objectives, the most important of which is the construction and development of a measure of readiness for e-learning that includes many components. Perceived benefit, where the analysis produced three factors, the first included the two dimensions of computer and internet competence, communication and interaction, the second factor included the vocabulary of the two dimensions of self-directed learning and learner control, and the third factor included the vocabulary of the dimensions of perceived benefit and motivation, and the confirmatory factor analysis was conducted, but after including the terms of my dimension Utilization and motivation in one dimension due to the high correlations between the items of the two dimensions in order to avoid the issue of high correlation, and several confirmatory factorial models were tested in which the model of the five factors associated with the rest of the models was tested, which confirms the multidimensional nature of the concept of readiness for e-learning, and this is consistent with all studies [4,10,12,29].

While Yum Acceptance was on the occasion of the exploratory factor analysis model with three factors, where the two dimensions of self-computer and Internet efficiency, and Internet communication efficiency can be included in one dimension called technological competence, and this was adopted by some studies such as (Allem et al) [4], while the factor analysis model proved The two-rank confirmatory is a good fit, and therefore it is possible to accept the unilateral construction in the sense of relying on the total degree of readiness for e-learning in the statistical analyses, and this is supported by the relatively high correlations between the five dimensions.

Also, the study aimed to reveal the levels of readiness components, and it became clear that the computer and internet competency dimension is available to a large extent, while the rest of the readiness dimensions for e-learning are moderate, and this is consistent with the results of studies [12,23,40], and contradicts the results of studies done by Ates-cobanoglu & Cobanoglu [16]; Küsel [18] and Fearnley & Malay [22], in the availability of electronic readiness to a high degree, but it is noteworthy that The least available components of readiness for e-learning are the communication dimension in the e-learning environment, especially for females. This may be due to the lack of familiarity with the basic technological skills for communication and raising discussions and inquiries between the student and the teacher, especially since this is a new teaching method in the educational system, and this requires training and practice, or it may be due The inability of some teachers to communicate with students as a result of their lack of computer and Internet skills.

The results of the study showed that there are differences between male and female students in the readiness for e-learning in favor of male students, and this is partially consistent with Kibici & Sarikaya [17], and contradicts the studies of Chung et al [20]; Fearnley & Malay, [22]; Tang et al [24]. This can be explained by the fact that male students have higher internet and computer skills than females, as the average experience of dealing with a computer among students is ((M=6.11 years), while females have ((M=5.44 years), and male students can have the ability to ask questions and participation in discussions more than females due to the nature of female students who are characterized by shyness and reluctance in discussions.

Although the study attempted to verify the psychometric components of readiness in the e-learning environment, it suffers from some limitations, including: First, the study procedures were conducted in a hybrid or mixed learning environment, where the courses are taught electronically and traditionally together.

References

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