Data on factors characterizing the eLearning experience of secondary school teachers and university undergraduate students in Jordan

Two datasets were obtained to develop an understanding of factors characterizing the eLearning experience of secondary school teachers and university undergraduate students in Jordan. Both datasets were collected via electronic questionnaires. The secondary school teachers’ dataset was collected toward the end of the second semester of the 2019/2020 academic year, and the university undergraduate students’ dataset was collected during the summer semester of the same year. Six hundred and sixty-six participants responded to the secondary school teachers’ questionnaire, and one thousand participants responded to the university undergraduate students’ questionnaire. A structural equation modelling approach was utilized to analyze the data. Future research might extend the two data models by collecting data about other factors and examine their impact on the eLearning experience of secondary school teachers and university undergraduate students in Jordan and other countries. Moreover, the datasets can be compared with other data from different developing and developed countries.


a b s t r a c t
Two datasets were obtained to develop an understanding of factors characterizing the eLearning experience of secondary school teachers and university undergraduate students in Jordan. Both datasets were collected via electronic questionnaires. The secondary school teachers' dataset was collected toward the end of the second semester of the 2019/2020 academic year, and the university undergraduate students' dataset was collected during the summer semester of the same year. Six hundred and sixty-six participants responded to the secondary school teachers' questionnaire, and one thousand participants responded to the university undergraduate students' questionnaire. A structural equation modelling approach was utilized to analyze the data. Future research might extend the two data models by collecting data about other factors and examine their impact on the eLearning experience of secondary school teachers and university undergraduate students in Jordan and other countries. Moreover, the datasets can be compared with other data from different developing and developed countries.

Value of the Data
• The datasets are important because they capture data about factors characterizing the eLearning experience of secondary school teachers and university undergraduate students in Jordan, which is useful in formulating an eLearning strategy in Jordan and other countries. • Online course developers, university instructors, school teachers, administrators, educational policy makers, and learning management system vendors can utilize the datasets in support of their decision-making processes. • Future research might extend the two data models by collecting data about other factors and examine their impact on the eLearning experience of secondary school teachers and university undergraduate students in Jordan and other countries. Moreover, the datasets can be compared with other data from different developing and developed countries.

Data Description
The data for secondary school teachers, ( Appendix A ), was collected toward the end of the second semester of the 2019/2020 schools academic year in Jordan. Such that, a questionnaire ( Appendix B ) was distributed targeting secondary school teachers -teachers who teach from eighth grade to twelfth grade. Those teachers used Microsoft Teams to deliver instruction as mandated by the Jordanian Ministry of Education, during COVID-19 lockdown in Jordan. The questionnaire was hosted on a Google form. The invitation to participate in the electronic questionnaire was distributed via social networks of secondary school teachers in Jordan. Six hundred and sixty-six teachers filled-in the questionnaire. Table 1 presents the demographic characteristics of secondary school teacher participants. The data for university undergraduate students, ( Appendix C ), was collected during the summer semester of 2019/2020 academic year in Jordan. Hosted on a Google form, a questionnaire ( Appendix D ) was put out seeking responses from public and private university undergraduate students in Jordan about their eLearning experience. The invitation letter to participate in the electronic questionnaire was sent via social networks of public and private university undergraduate students in Jordan. One thousand responses to the questionnaire were obtained. Table 2 presents the demographic characteristics of university undergraduate student participants. For both datasets, all constructs were operationalized using validated items from prior research. Table 3 presents the constructs for both datasets, their associated number of items, and the resources they were obtained from.
To establish convergent validity for each measurement model, four criteria were assessed: (1) standardized Factor Loading (FL) for each item which must be greater than 0.5, (2) composite reliability (CR) for each construct which must be greater than 0.7, (3) Cronbach's alpha for each construct which must be greater than 0.7, and (4) the average variance extracted (AVE) for each construct which must be above 0.5 [12] . With respect to secondary school teachers' measurement model, the absolute value of FL for the fifth item measuring the facilitating conditions construct (FC5), which is reverse coded, was below the specified threshold. As such, the item was removed from the model. By examining Table 4 , it can be concluded that convergent validity of secondary school teachers' measurement model has been established after removing FC5. Also, by examining Table 5 , it can be concluded that convergent validity of university undergraduate students' measurement model has been established as well.
The discriminant validity for each model was assessed by the heterotrait-monotrait (HTMT) ratio of correlations as it is the most rigor approach for achieving that goal [13] . The criterion in this approach is that the HTMT for each pair of constructs must be below 0.9 [13] . As presented in Tables 6 and 7 , in both models, the HTMT for each pair of constructs is below 0.9. As such, the discriminant validity for each measurement model has been established.
Structural equation modeling approach, in R language version 4.0.1, was applied to analyze the data for each model. Appendix E and Appendix F have the R code for analyzing secondary school teachers and university undergraduate students' datasets, respectively. Table 9 presents the analysis results of the university undergraduate students' structural model, and Table 10 presents the analysis results of the secondary school teachers' structural model.

Experimental Design, Materials and Methods
Two datasets were obtained to get an understanding of factors characterizing the eLearning experience of secondary school teachers and university undergraduate students in Jordan. The first dataset is concerned with secondary school teachers, and the second dataset is concerned with university undergraduate students. The first and second datasets were collected via two different questionnaires. Since the language of all prospected respondents is Arabic, both  questionnaires were translated into their native Arabic language. Attitude toward use in the first dataset, and user satisfaction in both datasets were operationalized using a 5-point semantic differential scale from 1 to 5, with higher numbers reflecting more positive evaluations. The rest of the constructs, in both datasets, were operationalized using a 5-point Likert scale from 1 ("strongly disagree") to 5 ("strongly agree"). Structural equation modeling approach, in R language version 4.0.1, was applied to analyze the data for each model.

Ethics Statement
Ethical approvals for collecting both datasets were obtained from Al-Hussein Bin Talal University in Jordan. In the first page of both electronic questionnaires, the objectives of the research were presented, stating clearly that participation was voluntary, and the answers would be kept confidential. Informed consent of all participants was obtained before participating in either questionnaire.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.