Unlocking future learning: Exploring higher education students' intention to adopt meta-education.

Despite the potential of meta-education to transform higher education, there remains a scarcity of research investigating students' adoption intentions. This study aimed to identify factors influencing students' intentions to adopt meta-education using an extended Decomposed Theory of Planned Behavior model (DTPB). Data was collected via an online survey of 596 higher education students from Jordan who were purposefully selected. Structural equation modeling using partial least squares analysis revealed attitude, social influence, and perceived behavioral control as key antecedents of adoption intention. Furthermore, newly added variables including perceived enjoyment, herd behavior, student autonomy, and student innovativeness showed efficiency in explaining variance in attitude, social influence, and perceived behavioral control. Overall, the extended model provided meaningful insights on factors driving students' willingness to adopt meta-education. The study contributes to theory by extending the decomposed TPB model in the context of emerging educational technologies. It also provides practical implications for policymakers and educators aiming to encourage meta-education adoption.

"Extending the Technology Acceptance Model (TAM) to Predict University Students' Intentions to Use Metaverse-Based Learning Platforms".

Metaverse, which combines a number of information technologies, is the Internet of the future. A media for immersive learning, metaverse could set future educational trends and lead to significant reform in education. Although the metaverse has the potential to improve the effectiveness of online learning experiences, metaverse-based educational implementations are still in their infancy. Additionally, what factors impact higher education students' adoption of the educational metaverse remains unclear. Consequently, the aim of this study is to explore the main factors that affect higher education students' behavioral intentions to adopt metaverse technology for education. This study has proposed an extended Technology Acceptance Model (TAM) to achieve this aim. The novelty of this study resides in its conceptual model, which incorporates both technological, personal, and inhibiting/enabling factors. The empirical data were collected via online questionnaires from 574 students in both private and public universities in Jordan. Based on the PLS-SEM analysis, the study identifies perceived usefulness, personal innovativeness in IT, and perceived enjoyment as key enablers of students' behavioral intentions to adopt the metaverse. Additionally, perceived cyber risk is found as the main inhibitor of students' metaverse adoption intentions. Surprisingly, the effect of perceived ease of use on metaverse adoption intentions is found to be insignificant. Furthermore, it is found that self-efficacy, personal innovativeness, and perceived cyber risk are the main determinants of perceived usefulness and perceived ease of use. While the findings of this study contribute to the extension of the TAM model, the practical value of these findings is significant since they will help educational authorities understand each factor's role and enable them to plan their future strategies.

Covid-19 distance and online learning: a systematic literature review in pharmacy education.

The Covid-19 outbreak necessitated the implementation of social distancing mechanisms, such as the enforcement of lockdowns in numerous nations. The lockdown has disrupted many parts of everyday life, but this unusual event has particularly affected education. The temporary closure of educational institutions ushered in dozens of new reforms, including a shift into the distance and online learning. This study investigates the transition from traditional education in physical classrooms to online and distance and online learning in pharmacy education during Covid-19, especially about the challenges and benefits of distance and online learning. We did Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for literature sources between 2020 and 2022 (n.14). The study elaborates on how the transition has influenced teachers and students of pharmacy education. The research also summarizes several recommendations, which may assist in minimizing the adverse impacts of lockdown and encourage streamlined processes to distance and online learning, particularly in pharmacy education.

Factors affecting digital technology access in vocational education.

If policies are not thoroughly designed, technology integration may fail. As a result, users' perceptions of technology, especially access to digital technology, are critical for technology integration in education. This study aimed to develop and validate a scale to model factors affecting digital technology access for instructional use in Indonesian vocational schools. The study also reports the structural model of the path analysis and tests of differences based on geographical areas. A scale adapted from prior studies was established, validated, and examined for its validity and reliability. A total of 1355 responses were measurable; partial least squares structural equation modeling (PLS-SEM) and t-test procedures were applied for the data analysis. The findings informed that the scale was valid and reliable. For the structural model, the strongest relationship emerged between motivational access and skills access, while the lowest existed between material access and skills access. However, motivational access has an insignificant effect on instructional use. The t-test results show that geographical areas were significantly different regarding all involved variables.

Integrated-Based Curriculum of Pharmaceutical Dosage Forms (ICPDF): What Factors Affect the Learning Outcome Attainment?

This study aimed to evaluate pharmacy students' perceptions regarding the correlations among the quality of faculty members, institutional resources, an integrated-based curriculum of pharmaceutical dosage forms (ICPDF), and learning outcome attainment. The current study participants have attended courses (semesters 2 to 6) through the ICPDF in the Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Indonesia. We distributed survey instruments to 212 pharmacy undergraduate students after one year of the curriculum implementation. We asked the students to fill in the instrument in which the indicators consist of a 7-point Likert scale. The data were analyzed using SmartPLS, which included measurement and structural models through PLS-SEM. The findings informed that the quality of faculty members and institutional resources significantly predict ICPDF. Similarly, ICPDF plays a significant role in affecting learning outcome attainment. The quality of faculty members and institutional resources were not related to learning outcome attainment. Significances of differences were informed among students' years in university regarding learning outcome attainment and ICPDF. However, insignificant differences emerged based on gender. The findings demonstrate the benefits of using the PLS-SEM approach to create a valid and reliable model, assessing the correlations between independent variables with the ICPDF and learning outcome attainment as two dependent variables.

Dataset on Islamic school teachers' organizational commitment as factors affecting job satisfaction and job performance.

This dataset aims to examine the effect of Organizational Commitment (OC) factors on Job Performance (JP) and Job Satisfaction (JS) at Islamic schools in Indonesia. The data propose that OC factors (Affective Commitment (AC), Normative Commitment (NC), and Continuance Commitment (CC)) have significant influences on JS and JP. Through the survey approach, 387 responses were obtained from Islamic schoolteachers in Indonesia. Face and content validity were initiated after the adaptation of the survey instrument. Further, a Partial Least Squares Structural Equation Model (PLS-SEM) was used to test the reliability and validity of the measurement model. For this purpose, the statistical process presents reflective indicator loadings, internal consistency reliability, and convergent and discriminant validity. Future researchers may reuse this dataset as a potential instrument to measure factors affecting job satisfaction and job performance within the context of education.

Using an extended Technology Acceptance Model to understand students' use of e-learning during Covid-19: Indonesian sport science education context.

This study was to explore factors predicting the use of e-learning during Corona Virus Disease 2019 (Covid-19) among sport science education students In Indonesia Higher Education Institutions (HEIs). The study was conducted through survey with 974 participating students from five Indonesian HEIs. An extended Technology Acceptance Model (TAM) with facilitating condition as the external factor was implemented to be the theoretical framework of this study. An analysis method through Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to measure and assess the proposed model. The findings informed that: (1) the TAM-based proposed scale has been successfully explained factors predicting the use of e-learning among Indonesian sport science students during the pandemic; (2) the finding of significant relationships between facilitating condition and perceived ease of use and between facilitating condition and perceived usefulness was reported; and (3) the significant relationships among core components of TAM were found except for one, relationship between perceived usefulness and attitude.

Science teachers' integration of digital resources in education: A survey in rural areas of one Indonesian province.

Digital resources can be implemented to support the quality of education, including for teachers teaching science. Studies have explored technology integration for schools in cities and urban areas; however, few studies focused on the technology integration in education of rural areas. Therefore, this study aimed at elaborating factors predicting Indonesian science teachers' integration of digital resources in education, especially for teaching activities in rural areas. Besides, demographic information was addressed to understand the differences regarding the integration of digital resources in education. We collected data from 217 respondents who are science teachers of senior and junior high schools in one province located in Sumatra, Indonesia. Using Smart PLS 3.0, we analyzed the data to understand the relationship between exogenous and endogenous variables. In addition, t-test was used to elaborate on the differences regarding the integration of digital resources based on demographic information. Findings from the path analysis show that attitude was the strongest driver predicting intention to use digital resources in education perceived by teachers in rural areas. Meanwhile, self-efficacy was reported to be insignificant for the intention to use. The only predictor that is positively significant for actual behavior was intention to use. Other factors (facilitating condition and knowledge and skills) did not affect actual behavior. In addition, the difference test informed that Mean values between participants' teaching experience was significantly different regarding the integration of digital resources of schools in rural areas. No significant differences were reported based on gender and level of school. The technology integration for schools in rural areas is different compared to the schools in cities and urban areas. With limited accesses and resources, this study might provide practical implications and recommendations for further research, especially on technology integration in schools of rural area.

DATASET for validation the relationship between workplace spirituality, organizational commitment, and workplace deviance.

The current dataset examines the relationship between workplace spirituality and workplace deviance through the improvement of organizational commitment. The instruments from previous studies were adapted and validated through content validity. Further, it was translated from English to Indonesian language. In the data preparation, the computation of Skewness and Kurtosis, as well as Histogram, was done. Reliability assessment was done through Cronbach's alpha. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were addressed for the three constructs; workplace spirituality, organizational commitment, and workplace deviance. In an academic standpoint, the dataset can extend in-depth contributions and references for further researchers as a basis of the empirical evidence in relation to the relationship between the workplace spirituality, organizational commitment, and the workplace deviance. It is also beneficial for a model for reducing the workplace deviance from employee perspectives in the context of developing countries. Access to this dataset may contribute to stakeholders in establishing policies to reduce the workplace deviance.

An Asian Perspective: The dataset for validation of Teachers' Information and Communication Technology Access (TICTA).

This dataset presents the validation process of a survey of factors affecting Indonesian K-12 school teachers' Teachers' Information and Communication Technology Access (TICTA). An initial instrument was developed through the adaptation of instruments from previous studies. Afterward, it was piloted to 120 teachers and tested for its reliability. For the main data collection, the instrument was distributed online and responded by 2775 Indonesian K-12 school teachers. The main data analysis was conducted for the measurement model using four assessments; reflective indicator loadings, internal consistency reliability, convergent, and discriminant validity. The Partial Least Square Structural Equation Model (PLS-SEM) was utilized for the analysis. The dataset is beneficial for educational regulators in providing appropriate access to ICT in K-12 education and for educational researchers for future research on technology access in teaching.

Data relating to factors affecting teachers' burnout: A sem analysis in an Asian context.

The dataset presents the relationship between Teacher Self-Concept (TSC) and Teacher Efficacy (TE) as the predictors predicting burnout. Three components of burnout involved are Emotional Exhaustion (EE), Depersonalization (DP), and Reduced Personal Accomplishment (RPA). Various statistical approaches such as Content Validity Index (CVI), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Covariance-Based Structural Equation Modeling (CB-SEM) were addressed. Eight hundred seventy six Indonesian teachers form 3 provinces were willing to get involved by filling in the instrument. The data can be used for the educational institutions and centers to issue policies overcoming burnout among teachers, teachers to understand factors affecting their burnout, and future researchers extend the model offered by this dataset. This dataset is co-submitted from Heliyon entitled "Teachers' burnout: A SEM analysis in an Asian context" [1].

Teachers' burnout: A SEM analysis in an Asian context.

Researchers in educational psychology have researched Teacher Self-Concept (TSC) and Teacher Efficacy (TE) as two main predictors predicting burnout. Guided by a model developed by Zhu, Liu, Fu, Yang, Zhang & Shi (2018), the researchers aimed at building a model involving TSC, TE, and three components of burnout; Emotional Exhaustion (EE), Depersonalization (DP), and Reduced Personal Accomplishment (RPA) through Structural Equation Modeling (SEM). The researchers investigated predicting factors of burnout by reporting TSC and TE that might directly affect the components and examine the probability of TE to become a mediator of the correlation between TSC and burnout. This research also examined whether the difference emerges constantly among demographic information (gender and teaching experience) regarding all involved variables. A sample of 876 teachers across three Indonesian provinces completed a printed form of questionnaires. Some statistical procedures namely Content Validity Index (CVI), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Covariance-Based Structural Equation Modeling (CB-SEM), and t-test were conducted. Findings informed that the model is valid and reliable. TSC could directly affect EE, DE, and RPA, as well as indirectly influence them mediated by TE. Besides, TE is also reported to have significant relationships with EE, DE, and RPA. No significant differences in terms of age and teaching experiences emerge, except for EE.

The dataset for validation of factors affecting pre-service teachers' use of ICT during teaching practices: Indonesian context.

This dataset describes two main variables, technological pedagogical and content knowledge (TPACK) and Beliefs on ICT, which may affect pre-service teachers' (PSTs) use of ICT (UICT) during teaching practices. TPACK assumes that PSTs should actively combine some domains of knowledge to design good quality of ICT-integrated courses lessons. Beliefs on ICT in this study consist of a mix of behavioral, normative, and control beliefs on ICT integration in education. In addition, UICT is defined as ICT used by PSTs during teaching practices. Three approaches were applied for the purification of the dataset; development of instruments, survey, and exploratory factor analysis (EFA). The dataset consists of demographic information, TPACK, Beliefs on ICT, and UICT. The dataset is beneficial to teacher educators in designing effective programs that best nurture PSTs' UICT during teaching practices. Researchers sharing similar sample characteristics in developing countries may adapt this dataset for more rigorous statistical analyses.