This study addresses the effectiveness of learning via educational software. Recent decades have seen the integration of technologies, which are changing teaching and transforming teachers into mediating, facilitating, and guiding figures by means of digital learning methods that serve as a major tool in schools, colleges, and universities. The current study focuses on instruction provided within the Israeli Air Force and examined the effectiveness of instruction provided via educational software in terms of learning products: Bloom’s revised taxonomy, Te’eni’s affective-cognitive model of organizational communication and the STEM model. We randomly divided the learners into three groups who studied the same topic: one group studied with the educational software only, the second with the educational software together with an instructor, and the third with an instructor who used a presentation. The learners took a test and four months later they took another test to examine the effectiveness of the instruction over time. The research results show that the recall levels and performance levels on the tests were almost identical in all groups, but in the categories of understanding and applying the addition of an instructor strongly contributed to achievements: Those who received instruction via educational software achieved the best results in the understanding category, while those who studied with an instructor who used a presentation achieved the best results on the test with regard to application of the studied material. The findings of this study can illuminate the effectiveness of using educational software in learning processes in all educational systems.

The primary goal of this study is to explore what makes teachers accept Google Classroom (GCR). GCR platform is an emerging technology that could support online learning activities by offering outstanding benefits such as usability, flexibility, and task adaptability. Many of the students in Indonesia have al-ready used the GCR platform since the government has tried to provide it as a free online learning tool to support learning activities during the pandemic. However, there is limited understanding of users' behavior, especially Indonesian students' acceptance of the GCR platform. The model is tested by administering the online questionnaire to 261 university students in Indonesia. The extended Unified Theory of Acceptance and Use of Technology Model (UTAUT) model has been applied to observe users’ acceptance of GCR. The result Performance expectancy (PE), Effort expectancy (EE) Social Influence (SI), Facilitating Conditions (FC), Trust of Internet (TI) and Trust of Government (TG) considerably affected users’ intention to use the GCR. Moreover, Trust of Internet (TI) and Trust of Government (TG) also knowingly impacted Performance expectancy (PE).

Modern technology is necessary and important for improving the quality of education. While machine learning algorithms to support students remain limited. Thus, it is necessary to inspire educational scholars and educational technologists. This research therefore has three main targets: to educate the holistic context of rural education management, to study the relationship of continuing education at the upper secondary level, and to construct an appropriate education program prediction model for high school students in a rural school. The data for research is the academic achievement data of 1,859 students from Manchasuksa School at Mancha Khiri District, Khon Kaen Province, Thailand, during the academic year 2015-2020. Research tools are separated into 2 sections. The first section is a basic statistical analysis step, it composes of frequency analysis, percentage analysis, mean analysis, and standard deviation analysis. Another section is the data mining analysis phase, which consists of discretization technique, XGBoost classification technique (Decision Tree, Gradient Boosted Trees, and Random Forest), confusion matrix performance analysis, and cross-validation performance analysis. At the end, the research results found that the reasonable distribution level of student achievement consisted of four clusters classified by academic achievement. All four clusters were modeled on predicting academic achievement for the next generation of students. In addition, there are four success models in this research. For future research, the researcher aims to develop an application to facilitate instruction for learners by integrating prediction models into the mobile application to promote the utilization of modern technology.

The increasing sophisticated learning technology and COVID-19 have pushed the teaching-learning process to use pedagogy, andragogy, and cybergogy approaches. The current research aims to investigate the relationship between the practices of these three approaches and student self-regulated learning. The structural equation model used indicates that pedagogy practices may affect the andragogy practices in teaching-learning process. Pedagogy approach shows no direct effect but has an indirect effect on students’ self-regulated learning. The indirect effect comes from the pedagogy-andragogy continuum and the impact of pedagogy instruction on cybergogy practices. Andragogy practices also gives a significant impact on students’ self-regulated learning and how the students use learning technology in cybergogy approach. Andragogy and the continuum of cybergogy promote students’ self-regulated learning. These results indicate that pedagogy-andragogy continuum can have an interplay with cybergogy. The interplay of these approaches may encourage students’ self-regulated learning. The current research can be a baseline to construct a new approach in teaching-learning process and its instructions in the classroom.

The purpose of the current study was to analyze social media content related to physical education. In the context of summative qualitative content analysis, I took advantage of big data analytics to access the data. Machine learning of this big data mapped the large content volume from four major social media platforms. The data was collected by extracting social media posts from January to December 2020. The big data analysis process sorted, categorized, and classified the enormous data into several preeminent topics regarding PE. These computerized analyzes were used to identify themes that were further analyzed using qualitative methods. The results revealed two overarching themes. These themes were (a) PE representation as a school subject and (b) the images of PE teachers on social media. The second theme consisted of three subthemes: masculine traits of PE teachers and negative and positive sentiments toward these teachers. I concluded that key aspects of PE discourse in virtually mediated reality share topical characteristics with what people have previously socially constructed. However, the themes offer a new addition to the literature in that the analysis offers a new perspective on ongoing debates about the social construction of PE through enormous large data sets.

Industry 4.0 has affected various aspects of life, including the organization of higher education. In the current era, higher education is required to transform themselves from using the conventional way of administration to the digitalized one. The said transformation also includes the services provided and management carried out by the organizations. The objective of this study is to measure the understudied mediation of digital innovation in the effect of the nexus of digital leadership and digital literacy on the performance of higher education. This quantitative research was conducted by distributing questionnaires to 234 faculty members of four higher education institutions in Malang City, Indonesia. Partial Least Squares – Structural Equation Modeling was applied to analyze the data. This study finds that digital leadership significantly affects the higher education performance and conclusively predicts digital innovation. As hypothesized, digital literacy has a significant effect on the higher education performance and digital innovation, and digital innovation plays a substantial role in the higher education performance. In addition, digital innovation mediates the influence of digital leadership and digital literacy on the higher education performance.

The world eagerly anticipates advancements in AI technologies, with substantial ongoing research on the potential AI applications in the domain of education. The study aims to analyse publications about the possibilities of artificial intelligence (AI) within higher education, emphasising their bibliometric properties. The data was collected from the Scopus database, uncovering 775 publications on the subject of study from 2000 to 2022, using various keywords. Upon analysis, it was found that the frequency of publications in the study area has risen from 3 in 2000 to 314 in 2022. China and the United States emerged as the most influential countries regarding publications in this area. The findings revealed that “Education and Information Technologies” and the “International Journal of Emerging Technologies in Learning” were the most frequently published journals. “S. Slade” and “P. Prinsloo” received the most citations, making them highly effective researchers. The co-authorship network primarily comprised the United States, Saudi Arabia, the United Kingdom, and China. The emerging themes included machine learning, convolutional neural networks, curriculum, and higher education systems are co-occurred with AI. The continuous expansion of potential AI technologies in higher education calls for increased global collaboration based on shared democratic principles, reaping mutual advantages.