This study considers the interaction between an e-learning system, the Blackboard system, and the students who use it in Saudi Arabia. While previous work exists, there is limited consideration of the assessment of the preferences of e-learning system usability variables based on students’ perspectives, especially in developing countries such as Saudi Arabia. This paper attempts to fill the gap by investigating the relative importance of the design criteria developed for e-learning system usability evaluation from the students’ perspective in Saudi tertiary education. Based on reviewed literature, a set of usability principles was developed that have had an influence in the students’ learning process and use of the e-learning system. The list includes system navigation, system learnability, visual design, information quality, instructional assessment and system interactivity. An exploratory study was carried out to identify the most important usability design characteristics from a student’s perspective and then evaluate the overall usability of the current e-learning system, based on this subset. A quantitative approach was adopted to weigh usability design characteristics, based on 181 learners’ perceptions. The sample consists of undergraduates who are users of a web-based e-learning system in a university in Saudi Arabia. The research instrument was tested for construct validity and reliability. The analysed results have shown that information quality is the most important dimension followed by the navigation of the e-learning system. The study has also revealed that the system learnability and visual design came third and fourth in order of importance of e-learning system usability assessment. Finally, the least important design categories that influenced the e-learning system usability assessment were instructional assessment and system interactivity. The empirical results of this study may help to provide insights for designers and evaluators leading to a more effective approach to improve the usability and uptake of the e-learning system.

The ability to think critically and creatively is essential for students to be able to face the challenges of the industrial revolution 4.0. Lectures must be designed to enhance students’ critical and creative thinking skills. This study aims to examine the implementation of problem-based learning in learning management information systems courses to improve students critical and creative thinking skills. The research design carried out was classroom action research. The subject in this study was students of Economics Education, Faculty of Economics, Universitas Negeri Semarang. The research was conducted in April-May 2019. The procedure for implementing class action research are two cycles (plan, action, observation, and reflection). The research data was taken by observation and interview methods. The data analysis method used is descriptive quantitative and qualitative methods. The results showed that two class action research cycles were well implemented. The application of the problem-based learning method can improve students’ critical and creative thinking skills. Students are able to solve a given case by doing the right analysis and being able to provide alternative solutions. Students consider the learning process to be more exciting and challenging. Students can express their opinions well in front of the class. The implication of this research is that lecturers can apply PBL with various combinations of learning strategies to improve students' critical and creative thinking skills.

Because of the Coronavirus Disease 2019 (COVID-19) outbreak, most universities were forced to choose Online Distance Learning (ODL). The study aimed to examine the response of university students to the new situation. A questionnaire was sent to the entire university student population. Based on responses from 606 students, it was revealed that use of all applications in ODL increased. However, only the use of MS Teams increased significantly, while the use of the other applications (email, Moodle, e-textbooks) increased in a range of low to medium in terms of effect sizes, and even nonsignificant for applications such as Padlet and Kahoot. Based on the replies of 414 respondents, a Model of Forced Distance Online Learning Preferences (MoFDOLP) based on Structural Equation Modeling was developed. With a chosen combination of predictors, we succeeded in predicting 95% of variance for Satisfaction, more than 50% for Continuance Preferences variance in MS Teams applications, and nearly 20% in the case of e-materials. Among hypothesized constructs, only Attitudes are a strong predictor of Satisfaction, while Organizational Support, Perceived Ease of Use and Learner Attitude toward Online Learning are not. Satisfaction is a good predictor of Continuance Preferences to use Information Technology after the lockdown ended.

The purpose of this research is to provide empirical evidence of the influence of perceived usefulness, convenience, experience, gender, and age on the intention to use e-learning after the government's school closure policy due to the Coronavirus disease (COVID-19) pandemic. Our sample is junior high school teachers in Indonesia and the data are analyzed using the Structural Equation Model (SEM). We show that perceived usefulness has a positive influence on e-learning intentions. The perceived convenience is not the reason for the teachers to use e-learning because they have no other alternative in carrying out their duties apart from e-learning. Besides, we have also found that gender and experience influence e-learning intentions. The teachers with different genders and experiences continue to implement e-learning as their way of teaching during the pandemic. The younger teachers have a higher intention of using e-learning. They have adequate digital abilities and are more confident in using e-learning, so they have a great intention in implementing e-learning for the delivery of the materials. Experienced teachers will find it easier to find the use and convenience of e-learning. Apart from experience, male teachers also report that it is easier to grasp the ease of e-learning. However, older teachers show more concern about their perception of system usability as a reason for using e-learning than system convenience.

A large number of articles in the field of science education reflect on scientific literacy as the main goal of science education (SE), although often with varying viewpoints. Nevertheless, researchers have begun to highlight subject-specific teaching practices that are expected to specifically enhance science subject teaching, including biology. The main aim of this theoretical article is to come on consensus and to conceptualise the term biological literacy (BL) more clearly and to present a theoretical concept of BL, composed on the basis of systematically analysed articles. This theoretical concept includes two dimensions of BL: (1) cognitive (cognitive skills, conceptual understanding, biological inquiry) and (2) affective dimension, based on systematic literature review (SLR). This theoretical concept also includes in addition four dimensions of BL: (3) sustainability; (4) interdisciplinarity, (5) career awareness and (6) nature of biology (NOB), based on literature review (LR) of recent decades, that was conducted to write theoretical overview of this research.

The aims of the recent study were; analyzing the effect of creative and online learning using Instagram on the ability to create new products in the technology and learning media course, and determining the level of users’ experience in processing visual media through the Canva application on android. Quantitative approach with this research was carried out through a quasi-experimental research model because it measured the post-test was applied to 58 students as the respondents in two experimental and control classes. This study used two instruments; the cognitive learning outcome questionnaire according to the subject to get the students’ ability to create new products, and the second instrument was taken from ueq-online.org in the Indonesian version to know the users' experience in processing visual media. The data were analyzed using one-way ANOVA, mean and standard deviation assisted by IBM SPSS Statistics 24.0. The results demonstrated that the effect of creative and online learning using Instagram on the ability to create new products in The Technology and Learning Media course was found significant at 0.028 <0.05 which meant it could be applied well. Meanwhile, the level of users' experience in processing the visual media application Canva on Android had the highest score on the Stimulation scale at 1.59, categorized "Very Good" and the lowest score was on the Novelty scale at 0.93, categorized "Above Average". With the strong creative encouragement to create new products, the students were able to process their own visual media which were tailored to the agreed digital visual designs and were published on Instagram.

This study investigates the contribution of teachers’ willingness to their professional competence in adapting to digital learning transformation during the Coronavirus disease (COVID-19) pandemic and its relationship to instructional leadership and self-efficacy. Data were collected by distributing online questionnaires consisting of 4 constructs, namely instructional leadership, self-efficacy, teacher’s willingness to change, and professional competence, and distributed to 221 Indonesian High School teachers. Structural Equation Model (SEM) was used for analysis using Analysis of Moment Structure (AMOS) version 23.0 to examine the hypotheses. The results showed that instructional leadership significantly influences teachers’ willingness to change with a positive impact on their professional competence. Furthermore, there is a significant effect on teachers’ willingness to change their professional competence, insignificant on self-efficacy with substantial impacts on their willingness to change. The analysis results through the Sobel test showed that the teachers’ willingness to change is an excellent mediating variable for self-efficacy in influencing professional competence. Conversely, it is not an excellent mediating variable for instructional leadership towards professional competence. The importance of teachers’ willingness to improve their professional competence is a new finding that significantly contributes to their professional development.

This study focuses on online supervisory written feedback on PhD supervisees’ performance, given explicitly through online communication, particularly during the first wave of COVID-19. This unusual situation has brought many different effects on students’ academic lives. This scenario has influenced both students’ and teachers’ mutual communication. A directed qualitative content analysis (DQCA) approach was adapted from previous research and modified for the present context. The current study planned to bring forth the supervisee and supervisors’ perception of the communication and feedback process, considering that online feedback and communication has been a new experience for most students. According to the findings, teachers/supervisors give feedback on students’ production, whereas teacher-student communication also seemed crucial for the performance improvising of learners. The result brought forth a wide range of social, educational, and surprisingly psychological issues both supervisees and supervisors faced during online communication during COVID-19.

Academic failure and school dropout, or early school leaving, are two of the situations that most concern countries and educational institutions worldwide, because of their prevalence and also their economic and social implications. Despite this prominent role that academic failure and school dropout have in societies, there seems to be no consensus on the literature on their conceptualization, definition, and relationship. Moreover, it is frequent to observe how both concepts are confused or overlap in the scientific literature and how many authors avoid defining these constructs, using them indistinctly. Therefore, this work analyses whether educational research considers them as two different concepts or if they are used indistinctly. For this purpose, 2,051 keywords from 450 articles were subjected to a systematic review and classified into the Education Resources Information Center (ERIC) thesaurus´ descriptors. The results reveal statistically significant differences in the descriptors according to the type of paper to which they correspond (academic failure or dropout). Thus, academic failure is associated with sociocultural, personal, and academic factors, while dropout is linked to employment and educational trajectories. These differences evidence that, although academic failure and school dropout refer to closely related educational problems, there are remarkable differences between them and between the treatment given to each of them in the scientific literature. Therefore, they should be considered as two different concepts. For all this, keyword analysis has proved to be a relevant element for the study of the structure of knowledge, allowing to clearly establish the differences between the two closely related concepts.

Online learning has become increasingly popular, making the learning process more attractive. One of the most popular learning media is artificial intelligence (AI). However, students do not accept this technology at all. Therefore, this study examined the factors influencing accounting students' acceptance of AI in learning. The survey was conducted with 147 higher-education students who use AI as a learning medium. The data were analyzed using SmartPLS 4.0 with the partial least square approach. The results showed that perceived usefulness influenced behavioral intention to use and satisfaction. However, perceived ease of use was only significant for satisfaction. Similarly, perceived confidence must be consistent with intention. Although it may influence perceived usefulness, other constructs, such as AI quality and personal innovativeness, can increase students' perceptions of the benefits and convenience of adopting AI in learning. Thus, this study contributes to the development of the technology acceptance model (TAM) and the information systems success model and is helpful to scholars, especially in applying AI in learning. They need to pay attention to the quality of AI, such as the accuracy of the information produced. Thus, the need to control the information from the AI only serves as a reference without requiring you to trust it completely.

This study aimed to investigate the relationship between teacher professional development, quality of lecture design, student engagement, teacher technical skills, pedagogical content knowledge and teacher satisfaction in using Artificial Intelligence (AI)-Powered Facilitator for designing lectures. The study used a non-random sample technique, and 208 participants answered a survey via Google Form after one semester, using a 5-point Likert scale to rate their responses. The structural equation model was used to analyze the data, and six factors were included in the study. The study confirmed hypotheses that teacher professional development, quality of lecture design, student engagement, and pedagogical content knowledge have a positive effect on teacher satisfaction. However, the study also revealed that teacher technical skills have a negative effect on teacher satisfaction, and pedagogical content knowledge has no significant effect. The proposed conceptual model explained 55.7% of the variance in teacher satisfaction Theoretical and practical implications were also discussed. These findings provide insights into the factors that contribute to teacher satisfaction in utilizing AI-Powered Facilitator for designing lectures and could inform the development of effective teacher training programs.

This study investigates the impact of external variables, technological pedagogical and content knowledge (TPACK) self-efficacy, and facilitating conditions on teacher adoption of online teaching technology during the COVID-19 pandemic. It employs explanatory research to characterize the effect of external variables on the variables of the technology acceptance model. 240 high school teachers filled out Google Forms survey questionnaires for six research variables and analyzed by the SmartPLS program. The result indicates that attitude and perceived usefulness significantly and positively influence behavioral intention. Perceived usefulness and ease of use have a strong positive effect on attitude. Furthermore, perceived ease of use has a considerable effect on perceived usefulness. Perceived usefulness and ease of use are not significantly influenced by external variables. Facilitating conditions significantly positively affect behavioral intention, whereas TPACK self-efficacy negatively affects behavioral intention.

In the early stages of adopting online learning, both learners and teachers displayed resistance, but the COVID-19 pandemic has forced a widespread shift to digital learning. To facilitate this transition, there is a growing focus on highlighting the effectiveness of online learning, which directly impacts learning outcomes. This study investigates online learning effectiveness through an integrated framework that considers online assessment practices and online course design as independent variables, with online learning support as a moderating variable. Understanding the effectiveness of online learning is crucial as hybrid learning becomes the "new norm" in education, combining online and offline methods for teaching the digital generation. Using a quantitative research design involving 232 students at Universiti Kebangsaan Malaysia, the study found that online assessment practices and course design significantly influence students perceived learning outcomes in an online learning environment. Additionally, online learning support positively moderates this relationship. These findings offer a comprehensive perspective on how online assessment practices, course design, and support systems contribute to the quality of higher education in Malaysia amidst evolving educational practices.

In an era where diversity and digitalization significantly influence higher education, understanding and adapting to various learning preferences is crucial. This study comprehensively analyzes 394 scholarly articles from 1984 to 2022 using bibliometric methods, providing a dynamic overview of the research patterns in learning styles within higher education. We identified four stages of development during this period: 1984–1995 (Low-interest), 1996–2005 (Early development), 2006–2018 (Development), and 2019–2022 (Intensification). Our analysis highlights that the United States, the United Kingdom, and Australia were the top three leading publishers of research on learning styles in higher education. The results reveal three main topics of publications: educational technology, learning environments, and subject behaviors. This research not only identifies emerging research topics but also underscores the importance of adapting instructional strategies to diverse learning styles to enhance educational outcomes in higher education.

This research aims to systematically review the integration of artificial intelligence (AI) in English language teaching and learning. It specifically seeks to analyze the current literature to identify how AI could be utilized in English language classrooms, the specific tools and pedagogical approaches employed, and the challenges faced by educators. Using the PRISMA-guided Systematic Literature Review (SLR) methodology, articles were selected from Scopus, Science Direct, and ERIC, and then analyzed thematically with NVivo software. Findings reveal that AI enhances English teaching through tools like grammar checkers, chatbots, and language learning apps, with writing assistance being the most common application (54.55% of studies). Despite its benefits, challenges such as academic dishonesty, over-reliance on AI (27.27% of studies), linguistic issues, and technical problems remain significant. The study emphasizes the need for ethical considerations and teacher training to maximize AI’s potential. It also highlights societal concerns, including the digital divide, underscoring the importance of equitable access to AI-powered education for learners of all socioeconomic backgrounds.

Artificial intelligence (AI) has revolutionized higher education. The rapid adoption of artificial intelligence in education (AIED) tools has significantly transformed educational management, specifically in self-directed learning (SDL). This study examines the factors influencing Indonesian higher education students' intention to adopt AIED tools for self-directed learning using a combination of the Theory of Planned Behavior (TPB) with additional theories. A total of 322 university students from diverse academic backgrounds participated in the structured survey. This study utilized machine learning it was Artificial Neural Networks (ANN) to analyze nine factors, including attitude (AT), subjective norms (SN), perceived behavioral control (PBC), optimism (OP), user innovativeness (UI), perceived usefulness (PUF), facilitating conditions (FC), perception towards ai (PTA), and intention (IT) with a total of 41 items in the questionnaire. The model demonstrated high predictive accuracy, with SN emerging as the most significant factor to IT, followed by AT, PBC, PUF, FC, OP, and PTA. User innovativeness was the least influential factor due to the lowest accuracy. This study provides actionable insights for educators, policymakers, and technology developers by highlighting the critical roles of social influence, supportive infrastructure, and student beliefs in shaping AIED adoption for self-directed learning (SDL). This research not only fills an important gap in the literature but also offers a roadmap for designing inclusive, student-centered AI learning environments that empower learners and support the future of SDL in digital education.

Integrating generative artificial intelligence (GenAI) in education has gained significant attention, particularly in flexible learning environments (FLE). This study investigates how students’ voluntary adoption of GenAI influences their perceived usefulness (PU), perceived ease of use (PEU), learning engagement (LE), and student-teacher interaction (STI). This study employed a structural equation modeling (SEM) approach, using data from 480 students across multiple academic levels. The findings confirm that voluntary GenAI adoption significantly enhances PU and PEU, reinforcing established technology acceptance models (TAM). However, PU did not directly impact LE at the latent level—an unexpected finding that underscores students’ engagement’s complex and multidimensional nature in AI-enriched settings. Conversely, PEU positively influenced LE, which in turn significantly predicted STI. These findings suggest that usability, rather than perceived utility alone, drives deeper engagement and interaction in autonomous learning contexts. This research advances existing knowledge of GenAI adoption by proposing a structural model that integrates voluntary use, learner engagement, and teacher presence. Future research should incorporate variables such as digital literacy, self-regulation, and trust and apply longitudinal approaches to better understand the evolving role of GenAI inequitable, human-centered education.

This article aims to establish a research map of the flipped classroom (FC) model in general education for the period 2014-2024, exploring publication trends, influential authors, organizations, countries, and prominent research topics, while also identifying academic gaps in this field. The study focuses on three main aspects: (1) publication trends and influential authors, organizations, and countries; (2) key research topics and academic gaps; (3) international collaboration networks in FC research. The research employs a bibliometric analysis method, utilizing the Scopus database and the VOSviewer visualization tool, to synthesize information and identify research trends. The results indicate that research on FC in K-12 education increased sharply from 2019, reflecting the impact of digital transformation in education during and after Covid-19. The United States, Hong Kong, and Taiwan are the leading research centers. Authors such as Bergmann, Bishop, and Hew have been highly influential. Prominent research trends include self-regulated learning, learner satisfaction, gamification, and the application of artificial intelligence. The international collaboration network in this field is growing, with strong participation from institutions from Southeast Asia, including Vietnam. The study recommends expanding the scope of analysis beyond Scopus and using qualitative methods and systematic reviews to further evaluate the FC model. The research will provide policymakers, teachers, and researchers with useful evidence for improving programs, enhancing professional development, and promoting digital transformation in general education.

The rapid integration of artificial intelligence (AI) technologies into the field of higher education is causing widespread public discourse. However, existing research is fragmented and lacks systematic synthesis, which limits understanding of how college and university students adopt artificial intelligence technologies. To address this gap, we conducted a systematic review following the guidelines of the PRISMA statement, including studies from ScienceDirect, Web of Science, Scopus, PsycARTICLES, SOC INDEX, and Embase databases. A total of 5594 articles were identified in the database search; 112 articles were included in the review. The criteria for inclusion in the review were: (i) publication date; (ii) language; (iii) participants; (iv) object of research. The results of the study showed: (a) The Technology Acceptance Model and the Unified Theory of Technology Acceptance and Use are most often used to explain the AI acceptance; (b) quantitative research methods prevail; (c) AI is mainly used by students to search and process information; (d) technological factors are the most significant factors of AI acceptance; (e) gender, specialty, and country of residence influence the AI acceptance. Finally, several problems and opportunities for future research are highlighted, including problems of psychological well-being, students’ personal and academic development, and the importance of financial, educational, and social support for students in the context of widespread artificial intelligence.