Proctored online exams have become widespread, especially after COVID-19, increasing exam security and efficiency. In recent years, artificial intelligence developments have increased these systems’ importance by expanding their use. Accordingly, this study aims to provide an in-depth understanding of the conceptual structure of artificial intelligence-supported proctored online examination (AI-SPOE) research. Systematic literature review and text mining were used as methods. Thirty-two studies in Scopus and Web of Science databases were analyzed according to the PRISMA technique. While all the studies were examined in the context of content analysis, abstracts and keywords of the relevant studies were analyzed by text mining. The current study found it effective in increasing academic integrity and exam security, but problems such as privacy violations, exam anxiety, and algorithmic biases were encountered. Although methods such as biometric verification and behaviour analysis successfully detect cheating, technical difficulties, and false positives negatively affect the user experience. In the future, more inclusive designs, transparent algorithms, and alternative evaluation methods are suggested. In conclusion, the study emphasizes the potential of AISPOE systems and highlights the importance of ethical and technical improvements.
The analysis of students’ learning traces in digital environments enables a better understanding of the factors influencing their academic performance. This paper proposes a hybrid predictive model combining Machine Learning (ML), Deep Learning (DL), and Generative Artificial Intelligence (GenAI) to predict students’ academic performance (GPA) by leveraging traces from six dimensions: demographic, cognitive, social, emotional, contextual, and normative. First, the Random Forest algorithm is used to select the most relevant features. Then, a combined model based on MLP, LSTM, and XGBoost is trained to optimize prediction accuracy. Generative AI (DeepSeek) is integrated to enrich contextual data and provide personalized recommendations. Model decisions are interpreted using SHAP, allowing for explainability of the predictions. The model is evaluated on a real dataset of 1,002,393 records, built from learning traces collected from student interactions with courses hosted on a Moodle platform. The results highlight a significant improvement in predictive performance (a coefficient of determination of 87%, an average precision of 89.2%, an overall recall of 87.5%, an F1-score of 88.3%, and an accuracy of 90.4%) compared to traditional approaches. This work underscores the value of hybrid techniques for advanced educational data analysis. The results open promising perspectives for integrating intelligent learning support systems, as well as for adapting and extending the model to other educational contexts, learning platforms, and academic levels.
Feedback plays a critical role in learning; however, its structure and implementation in distance education differ significantly from traditional educational practices due to the unique nature of online environments. This necessitates a systematic investigation into feedback mechanisms in distance education. This systematic review aims to explore the literature on feedback in distance education, focusing on the types, purposes, and timing of feedback, as well as their implementation and impact on students. We conducted a systematic search of relevant databases on May 5, 2024, without applying a publication year restriction, thereby including all eligible studies published up to that date in accordance with PRISMA guidelines. After applying inclusion and exclusion criteria, we included 91 studies and subjected them to content analysis. Existing literature guided the categorization of feedback, examining three dimensions: feedback types, purposes, and timing. Automated systems emerged as the most common source of feedback. Formative feedback was the predominant type, evaluation was the primary purpose, and academic performance was the most frequently measured outcome. Findings highlight the emphasis on feedback’s role in 1 improving student learning outcomes, engagement, and instructional practices. This review synthesizes key findings on feedback mechanisms in distance education, focusing on their critical role in enhancing learning outcomes. It underscores the need for innovative feedback strategies that balance personalization and scalability to address diverse learner needs across contexts. Future research should explore adaptive feedback approaches to ensure inclusivity and effectiveness in distance education settings.
This book provides an in-depth analysis of the principles and successful practices of online and distance education, covering key areas such as course design, pedagogy, online assessment, open education, and inclusive practices. The book Online and Distance Education for a Connected World is a valuable resource for Instructors in the field of designing e-learning courses.
This study explores the perceptions of implementing digital curricula in terms of Readiness (technical and pedagogical) and the obstacles to implementation from teachers' perspective in the Directorate of Education of Al-Jami'ah District, Jordan. The study employs a descriptive survey and includes a random sample of 370 male and female teachers working in the Directorate of Education of Al-Jami'ah District, Amman. A questionnaire was developed, focusing on two domains: teachers' Readiness (technical and pedagogical) and the challenges of implementing digital curriculum supports. The results indicate that the overall Readiness for implementing digital curricula, in terms of technical and pedagogical Readiness, and the degree of obstacles to implementation were rated as moderate. There were significant differences in Readiness based on gender and school type, favoring males and private schools. However, no significant differences were observed concerning teaching experience or academic qualifications. The study recommends that stakeholders in the Ministry of Education prioritize digital curricula and work towards effective implementation in the educational field.
This study investigates the determinants of e-learning adoption among pre-service mathematics teachers through an extended Technology Acceptance Model (TAM) framework. Using structural equation modeling with data collected from 508 pre-service mathematics teachers in Indonesia, we examined thirteen hypothesized relationships between system accessibility, enjoyment, perceived ease of use, perceived usefulness, attitude toward using, behavioral intention, and actual system usage. Results revealed that system accessibility ((3 = 0.535) and enjoyment ((3 = 0.319) significantly predicted perceived ease of use, explaining 58.9% of its variance. Perceived usefulness was primarily influenced by enjoyment ((3 = 0.468), perceived ease of use ((3 = 0.323), and system accessibility ((3 = 0.165). Attitude toward using was significantly affected by perceived usefulness ((3 = 0.489) and perceived ease of use ((3 = 0.265), while behavioral intention was predominantly determined by perceived usefulness ((3 = 0.469), attitude toward using ((3 = 0.277), and perceived ease of use ((3 = 0.106). Notably, the relationship between behavioral intention and actual system usage was borderline significant ((3 = 0.082, p = 0.050), supporting critique of TAM's limited ability to predict actual use. These findings highlight the importance of developing accessible, enjoyable mathematics e-learning systems while acknowledging the complex relationship between intention and actual usage behavior.
In the era of advancing globalization, developing emotional intelligence (EI) has become essential for adolescents to manage emotional stress, build social relationships, and adapt to changing environments. This study developed a Self-Directed Blended Learning (SDBL) model incorporating Intangible Cultural Heritage music to enhance students' EI and academic achievement. The research was conducted in two phases: First, the SDBL model was designed and validated, consisting of five components (setting learning targets, planning, gathering information, online and offline learning activities, and post-lesson reflection). Five education experts evaluated the model using a 5-point Likert scale, yielding an average score of 4.84, confirming its pedagogical quality. Second, the teaching experiment was conducted at Taiyuan University of Science and Technology. A total of 112 non-music undergraduate students, who voluntarily enrolled in the Shanxi Intangible Cultural Heritage Music Appreciation course, participated in the study. The students were randomly divided into an experimental group and a control group, with 56 students in each. After the intervention, both groups completed post-tests to measure their emotional intelligence and academic achievement. Emotional intelligence was assessed using a revised 25-item version of the Schutte Self-Report Emotional Intelligence Test (SSEIT). Five experts evaluated the test's content validity using the Item-Objective Congruence (IOC) method, with scores ranging from 0.6 to 1. Academic achievement was measured using test items from a nationally recognized Intangible Cultural Heritage (ICH) database maintained by the Chinese government. Five experts reviewed these items, and their IOC scores ranged from 0.8 to 1.0. The collected data were analyzed using t-tests and MANOVA. The results showed that the experimental group performed significantly better than the control group in both emotional intelligence and academic achievement. Additionally, a positive correlation was found between the two variables. The findings demonstrate that SDBL model incorporating Intangible Cultural Heritage music can enhance EI and academic achievement while simultaneously developing cognitive and emotional competencies, offering an innovative solution for preparing students to meet future challenges.
This umbrella review synthesizes evidence from 41 qualitative, meta-analytic, and mixed-method reviews to reveal the effectiveness of FL on cognitive, affective, and interpersonal domains across STEM, Medical and Health Sciences (M&H), and Social Sciences (SS). The results reveal that while flipped learning (FL) demonstrates positive effects on academic achievement in mathematics, science, and English language teaching, evidence for other domanial outcomes across fields remains inconclusive due to several limitations such as study scope, methodology and regional restrictions. Specifically, evidence across fields on other cognitive outcomes including autonomy, conceptual understanding, and higher-order thinking skills has the potential for fostering autonomy, conceptual understanding, and higher-order thinking skills, but the current evidence base is insufficient to definitively confirm these claims. Similarly, FL practices across fields seem conducive to enhancing affective and interpersonal domain outcomes; however, the predominance of qualitative evidence necessitates more quantitative and mixed method research for robust conclusions. Our synthesis also highlights the lack of in-in reporting and exploration of specific mechanisms by which FL cultivates domanial outcomes such as higher order thinking skills, motivation, and collaboration. Moreover, the evidence in the reviews highlights the absence of detailed reporting on the measurement metrics used to assess affective and interpersonal outcomes across fields in the reviews. Researchers should prioritize discipline-specific investigations, explore outcome-related mechanisms, develop standardized metrics, and bridge the gap between theory and practice through the integration of relevant learning theories into the design of FL strategies.
This study, conducted within Anadolu University's Open Education System, examines the effects of learners' attitudes, subjective norms, and perceived behavioral control, as conceptualized in the Theory of Planned Behavior (TPB), on their intention to participate in live sessions and their intention to engage in electronic word-of-mouth (eWOM). It also tests the direct effect of parasocial relationships with instructors on these intentions, as well as their mediating role between TPB variables and behavioral intentions. Using the quantitative research method with a correlational research design, data were collected via an online survey from 475 open and distance learners who had attended at least one live session during the 2023-2024 fall semester. Data were analyzed using SPSS 25 and SmartPLS 4.0. Findings show that positive attitudes toward live sessions, high perceived behavioral control, and strong parasocial relationships significantly and positively influence both participation and eWOM intentions. Subjective norms had no direct significant effect but showed a significant indirect effect through parasocial relationships. The model demonstrates that parasocial relationships strongly predict behavioral intentions and serve as a key mediating variable, enhancing the effects of TPB variables. In conclusion, emotional bonds with instructors and screen-based social interactions strengthen participation and eWOM intentions. Interactive, learner-centered, and socially engaging strategies are recommended to enhance the effectiveness of live sessions in open and distance learning, capitalizing on digital communication opportunities.
Growing interest in online education has led to considerable research on factors influencing students' learning outcomes, yet research on the relationship between teaching presence and student satisfaction and academic achievements in synchronous online English classes in developing countries is scanty. The current study aimed to address this gap by employing a quantitative design and Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis. A total of 182 surveyed respondents from a private Vietnamese university, enrolling in various general English levels in synchronous online modes for at least one semester, completed a closed-ended five-point Likert scale questionnaire survey over a two-month period. The findings indicate that teaching presence was significantly associated with both extrinsic and intrinsic motivation, as well as student satisfaction in synchronous online English classes, although it does not directly influence academic performance. Extrinsic motivation showed a significant positive relationship with academic achievement, whereas intrinsic motivation shows no significant effect. Student satisfaction functions as a mediator between teaching presence and academic performance, with higher satisfaction correlating with improved academic outcomes. The research underscores the importance of teaching strategies that promote satisfaction and extrinsic motivation to bolster student success in online learning environments, specifically in the Vietnamese context, where online learning mode is burgeoning. Moreover, this study contributes to current research by providing additional empirical evidence on theoretical insights and applied applications for online English learning environments by focusing on the unique challenges and opportunities that synchronous online classes present in the Vietnamese educational system, thereby addressing a gap in research from developing countries.
This study aimed to investigate the effects of flipped learning on mathematics anxiety among primary school students. An embedded mixed-methods design was employed for this study. The participants in the 8-week intervention were 3rd-grade primary school students attending a public school in Turkiye. Data for the study was collected through the use of the mathematics anxiety scale, interview form, student diaries, and observation instrument. The collected data was analysed utilizing the Wilcoxon Signed Rank Test for quantitative analysis and inductive content analysis methods for qualitative analysis. The quantitative analysis indicated that the flipped learning significantly reduced mathematics anxiety among primary school students and enhanced student participation. The qualitative analysis produced four main themes: in-class learning activities, student participation, technology and internet usage, and learning resources. The findings revealed that the incorporation of in-class learning activities in the flipped learning facilitated a better comprehension of mathematical concepts among students and fostered positive attitudes toward mathematics lessons.
Interest in online learning has grown over the past decades, with the heutagogical approach gaining traction, especially in doctoral programs requiring learner autonomy. This study aims to explore doctoral students' lived experiences with the heutagogical approach in online learning, including their perceptions of its effectiveness and their interpretation of learning outcomes and quality assurance processes. Using a phenomenological design, data were collected from graduate students at Universitas Negeri Yogyakarta, Indonesia, through indepth interviews and classroom observations over one semester. Observations revealed three key phases: (1) design (students co-developed learning objectives and project scopes aligned with their dissertations); (2) development (students conducted self-directed research with weekly discussions and iterative feedback); and (3) implementation (peer evaluations and final project submission for formal review). Students perceived the approach as effective, recognizing its flexibility and autonomy, while also acknowledging challenges such as demands for self-regulation, technical constraints, and limited face-to-face interaction. The approach proved effective in balancing independent learning with academic achievement, reflected in scientific publications and intellectual property rights. This study highlights the heutagogical approach as a viable pedagogical model for doctoral online learning, emphasizing the critical balance between learner autonomy, structured guidance, and institutional support to ensure sustained academic excellence.
The right to education is fundamental and must be provided to all without restriction. Distance learning methodologies present key learning opportunities for those with limited access to conventional education, such as prison inmates, and come in response to the demand for new solutions in the field of educational activities. The ever-increasing need for new forms of education highlights distance learning in prison schools as a key pillar of the new learning process. Education in prisons as a way of reintegrating offenders is an essential means of changing behavior patterns as well as for acquiring knowledge and developing-improving skills, while aiming at the smooth professional integration of inmates. The present study presents a review of distance education in prisons, as well as qualitative research examining aspects related to distance education in a prison of central Greece, which includes observation as well as interviews with thirty-six inmates. The results of the qualitative research show that Greek language courses are essential for reading, writing and communication with more teaching time for developing-improving the skills of the inmates. Distance education is an essential tool for inmates to acquire the necessary skills for social reintegration and smooth professional integration.
Today the world is witnessing a rapid change in industrial needs. Traditional education has failed to meet the current industrial demands. Micro-credentials have emerged as an innovative solution to address the evolving demands of higher education and workforce development to ultimately address the requirements of the changing industry. This research paper investigates the role of micro-credentials in strengthening educational quality and employability by offering targeted, competency-based certifications that complement traditional degree programs. Through a comprehensive analysis using mixed methods with a convergent design, the study reveals that micro-credentials facilitate flexible learning pathways, promote skills development, and provide institutions with a mechanism to align curricula with industry requirements. The findings indicate that students value the opportunity to receive specialized skills rapidly, while employers appreciate the direct correlation between micro-credential achievements and workforce readiness. However, the research finds out the challenges of standardization, quality assurance, and institutional integration that must be addressed to fully realize the potential of micro-credentials. Emerging as a catalyst for lifelong learning, micro-credentials not only empower learners to adapt to the rapidly changing technological settings but also offer universities a viable strategy to augment their relevance in a competitive academic environment. This paper concludes that effective implementation of micro-credentials can bridge the gap between academic training and realworld demands, as a whole contributing to a more resilient and vital economic environment. The insights of this study provide actionable recommendations for the inclusion of micro-credentials in higher education.
This research examines key ethical points related to using generative artificial intelligence (AI) in education. As AI technologies advance, their application in creating personalized learning materials and automating educational tasks raises significant ethical challenges, including data privacy, algorithmic bias, authorship, and the evolving role of educators. This research presents a comprehensive ethical framework designed to guide educational institutions in the responsible implementation of generative AI. Through a systematic analysis of existing global guidelines, academic literature, and the identification of key gaps, this study develops actionable strategies to mitigate potential risks and ensure equitable AI-driven learning experiences. The proposed framework emphasizes context-specific guidelines, practical implementation steps, and continuous stakeholder engagement. A detailed case study illustrates the framework's application in a real-world educational scenario. This research contributes to the nascent field of ethical AI in education by providing a practical tool for institutions to navigate the complex ethical landscape, fostering a culture of responsible AI adoption and enhancing student learning outcomes while safeguarding fundamental rights and values.
Education in modern society has an exceptionally important mission-to shape a responsible and selfsufficient individual equipped with critical thinking and a protective immunity against numerous social myths and threats. The fundamental task of education is to prepare future specialists for life in rapidly changing conditions. This article is dedicated to analysing access to the educational process during wartime by addressing the following objectives: identifying current challenges and the foundations of the influence of globalization processes and domestic factors on Ukrainian education; exploring the impact of information technologies on the development of education; and outlining the main directions for modernizing national education. A systemic-structural approach was employed as a general scientific method which made it possible to identify problematic issues in ensuring access to the educational process during wartime, to analyze the development of Ukrainian education in the context of integrative educational processes, and to formulate proposals aimed at improving the modern national education model. The results of the analysis have led to conclusions regarding the necessity of uniting the efforts and resources of various countries to establish a unified educational system, universal quality assurance standards, and integrative and internationalization processes in education. Several key issues have been highlighted: training and/or retraining of academic staff through academic mobility, collaboration within educational and research grants, professional development programs, and internships. This will ensure high-quality education and the adoption of new teaching methods and formats; development and further improvement of technical infrastructure to support educational processes effectively.
The integration of artificial intelligence (AI) tools like ChatGPT into education presents significant opportunities to foster interactive and student-centered learning. This study surveyed 61 educators during an online AI workshop to examine their baseline prompt-design skills, explore their perceptions of student use of ChatGPT, and identify associated challenges and strategic opportunities. Importantly, the six-element prompt design framework was deliberately shared only after data collection, ensuring that the study captured prompt-design abilities at a true baseline without prior intervention, which constitutes a novel contribution of this research. Findings reveal that although most educators expressed enthusiasm for ChatGPT's potential, there was notable variability in their ability to design effective prompts. Specifically, over 77% of the initial prompts were vague and lacked essential elements such as context, role, or format, indicating a substantial gap in prompt-engineering competence. Some respondents faced difficulties in crafting contextual and specific prompts, while others demonstrated creative uses of ChatGPT to inspire classroom activities and support student discussions. Additionally, ethical concerns, such as plagiarism, overreliance, and the reliability of ChatGPT-generated answers, emerged as critical issues. The study highlights the urgent need for targeted digital literacy training, prompt-design strategies, and institutional policies that promote the ethical and pedagogically sound use of ChatGPT in both physical and online classrooms. These findings provide a unique and timely foundation for guiding AI adoption in education by underscoring the necessity of equipping educators with tailored digital literacy training and clear ethical guidelines, which are critical for fostering innovative, responsible, and context-sensitive teaching practices.
This study aims to adapt the six-factor "Students' Sustainable Engagement in E-Learning Scale" (SSE-eL-S) developed by Lee et al. (2019) from English to Turkish language and culture and validate its psychometric properties to be conducted as a reliable research instrument. It was designed with a three-step approach involving translation and cross-cultural adaptation, pre-testing, and field testing. First, the SSE-eL-S was cross-culturally adapted to the Turkish language and culture with forward and backward translation as well as functional, conceptual, and linguistic assessment. Subsequently, in the pre-testing phase, the English and Turkish versions of the scale were administered to a sample of 43 university students, and paired sample t-tests with Pearson correlation analyses were conducted to ensure content equivalence and reliability. In the field testing phase, data were collected from two independent student samples. Exploratory factor analysis and internal consistency analysis were applied to the first group (n=226), resulting in a four-factor structure. Confirmatory factor analysis was subsequently performed on the second group (n=206) to validate the factor structure. The model exhibited acceptable fit indices (chi(2)/df=2.065; CFI=0.934; IFI=0.935; RMSEA=0.072), and internal consistency coefficients ranged between 0.799 and 0.936. Furthermore, convergent and discriminant validity were confirmed through AVE, CR, MSV, and Fornell-Larcker analyses. The finalized Turkish version consisted of 20 items categorized into four factors: psychological motivation, peer collaboration, cognitive problem-solving, and learning management. The adapted SSE-eL-S provides a reliable and valid tool to measure sustainable student engagement in e-learning environments in higher education.
Although Anadolu University is known for having one of the world's most prestigious and largest-scale Open Education Faculties, it also offers distance learning-based non-thesis master's programs within its graduate education framework. The aim of this study is to explore the satisfaction of learners enrolled in distance education non-thesis master's degree programs at Anadolu University regarding online courses delivered over the internet. This research was carried out using a cross-sectional survey model. 488 learners voluntarily participated in the study. The data obtained to determine the satisfaction of the learners towards online courses were collected using the 'Satisfaction Scale for E-Courses'. As a result of the study, it was found that learners' satisfaction with online courses differed significantly according to age, occupational status and monthly income level variables. Learners in the older age group were more satisfied with online courses than those in the young and young middle age group. Regarding occupational status, learners working in the public sector were more satisfied with the materials and communication tools used in online courses compared to non-employed learners. In addition, learners with high monthly income were found to be more satisfied with online courses compared to learners with low monthly income. By focusing on the satisfaction of online non-thesis master's students in Anadolu University's large-scale open and distance education system, this research provides innovation and contribution to the limited number of studies on online master's education in Turkiye with original and up-to-date findings.
Integrating artificial intelligence (AI) into higher education can revolutionise traditional learning paradigms by enhancing self-directed learning and fostering student autonomy. This systematic research paper examines the role of AI in supporting these educational shifts, analysing its impact on student engagement, personalised learning experiences and academic performance. Through a comprehensive review of existing literature, this study explores various AI-driven tools and applications that enable students to take greater control of their learning processes. The promise of AI in fostering student autonomy is significant, with key focus areas that include adaptive learning systems, AI-powered feedback mechanisms, and intelligent tutoring systems, all of which contribute to a more personalised and autonomous learning environment. The findings underscore AI's transformative potential to reshape higher education, highlighting its ability to empower students through personalised learning pathways and cultivate essential 21st-century skills. This potential of AI to enhance students' performance should instil a sense of optimism about the future of education.
