Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ1The Relational Online Learner: Self-Directed Learning, University Belonging, and Student Engagement in Online Higher Education from a Self-Determination Theory Perspective
Kayalı, Bünyami; Yavuz, Mehmet; Üstün, Ayşin Gaye; Uçar, Hasan; Erdoğdu, Erdem; Aydemir, Mesut; Bozkurt, Aras
We examined the relationships between self-directed learning skills, university belonging, and student engagement in open online and distance learning environments within the framework of Self-Determination Theory. The research was conducted using a correlational survey model. Data were collected from 696 university students enrolled at a large-scale open, online and distance learning institution in Türkiye. We used three instruments measuring self-directed learning skills, university belonging, and student engagement. The data were analyzed using partial least squares structural equation modeling. The findings indicate that university belonging significantly and positively predicts all dimensions of student engagement. While self-control skills, learning skills, and sustaining the desire to learn significantly explain university belonging, metacognitive awareness and ability to identify sources were found to have no significant effect. Furthermore, it was found that university belonging acts as a significant mechanism in the relationships between self-control skills, learning skills, and sustaining the desire to learn, and student engagement. In this open and distance learning context, student engagement was not explained by individual learning skills alone. University belonging may therefore be one mechanism that partly accounts for the association between self-directed learning and academic participation. The study contributes by testing self-directed learning at the level of its dimensions, by identifying university belonging as a mechanism that partly accounts for its association with engagement, and by situating this model in a large-scale open and distance learning setting.
- Derleme MakaleAçık Erişim2026Scopus SJRQ2From Technology Adoption to Organizational Governance: Mapping the Intellectual Structure and Shifting Paradigms of AI Governance in Educational Leadership
Kayalı, Bünyami; Yavuz, Mehmet; Bozkurt, Aras
This study maps the intellectual structure and thematic evolution of the intersection between artificial intelligence (AI) and educational leadership in related literature. Employing a hybrid research design that integrates bibliometric analysis with Latent Dirichlet Allocation (LDA)-based topic modeling, we examined 757 unique records retrieved from the Web of Science and Scopus databases for the period 2017–2026. Through computational optimization, the corpus was delineated into six emerging research themes that dictate the field’s shifting paradigms: “Digital Leadership & Transformation,” “Teaching & Student Learning,” “AI Acceptance & Self-Efficacy,” “Data Management & Machine Learning,” “School Leadership & Principals,” and “Technical & Computational Fields.” While “Digital Leadership & Transformation” dominates over half of the literature, a sharp proportional decline in “School Leadership & Principals” uncovers a critical structural imbalance between abstract institutional transformation discourse and the actual human agents executing it. Furthermore, a severe underrepresentation of technical, governance, and transparency themes exposes a critical “opaque box” crisis of institutional agency. The study concludes that the field remains theoretically fragmented and calls for an integrated socio-technical framework reconciling leadership governance with the complex realities of AI systems.
- MakaleAçık Erişim2026Scopus SJRQ1Examining the relationships between artificial intelligence literacy, AI-TPACK, job satisfaction, and well-being among teachers through structural equation modeling
Kayalı, Bünyami; Balat, Şener; Yavuz, Mehmet
This study explores the relationships between teachers' artificial intelligence literacy (AIL), AI-TPACK competencies, job satisfaction, and well-being. Survey data from 350 in-service teachers were analyzed using PLS-SEM to examine how AI-related competencies are associated with professional and psychological outcomes. The findings reveal that AIL strongly predicts AI-specific knowledge, especially AI-TCK, AI-TK, AI-TPCK, and AI-TPK, with a moderate influence on traditional pedagogical knowledge. At the job satisfaction level, AI-TCK and general pedagogical knowledge are positively associated with satisfaction, while AI-TK has a negative relationship. Mediation analyses show that AI competencies influence well-being through job satisfaction. AI-TCK and pedagogical knowledge are positively associated with well-being indirectly through job satisfaction, whereas AI-TK shows a negative indirect association along the same pathway. The study suggests that AI-related competencies are linked to teachers' work-life outcomes, and highlights job satisfaction as the key mediator between AI competence and well-being. Because the design is cross-sectional and self-reported, these relationships are interpreted as associations rather than causal effects. The study advocates for integrating AI in ways that support pedagogical development rather than just technical skills.
- MakaleAçık Erişim2026Scopus SJRQ1The 20-Year Evolution of Distance Education: A Bibliometric and LDA-Based Topic Model Analysis
Yavuz, Mehmet; Balat, Şener; Kayalı, Bünyami; Gülen, Emirhan
Although research on distance education has increased substantially in recent years, comprehensive analyses capturing the overall thematic development of the field remain limited. Modeling studies are necessary to address this gap and to uncover long-term research trends. This study aimed to reveal the trends of research conducted in the field of distance education in the last two decades by using machine learning methodology and bibliometric analysis. The unique aspect of the study was its use of the Latent Dirichlet Allocation (LDA) algorithm, a machine learning method for analysing large-scale data. Within the scope of the study, 54,444 articles in the Web of Science database were analysed. Bibliometric analysis revealed that distance education had gained significant momentum, especially in the post-pandemic period, with a wide range of applications in different disciplines such as education, management, and health. As a result of LDA analysis, 19 thematic topics were identified. Among these, digitalization, artificial intelligence, Web-based learning, and social interaction and collaboration stood out. Time series analysis showed an increasing trend for topics such as artificial intelligence and emergency distance education over the years, with less interest shown in topics such as Web-based learning and program design. The study emphasized that distance education is in a technology-driven transformation process and pointed to areas that will offer new opportunities for researchers. Furthermore, recommendations for decision-makers, such as investments in digital infrastructure, integration of artificial intelligence-based systems, and the establishment of quality standards, also emerged.
- MakaleAçık Erişim2026Scopus SJRQ1Investigating the relationship between social presence and transactional distance in online distance learning environments through structural equation modeling analysis
Yavuz, Mehmet; Kayalı, Bünyami; Üstün, Ayşin Gaye; Uçar, Hasan; Erdoğdu, Erdem; Bozkurt, Aras
While digitalization has increased flexibility in online distance learning, it has also brought challenges such as emotional strain, interactional issues, and social isolation. In this context, students’ sense of social “presence” is vital for sustaining their well-being and ensuring high-quality interaction. Grounded in Transactional Distance Theory (TDT) and the Community of Inquiry (CoI) framework, this study modeled the relationships between social presence (SP) and interaction types within the using Structural Equation Modeling (SEM). The Social Presence and Transactional Distance Scales were used as data collection instruments. The sample comprised 574 online distance learners, with data collected via an online questionnaire. The results of the SEM analysis showed that SP was a significant positive predictor of all interaction types. Findings show that SP most strongly influences student–student and student–learning environment interactions, while also significantly enhancing student–instructor, student–content, and student–interface interactions. These results indicate that online learning environments should support not only content delivery but also meaningful social interaction. The study highlights the value of SP-oriented design in open, online and distance education systems and offers strategic recommendations for instructors, content developers, and digital learning administrators. Future research should compare synchronous and asynchronous learning structures.
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İncele - MakaleAçık Erişim2026Scopus SJRQ1Trust in AI: scale development and validation for online distance learners
Üstün, Ayşin Gaye; Yavuz, Mehmet; Kayalı, Bünyami; Uçar, Hasan; Erdoğdu, Erdem; Bozkurt, Aras
Trust in artificial intelligence (AI) has emerged as a critical factor influencing students’ interactions with AI-supported learning environments. However, validated instruments for measuring student trust in AI within educational contexts remain limited. This study developed and validated a multidimensional scale to assess student trust in AI systems in online distance education. Scale development followed a sequential multi-stage design, including item generation based on literature review, expert evaluation for content validity, and psychometric validation through exploratory and confirmatory factor analyses. A total of 837 distance learning students participated in the study (EFA = 412; CFA = 307; validation sample = 118). Exploratory factor analysis supported a five-factor structure explaining 63.20% of the total variance. Confirmatory factor analysis with an independent sample indicated good model fit (/df = 2.17; CFI =.95; TLI =.94; RMSEA =.06). The scale demonstrated high internal consistency (=.94; =.94). Multi-group confirmatory factor analysis supported metric invariance across gender, indicating comparable measurement across groups. No significant gender differences were observed, and trust in AI was not significantly associated with grade point average or system engagement frequency. These results suggest that trust represents a distinct perception that may influence how students engage with AI-supported learning environments. The resulting 21-item scale provides a reliable instrument for investigating student trust in AI and offers practical implications for the design and implementation of AI-supported learning in higher education.
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İncele - MakaleYalnız künye2026Scopus SJRQ1Development and Validation of AI Acceptance Scale (AI-AS) for Learners in Higher Education
Kayalı, Bünyami; Yavuz, Mehmet; Üstün, Ayşin Gaye; Uçar, Hasan; Erdoğdu, Erdem; Bozkurt, Aras
The use of artificial intelligence (AI) applications in educational activities is increasing day by day. This situation highlights the need for valid and reliable measurement tools to determine students' levels of acceptance of this technology. This study aims to develop a standard measurement tool to determine students' levels of acceptance of AI. To this end, the systematic scale development steps proposed by DeVellis and Thorpe (2021) were followed. Exploratory factor analysis conducted with 560 participant responses and confirmatory factor analysis conducted with 950 participant responses resulted in a 26-item, 5-factor structure. The factors included in the scale were listed as "usability", "ease of use", "intention and attitude", "facilitating conditions" and "trust and data privacy". The findings revealed that the scale had high internal consistency and exhibited satisfactory model fit indices. The results indicate that the scale is a valid and reliable measurement tool for determining students' levels of AI acceptance. The study offers practical implications for researchers and practitioners and highlights directions for future work on AI adoption in higher education environments.
- Derleme MakaleAçık Erişim2026Scopus SJRQ1Artificial Intelligence Supported Online Assessment and Evaluation in Higher Education: A Systematic Review With SWOT Analysis
Üstün, Ayşin Gaye; Kayalı, Bünyami; Yavuz, Mehmet
Artificial intelligence-supported online assessment and evaluation (AI-SOAE) systems have the potential to transform assessment and evaluation processes in higher education; however, there is a need for a comprehensive evaluation of the pedagogical, technical, and ethical dimensions of these systems. In this regard, the study systematically examined the characteristics of AI-SOAE systems within the framework of a SWOT analysis. A systematic literature review method was used in the research. Ninety-eight studies published between 2018 and 2025 in the Web of Science (WoS), Scopus, and Education Resources Information Centre (ERIC) databases were analysed in accordance with PRISMA guidelines. The findings show that the strengths of AI-SOAE systems include personalised feedback, advanced assessment processes, increased efficiency, and data-driven decision support mechanisms. In contrast, technical infrastructure limitations, algorithmic biases, data management issues, and negative user experiences were identified as the weaknesses of the systems. The analysis also reveals that AI-SOAE systems offer significant opportunities in terms of pedagogical transformation, digitalisation, and institutional competitiveness; however, they face threats such as data security, ethical concerns, risks to academic integrity, and limited empirical evidence. Consequently, AI-SOAE systems offer context- and application-dependent potential for supporting assessment and evaluation practices in higher education. Effectively realising this potential depends on strengthening technical infrastructure, managing algorithmic risks, and developing transparent, ethical, and institutional policy frameworks. By synthesising the literature in the field of AI-SOAE in a structured manner, this study provides a comprehensive assessment for researchers, educators, and policymakers. © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
AtıfScopus1WoS1OpenAlex3İncele - MakaleAçık Erişim2023Scopus SJRQ1Investigation of student experiences with ChatGPT-supported online learning applications in higher education
Kayalı, Bünyami; Yavuz, Mehmet; Balat, Şener; Çalışan, Mücahit
The purpose of this study was to determine university students' experiences with the use of ChatGPT in online courses. The sample consisted of 84 associate degree students from a state university in Turkey. A multi-method approach was used in the study. Although quantitative data were collected using the Chatbot Usability Scale, qualitative data were collected using a semi-structured interview form that we developed. The data were analysed using descriptive and content analysis methods. According to the findings, ChatGPT exhibits advantages such as a user-friendly interface and fast, concise, relevant responses. Moreover, emphasizing its contribution to the learning process, the information provided was sufficient and topic-oriented. The understandability of the chatbot’s functions and the clarity of their communication were emphasized. However, there are disadvantages such as performance issues, frequency of errors and the risk of providing misleading information. Concerns have also been raised about the potential difficulties chatbots may face in ambiguous conversations and providing insufficient information on privacy issues. In conclusion, ChatGPT is recognised as a potentially valuable tool in education based on positive usability impressions; however, more research is needed for its safe use. Implications for practice or policy • Based on positive usability impressions, students and instructors can use ChatGPT to support educational activities. • ChatGPT can promote and enhance students' personalised learning experiences. • ChatGPT can be used in all higher education courses. • Users should be cautious about the accuracy and reliability of the answers provided by ChatGPT. • Decision-makers should take precautions against risks such as privacy, ethics, confidentiality and security that may arise from using artificial intelligence in education.
AtıfScopus79WoS48OpenAlex76İncele - MakaleAçık Erişim2023Scopus SJRQ3An Investigation of Digital Transformation Activities of Higher Education in Türkiye
Yavuz, Mehmet; Kayalı, Bünyami; Karaman, Selçuk
This study aimed to examine the digital transformation activities of universities. The document analysis method was used in the study, one of the qualitative research methods. In this context, the websites of 207 universities were examined using the "Digital transformation activities form of universities" developed by the researchers. The data were analyzed by content analysis method. As a result of the analysis, six broad themes, also called dimensions in the current study, emerged, such as research, education, culture, process, technology, and community service regarding digital transformation activities of higher education institutions. Infrastructure studies, educational activities for research and supply and the use of smart systems to provide various cooperation opportunities are discussed within the scope of the research dimension. There are online scientific studies, online education programs, digital resources, and open access opportunities in the education dimension. As for the culture dimension, online manager activities for corporate culture, training of trainers, culture-arts and scientific activities took place. With the transfer of business processes to the digital environment, corporate promotion and communication activities were included in the process dimension. In the technology dimension, smart systems, smart campus applications and innovative technologies took place. Finally, the scope of community service compromised related units, university-industry cooperation activities and social activities. As a result, universities carry out activities to support processes such as education, research, management, and cooperation and to realize them in digital environments. For this purpose, it can be said that they perform effective and efficient processes by using various intelligent systems.
AtıfScopus7OpenAlex10İncele - MakaleAçık Erişim2021Scopus SJRQ4The scale of evaluating instruction in pandemic process: Development, validation, and reliability; [Mokymo vertinimo skalė pandemijos procese: Plėtra, pagrįstumas ir patikimumas]
Tutal, Özgür; Kayalı, Bünyami; Yavuz, Mehmet; Hasançebi, Mehmet; Yeşildağ-Hasançebi, Funda
Annotation. The study aims to develop a scale for determining student views on distance instruction, hybrid instruction, and back-to-school process after the pandemic. The study data were obtained from 442 students. EFA and CFA were performed to determine the scale‘s construct validity. Moreover, to decide the data collection tool‘s reliability, item analysis was conducted and internal consistency coefficients were estimated. Based on the results obtained from the validity and reliability analysis of the scale, it can be said that the Scale of Evaluating Instruction in Pandemic Process (SEIPP) is valid and reliable.
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