Bünyami Kayalı

Bu araştırma, ters yüz sınıf uygulamasının üniversite öğrencilerinin akademik başarıları, akademik öz-yeterlik algıları ve çevrim içi öz-düzenleme becerilerindeki değişim üzerindeki rolünü incelemeyi amaçlamaktadır. Araştırma, bir üniversitenin Bilgisayar Programcılığı bölümünde öğrenim gören 43 öğrenci ile yürütülmüş ve deneysel araştırma yöntemlerinden tek grup ön test-son test deseni kullanılmıştır. Dört haftalık uygulama sürecinde öğrenciler ders içeriklerini ders öncesinde çevrim içi ortamda incelemiş, ders saatlerinde ise bilgisayar laboratuvarında uygulamalı etkinliklere katılmışlardır. Bulgular, ters yüz sınıf uygulaması sonrasında öğrencilerin akademik başarılarında ve çevrim içi öz-düzenleme becerilerinde istatistiksel olarak anlamlı artışlar olduğunu göstermiştir. Buna karşılık akademik öz-yeterlik algısında gözlenen artış istatistiksel olarak anlamlı bulunmamıştır. Elde edilen sonuçlar, dört haftalık ters yüz sınıf uygulamasının öğrencilerin öğrenme süreçlerini planlama, izleme ve düzenleme gibi öz-düzenleme becerilerini desteklediğini ve bu gelişimin akademik başarıya olumlu yansıyabildiğini göstermektedir. Bununla birlikte akademik öz-yeterlik gibi görece daha kalıcı duyuşsal yapıların anlamlı biçimde değişebilmesi için daha uzun süreli ve sistematik uygulamalara ihtiyaç duyulabileceği düşünülmektedir. Sonuç olarak çalışma ters yüz sınıf modelinin özellikle uygulamalı ve teknoloji temelli derslerde öğrencilerin bilişsel ve öz-düzenleyici öğrenme çıktılarının desteklenmesinde işlevsel bir öğretim yaklaşımı olarak değerlendirilebileceğine işaret etmektedir.

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.

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.

When educational leadership is tasked with navigating the most profound technological shift of our time, a critical question arises: Are we so captivated by the algorithmic capabilities of artifici...

This study develops a multi-level Emergency Remote Teaching (ERT) framework for K–12 distance education, grounded in the experiences of students, teachers, parents, and administrators during the COVID-19 pandemic. Prior ERT research has predominantly focused on single stakeholder groups or produced descriptive findings without synthesizing perspectives into a structured response model. Using a qualitative case study design, semi-structured interviews were conducted with 100 participants (25 per stakeholder group) selected through maximum variation sampling. Thematic content analysis was employed to identify convergent and divergent priorities across groups, which were subsequently mapped onto the five layers of the Holistic Analytical Layer Framework (HALF): policy, system, program, course, and activity, based on their functional relevance to each layer. Findings indicate that digital infrastructure functions as a foundational system-level prerequisite shared across all stakeholder groups, while pedagogical, psychosocial, and governance priorities diverge by role. Teachers emphasized assessment reliability and instructional continuity; students highlighted engagement and psychosocial support; parents stressed communication and guidance mechanisms; while administrators prioritized governance flexibility and decision-making autonomy. The resulting framework positions ERT not as an isolated emergency improvisation but as a coordinated, multi-layered response architecture that aligns stakeholder expectations across interconnected educational layers and supports the coordination of policy, infrastructure, programmatic planning, and instructional practices during crisis periods. The study contributes to the distance education literature by transforming multi-stakeholder perspectives into an integrated framework and extending the application of HALF to emergency education contexts. For e-learning practice, the framework offers school administrators and policymakers a concrete diagnostic tool for auditing crisis-preparedness across governance, infrastructure, and instructional layers, while offering teacher educators a basis for designing professional development that addresses both technical and psychosocial readiness for remote instruction. Given its single-region, qualitative design, the framework should be understood as a context-sensitive foundation for future empirical validation rather than a universally transferable model.

This study examines the impact of technology-based course design in online speaking classes on students’ Foreign Language Enjoyment and Foreign Language Speaking Anxiety. Using a mixed-methods approach, the study employed a single-group pre-test-post-test experimental design and a case study method. Quantitative data were collected through foreign language enjoyment and speaking anxiety scales and analyzed with dependent group t-tests. In contrast, qualitative data from interviews and reflection forms were analyzed through content analysis. Findings showed a significant reduction in foreign language speaking anxiety and a non-significant increase in foreign language enjoyment following the intervention. Additionally, students had positive stances regarding technology-based online foreign language speaking classes. Specifically, students described these classes as beneficial, stress-free, participatory, motivating, and encouraging. Considering the results, it can be said that technology in foreign language teaching has beneficial and promising potential for students’ language learning performance. Based on the results, it is recommended that decision-makers and program developers popularize the use of technology in foreign language teaching and integrate it into the current curriculum.

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.

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.