Search results

This study aimed to determine the expectations of students from mathematics teachers in the planning phase of lesson study (LS) in mathematics classrooms.

This study reported only a part of large-scale action research. The participants were Grade 8 students selected by the convenience sampling method. The data were obtained through open-ended questions. The content analysis method was used to analyze the data.

Four categories emerged: connection, technology-supported teaching, use of concrete materials, practice, and teacher behavior and teaching style.

This study reveals how students in a different culture and education system, such as Türkiye, want to learn mathematics in the LS process of Japanese origin. It also gives some important clues for applying LS in a different culture.

This study may attract the attention of educational stakeholders who want to implement LS in mathematics classrooms by considering student perspectives.

Due to the nature of LS, this study may emphasize teacher–student and teacher–teacher interactions. Thus, it can draw attention to the importance of social learning environments where students take responsibility and interact.

This study emphasizes the importance of listening to student voices in LS. Some ideas about mathematics teaching in Turkey should also be given. Finally, it can provide a good basis for understanding and comparing LS practices in different cultures and understandings.

Refugee students’¹ education is disrupted by multiple spatial and temporal transitions – no matter where they come from, where they go, and what cause their forced migration. The first major disruption occurs with their flight from origin countries due to security or other reasons. The COVID-19 pandemic led to another dramatic disruption with a heightened risk of weakening hard-earned progress. This chapter examines an example of post-migration education opportunities for urban refugee students across two monolingual contexts at two critical junctures from their arrival in the destination countries to the COVID-19 outbreak. It discusses the readiness and responsiveness of Turkish and German education systems by examining the instances and consequences of disruption in refugee students’ education in İstanbul and Hamburg. A multiple case study design with critical policy analysis (CPA) was used. In addition to the available national statistics, empirical data drew on semi-structured in-depth interviews with refugee students and teachers. The background section maps out the trajectory of refugee education with a synthesis of the formal educational policies in Turkey and Germany toward the inclusion of refugee students into public schools. The findings first elaborate on the readiness of the Turkish and German contexts and a scoping review of the literature on the major issues in both settings. The second part explores the responsiveness of both education systems to refugee children’s education during the COVID-19 pandemic in both cases, namely, in İstanbul and Hamburg.

Although there has been considerable growth in the higher education systems of Turkey and China in about the last two decades, there is still a room for development in enabling equity in all regions, increasing opportunities and resources regardless of socio-economic status or gender differences. Students coming from disadvantaged backgrounds do not have enough tools to change their fate for the better due to the accumulated barriers they face. Given this background, the chapter discusses how the barriers to equitable HE admissions relate to each other and which one has a greater negative impact over the Accumulated Conversion Barriers Modal we propose defined by personal, discriminatory, institutional, and geographical barriers. The perspectives of Turkish and Chinese HE stakeholders were examined through 21 in-depth interviews that were subjected to content analysis and interpreted in a comparative style using the lens of Capabilities Approach of Sen. We also offer policy suggestions to increase students’ conversion capacities for better outcomes to serve both the national and the international educational contexts owing to the adaptable nature of our modal to other countries experiencing similar issues in their higher education systems.

Recently, online learning and online environments have become even more important. Students' engagement, fear of missing out and Internet addiction are seen as interrelated components that affect students' online teaching and learning process. In this context, university students' engagement, fear of missing out and Internet addiction in online environments, the relationship among them and students' demographic characteristics, online environment usage status and Internet usage profiles as their predictors are examined in this study.

This is a relational study and is carried out with 179 university students. Personal information form, student's engagement, fear of missing out and Internet addiction scales were used as data collection tools. Descriptive statistics, t-test, one-way ANOVA, correlation, hierarchical linear multiple regression analysis are used for the analysis.

According to the results, variables related to students' demographic characteristics, online environment usage status and Internet usage profiles together significantly predict the students' engagement, fear of missing out and Internet addiction in online environments. When students think positively about taking courses online, their engagement increases accordingly and their fear of missing out levels decrease. Increase in student's academic achievement leads to decline in Internet addiction.

In practice, examining the related variables about students in terms of engagement to the learning environment, fear of missing out and Internet addiction could bring a new perspective to studies on problematic use of the Internet and technology such as nomophobia and digital distraction. The results of this study reveal how and which components to be focused on for increasing the university students' engagement, reducing Internet addiction and fear of missing out in online learning environments.

The findings of this study provide a versatile perspective with the variables of student participation, fear of missing out, Internet addiction and their predictors in online learning environments, which are becoming widespread and increasingly important today and shed light on future researches.

Despite high aspirations to pursue personal development, self-sustaining employment, socio-economic integration, and stable futures in their host, origin, or resettlement countries through higher education, intersecting legal, economic, linguistic, and sociocultural barriers severely constrain refugees’ options. There is limited research on how refugee students overcome these barriers to access higher education, particularly in displacement settings like Turkey, which perpetuates a deficit view of these learners. This chapter seeks to address this gap and challenge deficit ideologies through an asset-focused perspective on the stories of 10 Syrian young adults accessing higher education during forced displacement in Turkey using a composite narrative portrait crafted based on common experiences running across the participants’ individual narratives. The narrative illustrates the importance of equitable policies, quality language instruction, inclusive pedagogies, and supportive interpersonal relationships for young people aspiring to invest in their futures during displacement as well as the resourceful and dynamic strategies they devise.

This study aims to present a twofold machine learning (ML) model, namely, EDU-AI, and its implementation in educational buildings. The specific focus is on classroom layout design, which is investigated regarding implementation of ML in the early phases of design.

This study introduces the framework of the EDU-AI, which adopts generative adversarial networks (GAN) architecture and Pix2Pix method. The processes of data collection, data set preparation, training, validation and evaluation for the proposed model are presented. The ML model is trained over two coupled data sets of classroom layouts extracted from a typical school project database of the Ministry of National Education of the Republic of Turkey and validated with foreign classroom boundaries. The generated classroom layouts are objectively evaluated through the structural similarity method (SSIM).

The implementation of EDU-AI generates classroom layouts despite the use of a small data set. Objective evaluations show that EDU-AI can provide satisfactory outputs for given classroom boundaries regardless of shape complexity (reserved for validation and newly synthesized).

EDU-AI specifically contributes to the automation of classroom layout generation using ML-based algorithms. EDU-AI’s two-step framework enables the generation of zoning for any given classroom boundary and furnishing for the previously generated zone. EDU-AI can also be used in the early design phase of school projects in other countries. It can be adapted to the architectural typologies involving footprint, zoning and furnishing relations.

The purpose of this study is to develop a valid and reliable scale that can measure the level of respect for differences of middle school students. The validity and reliability studies of the scale draft prepared as a result of the opinions received were carried out on 964 middle school students.

The sequential exploratory mixed method was used. In the study, attitude items were created by examining the studies in the literature with qualitative methods, and the scale was then finalized by applying tests for the attitude scale prepared with quantitative methods.

As a result of the exploratory factor analysis made on the data obtained, a structure consisting of 20 items and four factors as “gender”, “disability status”, “physical appearance” and “cultural identity” were reached. Also, it was seen that the scale identified 56% of the total variance. Confirmatory factor analysis that was performed on another sample showed that the sample fit of the scale was at an acceptable level. The reliability coefficient of the scale was calculated as Cronbach’s α = 0.815; composite reliability was ranged between 0.766 and 0.894 and the internal consistency of the scale was quite high. It was concluded that the scale developed to determine the level of respect for differences is a valid and reliable measurement tool.

This study is considered significant in terms of presenting a valid and reliable scale that will reveal the attitudes of middle school students towards differences in the literature.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

In this chapter, we plan to describe the effects of war on tourism. Battlefield tourism was discussed within the effects of War on tourism. It is aimed to analyse the wars in world history and the consequences of these wars. War is a modern security problem with long-term severe consequences engraved on society. Wars, on any ground, always impact people's memories and activities (Smith, 1998). Wars leave their mark on society and are among the never forgotten events in history (Aliağaoğlu, 2008). Some of these events end with victory, while others end sadly. Wars affect tourism both positively and negatively. This chapter is essential to identify the contribution of tourism in battlefields, a different type of tourism, to the countries. In the context of battlefield tourism, this chapter will present a framework. It attempts to explain battlefield tourism through case studies. Document review and case study methods will be used. It is assumed that this chapter will bring the battlefields to tourism.

This study intends to systematically review empirical evidence on transformational school leadership (TSL) in a centralized educational context.

The study includes a topographical mapping and a narrative review of thirty-seven research articles published in Turkey between 2000 and 2021. The review strategy included systematically searching the Turkish Academic Network and Information Center (ULAKBIM) and Scopus. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram guided the review and reporting process.

Three key themes emerged: (1) perceptions, attitudes and behaviors; (2) psychological resources, learning and innovation and (3) school climate and culture. The results indicated that the research evidence on TSL in the review did not correspond well with the evidence on TSL in non-Western contexts. The authors also discussed the state of research narratively to provide an overview of the topical foci and methodological and conceptual trends.

First, although the databases of the study are claimed to be regularly updated locally, these two search databases may inevitably have missed some journal articles relevant to the inclusion criteria. A possible solution is to include more databases like ERIC, PsycArticles, SAGE, ScienceDirect, PsycINFO, Web of Science and ProQuest. Second, the authors decided to limit the exploration of journal articles on transformative leadership to peer-reviewed journal articles. The authors might have missed a wider range of insights available in book chapters, books, research papers, dissertations and gray literature. Third, as there were many levels of research foci, the authors did the coding on research foci and methods. However, the approach may not allow authors to explore the relationships between research foci and method design. One possible solution is to do all coding in one file so that more complex analysis can follow. Finally, the authors agree that TSL is only one leadership style that is related to leadership effectiveness and that effective leadership is multi-faceted and requires a combination of leadership styles. Accordingly, the evidence in the study sheds light on only one dimension of leadership effectiveness.

Our review is part of an effort to demonstrate the importance of a culturally-situated understanding of TSL and to develop a globally validated knowledge base. It identifies and summarizes research in a non-Western school context defined by centrality, lack of autonomy and accountability.