International Journal of Learning Spaces Studies

Abstract In the modern digital economy, the protection of privacy and security in data sharing has become a major concern, particularly within supply chains that rely on extensive data exchange between stakeholders. As supply chains evolve, the integration of advanced technologies like artificial intelligence (AI) and machine learning has revolutionized how companies predict demand, manage inventory, and optimize operations. This paper investigates the use of federated learning in supply chain management to address privacy and efficiency concerns. Federated learning allows decentralized data processing across multiple nodes, ensuring data privacy while maintaining high model accuracy. By employing privacy-preserving techniques such as differential privacy and encryption, the proposed model safeguards sensitive information from adversarial attacks, including model inversion and backdoor threats. The study also demonstrates the model’s effectiveness in reducing communication overhead, making it suitable for distributed supply chain systems. Although the findings are promising, further research is needed to optimize privacy-accuracy trade-offs, especially when dealing with non-IID data.

Abstract This study examined bottlenecks faced by teachers and learners on using phonics instruction in early classes in Tanzania. The study was undertaken in 25 public primary schools of Mbeya city and Rungwe district involving 85 Primary Early Grade Teachers (PEGTs) and. Random sampling technique was utilized to sample 180 class one and two pupils two primary school quality assurers (PSQAs) obtained purposively. Mixed method approach was deemed relevant employing Questionnaire, interview and observation as data collection methods. Epi-data software version 3.1 and Statistical Package for Social Sciences (SPSS) version 20 helped in analyzing quantitative data and qualitative data were analyzed thematically. The findings indicated that teachers encountered unconducive teaching environment including lack of teaching and learning resources, lack of inclusive skills, unrealistic teacher-pupils’ ratio with congested classrooms. The study recommends for intensive training on 3Rs and accommodation of pupils with disabilities to all EGTs. A study on teachers' challenges in teaching other literacy skills such as writing and Numeracy is desirable.

Abstract This research aims to design a data analysis-based learning model for training in accounting fraud detection in digital environments. The research was conducted in an exploratory and survey manner. The qualitative part was conducted using the Delphi method and the quantitative part using the interpretive structural model and structural equations method. In the qualitative part, the population studied was managers and experts in financial management and information technology management, 12 people were selected using the available judgment sampling method. Using the library technique, the components affecting the model were identified. First, the identified components were screened and evaluated using the Delphi technique. Then, modeling was carried out using the Delphi method and the interpretive structural method. The software used were EXCEL and MICMAC. Using the Cochran formula, 384 people were selected. Based on a researcher-made questionnaire based on qualitative analysis, data analysis was performed using coding and path analysis. Data analysis was performed using coding and path analysis. Based on sampling, the structural equation modeling technique in SMARTPLS software was used to fit the proposed model. The ten main criteria, including big data analytics, fraud machine learning, audit artificial intelligence, smart transaction tracking, encryption and transparency, financial process automation, hidden behavioral data mining, digital anomaly detection, financial blockchain platform, and continuous real-time monitoring, are classified into seven levels. This hierarchical classification shows that some criteria play a fundamental and fundamental role in the success of fraud detection systems, while others affect the operational, analytical, and monitoring layers more.

Abstract The 21st century has brought rapid changes in technology, society, and education, transforming the role of teachers and the expectations placed upon educational systems. Among the skills that teachers need today, creativity stands out as a core competency, enabling educators to nurture innovative thinking, adaptability, and problem-solving in their students. This study explores how teacher education programs in seven countries—Singapore, Finland, Scotland, Australia, Japan, Malaysia, and Iran—support the development of creativity among pre-service teachers.
A descriptive–analytical comparative review was conducted, drawing on peer-reviewed articles, national curriculum frameworks, policy documents, and official teacher education reports published between 2010 and 2024. Data were collected using AI-assisted research tools alongside traditional academic databases. The documents were examined through thematic content analysis to identify key strategies, pedagogical approaches, and institutional practices aimed at fostering creativity in teacher education.
The findings show that while all countries have made efforts to strengthen teacher preparation, notable differences exist in program structure, practical experience integration, and the emphasis on reflective teaching. Shared features include curricula that actively incorporate creativity, strong connections between theory and classroom practice, and encouragement of professional autonomy. Specific approaches include Singapore and Finland’s research-driven and standards-based models, Japan’s inclusion of moral and citizenship education in creativity development, Australia and Malaysia’s innovation-oriented policies, and Scotland’s focus on teacher reflection to guide curriculum reform.
This comparative analysis illustrates the variety of ways teacher education programs cultivate creativity and provides insights into effective strategies for preparing teachers capable of meeting the evolving demands of contemporary education.

Abstract This study presents a comparative analysis of primary education systems in Iran and the United Arab Emirates (UAE), with particular emphasis on educational technology integration and quality assurance mechanisms. Employing a descriptive-analytical comparative design grounded in Bereday's Comparative Method, data were collected through the triangulation of national policy documents, international reports (UNESCO, OECD), and peer-reviewed studies. The analysis focuses on pedagogical approaches, teacher–student interaction, and the application of information and communication technologies (ICT) in primary education. Findings indicate that the UAE adopts a semi-decentralized, competency-based educational model characterized by systematic technology integration and multi-layered quality monitoring frameworks such as KHDA and ADEK. In contrast, Iran operates within a highly centralized, content-based system in which technology adoption is often symbolic and constrained by infrastructural limitations and teacher-centered pedagogy. The study concludes that effective digital transformation in Iran requires sustained professional development in digital pedagogy and the establishment of feedback-oriented quality assurance mechanisms that directly link technology use to student learning outcomes. The findings offer comparative insights and policy-relevant implications for improving educational quality in developing contexts.