Despite the growing integration of programming education within STEM curricula worldwide, there remains a significant gap in the availability of comprehensive and empirically validated assessment tools, particularly those capable of evaluating students’ broader computational thinking competencies. Addressing this gap, this study introduces a novel rubric grounded in the framework of algorithmic literacy—defined by Kalaš (2011) as a set of skills that enable individuals to manage behavior and attain goals within dynamic, interactive environments. The rubric, comprising four key dimensions—Subject Knowledge, Programming Design, Programming Techniques, and the Learning Process—was developed to assess students’ algorithmic literacy in the context of Scratch-based STEM learning activities. The study was guided by two primary research questions: ⑴ To what extent can the developed rubric effectively assess students' algorithmic literacy in Scratch-based STEM education? and ⑵ What similarities and differences exist between Japanese and Indonesian students in their programming outcomes, and what contextual factors contribute to these patterns? A total of 228 students—132 from Japan and 96 from Indonesia—participated in three intervention lessons focused on programming regular polygons using Scratch. Evaluation of their work revealed high proficiency in Programming Techniques, particularly in utilizing sequential commands and loops, while students exhibited difficulties in integrating complex mathematical concepts and multi-shape design tasks. Comparative analysis highlighted the influence of curriculum structure, access to technological resources, and pedagogical approaches on student performance across the two contexts. The findings contribute a validated and practical tool for assessing algorithmic literacy, offering critical insights to inform the design of culturally responsive and pedagogically effective programming education initiatives.

Information systems and information technology have become indispensable in modern business operations, serving as critical tools for enhancing efficiency, streamlining processes, and supporting strategic decision-making. To ensure these technologies effectively meet organizational needs, enterprise architecture design plays a key role in aligning business goals with IT systems. This alignment not only improves operational efficiency but also lays the foundation for long-term organizational success. This study employs The Open Group Architecture Framework (TOGAF), focusing on its Architecture Development Method (ADM), to design an enterprise architecture tailored for PT Perkebunan Kaltim Utama. TOGAF ADM offers a structured, iterative approach to architecture development, encompassing phases from the initial planning stage to the final design and implementation analysis. Each phase is designed to integrate business processes with IT systems, enabling a cohesive and adaptive framework. PT Perkebunan Kaltim Utama, a company specializing in palm oil mill maintenance, faces significant operational challenges due to its reliance on manual processes and lack of integration. These inefficiencies hinder productivity and affect the company’s ability to meet strategic goals. This research systematically identifies the functional and technological requirements for PT Perkebunan Kaltim Utama’s business activities, laying the groundwork for an integrated solution. The proposed architecture design addresses these inefficiencies by providing a comprehensive blueprint for implementing a unified system. This system will not only enhance PT Perkebunan Kaltim Utama’s operational performance but also support its strategic objectives, enabling the company to remain competitive and responsive to industry demands. By integrating TOGAF ADM into its processes, PT Perkebunan Kaltim Utama can ensure a more effective alignment of business and IT, paving the way for sustainable growth and improved decision-making capabilities.

This study focuses on the opportunities and challenges of online learning in Bangladeshi higher education, particularly in achieving Sustainable Development Goal 4, emphasizing inclusive, equitable, and quality education for all. A well-structured questionnaire was administered to collect data from undergraduate students at a public university in Bangladesh. Students’ responses were analyzed quantitatively, using descriptive statistics and Chi-Square tests. Quantitative findings on student perceptions reveal significant benefits in terms of flexibility, self-paced learning, cost-effectiveness, and global networking opportunities, improving access and learning experiences among students from remote or marginal areas. On the other hand, the results draw attention to certain significant issues, such as dependence on technology, unsuitable learning environments, social isolation, and receiving feedback very late, which reduce online education success. These challenges underpin the pressing need for considerable investment in the digital infrastructure of affordable access to technology and blended models of learning, integrating online and offline resources that will meet the diverse needs of learners. Improvement in feedback mechanisms, facilitation of online communities of collaboration, and development of digital literacy among both students and educators contribute to better learning outcomes. The research has proposed actionable measures to enhance online education in Bangladesh, such as expanding internet connectivity, providing subsidies for devices, and implementing innovative teaching methods. Addressing these challenges while leveraging the benefits of online learning has the potential to transform higher education in Bangladesh. This will ensure greater accessibility, equity and inclusivity, thereby contributing significantly to the achievement of Sustainable Development Goal 4.

Advancements in educational assessment methodologies, driven by high-speed data networks, have enabled the efficient management and analysis of large datasets, replacing traditional testing methods. Even though they are frequently used, traditional statistical methods have the potential to incorporate biases into assessments of the caliber of universities. To address these limitations, the application of automated technologies is necessary for identifying key factors influencing institutional quality. Developing effective educational programmes in higher education requires quality assurance. Academic performance evaluation using Machine Learning (ML) and Artificial Intelligence (AI) techniques yields more accurate predictive models than traditional methods. This research proposes a hybrid approach that integrates Long Short-Term Memory (LSTM) neural networks with Novel Modified Particle Swarm Optimization (NMPSO) to optimize model architecture, enabling more precise and unbiased assessments of institutional quality. The objective of the proposed methodology is to improve the objectivity and reliability of institutional quality evaluations in higher education.

The rapid growth in IoT applications has brought enormous challenges especially with achieving scalability and security in communicating devices. Traditional centralized security models are inadequate for managing the vast volume of data and diverse communication protocols in IoT environments, making them vulnerable to attacks such as Distributed Denial of Service (DDoS) and unauthorized access. Blockchain technology offers a decentralized alternative with its inherent properties of immutability, transparency, and decentralized consensus, providing a robust security solution for IoT communication. This paper presents a novel blockchain-based framework designed to secure IoT communication by addressing key challenges such as data integrity, privacy, and scalability. The proposed system integrates Ethereum’s blockchain, Zero Knowledge (ZK)-Rollups for Layer 2 scaling, and edge computing to optimise both performance and energy efficiency in large-scale IoT networks. The framework achieves a transaction throughput of 2,500 transactions per second with a median latency of 850 milliseconds. ZK-Rollups ensure that 99.8% of transactional data remains off-chain, improving privacy while reducing computational overhead. The system maintains 99.7% uptime during DDoS attacks and reduces energy consumption by 95% compared to traditional Proof of Work (PoW) blockchain systems. These findings indicate that the proposed blockchain-based framework is scalable, energy-efficient, and secure, making it a promising solution for large-scale IoT deployments in sectors such as smart cities, industrial automation, and healthcare.