This study employed a single case study qualitative approach, involving five Diploma students from Universiti Teknologi MARA (UiTM), Malaysia. The findings reveal that the flipped classroom approach generates varied emotional outcomes in ESL writing courses, fostering motivation, engagement, confidence, and relief for some learners, while also producing stress and frustration among those who require greater instructional structure. Interviews further indicated that the approach promotes engagement, collaboration, self-directed learning, and writing confidence, yet challenges persist in adaptation, brainstorming, and teamwork. These results suggest that flipped learning encourages more active and responsible learner behaviors but demands tailored scaffolding to ensure inclusivity and effectiveness. From a cognitive perspective, Burnard's five-stage thematic analysis highlighted that the flipped classroom enhances engagement, facilitates deeper understanding, and supports writing development through autonomy and collaboration, although some students struggled with reduced structure and the reliance on video-based materials. Taken together, the evidence suggests that while flipped learning holds strong potential to cultivate both emotional and cognitive gains in ESL writing, its effectiveness is contingent upon striking a balance between learner autonomy and structured guidance. Accordingly, the adoption of the flipped classroom in ESL writing should integrate systematic instructional support, explicit training in collaborative and brainstorming strategies, and flexible adaptation of materials to accommodate diverse learner needs, thereby maximizing the pedagogical benefits of the model while mitigating its challenges.
This study aimed to examine the effects of the B-CompThink module integrated with technology on students' computational thinking skills in the topic Basic Concepts of Computational Thinking. A quasi-experimental design was employed, involving three groups: the B-CompThinkT group, the B-CompThink group, and the control group. The study sample consisted of 90 first-year secondary school students enrolled in the subject. The research instruments included pre-tests, post-tests, and delayed post-tests developed based on the format of the Academic Session Final Examination (UASA). Data were analyzed using MANCOVA, and the results showed that the B-CompThinkT group demonstrated a significant improvement in achievement scores compared to the other groups in both the post-test and delayed post-test. The findings suggest that integrating the B-CompThinkT module into teaching and learning enhances students' computational thinking skills.
This paper introduces ComputUp, a digital board game designed to foster computational thinking (CT) and enhance STEM education through arithmetic-driven gameplay and interdisciplinary Chance card challenges. Unlike traditional chance-based games, ComputUp requires players to generate their own movement through strategic calculations, promoting mathematical reasoning and problem solving. The game integrates CT components such as decomposition, pattern recognition, abstraction, and algorithmic thinking into its core mechanics. Chance challenges are drawn from science, history, and practical knowledge domains, encouraging cross-curricular learning and real-world application. With features that support both individual and collaborative play, as well as built-in analytics to support formative assessment, ComputUp serves as a versatile educational tool for classroom and extracurricular STEM learning, and for team building.
This study explores a learning technology designed to support teachers' reflection and instructional improvement through video-based activities. It focuses on synchronous online mathematics instruction, which has the potential to provide students with additional and sustainable learning opportunities in the post-pandemic era. We enhanced a visual learning analytics (VLA) platform-developed based on established visualization principles-to analyze online classroom discourse data from lesson recordings. The platform enables teachers to review and reflect on their teaching practices. The study involved 15 secondary school teachers who participated in our professional development programs. Data were collected through written feedback to gain insights into the teachers' perspectives. According to their user experiences, the teacher participants shared both the benefits and challenges of using the platform, along with suggestions for improvement. The findings indicated that they valued the technological (e.g., effective design and functionality), educational (e.g., transcription and classification support for review), and social (e.g., collaborative lesson reflection) affordances provided by the VLA platform. Their suggestions (e.g., integrating AI technologies to support analysis and facilitating idea exchange on the platform) offered valuable directions for further enhancement. This study thus serves as a demonstration of how VLA technology can be applied to analyze classroom interactions and support instructional improvement in teacher professional development.
This article highlights the role and importance of the education statistics system in the decision-making process. The current state of the education statistics system has been analyzed, and approaches and measures needed to improve its effectiveness have been recommended. The study emphasizes that ensuring the consistency and accuracy of statistical data is crucial in developing evidence-based education policies. Additionally, the importance of digitization of information, developing staff qualifications, and implementing an effective management system based on international experiences, particularly Korean education statistics system standards, has been noted. This article presents proposals and recommendations for advancement trends in statistical infrastructure to support evidence-based policy decisions for Uzbekistan's education system.
The integration of multimedia-based simulations in science education has been widely recognized for its potential to enhance student engagement, conceptual understanding, and teacher adoption of digital tools. This study evaluates the effectiveness of a multimedia physics platform in a secondary school with 8(th) and 9(th) grade students in Tashkent, along with survey responses from teachers. Results indicate that variation in the platform's effectiveness by grade level. Teacher surveys highlight high satisfaction with usability and instructional value. Students using the platform demonstrated increased participation in discussions and problem solving activities. While the multimedia platform enhances engagement and learning, its impact is inconsistent across student groups, warranting further investigation into content alignment, usability improvements, and differentiated instructional strategies.
History is often perceived as a difficult subject for primary school students, particularly when dealing with abstract topics such as the Prehistoric Era and the Ice Age. This study examines the need for an Augmented Reality (AR)-based history learning module for Year 4 students in Malaysian primary schools. Using a qualitative design, data were gathered through semi-structured interviews with five history teachers and two students. The findings indicate a heavy reliance on textbooks, limited use of interactive tools, and challenges in delivering complex content effectively. Students described lessons as monotonous and demonstrated underdeveloped historical thinking skills due to passive learning environments. However, both teachers and students expressed strong interest in AR technology for its potential to enhance visualization, engagement, and understanding. In order to promote more engaging and meaningful history learning experiences at the primary level, the study highlights the necessity of an AR module that is aligned with the curriculum.
This study aims to evaluate Artificial Intelligence-based pedagogy from the Cultural Historical Framework perspective for exploring the art of teaching in future education. Bibliometric analysis and systematic review were conducted to explore the artificial intelligence in education and cultural-historical activity theory. It is revealed that the main areas are teaching, partnership, language education, learning, machine learning, human-centered approach, teaching, learning activities, deep learning, language, computer-mediated instruction, and ethics in AI. AI-supported teaching is used as object. Subjects are teachers, peers, educational leaders, professors, mentors, children, students, and school leaders. Communities as stakeholders in implementing AI pedagogy. The division of labour refers to collaboration between educators, teachers, school leaders and educators as stakeholders in the design, implementation, and teaching of AI pedagogy. Translation materials, drawing formats, text annotations, and teaching methods utilizing AI technologies are tools. Assignments, e-textbooks, elearning courses, a comprehensive human-AI collaborative instructional design model, review of existing research studies, drawing, and collaborative word analysis techniques are also tools. Theoretical frameworks become rules, and outcomes are interactive, personalized, and equitable. Teaching-learning experiences are essential to develop critical thinking, higher-order skills, improve engagement, communication, and expansive learning. It is underlined that strategic planning policy in schools, design policy, collaboration and team teaching, capacity building in ethics, culture, and background in the use of AI are key focuses for the quality of AI pedagogy in future studies.
The topic of generative artificial intelligence, including ChatGPT, has drawn significant attention from scholars and the media. Nonetheless, there exists a need to better understand students' usage of ChatGPT and the potential implications, constructive and adverse, of its utilization. This study examined the reasons for and concerns of ChatGPT usage among university students based on evidence from two separate investigations. The initial study used an eight-item scale to measure ChatGPT use by 165 students from a university. The second study utilized a three-wave time-lagged data to gather data on 494 students from a university, and the validity of the scale was affirmed and the study's assumptions tested. The second study further explored the impact of academic pressure, time urgency, incentive sensitivity, and quality sensitivity on the use of ChatGPT. It also examined whether ChatGPT use affected students' adjournment, memory performance, and academic performance. The initial study provided strong evidence that the scale of ChatGPT use was valid and reliable. In addition, the second investigation determined that students were more apt to make use of ChatGPT when there was a heightened level of academic pressure and time constraint. Students with stronger sensitivity to incentives were less inclined to use ChatGPT. As hypothesized, ChatGPT was associated with higher levels of deferment and forgetting, which led to diminished academic achievement. Lastly, task burden, pressure of time, and reward sensitivity had an indirect impact on the outcomes of the students via their use of ChatGPT.
This qualitative study explores the multifaceted challenges and strategic approaches related to integrating computational thinking (CT) into Malaysia's national curriculum, with a particular focus on its alignment with STEM (science, technology, engineering, and mathematics) education. Despite ongoing efforts to embed CT within the educational framework, significant obstacles persist. These include widespread misconceptions among educators about the nature and scope of CT, a critical shortage of specialized expertise, and an overemphasis on theoretical instruction, often at the expense of practical application. These barriers hinder the effective integration of CT, undermining both instructional quality and student engagement in STEM subjects. To address these challenges, the study identifies key strategies that can enhance the integration of CT into STEM education. Central to these strategies is the implementation of early and continuous professional development programs for educators, aimed at fostering a deeper and more comprehensive understanding of CT. Additionally, the adoption of practical, project-based learning modules is essential to bridging the gap between theoretical knowledge and real-world STEM applications. Such initiatives are expected to create a more dynamic and engaging learning environment, enabling students to apply CT principles across various STEM disciplines. Future research should further explore strategies for equitable resource allocation to ensure that CT education is accessible to all students, regardless of geographic or socioeconomic factors. By addressing these critical areas, this study aims to lay a solid foundation for the effective integration of CT into Malaysia's STEM education system, ultimately preparing students to thrive in an increasingly digital and complex world.
Recent advancements in generative artificial intelligence (GenAI), particularly large language models (LLMs), have transformed the landscape of AI-driven educational applications. In this paper, we report on the design and use of a general and adaptable client-server web application architecture that harnesses LLMs for automated educational content generation. This architecture seamlessly integrates modern web technologies with AI-driven content creation workflows, enabling instructors to generate instructional materials and assessment items efficiently. The system leverages retrieval-augmented generation (RAG) to incorporate relevant course materials, ensuring that generated content aligns with predefined learning objectives and pedagogical frameworks. Additionally, prompt engineering techniques are employed, leveraging structured course modeling, and human-AI interaction in optimizing the quality and usability of AI-generated content. To evaluate the effectiveness of this architecture, we discuss the outcomes of multiple research studies that implement this framework in a research setting. These studies examine various use cases, AI integration strategies, and iterative improvements in content generation, highlighting both the potential and challenges of LLM-driven educational applications. Furthermore, the application of this architecture to real-world educational settings is discussed. By providing a scalable, adaptable, and research-driven approach, this work contributes to the ongoing development of AI-enhanced learning environments, paving the way for future innovations in automated content generation, adaptive learning, and AI-assisted instruction.
The Western Coast of Peninsular Malaysia faced significant challenges during the pandemic, as the most densely populated region in Malaysia, impacting the education system. Many educators have been instructed to use Google Classroom as the main platform to conduct lessons. Thus, it is crucial to report the teachers' viewpoints on Google Classroom, specifically on the benefits and issues to recommend suggestions for improvements. A grounded-theory qualitative research design was employed to determine the viewpoints of 11 West Coast school teachers with diverse teaching backgrounds using semi-structured interviews. This research involved the use of ATLAS.ti to analyse the data. The beneficiaries were teachers, students, and parents, respectively; while the issues were within the application of Google Classroom, within the school, and beyond the school. The recommendations were for Google Classroom, schools, and the government. This study reveals that despite the importance of using educational technologies in today's classrooms, acknowledging the issues voiced by West Coast teachers on Google Classroom is of definite importance. By considering the suggestions, remedies must be taken to improve the state and quality of teaching and learning by providing valuable insights into the practices and policies in online education.
In film and television education, shot scale identification is a key link in developing students' visual literacy and narrative understanding. However, in teaching, shot scale recognition mainly relies on Professors' manual labeling in advance, which has the problems of low automation, high subjectivity, and insufficient efficiency, limiting the depth and breadth of teaching and research. This study, based on the MovieShots Dataset, designed and developed an automatic shot scale recognition model using deep learning. It utilizes YOLOv5 to achieve the automatic classification of Long Shot (LS), Full Shot (FS), Medium Shot (MS), Close-Up(CU), and Extreme Close-Up(ECU). Additionally, it combines PySceneDetect technology for shot segmentation and video analysis. The experimental results indicate that the model's average precision (mAP@0.5) on the test set reaches 85.7, significantly improving the analysis efficiency. Subsequently, the model was applied in a simulated cinematic education classroom where one instructor and ten students utilized it for shot scale recognition. According to the experimental results, the model recognition effect and the Professor recognition results reached 90% recognition accuracy, and the time efficiency was greatly improved. At the same time, according to the results of the questionnaire survey, students have a better acceptance of using the model for teaching assistance. At the same time, according to the subjective interview feedback, Professors and students tend to use this efficient automatic learning aid to reduce the burden of manual annotation.
This study employs bibliometric methods to conduct a statistical analysis of 1,257 papers published at The IEEE International and 2024, aiming to explore the conference's contributions to the field of educational technology and identify potential future research trends. The findings reveal key research topics that have dominated scholar attention over the past decade, including virtual reality and augmented reality, learning analytics, learning design, game-based learning, machine learning, digital fabrication, and computational thinking. The study also analyzes topics that have experienced diminishing research interest and identifies emerging themes poised for growth. The research areas as likely to advance further in the future include: multimodal learning analytics, virtual reality and augmented reality, learning design, applying artificial intelligence to empower other research fields, and technology-supported education for people with disabilities. These conclusions offer valuable insights for organizing future iterations of ICALT and guiding the development of the educational technology field.
This study explores the pivotal role of proactive information gathering in developing teachers' Digital Learning Agility (DLA) within the context of contemporary education. Through qualitative interviews, observational data, and photographs collected from Malaysian schools, the research highlights how teachers engage in two primary types of information gathering: first, insights gained from attending webinars and workshops aimed at effectively utilising digital tools; and second, technical information derived from monitoring students' attendance and participation in the classroom. The findings reveal that effective data utilisation not only empowers educators to make informed decisions but also fosters a culture of data-driven decision-making that is essential for improving student engagement and learning outcomes. Furthermore, the support from school administrators and the collaborative involvement of parents are identified as critical factors that strengthen this process. By embracing data-informed approaches, teachers can create dynamic, student-centred learning experiences that are crucial for success in the digital age. This study underscores the necessity for educational institutions to prioritise the development of DLA among teachers, ensuring they are well-equipped to navigate the complexities of modern education.
This paper investigates the computational thinking proficiency of pre-service teachers at the Faculty of Educational Studies, Universiti Putra Malaysia, through a multi-methods research design. The primary objective is to assess the understanding and perceptions of computational thinking among pre-service teachers and its implications for problem-solving proficiency in various academic disciplines. The study reveals that pre-service teachers possess above midpoint computational thinking proficiency. It emphasizes the importance of integrating computational thinking into teacher training programs to adequately prepare pre-service teachers for the digital era. The role of prior knowledge and experience in shaping computational thinking proficiency among individuals entering the teaching profession is highlighted. The findings underscore the necessity of incorporating computational thinking into teacher education curricula to meet the evolving demands of the digital landscape. Despite challenges related to varying levels of prior knowledge and limited exposure, there is a growing recognition of the significance of computational thinking in education. Teacher training initiatives are progressively integrating computational thinking into their programs, offering practical experiences and interdisciplinary approaches to empower pre-service teachers with essential proficiency for contemporary educational practices. Continuous learning and collaborative efforts are identified as crucial components in shaping the future of computational thinking in teacher education, ensuring educators remain at the forefront of educational advancements. Ultimately, the aim is to equip future generations with the computational thinking proficiencies necessary for navigating a dynamic and ever-changing world.
Embracing the future of education involves harnessing the power of learning analytics, personalization, and collaboration. Learning analytics enables educators to glean valuable insights from student data, tailors learning experiences to individual needs, and fosters interactive learning environments. Dr. Elvira Popescu is a distinguished Full Professor at the Computers and Information Technology Department, University of Craiova, Romania. Her extensive research portfolio encompasses technology- enhanced learning, adaptive educational systems, learner modeling, computer-supported collaborative learning, learning analytics, and intelligent and distributed computing.
As artificial intelligence (AI) continues to revolutionize various sectors, its potential to transform education is increasingly recognized. However, the ethical implications of AI integration in educational settings, particularly in underprivileged contexts, demand careful examination. This paper explores the ethical considerations and future directions of AI implementation in education, drawing insights from the Philippines as a case study. Through an in-depth analysis of interviews with Professor Maria Mercedes T. Rodrigo, a professor and researcher specializing in educational technology and AI, this study delves into key ethical challenges such as informed consent, equity, and bias mitigation. The study highlights the need for strong ethical guidelines to ensure the fair use of AI in education. Informed consent is crucial to ensure that students and educators are aware of how AI tools are used and what data is collected. Equity concerns focus on providing equal access to AI resources for all students, regardless of their socio-economic background. Bias mitigation is essential to prevent AI from perpetuating existing inequalities. In addition, the paper recommends advancing ethical guidelines, empowering educators and students through AI literacy, and promoting international collaboration to share best practices. Addressing these issues helps educators, policymakers, and stakeholders navigate the ethical complexities of AI integration, harnessing its potential to enhance learning outcomes and promote inclusivity in education.
There is a lack of knowledge on the difference in Malaysian undergraduates' academic achievement and learning interest between physical (P), online synchronous (OS), and online asynchronous (OA) learning modes in Hybrid-flexible (HyFlex) lab sessions. Together with the inherent challenges of HyFlex learning, there is a need to conduct this study. This multimethod design study was conducted at a public university in Malaysia. The quantitative and qualitative strands of this study involved 65 and eight participants respectively. Multivariate analysis of variance showed no significant difference in students' academic achievement among the three learning modes whereas a significant difference in their learning interest was found between P and OA learning modes. Thematic analysis indicated that the multiple learning modes, mandatory learning evidence, instructor feedback, and students' priorities contributed to students' academic achievement across learning modes. The flexibility in learning enhanced students' overall learning interest. The physical presence of peers and instructors and students' familiarity with P learning also enhanced students' learning interest in the P mode. On the other hand, the feelings of disconnection and technical issues experienced by the students reduced their interest in learning in the OA mode. This study implies the opportunity for developing countries to expand access to education costeffectively and enhance future leaders' digital literacy. Nevertheless, the findings also imply challenges such as time and financial constraints, the existing digital divide, and the potential resistance from traditional educators.
eXtended Reality (XR) is a rapidly developing field encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR). They share common features and requirements but have different technologies and objectives. These technologies are widely used in gaming and non-gaming content in training and simulations. With the improvements in computing power and ease of access, the gaming industry is going through a revolutionary stage. In this context, teaching and training the next generation of developers is crucial in improving access and outreach of game development among the younger population. Jerry Medeiros is an experienced professional and leader in game development, XR training, and simulations. He is a Unity-certified instructor and is passionate about teaching emerging technologies. In this interview, he introduces his own experience in this emerging field and shares insights on the community, teaching, and training gaming development, and some resources and recommendations to aspiring candidates.
