Search Results (457)

Immersive technologies—such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR)—are increasingly adopted in cultural heritage settings to support education, public engagement, and digital preservation. This scoping review systematically maps existing research on immersive learning within cultural heritage contexts, identifying major trends, pedagogical approaches, and reported outcomes. Following the PRISMA-ScR framework, nineteen studies were selected from 235 publications published between 2016 and 2025 across four databases: ACM Digital Library, Web of Science, ProQuest, and Scopus. Findings reveal a predominant focus on enhancing learner motivation, engagement, and the perceived authenticity of immersive experiences. However, empirical validation of learning outcomes—particularly regarding sustained knowledge retention, critical reflection, and inclusive participation—remains scarce. Persistent gaps are also evident in accessibility and scalability, alongside ethical concerns related to cultural sensitivity, power asymmetries, and the representation of diverse heritage voices. By foregrounding participatory and co-creation approaches, this review highlights how collaborative design processes can enhance learner engagement and support the sustainable digital preservation of cultural heritage. Full article

The fundamental purpose of education—preparing new generations to be contributing members of society—remains constant, yet achieving this has become increasingly complex amid multifaceted technological, cultural, economic, and social transformations. Educational leaders worldwide continuously seek innovative pedagogical models addressing diverse learner needs and rapid societal changes. However, this article challenges the assumption that educational quality requires constant novelty, arguing that solutions lie in the innovative integration of established pedagogical theories developed over the past 150 years by scholars such as Dewey, Vygotsky, Piaget, Feuerstein, Gardner, Freire, and others. The article’s primary objective is to encourage education leaders and teacher educators to reconceptualize innovation by prioritizing pedagogical integration over continuous adaptation to rapidly expanding domain-specific knowledge and emerging technologies. Accordingly, this article employs a conceptual synthesis of major pedagogical approaches to equip educators with theoretical foundations and practical tools to foster learner independence, critical thinking, and holistic development across cognitive, emotional, and social domains. It will also promote inclusion through a practical framework integrating pedagogical theories, addressing diversity from a dual perspective, recognizing that both teachers and learners bring unique characteristics, strengths, and needs. Moreover, developing independent learners requires empowering teachers to cultivate unique professional methodologies grounded in integrated pedagogical understanding, so that a shift from innovation-centered to integration-centered teacher education may serve as a sustainable path toward educational quality and academic excellence. Full article

In the contemporary educational landscape, quality education requires a shift toward pedagogical models that develop knowledge and prepare students for the complexities of professional careers. This manuscript is an exploratory investigation of an educational innovation centred on podcast production within a Master of Science course (21 students). By moving beyond traditional oral assessment, this intervention fosters active learning and soft skills, such as metacognition, scientific communication, and collaborative problem-solving, which are essential for contributing to a sustainable society. The study examines the translation of a written technical essay on sustainable materials into digital storytelling. Students engaged in a multi-stage process of research, synthesis, and peer review. Marks across three consecutive course editions using the same evaluation rubric were compared, and qualitative feedback was gathered from 15 students through a questionnaire. The learners reported enhanced topic mastery (93.3%) and critical thinking (80%). By bridging the gap between academic research and non-specialist communication, this activity provides a scalable model across diverse disciplines. This research showcases how podcasting can overcome traditional learning barriers, ensuring that higher education remains responsive to the evolving requirements of our global society. Full article

This study presents a multimodal deep learning framework for automatic proficiency and style classification of parallel Bilingual Tamil–Hindi learner data. The proposed system employs a dual-headed neural architecture to simultaneously predict proficiency levels (Basic, Advanced) and stylistic categories (Formal, Literary) using shared feature representations. A curated dataset of bilingual text samples is utilized, along with synthetic speech generated through text-to-speech (TTS) to enable controlled multimodal experimentation. Five deep learning architectures are evaluated under text-only, audio-only, and learnable fusion settings. Experimental findings indicate that text-based models consistently achieve strong performance in both proficiency and style classification tasks. In contrast, the audio-only model demonstrates limited effectiveness, highlighting the constraints of synthetic acoustic features in capturing meaningful linguistic information. The fusion models provide only marginal improvements over text-based approaches, suggesting that textual representations play a dominant role in proficiency and stylistic classification within controlled datasets. These results emphasize the importance of linguistic features over acoustic signals for automated language assessment in low-resource settings. The proposed framework provides a scalable and reproducible approach and offers a foundation for future work incorporating real speech data and more diverse linguistic inputs. Full article

Inclusive education requires not only classroom-level adaptations but also coordinated interdisciplinary practices that strengthen the institutional conditions supporting diverse learners. However, counselor–teacher collaboration in many schools remains informal and episodic, limiting its potential contribution to an inclusive school climate. This study evaluated the effectiveness of a 12-week Structured Counselor–Teacher Collaboration (SCTC) program designed as a cyclical and replicable interdisciplinary model. A multi-site cluster quasi-experimental design with matched non-equivalent control groups was implemented in 12 public inclusive junior secondary schools in Yogyakarta, Indonesia (6 intervention; 6 control), involving 360 teachers (n = 180 per condition) and 24 school counselors as facilitators. Teachers completed the 35-item Inclusive School Climate Scale (ISCS) at pre-test and post-test. Data were analyzed using two-level linear mixed-effects modeling (teachers nested within schools) with pre-test scores as covariates. Results showed that the intervention significantly improved inclusive school climate compared with routine practice (B = 0.41, p < 0.001), yielding a moderate-to-large adjusted effect (Hedges’ g = 0.76). Dimension-level models indicated the largest gains in collaborative professional culture and perceived belonging. Implementation fidelity was high (82–91%). These findings suggest that institutionalizing structured counselor–teacher collaboration can serve as a promising approach for enhancing inclusive school climate in secondary school contexts. Full article

Generative artificial intelligence (AI) is increasingly used in higher education as an interactive tutoring partner rather than a passive information tool. While AI offers opportunities to support learning, concerns remain regarding cognitive offloading, reduced engagement, and unreflective use. Although instructional scaffolding is a well-established design principle for supporting complex learning, its role in shaping cognitive and metacognitive processes in AI-supported settings remains underexplored. This quasi-experimental pre–post study examined how varying levels of scaffolding influence learning outcomes and motivational, cognitive and metacognitive processes during AI-tutored learning. A total of 175 first-semester students from two faculties and diverse academic backgrounds completed the same academic task within a four-hour university session under one of three conditions: (1) full scaffolding, including a structured prompting template based on the Goal–Context–Constraints (GCC) strategy, iterative refinement, and reflective guidance; (2) light scaffolding, including the GCC prompting template; or (3) no scaffolding template as the control condition. Measures included knowledge gain, motivation, cognitive load, critical thinking, and reflective use. Data were analysed using ANOVAs, ANCOVAs, regression models, and PROCESS moderation and mediation analyses. Across the conditions, students showed significant gains in knowledge, critical thinking, and reflective use, while motivation remained stable and intrinsic and extraneous cognitive load decreased; no significant differences between scaffolding conditions were observed. The scaffolding conditions did not produce significant interaction effects, although descriptive trends suggested higher gains in higher-order knowledge under scaffolded conditions. Overall, the findings suggest that short-term learning gains in AI-supported settings may not depend on scaffolding intensity alone, but rather on how learners engage with AI during the learning process. Full article

In the last few years, universities have increasingly implemented online learning environments, allowing students to study at their own pace. These environments utilize technological tools and implement methods to support training, deliver content, and promote the acquisition of new knowledge and skills. As an example of these technologies, eye tracking has emerged as a powerful tool for studying visual attention, cognitive processes, and learning behaviors. The main aim of this study is to provide a scoping review of recent eye-tracking research across diverse learner populations, ranging from K-12 students to university-level learners and educators. The present study examined recent advances in eye-tracking technologies, focusing on their potential, especially when combined with artificial intelligence (AI) techniques such as machine learning. It analyzed 54 empirical studies in the last few years, highlighting their applicability, strengths, and limitations. The research findings highlight the promise of eye-tracking technology to transform educational practices by providing data-driven insights regarding student behavior and cognitive processes. Future research must address implementation and data-analysis challenges to maximize the educational benefits of eye tracking. Full article

Online asynchronous learning offers considerable flexibility but frequently faces challenges in sustaining engagement, interactivity, and inclusivity across diverse learner populations. This study introduces the OPTIMAL framework—an Online, Pedagogy- and Technology-Integrated, Microcurricula Approach for interactive and inclusive Learning—synthesising universal design for learning, active learning, and constructive alignment with technology integration frameworks (TPACK and PICRAT), operationalised through a microcurricula-as-a-service architecture. A three-year longitudinal case study (2022/23 to 2024/25) examined the application of the framework to a data literacy and analytics module serving over 5000 students across more than 15 programs and five faculties at Dublin City University. The module design constructively aligned learning outcomes, content, and technology at three levels to support multiple learning pathways, formative assessment, and transdisciplinary engagement, deliberately fostering transformative uses of technology in a fully asynchronous environment. Mixed-methods evaluation—combining learning analytics, surveys (n = 1743), and qualitative feedback—demonstrated sustained positive outcomes across all three years, including 95–99% completion rates, consistently high satisfaction, and longitudinal gains in engagement and pass rates. These findings demonstrate how the deliberate integration of pedagogical theory, technological frameworks, and modular curriculum architecture can deliver scalable, inclusive, and high-engagement online education, offering both a transferable, evidence-based model for educators and curriculum designers and longitudinal empirical validation for researchers. Full article

Syntactic complexity is a crucial aspect of assessing writing quality in Chinese as a second language. While existing literature predominantly focuses on synchronic features of syntactic complexity, particularly changes in complexity indices, less attention has been paid to its diachronic development and interactions among these indices. Drawing upon Dynamic Systems Theory, this exploratory longitudinal study traces the developmental trajectories of syntactic complexity indices and their interactions in CSL writings of 15 native Cambodian speakers within a single instructional context. The main results are as follows: (i) The syntactic complexity indices exhibited fluctuating and nonlinear growth. Among them, the length of topic chain clauses showed notable variation (range: 1.12 to 9.05), while relatively small changes (range: 0.4 to 2.39) occurred in the number of topic chain clauses. (ii) The development trends in the number of topic chains and the number of zero components showed no significant difference (p = 0.086, Cohen’s d = 0.31). In contrast, the development trends in the number of topic chain clauses and the length of topic chain clauses differed significantly (p = 0.039, Cohen’s d = 0.65). (iii) Individual differences in syntactic complexity were observed among learners in similar learning environments. These findings provide a detailed, context-bound description of the dynamic and complex syntactic development observed in the Chinese writing of 15 participants. The study highlights the value of employing multiple perspectives to capture such complexity and underscores the need for future research with more diverse samples and designs to test the generalizability of these patterns. Full article

Creativity is a key goal in contemporary mathematics education, yet teachers often rely on intuition when selecting tasks intended to elicit creative thinking. This study proposes Creativity-Eliciting Potential Entropy (CEPE), an entropy-inspired theoretical framework that conceptualises the creative potential of mathematical tasks in terms of their structural openness across the stages of Start, Process, and Goal. CEPE focuses on the diversity of possible solution strategies, assumptions, and representations provided by a task, rather than on learners’ observed performance. An exploratory study was conducted with 32 mathematics teachers from Japan and Germany, who ranked three task types—a geometrical proof task, a mathematical modelling task, and a Fermi problem—according to their perceived creativity-eliciting potential. Teachers’ rankings showed a weak but statistically significant consensus and a significant alignment with CEPE-based predictions, particularly in the high evaluation of Fermi problems. However, substantial individual variability remained, reflecting teachers’ consideration of learner characteristics and instructional constraints. The findings suggest that CEPE provides a theoretically grounded lens that supports, but does not replace, teachers’ professional judgement in task selection. Full article

Guaranteeing equitable access to computational thinking (CT) remains a persistent challenge in computing education, particularly across socioeconomically diverse school contexts. Although prior research has demonstrated the effectiveness of block-based and physical computing environments, limited empirical evidence has examined whether structured instructional mediation can compensate for contextual disparities. This quasi-experimental pre–post study addresses this gap by analyzing CT development in three socioeconomically diverse primary schools in Chile ($N=88$, third grade), including private urban, public urban, and rural public institutions. Students engaged in scaffolded Scratch programming and Arduino simulation activities designed to explicitly support abstraction, sequencing, and debugging processes. These activities were framed within a broader STEAM learning approach, integrating computational thinking with problem-solving, experimentation, and interdisciplinary reasoning. Statistical analysis revealed significant differences in instructional time across contexts ($F(2,85)=14.62$, $p<0.001$, ${\eta }^2=0.26$), indicating structural disparities in pacing. However, no statistically significant differences were observed in CT gains ($F(2,85)=0.31$, $p=0.74$), suggesting that structured pedagogical scaffolding buffered contextual inequalities. These findings provide empirical evidence from a Latin American non-WEIRD context and advance the conceptualization of instructional mediation as a compensatory mechanism for equity in early computing education. This study contributes to digital equity research by demonstrating that instructional design quality may play a more decisive role than infrastructural availability in enabling computational thinking development for all learners. Full article

Generative Artificial Intelligence (GenAI) is reshaping how students engage with learning both within and beyond traditional classroom settings. In a time when the development of transferable skills is essential for enabling students to thrive in varied and rapidly evolving environments, the potential of GenAI to enhance learning engagement remains insufficiently understood. Despite rising interest in interactive, personalised learning companions that enable deep engagement and ongoing skills development, scholarly research remains limited. This gap constrains effective institutional use of GenAI, reinforces black-box thinking, and restricts understanding of meaningful student engagement and skills acquisition. This paper investigates how a GenAI-enabled AI teacher supports student learning engagement, focusing on behavioral engagement as evidenced by learner interaction and participation patterns across diverse international higher education institutions. Using a combination of quantitative engagement metrics and qualitative learner reflections, the study examines how GenAI supports personalised learning, sustained interaction, autonomy, and cognitive engagement among students with varying educational backgrounds. The findings demonstrate that GenAI-based teaching systems can promote meaningful learning engagement, enhance motivation, and strengthen the development of transferable and employability skills. The study contributes empirical evidence to current debates on GenAI integration, teacher practices, and student engagement, offering implications for curriculum design and institutional adoption of GenAI-enabled learning tools. Full article

With the rapid technological advancements, persistent retention disparities, and career stability concerns among Generation Z learners, higher education in the United States needs a re-examination of student success. Student support efforts and previous student-centered frameworks require re-examination in light of the current socio-cultural context. In response, this paper proposes the CIRCLE model. This conceptual model is faculty-driven and includes evidence-based practices that predict successful outcomes by benefiting students’ socio-emotional factors. The model stems from an integrated conceptual framework that synthesizes established student success theories, contemporary research on faculty–student relationships, and digital integration in higher education. Traditional student-centered theories are merged with contemporary digital integration models and applied to the realities of Generation Z and first-generation college students. From this, the author delivers a clear, context-responsive plan for faculty supporting a diverse cohort of learners, as we all live in today’s post-pandemic, digitally immersed world. Full article

This scoping review critically synthesizes 34 studies (2015–2026) examining the metaverse’s role in fostering six core STEM competencies, moving beyond descriptive reporting to interrogate whether these technologies constitute genuine pedagogical transformation, whose learners are served or excluded, and how isolated interventions connect into lifelong learning pathways. Following PRISMA-ScR guidelines, our analysis reveals that while technology literacy and collaboration appear in 91.2% of our selected studies, mathematical application is addressed in fewer than half (44.1%), raising unanswered questions about whether this pattern reflects an equitable distribution of mathematical learning opportunities across diverse learner populations—a question the current evidence base cannot answer but one that warrants urgent investigation. The evidence demonstrates substantial immediate learning gains through embodied presence and risk-free experimentation, yet a deeper reading suggests this often represents technological optimization of traditional goals rather than epistemological transformation. More troublingly, the concentration of inclusivity evidence on select populations—while rendering students with physical disabilities, Indigenous learners, and refugee students entirely invisible—reveals an equity paradox where immersive technologies may inadvertently amplify existing disparities. The absence of any longitudinal data linking short-term engagement to sustained STEM participation leaves the field’s claim to transformative impact unsubstantiated. This review argues for moving beyond fragmented interventions toward designing coherent, equitable learning pathways that fulfill the metaverse’s potential for all learners. Full article

Mathematics is a diverse discipline that requires a variety of cognitive abilities and presents varying levels of difficulty. Understanding how different cognitive profiles relate to specific patterns of mathematical performance is important for developing effective educational interventions. This study extends our previous research, in which we identified subgroups of children with mathematical difficulties based on their cognitive abilities. We examined 146 Israeli elementary school children in grades 3 and 4, classified into four subgroups: Reading Accuracy Difficulties (RAD), Mild Mathematical Difficulties (MMD), Non-Verbal Reasoning Difficulties (NVRD), and Typically Developing children (TD). Participants were assessed on arithmetic facts, computational fluency, procedural skills, estimation, and numeration. We observed varied performance patterns among subgroups. The RAD group showed the most severe impairments across all mathematical domains, along with reading comorbidity and cognitive difficulties. The MMD group, which maintained intact cognitive skills, faced notable challenges in computation, performing significantly below the TD group but better than the RAD group. The NVRD group, despite limitations in nonverbal reasoning, outperformed other difficulty groups on fact retrieval and estimation. Performance on multiplication and division tasks consistently followed a hierarchical pattern across all difficulty groups, with the RAD group facing the greatest challenges. These findings demonstrate that mathematical difficulties vary across cognitive profiles and that distinguishing between profiles through targeted assessment enables the development of differentiated interventions tailored to each learner’s specific cognitive profile. Full article