The rapid development of digital technology has driven innovation in education, particularly in the creation of interactive learning media that can enhance students’ motivation and engagement. One prominent approach is gamification, namely the application of game elements in the learning process to create more engaging and meaningful learning experiences. This study aims to design and develop game-based instructional media in the Role-Playing Game (RPG) genre that integrates Information and Communication Technology (ICT) subject matter at SMK Negeri 6 Padang. The development method employed is the Multimedia Development Life Cycle (MDLC), which comprises the stages of conceptualization, design, material collection, assembly, testing, and distribution. The resulting game presents an interactive storyline, educational missions, and quizzes based on course content as part of an enjoyable learning system. The findings indicate that this medium improves interactivity and learning effectiveness, as evidenced by expert evaluations and student responses categorized as “feasible for use,” with an average feasibility score of 94.04%. The implementation of gamification through this RPG is concluded to be an innovative solution in digital learning that has the potential to strengthen students’ understanding of ICT content in a more enjoyable, meaningful manner that is aligned with the demands of twenty-first-century learning.

Background: Indonesia continues to face persistent challenges in students’ mathematical problem-solving and reasoning abilities, as reflected in declining international assessment results. These issues indicate a need for instructional strategies that more effectively cultivate higher-order thinking. CPS and PBL are widely used approaches, yet their combined effects with cognitive styles remain underexplored. Aim: This study aims to compare the effectiveness of Collaborative Problem Solving (CPS) and Problem-Based Learning (PBL) on problem-solving and mathematical reasoning abilities, and to examine the role of Field-Independent (FI) and Field-Dependent (FD) cognitive styles, including their interaction with instructional strategies. Method: A quasi-experimental 2×2 factorial design was implemented with 119 seventh-grade students assigned to CPS or PBL and classified into FI or FD groups using the GEFT instrument. Data on problem-solving and reasoning were collected through validated essay tests. MANOVA was used to analyze main and interaction effects. Results: CPS produced significantly higher gains than PBL in both problem-solving and reasoning. FI students outperformed FD students across both strategies. A significant interaction effect was found, showing that FI learners benefit most from CPS, whereas FD learners perform relatively better under PBL, although still below FI peers. Conclusion: CPS offers a more structured and effective pathway for developing higher-order mathematical thinking. Cognitive style strongly influences learning outcomes, highlighting the need for differentiated support. Instructional designs that integrate structured collaboration and cognitive-style profiling are recommended to optimize students’ mathematical problem-solving and reasoning abilities

This research aimed (1) to study the level of professional leadership characteristics among industrial teachers in the era of Industry 4.0, (2) to analyze key components of professional leadership, and (3) to propose guidelines for promoting leadership development among vocational instructors. A mixed-methods design was employed, collecting data from 150 industrial teachers under the Office of Vocational Education Commission in Bangkok and its vicinity. A Likert-scale questionnaire and in-depth interviews with five model teachers and two school administrators were used. Quantitative data were analyzed using descriptive statistics and Exploratory Variable Analysis (EFA), while qualitative data were analyzed through content analysis. The findings revealed that the teachers exhibited a high level of professional leadership. Major components included instructional leadership, transformational leadership, servant leadership, and coaching/mentoring roles, all positively correlated with the effectiveness of Active Learning and Project-based Learning approaches. It is recommended that leadership development programs for industrial teachers emphasize collaboration with industry partners, motivation building, and professional mentorship for learners.

Deep-rooted educational inequality remains a major challenge in Thailand's remote and rural areas. Against this backdrop, this academic article explores the potential of Microlearning, or modularized learning, as an educational innovation that could help bridge existing learning disparities. Employing a systematic literature review methodology, the article synthesizes theoretical principles, evidence from applied case studies, and practical insights to demonstrate that concise, accessible, and mobile-based learning content directly aligns with the needs, lifestyles, and constraints of learners in remote contexts. This article connects the concept of Microlearning with established educational and psychological theories to explain its potential to enhance learning effectiveness. Drawing from Cognitive Load Theory and Self-Determination Theory, the study highlights the cognitive and motivational mechanisms underlying Microlearning's success. The findings suggest that Microlearning, when contextually adapted, can enhance learner motivation, autonomy, and retention. In addition, the article proposes a practical framework for implementing Microlearning that integrates contextually relevant content design, accessible technology, strategies for assessment and feedback, and a redefined role for facilitators. It also identifies possible challenges and provides policy recommendations for educators and government agencies. Ultimately, this article proposes Microlearning not only as a technological innovation but also as a human-centered approach to achieving long-term educational transformation.

This study aims to analyze the influence of learning planning and organization on the effectiveness of kitab kuning (classical Islamic text) learning at Pondok Pesantren Madinatul Munawwarah, Bukittinggi City. The research employs a quantitative approach with an explanatory research design. The population includes teachers of kitab kuning and students (santri) directly involved in the learning process, with samples selected through purposive sampling techniques. Data were collected using questionnaires and documentation, and analyzed using multiple linear regression to determine the relationships among variables. The results indicate that learning planning has a significant effect on learning effectiveness, as reflected in teacher preparedness, clarity of objectives, and the suitability of learning materials and methods. Furthermore, learning organization also shows a significant influence through effective coordination among educators, time management, and the utilization of facilities and infrastructure. Simultaneously, both variables contribute positively to enhancing the effectiveness of kitab kuning learning. The findings highlight the importance of well-structured planning and organization as strategic foundations for achieving effective kitab kuning learning in Islamic boarding schools.

The success of the learning process largely depends on teachers’ ability to understand and adapt their teaching strategies to students’ learning styles, particularly at MIN 12 Nagan Raya. This article examines three primary learning styles—visual, auditory, and kinesthetic—which reflect students’ preferred ways of receiving, processing, and retaining information. Using a literature-based approach supported by empirical analysis, this study identifies practical strategies teachers can apply to improve learning effectiveness. The findings indicate that aligning teaching methods with students’ learning styles significantly enhances learning motivation, comprehension, and academic performance. Visual learners benefit from the use of images, diagrams, and written explanations; auditory learners respond well to discussions, storytelling, and verbal instruction; while kinesthetic learners achieve better understanding through hands-on activities and movement-based tasks. Implementing these strategies also fosters positive teacher–student relationships, as students feel more valued and supported when their learning preferences are acknowledged. Moreover, differentiated instruction helps create an inclusive learning environment where all students have equal opportunities to succeed. However, the article also highlights several challenges faced by teachers, including limited preparation time and insufficient training on learning-style-based pedagogy. To address these issues, the study suggests providing professional development programs, strengthening collaboration among teachers, and integrating simple yet effective techniques into classroom activities. By applying the appropriate strategies, teachers can help students reach their maximum potential and improve the overall quality of the learning experience

Purpose This study aims to investigate how inclusive design features in digital training systems influence learner engagement, perceptions of fairness and training effectiveness, focusing on neurodiverse employees. The research contributes to ongoing debates about learning equity in increasingly diverse and hybrid workplaces by examining behavioral and interface-level inclusivity. Design/methodology/approach A cross-sectional survey of 180 professionals from diverse industries was conducted in India, an emerging economy undergoing rapid digital transformation in workforce development. Validated scales measured perceived inclusivity, engagement, fairness and learning effectiveness. A variance-based partial least squares structural equation modeling approach using SmartPLS 4, together with moderated mediation analysis, was used to test the hypothesized relationships, with neurodiversity as a moderator. Findings Inclusive features – such as adaptive feedback, multilingual support and culturally responsive content – significantly enhanced engagement (β = +0.52) and perceptions of fairness and psychological safety (β = +0.47), both of which improved learning outcomes (β = +0.42 and β = +0.38, respectively). Neurodiverse learners experienced amplified benefits (Mod β = +0.29), confirming a moderating effect and supporting the value of equity-focused training systems. Research limitations/implications Although limited to a single-country context, the findings have broader implications for workplace learning design across culturally and cognitively diverse labor markets. Future research should explore cross-cultural validation and longitudinal outcomes. Practical implications Organizations should embed inclusive design features as core components of digital training platforms. Personalized and culturally sensitive interfaces enhance learning return on investment by promoting fairness, engagement and accessibility, especially for underrepresented employee segments. Originality/value This study bridges inclusive instructional design and digital workplace learning by modeling how interface-level features impact learning equity. It introduces neurodiversity as a moderating variable and expands the theoretical understanding of psychological safety and learner engagement in corporate e-learning environments.

Multimodal-based deep learning detected disrupted precuneus connectivity and its related genetic profiles for predicting adults with ADHD.

Detecting Brain Tumors is essential in medical imaging, as early and accurate diagnosis significantly improves treatment decisions and patient outcomes. Convolutional Neural Networks have demonstrated high efficiency in this domain, but their lack of interpretability remains a significant drawback for clinical adoption. This study explores the integration of Explainability techniques to enhance transparency in CNN-based classification and improve model performance through advanced optimization strategies. The primary research question addressed is how to improve the accuracy, generalization, and interpretability of CNNs for brain tumor Detection. While previous studies have demonstrated the effectiveness of deep learning for tumor detections, challenges such as class imbalance and overfitting of CNNs persist. To bridge this gap, we employ different dynamic learning rate modifiers, perform architectural enhancements, and apply XAI techniques, including Grad-CAM and LIME. Our experiments are conducted on three publicly available multiclass tumor datasets to ensure the generalizability of the proposed approach. Among the tested architectures, the enhanced ResNet model consistently outperformed others across all datasets, achieving the highest test accuracy, ranging from 99.36% to 99.65%. The techniques such as unfreezing layers, integrating various blocks, pooling, and dropout layers enhanced feature refinement and reduced overfitting. By incorporating XAI, we improve model interpretability, ensuring that clinically relevant regions in MRI scans are highlighted. These advancements contribute to highly reliable AI-assisted diagnostics, addressing significant challenges in medical image classification.

Document classification in low-resource languages remains a critical challenge due to the scarcity of annotated datasets, language-specific resources, and linguistic tools. This study investigates the effectiveness of zero-shot learning (ZSL) for multilingual document classification, with a specific focus on low-resource Southeast Asian languages: Javanese, Sundanese, and Malay. We adopt a zero-shot cross-lingual transfer approach, using English-labeled data as the source domain and evaluating on unseen target-language documents without any supervised fine-tuning. Specifically, we employ two state-of-the-art multilingual transformer models, XLM-RoBERTa (XLM-R) and Multilingual T5 (mT5), to evaluate their ability to generalize across linguistically distant languages. Experimental results show that XLM-R achieves higher average accuracy (≈78%) and F1 Score (≈0.76) than mT5 (≈74% accuracy, 0.72 F1), demonstrating stronger transferability and stability. Both models exhibit efficient inference speed and manageable computational costs, indicating potential for deployment in resource-constrained environments. The findings introduce an early benchmark for zero-shot multilingual document classification in Southeast Asian languages and highlight the feasibility of inclusive NLP systems that bridge the data gap for underrepresented linguistic communities.

This study proposes an Improved Deep Transfer Network (IDTN) to enhance decoding accuracy, calibration efficiency, and adaptability of intracortical brain machine interface (iBMI) systems while reducing the reliance on new labeled samples. IDTN integrates two core components: Structural Joint Discriminative Maximum Mean Discrepancy (SJDMMD) and Kernel Norm Improved Multi-Gaussian Kernel (KNK). SJDMMD extends the standard MMD framework by incorporating a structure-enhanced soft label weighting mechanism that simultaneously minimizes intra-class distributional shifts and maximizes inter-class margins for precise cross-domain alignment. KNK employs multi-Gaussian kernels with kernel norm regularization to enhance high-dimensional feature representations and sharpen inter-class boundaries, thereby improving the effectiveness of SJDMMD. Evaluated on neural datasets from two rhesus macaques, IDTN achieved superior performance in both intra- subject and inter-subject transfer scenarios, consistently outperforming state-of-the-art methods in decoding accuracy. IDTN also exhibited consistent decoding stability across daily recording sessions. Ablation studies further confirm that SJDMMD improves inter-class separability and intra-class coherence, while KNK contributes to more effective kernel mapping in complex feature spaces. These findings underscore the effectiveness of structure-aware transfer learning for neural decoding. They also highlight the potential of IDTN for deployment in real-world iBMI applications, particularly in data-limited or cross-subject environments.

The role of the patient and carer voices in modern pharmacy education.

This study examined factors influencing the utility of ICT in enhancing teaching and learning effectiveness in secondary schools in the state of Amhara, Ethiopia. It specifically explored the effects of financial resources, infrastructure, personnel, policy, learning, evaluation, and support, as well as teachers’ and students’ perceptions of ICT utility. A correlational research design was employed, involving 739 teachers and 758 students selected through multistage sampling. Data were collected using a questionnaire and then analyzed using confirmatory factor analysis, independent-samples t-tests, and multiple regression analyses. Findings showed that the specified antecedents explained 65.9% and 52.5% of the variance in ICT utility for teachers and students, respectively. Support and access to ICT infrastructure significantly predicted ICT use, while ICT policy was the least influential factor. The t-test indicated a small difference between teachers’ and students’ perceptions (t = 2.771, d = 0.143), with both groups reporting below-average confidence in ICT’s usefulness. The study concludes that while ICT has the potential to enhance teaching and learning in secondary schools, its effective utilization is hindered by inadequate infrastructure, limited support, and weak policy implementation. To maximize ICT’s educational impact, policymakers and school leaders must adopt practical, evidence-based strategies that bridge the gap between policy intentions and classroom realities. They should strengthen technical and administrative support, provide adequate infrastructure, and offer ongoing training. Policymakers and educational leaders must also review ICT policies to ensure alignment with practical implementation needs.

Traditional domain adaptation learning methods have a strong dependence on data labels. The transfer process can easily lead to a decrease in training set performance, affecting the effectiveness of transfer learning. Therefore, this study proposes a domain adaptation model that combines feature disentangling and disentangling subspaces. The model separates the content and style features of images through disentangling, effectively improving the quality of image transfer. From the results, the proposed feature disentangling algorithm achieved pixel accuracy of over 84% for semantic segmentation of 14 categories, including roads, sidewalks, and buildings, with an average pixel accuracy of 85.2%. On the ImageNet, the precision, recall, F₁ score, and overall accuracy of the research algorithm were 0.942, 0.898, 0.854, and 0.841, respectively. Compared with the One-Class Support Vector Machine, the precision, recall, F₁, and overall accuracy were improved by 8.4%, 10.3%, 27.8%, and 10.9%, respectively. The proposed model can accurately recognize and classify images, providing effective technical support for image transfer.

Education is a fundamental right that must be fought for by every child in the nation, and throughout its development, it continues to undergo innovations and evaluations toward better quality. One educational model that is growing in Indonesia is the boarding school system, which has its own characteristics, advantages, and challenges. This study aims to describe the social studies (IPS) teachers’ strategies in boarding school–based learning, the implementation of IPS learning within the boarding school environment, and the obstacles faced by IPS teachers at MTs Darul Hikmah. This research employs a qualitative approach with a case study design. The study was conducted at Pondok Modern Darul Hikmah Tulungagung, with seventh-grade students as research subjects. Data collection techniques included observation, interviews, and documentation. The results show several factors that influence the low effectiveness of IPS learning in boarding schools, including the dense institutional activity schedule that reduces students’ learning focus, teachers’ limited mastery of the subject matter and classroom management, and the dual curriculum implemented simultaneously within the institution. Efforts to improve IPS learning effectiveness include enhancing teacher discipline when entering and leaving the classroom, utilizing audio-visual learning media, and connecting learning materials with current issues to make the lessons more relevant and engaging. Furthermore, the study reveals several advantages of boarding school–based education, such as fostering students’ independence, developing social awareness, providing deeper religious instruction, and integrating both general and religious education. Thus, IPS learning in boarding schools has the potential to develop more optimally with the support of appropriate learning strategies and more structured institutional management.

Augmented Reality has been widely investigated as an educational technology that overlays digital content such as 3D models, animations, audio narration, and interactive quizzes onto physical media. Most existing studies on augmented reality books focus on formal education (schools, classrooms, and STEM learning). Only a small number examine how augmented reality books influence learning, engagement, or knowledge recall among public library users, especially younger children and casual readers. While Design Thinking is often used to create prototypes, few studies use its Testing phase to systematically evaluate learning effectiveness through repeated measurement. The present study aims to evaluate the effectiveness of an augmented reality book in enhancing students’ knowledge recall and understanding of key places of interest. A set of questionnaires is being used to measure change in knowledge recall and understanding to the augmented reality experience. This study employed a quantitative research design to explore the effectiveness of an augmented reality book using a pre-test and post-test design. 70 participants in this study represented a broad spectrum of library users, supporting the inclusiveness of the study sample. Findings from pre-test and post-test results showed a significant increase in students’ post-test scores, indicating improved knowledge recall and understanding. The positive gain score and paired t-test results further confirmed that the augmented reality book enhanced learning performance.

Although protein-RNA interactions are crucial for many biological processes, predicting their binding free energies (ΔG) is a challenging task due to limited available experimental data and the complexity of these interactions. To address this issue, we developed a machine learning-based model designed to predict energy-based scores for protein-RNA complexes, called PANTHER score. By applying a local-to-global approach, the here proposed methodology can be subdivided into four steps: (1) we derived 87,117 pairwise local interaction energies out of 331,744 obtained from molecular dynamics simulations for a training set composed by 46 curated protein-RNA complexes; (2) we trained ML models derived from pairwise interaction features to predict the local interaction energies without performing MD runs; (3) we integrated the predicted local interaction energies with our here proposed local-to-global methodology, to calculate the model-specific PANTHER score; (4) we test the model-specific PANTHER score on a test set of 7 complexes (5) we further exposed all the models to an external stress set which includes 110 complexes with experimental ΔG allowing for final selection of the optimal model for implementation in the PANTHER scoring pipeline. Among all the multiple regression models developed here and evaluated on the test set, Random Forest Regression exhibited the highest predictive performance as a model-specific PANTHER score, with a Pearson correlation coefficient of (r) of 0.80 and mean absolute error (MAE) of 1.79 kcal/mol. Furthermore, the Random Forest Regression model maintained strong predictive capabilities on the stress set as well with (r) of 0.64 and MAE of 1.63 kcal/mol. Benchmarking against existing tools on the stress test set, the PANTHER score demonstrated superior accuracy and reliability. This study highlights the effectiveness of machine learning in addressing data limitations through innovative strategies, positioning here proposed PANTHER score as a valuable tool for predicting protein-RNA binding affinities in biomolecular research and drug discovery.

Background: Postpartum haemorrhage (PPH) remains a leading cause of maternal mortality, particularly in low-resource settings. Active Management of the Third Stage of Labour (AMTSL) is a critical intervention for reducing PPH, yet midwives in underserved areas often face challenges accessing updated, practical training.Objective: This study explored midwives’ perceptions of a digital micro-learning video intervention (m-AMTSLV) on AMTSL and its effectiveness in improving knowledge and clinical practice in southern Jordan.Methods: A qualitative descriptive design involving semi-structured interviews with 13 midwives from two government hospitals was used. Purposive sampling was used to recruit midwives who had completed the m-AMTSLV intervention, worked full-time on labour and postpartum units, and had direct care of childbirth. Data were analysed using thematic analysis based on Braun and Clarke’s six-phase framework. Results: Four major themes emerged. Theme 1: effectiveness of Digital Learning in Real-World Practice, subtheme 1: realistic simulation that boosted clinical confidence and improved maternal care outcomes, and subtheme 2: flexible, self-paced access via phone without disrupting work or family duties. Theme 2: suitability for remote and low-resource settings, subtheme 1: addressing training gaps, and subtheme 2: practical Implementation without advanced tools. Theme 3: user-centred design and language accessibility, subtheme 1: preference for Arabic or bilingual content, and subtheme 2: engaging scenario-based videos. Finally, theme 4: technical and logistical challenges, subtheme 1: internet connectivity issues, and subtheme 2: limited access to traditional training opportunities. Conclusion: The m-AMTSLV intervention was perceived as effective, accessible, and relevant for enhancing midwives’ clinical competence in managing PPH, particularly in areas with limited resources. Integrating such micro-learning tools into national midwifery education strategies could strengthen maternal care outcomes in underserved settings.

This study systematically reviews the effectiveness of Project-Based Learning (PjBL) and its variants, including POPBL and PjBL-STEM, in improving problem-solving skills, higher-order thinking skills, and learning outcomes in Mathematics and Science. Drawing on ten journal articles encompassing Systematic Literature Reviews, meta-analyses, and empirical studies such as quasi-experiments and classroom action research, the findings consistently show that PjBL is more effective than conventional methods, particularly for complex subject matter. Meta-analytic evidence also indicates a large effect size in fostering scientific creativity. Beyond cognitive gains, PjBL supports the development of positive student character. However, its implementation within the Merdeka Curriculum still faces challenges, particularly teacher administrative burdens, limited time allocation, and students’ readiness for collaborative work.

Root canal irrigation is a crucial component of endodontic treatment, yet it is often insufficiently addressed in dental education. Although its clinical significance is well recognized, dedicated simulation platforms for irrigation training remain scarce. This study aimed to develop a virtual simulation system for root canal irrigation and to evaluate its effectiveness in improving dental students' knowledge and procedural competence. Thirty-four dental students (26 undergraduates and 8 postgraduates) participated in this prospective study. After receiving standardized theoretical instruction, all students completed baseline and post-training assessments using three-dimensional printed tooth models. Training sessions were conducted with the newly developed virtual simulation system. Outcome measures included theoretical knowledge scores, pre- and post-training practical scores, and simulation-based performance scores. Data were analyzed using paired t-tests, Pearson correlation analyses, subgroup comparisons, and a post-hoc power analysis conducted with G*Power 3.1. In addition, a 26-item Likert-scale questionnaire was administered to assess usability and learner perceptions. Significant improvements were observed in both theoretical knowledge (mean increase: 0.59 points, p < 0.001) and practical performance (mean increase: 3.15 points, p < 0.001). Simulation-derived performance scores demonstrated a strong positive correlation with post-training practical outcomes (r = 0.73, p < 0.001). Questionnaire analysis indicated consistently high ratings for learning effectiveness, usability, instructional value, and overall satisfaction, with Cronbach's alpha values above 0.80 across all domains. This virtual simulation system shows preliminary value in improving theoretical and procedural learning in root canal irrigation, with strong acceptability among students. Further validation through larger, multi-center and long-term studies is needed to establish its broader educational impact.