Traditional Learning Methods Research Articles

Virtual reality in medical education: Effectiveness of Immersive Virtual Anatomy Laboratory (IVAL) compared to traditional learning approaches

Immersive Virtual Anatomy Laboratory (IVAL) is an innovative learning tool that combines virtual reality and serious games elements to enhance anatomy education. This experimental study compares IVAL with traditional learning methods in terms of educational effectiveness and user acceptance. An experimental design was implemented with 120 undergraduate health-science students, randomly assigned to two groups: an experimental group using IVAL, and a control group following traditional learning methods. Data collection focused on quantitative measures such as pretest and posttest vocabulary assessment scores and task completion times, alongside qualitative measures obtained through a user experience questionnaire. This study utilizes the Technology Acceptance Model (TAM), incorporating variables such as Perceived Usefulness and Perceived Ease of Use. Results revealed significant improvements in the experimental group, with a 55.95% increase in vocabulary scores and an 18.75% reduction in task completion times compared to the control group. Qualitative data indicated that IVAL users reported greater Perceived Usefulness of the technology, improved Perceived Ease of Use, a more positive Attitude Towards Using IVAL, and stronger Behavioral Intention to continue using IVAL for anatomy learning. This study demonstrates that the integration of immersive virtual reality in the IVAL approach offers a promising method to enhance anatomy education. The findings provide insights into the effectiveness of immersive learning environments in improving learning outcomes and user acceptance. While further research is needed to explore long-term effects, this innovative approach not only enhances the effectiveness and enjoyment of anatomy learning but also provides valuable data on optimizing educational technology for improved learning outcomes.

The Difficulties That Faced Libyan Students in Learning English Vocabulary and Understanding Academic Texts

This study examines Libyan students' difficulties in acquiring the English language and comprehending academic writing. The research delineates particular vocabulary-related challenges and their effects on reading comprehension and academic achievement. A quantitative methodology was employed, utilizing a questionnaire administered to 32 students from the English department at Aljufra University. The results demonstrate that students face considerable difficulties in enhancing their vocabulary, employing words correctly, and comprehending phrasal verbs. The challenges are frequently intensified by cultural and linguistic disparities between Arabic and English, impeding students' ability to identify equivalent concepts in both languages. It also reveals that many students rely on traditional learning methods, such as dictionaries, flashcards, and repetition. While these strategies are somewhat effective, they are insufficient for mastering the complexities of academic English. The research highlights the necessity for educational institutions to provide additional support, including targeted vocabulary instruction, workshops, and one-on-one tutoring, to enhance students' academic writing and comprehension skills. Over 60% of the participants expressed the need for more excellent institutional support in academic writing, emphasizing the importance of structured resources to improve their learning outcomes. The results further emphasize the challenges associated with phrasal verbs and complex vocabulary, which remain common obstacles for non-native speakers. Addressing these areas through culturally responsive teaching techniques and providing real-world examples could make learning more relatable for students. The research underlines the importance of a comprehensive approach to vocabulary learning that incorporates both traditional and modern methods. Educators' focused effort to provide vocabulary support, academic assistance, and culturally relevant instruction is essential for improving Libyan students' vocabulary, enhancing their comprehension of scholarly texts, and ultimately enabling their educational success.

Assessing traditional and machine learning methods to smooth and impute device-based body condition score throughout the lactation in dairy cows

Regular monitoring of body condition score (BCS) changes during lactation is an essential management tool in dairy cattle; however, the current BCS measurements are often discontinuous and unevenly spaced in time. The imputation of BCS values is useful for two main reasons: i) achieving completeness of data is necessary to be able to relate BCS to other traits (e.g. milk yield and milk composition) that have been routinely recorded at different times and with a different frequency, and ii) having expected BCS values provides the possibility to trigger early warnings for animals with certain unexpected conditions. The contribution of this study was to propose and evaluate potential methods useful to smooth and impute device-based BCS values recorded during lactation in dairy cattle. In total, 26,207 BCS records were collected from 3,038 cows (9,199 and 14,462 BCS records on 1,546 Holstein and 1,211 Montbéliarde cows respectively, and the rest corresponded to other minority cattle breeds). Six methods were evaluated to predict BCS values: the traditional methods of test interval method (TIM), and multiple-trait procedure (MTP), and the machine learning (ML) methods of multi-layer perceptron (MLP), Elman network (Elman), long-short term memories (LSTM) and bi-directional LSTM (BiLSTM). The performance of each method was evaluated by a hold-out validation approach using statistics of the root mean squared error (RMSE) and Pearson correlation (r). TIM, MTP, MLP, and BiLSTM were assessed for the imputation of intermediate missing values, while MTP, Elman, and LSTM were evaluated for the forecasting of future BCS values. Regarding the machine learning methods, BiLSTM demonstrated the best performance for the intermediate value imputation task (RMSE = 0.295, r = 0.845), while LSTM demonstrated the best performance for the future value forecasting task (RMSE = 0.356, r = 0.751). Among the methods evaluated, MTP showed the best performance for imputation of intermediate missing values in terms of RMSE (0.288) and r (0.856). MTP also achieved the best performance for forecasting of future BCS values in terms of RMSE (0.348) and r (0.760). This study demonstrates the ability of MTP and machine learning methods to impute missing BCS data and provides a cost-effective solution for the application area.

The Padlet Media Design Based on Bima Local Literature to Improve Student Writing Skills

This research aims to improve students' writing skills through the use of Padlet media which is integrated with local wisdom of Bima regional literature. The background of this research is based on the low writing ability of students caused by monotonous traditional learning methods and the lack of use of interactive media. This research uses the Research and Development (R&D) method with the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) development model. Local literature, such as folklore and poetry, is integrated into Padlet's platform as teaching materials to provide relevant cultural context for students. The participants in this study are 30 students of the Indonesian Language and Literature Education Study Program at STKIP Harapan Bima. The results of the study showed a significant improvement in students' writing skills after the use of Padlet based on local wisdom. The average writing score of students increased by 25%, especially in the aspects of cohesion, coherence, and idea development. In addition, students also reported increased motivation and active involvement in writing learning through Padlet's collaborative features, such as writing sharing and feedback. However, there are several technical obstacles faced, such as limited internet access and lack of digital literacy among students. The results of this study have implications that the integration of digital technology and local wisdom can be an innovative strategy in improving students' writing skills, as well as fostering their cultural awareness.

Enhanced landslide susceptibility mapping in data-scarce regions via unsupervised few-shot learning

Given the critical need to assess landslide hazards, producing landslide susceptibility map (LSM) in regions with scarce historical landslide inventories poses significant challenges. This study introduces a novel landslide susceptibility assessment framework that combines unsupervised learning strategies with few-shot learning methods to increase the accuracy of LSM in these areas. The framework has been practically validated in a representative geological disaster-prone area along the West-East Gas Pipeline in Shaanxi Province, China. We employed three advanced few-shot learning models: a support vector machine, meta-learning, and transfer learning. These models implement feature representation learning for weakly correlated influencing factors through an unsupervised approach, thereby constructing an effective landslide susceptibility assessment model. We compared traditional learning methods and used the receiver operating characteristic (ROC) curve and SHAP values to quantify the effectiveness of the models. The results indicate that the meta-learning algorithm outperforms both the SVM and transfer learning in areas with limited landslide data. The integration of unsupervised strategies significantly improves performance, achieving area under the curve (AUC) values of 0.9385 and 0.9861, respectively. Compared with using meta-learning alone, incorporating unsupervised learning strategies increased the AUC by 4.76%, enhancing both the predictive power of the model and the interpretability of the features. Meta-learning under unsupervised conditions effectively mitigates the evaluation difficulties caused by insufficient landslide records, providing a viable path and empirical evidence for performance improvement in similar data- scarce regions worldwide.

A rolling bearing fault diagnosis method for imbalanced data based on multi-scale self-attention mechanism and novel loss function

Deep learning methods are widely used in the field of rolling bearing fault diagnosis and produce good results when faced with datasets with roughly equal numbers of normal and faulty samples. However, real-world data often has a serious imbalance, with the number of fault samples being significantly less than the number of normal samples. This dataset imbalance challenges the performance of traditional deep learning methods. To address this problem, this paper proposes an efficient imbalanced data rolling bearing fault diagnosis method. The method consists of two parts: a deep learning network based on a multi-scale self-attention mechanism and a novel loss function. In terms of the deep learning network, firstly, the one-dimensional vibration signal is converted into a two-dimensional image through the Gramian angular field. This conversion maximises the inherent feature extraction capability of the network. Subsequently, the multi-scale learning capability of the network is enhanced by implementing different expansion rates for the head of the multi-scale self-attention mechanism. This nuanced approach allows the network to capture the underlying information more efficiently. Finally, the inclusion of Ghost bottlenecks and feature pyramid networks (FPNs) helps to optimise network efficiency and improve generalisation performance. A novel loss function is also proposed to make the method more suitable for imbalanced data. During the training process, the classification of samples in each class is analysed using the recall metric of imbalanced classification and the real-time recall is used as a weight to weaken the dominance of the majority class. This weighting ensures the adaptability of the method to imbalanced datasets. The proposed method is evaluated using rolling bearing datasets from Case Western Reserve University, USA, and Southeast University, China. Comparison results with other state-of-the-art deep learning methods show that the proposed method has a robust performance when dealing with imbalanced data.

Integrating temporal deep learning models for predicting screen-out risk levels in hydraulic fracturing

Amid the transformative shift in global energy structures, the exploitation and utilization of shale gas, an essential unconventional natural gas resource, have drawn widespread attention from both industrial and academic circles. However, screen-out incidents during hydraulic fracturing operations pose significant obstacles to extraction efficiency and safety. Traditional prediction methods, which rely on empirical estimations and simplified models, are deficient in accuracy and real-time applicability. Addressing this, our study introduces a novel deep learning ensemble integrating Gated Recurrent Units (GRU), Transformer, and One-Dimensional Convolutional Neural Networks (1D-CNN) for precise screen-out prediction. This approach markedly improves predictive accuracy by efficiently processing time-series data and capturing the complex dynamics of fracturing processes. Furthermore, the application of the correlation coefficient method and random forest algorithm for feature selection optimizes model input and further enhances prediction accuracy and operational efficiency. Our comparative analysis demonstrates the model’s superiority, achieving an F1 score of 0.951 and a loss of 0.430, clearly surpassing traditional and other deep learning methods. This integration of advanced neural architectures and feature selection techniques not only advances screen-out prediction but also yields practical insights for optimizing shale gas extraction strategies and enhancing safety.

Evaluation of coarse aggregate properties in hardened concrete based on segment anything model (SAM)

The applicability of the segment anything model (SAM) algorithm, a novel technique for analyzing the characteristics of coarse aggregate from RGB images of concrete cross-sections, was evaluated. Unlike traditional deep learning method, the SAM algorithm effectively and clearly separates coarse aggregate and mortar without requiring a large database, extensive pre-training, conversion of RGB images to grayscale, or manual threshold setting. Additionally, a new technique was applied to separate the pores recognized as aggregates by using the image difference according to the change in vertical and side lighting. Consequently, the area ratio of coarse aggregate, after the separation and removal of pores from the aggregate area, exhibited an error of approximately 7.0 % when compared to the actual volume ratio of the aggregate. However, in contrast to the calculation of the area ratio of coarse aggregate, the SAM algorithm demonstrated significant errors in size analysis, leading to distortions in the sieve curve. Nevertheless, the SAM algorithm indicates potential for application in various concrete-related fields of concrete in the future.

Data-Driven Approach to Development of a Risk Score for Periprosthetic Joint Infections in Total Joint Arthroplasty Using Electronic Health Records

BackgroundPeriprosthetic joint infection (PJI) is an uncommon, but serious complication in total joint arthroplasty. Personalized risk prediction and risk factor management may allow better preoperative assessment and improved outcomes. We evaluated different data-driven approaches to develop surgery-specific PJI prediction models using large-scale data from the electronic health records (EHRs). MethodsA large institutional arthroplasty registry was leveraged to collect data from 58,574 procedures of 41,844 patients who underwent at least one primary and/or revision hip and/or knee arthroplasty between 2000 and 2019. The registry dataset was augmented with additional clinical, procedural, and laboratory data from the EHRs for more than 100 potential predictor variables. The main outcome was PJI within the first year after surgery. We implemented both traditional and machine learning methods for model development (lasso regression, relaxed lasso regression, ridge regression, random forest, stepwise regression, extreme gradient boosting, neural network) and used 10-fold cross-validation to calculate measures of model performance in terms of discrimination (c-statistic) and calibration. ResultsAll models discriminated similarly in predicting PJI risk, with negligible differences of less than 0.08 between the best- and worst-performing models. The relaxed and fully relaxed lasso models using the Cox model structure outperformed the other models with concordances of 0.787 in primary hip arthroplasty and 0.722 in revision hip arthroplasty, with the number of predictors ranging from nine to 41. The concordances with the relaxed lasso models were 0.681 in primary and 0.699 in revision knee arthroplasty, with a higher number of predictors in the models. Predictors included in the models varied substantially across the four surgical groups. ConclusionsThe incorporation of additional data from the EHRs offers limited improvement in PJI risk stratification. Furthermore, improvement in PJI risk prediction was modest with the machine learning approaches and may not justify the added complexity.

Adaptive scheduling for Internet of Vehicles using deconfounded graph transfer learning

With the rapid development of Internet of Things technology, the Internet of Vehicles has emerged as a significant application. The Internet of Vehicles connects vehicles with traffic infrastructure and other vehicles, enabling real-time information sharing and vehicle dispatching. However, in the Internet of Vehicles environment, traffic scheduling systems face challenges such as sparse raw data and environmental confounding bias, which limit the effectiveness of traditional graph-based reinforcement learning methods. In this paper, we propose a Cross-City Federated Continual Learning Framework for Spatiotemporal Graph Transfer Learning called CCFTL, which removes confounding effects on reinforcement learning and enhances few-shot transfer learning in data-scarce cities. Our approach aims to significantly improve the adaptability and scalability of cross-city vehicle networking technologies in ever-changing environments. This study acts as an innovative research of the deconfounding strategy with spatiotemporal graph meta-knowledge learning, which enables optimization of cross-city meta-knowledge transfer through a federated continual learning approach. This approach effectively reduces learning bias caused by regional differences, which enhances the model’s generalization and adaptability to complex, heterogeneous urban scenarios. Experimental results show that our framework significantly outperforms traditional methods, especially in improving the efficiency and accuracy of traffic dispatch systems in Internet of Vehicles environments with scarce data and the presence of confounding factors.

A coordination attention residual U-Net model for enhanced short and mid-term sea surface temperature prediction

Sea surface temperature (SST) is crucial for studying global oceans and evaluating ecosystems. Accurately predicting short and mid-term daily SST has been a significant challenge in oceanography. Traditional deep learning methods can handle temporal data and spatial features but often struggle with long-range spatiotemporal dependencies. To address this, we propose a coordination attention residual U-Net(CResU-Net) model designed to better capture the dynamic spatiotemporal correlations of high-resolution SST. The model integrates coordinate attention mechanisms, multiple residual modules, and depthwise separable convolutions to enhance prediction capabilities. The spatiotemporal variations of SST across different areas of the South China Sea are complex, making accurate predictions challenging. Experiments across various regions of the South China Sea show the model’s effectiveness and robust generalization in predicting high-resolution daily SST. For a 10-day forecast period, the model achieves approximately 0.3 °C in RMSE, outperforming several advanced models.

A novel graph convolutional network-based interpretable method for chiller energy consumption prediction considering the spatiotemporal coupling between variables

The prediction of chiller energy consumption is critical for lowering the building's energy consumption. Deep neural networks have been widely applied in this field. However, traditional deep learning methods only consider operational data and do not incorporate empirical knowledge and working condition information that are beneficial for chiller energy consumption prediction. The graph convolutional network (GCN) can handle this problem well, as it is able to incorporate empirical knowledge and working condition information into the network. However, it is important to avoid the excessive influence of empirical knowledge and working condition information on the model's accuracy. To adjust the weight of the influence of the association graph and training set on model accuracy in the GCN network, a coefficient θ was introduced. Herein, a novel GCN (θ-GCN)-based method for the energy consumption prediction of chillers is proposed. This proposed method first combines empirical knowledge and working condition information to obtain the adjacency matrix among operational data and then constructs an association graph based on this adjacency matrix. Secondly, due to the temporal correlation of the collected dataset, a gated recurrent unit (GRU) is used for feature extraction. Lastly, the features extracted through GRU are input into the constructed association graph, and the θ-GCN network is used for training and energy consumption prediction. The proposed method was experimentally verified in an actual chiller system and compared with existing methods; the outcomes indicated that the established method could obtain better prediction accuracy.

End-to-end multiple object tracking in high-resolution optical sensors of drones with transformer models

Drone aerial imaging has become increasingly important across numerous fields as drone optical sensor technology continues to advance. One critical challenge in this domain is achieving both accurate and efficient multi-object tracking. Traditional deep learning methods often separate object identification from tracking, leading to increased complexity and potential performance degradation. Conventional approaches rely heavily on manual feature engineering and intricate algorithms, which can further limit efficiency. To overcome these limitations, we propose a novel Transformer-based end-to-end multi-object tracking framework. This innovative method leverages self-attention mechanisms to capture complex inter-object relationships, seamlessly integrating object detection and tracking into a unified process. By utilizing end-to-end training, our approach simplifies the tracking pipeline, leading to significant performance improvements. A key innovation in our system is the introduction of a trajectory detection label matching technique. This technique assigns labels based on a comprehensive assessment of object appearance, spatial characteristics, and Gaussian features, ensuring more precise and logical label assignments. Additionally, we incorporate cross-frame self-attention mechanisms to extract long-term object properties, providing robust information for stable and consistent tracking. We further enhance tracking performance through a newly developed self-characteristics module, which extracts semantic features from trajectory information across both current and previous frames. This module ensures that the long-term interaction modules maintain semantic consistency, allowing for more accurate and continuous tracking over time. The refined data and stored trajectories are then used as input for subsequent frame processing, creating a feedback loop that sustains tracking accuracy. Extensive experiments conducted on the VisDrone and UAVDT datasets demonstrate the superior performance of our approach in drone-based multi-object tracking.

The Impact of Generative Artificial Intelligence on Education: A Comparative Study

Generative Artificial Intelligence (GAI), also known as creative AI, is a technology capable of independently producing original and creative content, such as text, images, videos, music, or code. It uses machine learning algorithms to analyze and learn from a vast amount of data to generate similar or even innovative content. This type of AI is used in several fields, such as health for the production of new drugs, design to generate adaptive designs, video games to create immersive environments, and artificial intelligence to generate training data for new networks. Seizing the opportunities offered by AI innovations in education is crucial. We cannot afford to let time pass without doing anything and without benefiting from this technique. It is time to enhance traditional learning methods and adopt more innovative approaches that take advantage of the potential of AI, like content and exam generation, and also improve students' learning experience by exploiting their closeness to the technology. This paper aims to answer the question of when and how to use artificial intelligence responsibly, considering it as a technology and limiting its excessive use. Additionally, we present the impact of GAI through a comparative study of student performance in three exam types: a classical MCQ exam written by the teacher, an adaptive MCQ exam generated by GAI from a topic, and a third MCQ exam generated by GAI based on the content of the provided documents. This study is conducted on students undergoing a software testing training program. By evaluating their performance in the AI-generated adaptive MCQ exams and the traditional MCQ exam, we can gain insights into the potential of this technology to enhance educational practices and provide personalized learning experiences. This research will contribute to the ongoing discussion on responsible and effective utilization of GAI in education, paving the way for future advancements in the field.

Unsupervised domain adaptation for drive-by condition monitoring of multiple railway tracks

Monitoring railway tracks through drive-by vibration data collected by in-service trains offers a cost-effective and adaptable solution for inspecting multiple railway lines. However, numerous existing drive-by monitoring methods rely on supervised learning models, necessitating extensive labelled data for each line. In this paper, a novel framework is proposed based on Unsupervised Domain Adaptation (UDA) concept which facilitates the transfer of a geometric defects diagnosis model learned from one line to a new line without the need for any labelled data from the new line. The proposed framework learns the dynamic-based features that are sensitive to damage and also invariant to different railway tracks. It comprises three components: data pre-processing, UDA implementation, and damage diagnosis. The framework uses the data from the source domain, including corresponding labels, as well as the unlabelled data from the target domain as input. The outputs of the framework consist of the predicted labels for the target domain. The performance of the proposed framework is evaluated using a comprehensive dataset of field measurements of a high-speed train passing 4 different lines within the French high-speed rail network. The proposed UDA framework is implemented using four common UDA algorithms including Information-Theoretical Learning (ITL), Geodesic Flow Kernel (GFK), Transfer Component Analysis (TCA), and Subspace Alignment (SA). The results show that the proposed framework has a 14% increase in the anomaly detection accuracy compared to traditional unsupervised learning methods in which UDA is not used. Furthermore, this study investigates the impact of incorporating a percentage of target data labels during training (semi-supervised domain adaptation), along with various sensor layouts and different tuning parameters, on the accuracy of the proposed approach. The results show that the proposed framework can significantly facilitate the monitoring of railway track conditions using the data collected by in-service trains which could be great interest of railway owners.

GeomorPM: a geomorphic pretrained model integrating convolution and Transformer architectures based on DEM data

As the domain of artificial intelligence has advanced, the integration of deep learning techniques into terrain and landform analysis has become more prevalent. Nevertheless, many existing methods are fully supervised and designed for specific tasks; thus, their transferability is limited and massive annotated samples are required. This study introduces a geomorphic pretrained model (GeomorPM) capable of performing multiple tasks. First, an architecture was designed that combined a convolution-based Vector Quantised-Variational Autoencoder (VQVAE) with a Transformer-based masked autoencoder (MAE) framework, allowing it to autonomously learn local details and global patterns from large-scale digital elevation model (DEM) data. Subsequently, GeomorPM, based on the VQMAE architecture, was pretrained on massive DEM data and fine-tuned for three specific tasks: DEM void filling, DEM superresolution, and landform classification. GeomorPM outperformed the traditional and other deep learning methods in all three tasks, demonstrating the superior learning ability and transferability of the model. This study provides a practical framework for developing pretrained models based on DEMs that can be expanded to other continuous geoscientific data.

PLIC-FSR-SYS: System reliability analysis based on parallel learning of influential components with filtered sample region

In practical engineering, system reliability analysis is highly concerned since many structures or products have multiple failure modes. Accordingly, this paper develops an innovative method for system reliability analysis by parallel learning of influential component limit-state functions with filtered sample region (PLIC-FSR-SYS) based on Kriging modeling. Different from the traditional adaptive learning methods that train only one component in each iteration when constructing the surrogate of the composite limit-state function, a new strategy is explored to adaptively identify several important components in one iteration so as to train them simultaneously. In the meanwhile, a filtering formula is explored to determine the fatal region so that the unimportant samples can be removed to further accelerate the training process. Based on the join forces of parallel learning of influential components and avoiding the training at unimportant samples, PLIC-FSR-SYS can achieve a fairly efficient system reliability analysis with multiple failure modes. Finally, four different case studies, including an engineering application to the ultra-voltage on-load tap-changer, are conducted to prove the effectiveness of the proposed method. The results indicate that compared to traditional adaptive learning methods, the proposed method makes a significant efficiency improvement for system reliability analysis with multiple failure modes.

Military Equipment Entity Extraction Based on Large Language Model

The technology of military equipment entity extraction, a crucial component in constructing military knowledge bases, holds significant research value and theoretical importance for guiding the development and improvement of equipment support forces. In the military domain, equipment entities exhibit a phenomenon of nesting, where one entity is contained within another, and abbreviations or codes are frequently used to represent these entities. To address this complexity, this paper proposes a method named CoTNER for extracting entities. Initially, a large-scale language model is used to perform data augmentation with chain-of-thought on the original dataset, providing additional semantic and contextual information. Subsequently, the augmented dataset is fine-tuned on a small-scale language model to adapt it to the task of military equipment entity extraction and to enhance its ability to learn complex rules specific to the domain of military equipment. Additionally, a high-quality data filtering strategy based on instruction-following difficulty scoring is proposed to address the catastrophic forgetting issue that may occur during the fine-tuning of large language models. The experimental results demonstrate that the proposed military equipment entity extraction method outperforms mainstream traditional deep learning methods, validating the effectiveness of CoTNER.

Rekindling Connections to Languages through Socio-Cultural Immersion Using Game-Based Learning and Virtual Reality: Cipher VR Case Study

Traditional language learning methods often fall short in engaging learners, especially in the context of indigenous languages like Irish. In this study we show how the language learning game Cipher VR combines digital game-based language learning with Virtual Reality (VR) to reconnect learners with indigenous languages, using the Irish language as a case study. Initially designed for English, Cipher has undergone several iterations to adapt to the Irish context, and is now completing its metamorphosis into a VR platform aimed at meeting the needs of less-resourced and endangered languages. This paper explores Cipher VR's development, emphasising the learner-centric design that merges educational aims, engaging gameplay and a culturally grounded approach to create an immersive and motivating learning experience.

Transforming Islamic Education in Schools: Challenges and Opportunities in the Era of Society 5.0

This research analyzes the challenges and opportunities of Islamic education in schools in the Society 5.0 era, which is characterized by the use of industrial revolution 4.0 innovations to overcome social problems. Using a qualitative approach with literature study methods and content analysis, this research examines literature related to the problems of Islamic religious education in schools. The research results identified six main challenges: limited infrastructure, inadequate conditions in rural schools, lack of technology-based learning media, lagging behind in the development of science and technology, use of traditional learning methods, and weak quality of human resources in mastering learning technology. In conclusion, the transformation of Islamic education in the Society 5.0 era requires improving the quality of human resources in learning technology, the use of digital books and technology-based learning media. Systematic efforts are needed to overcome the technology gap and increase the competitiveness of Islamic education in this era.