MethodsX最新文献

{"title":"IoT security approach based random distribution of communication frequency","authors":"Manga Tonga V Joseph Salomon ,&nbsp;Mpoh Lobe Henri Pascal-Will ,&nbsp;Malong Yannick ,&nbsp;Maka Maka Ebenezer ,&nbsp;Ihonock Eyembe Luc ,&nbsp;Essiben Dikoundou Jean-François ,&nbsp;Paul-Marie Moulema Douala ,&nbsp;Yong Suk Joe","doi":"10.1016/j.mex.2025.103465","DOIUrl":"10.1016/j.mex.2025.103465","url":null,"abstract":"<div><div>IoT is a technology that can be found everywhere in our daily lives. The bulk of Internet of Things (IoT) technologies relies on wireless transmissions; however, these are often subjected to security threats such as communication intercepts. The aims of this paper is to address these threats by suggesting a method based on a random distribution of communication frequencies. The proposed approach was tested using LoRa technology within an experimental testbed. The validation results show that randomly changing communication frequencies introduce a processing delay of less than one second during transmission and reception. Furthermore, the experiments showed that the proposed strategy enhances coexistence with other LoRa devices. The performance achieved is promising for secure and efficient data transmission applications.<ul><li><span>•</span><span><div>The concentrate on the vulnerabilities that arise in the network of IoT systems at the physical layer</div></span></li><li><span>•</span><span><div>We propose a method to close this security gap by using a random allocation of communication frequency between LoRa cards.</div></span></li><li><span>•</span><span><div>The results show that the coexistence between IoT is possible and the random allocation of communication frequencies introduced less than a second in transmission and reception of packets.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103465"},"PeriodicalIF":1.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques","authors":"Rabita Hasan,&nbsp;Sheikh Md. Rabiul Islam","doi":"10.1016/j.mex.2025.103468","DOIUrl":"10.1016/j.mex.2025.103468","url":null,"abstract":"<div><div>Classifying emotions based on EEG signals is really important for enhancing our interactions with computers, monitoring mental health and creating applications in affective computing field. This study explores improving emotion recognition performance by applying traditional machine learning classifiers and boosting techniques to EEG data from the DEAP dataset. To categorize emotional states, we used four classifiers: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XGBoost and Gradient Boosting. Differential entropy and Higuchi's fractal dimension are two important time-domain parameters that we extracted after applying a segmentation technique to capture the temporal interdependence of EEG data. These features were selected for their ability to reflect intricate neural dynamics associated with emotional processing. A five-fold cross-validation procedure was applied to estimate the model's performance and hyperparameter tuning was conducted to optimize classifier efficiency. XGBoost achieved the highest accuracy 89 % for valence and 88 % for arousal demonstrating its superior performance. Furthermore, cross-subject evaluation on the SEED dataset reinforced the approach’s robustness, where XGBoost achieved 86 % accuracy using HFD and 84 % using DE. These results emphasize the effectiveness of combining advanced feature extraction methods with boosting algorithms for EEG-based emotion recognition, offering promising directions for the development of real-world emotion-aware systems. The key findings of this research are as follows:<ul><li><span>•</span><span><div>Differential Entropy and Higuchi’s Fractal Dimension proved effective in capturing emotional brain dynamics</div></span></li><li><span>•</span><span><div>XGBoost outperformed other classifiers in both DEAP and SEED datasets</div></span></li><li><span>•</span><span><div>The proposed method demonstrates robustness across subject variations and datasets</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103468"},"PeriodicalIF":1.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A study protocol of developing contemporary family pocketbook for preparing pregnancy and childbirth for Indonesian women","authors":"Dhiana Ayu Novitasari ,&nbsp;Novita Nur Rohma ,&nbsp;Harti Rahmi Aunurul Lisa ,&nbsp;Fitriana Murriya Ekawati","doi":"10.1016/j.mex.2025.103467","DOIUrl":"10.1016/j.mex.2025.103467","url":null,"abstract":"<div><div>Pregnancy and childbirth are two significant events for women and families, that readiness for these two events is linked to its successful outcomes. Worldwide, most of pregnancies are unplanned, which in some cases, limit the optimum pregnancy care affecting the prevention for maternal morbidity and mortality. This protocol aims to describe a method to develop a pocketbook for women and family for preparing pregnancy and childbirth. The study design follows the model of Research and Development (R&amp;D) using 4D development model: namely define, design, develop and disseminate. Data collection applied up-to focus group (FG) discussion with women, families and healthcare professionals in Bantul District Yogyakarta. The first and second FG will discuss about the importance of the pocketbook for women, and its expected content. The third-and fourth FG will finalize the book and its potential implementation in practice. Analysis of the data applied thematic models. The study period will take place from July 2024-July 2025. This study is expected to develop a comprehensive and easy-to-understand family pocketbook, containing important information related to pregnancy and childbirth preparation. Subsequently the pocketbook is expected to help prepare families’ readiness in supporting women during pregnancy and childbirth in Indonesia.</div><div>Three points:<ul><li><span>•</span><span><div>There is a need to prepare women and families about planned pregnancy and childbirth, aiming to provide information and appropriate arrangements.</div></span></li><li><span>•</span><span><div>This protocol aims to describe a method to develop a pocketbook for women and family for preparing pregnancy and childbirth using 4D development model.</div></span></li><li><span>•</span><span><div>The expected developed pocketbook is able to assist women and families’ readiness during pregnancy and childbirth and subsequently optimize the success of pregnancy care.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103467"},"PeriodicalIF":1.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance","authors":"Bala Subramanian C,&nbsp;Bharathi ST,&nbsp;Shanmugapriya S","doi":"10.1016/j.mex.2025.103461","DOIUrl":"10.1016/j.mex.2025.103461","url":null,"abstract":"<div><div>Cloud computing continues to rise, increasing the demand for more intelligent, rapid, and secure resource management. This paper presents AdaPCA—a novel method that integrates the adaptive capabilities of AdaBoost with the dimensionality-reduction efficacy of PCA. What is the objective? Enhance trust-based access control and resource allocation decisions while maintaining a minimal computational burden. High-dimensional trust data frequently hampers systems; however, AdaPCA mitigates this issue by identifying essential aspects and enhancing learning efficacy concurrently. To evaluate its performance, we conducted a series of simulations comparing it with established methods such as Decision Trees, Random Forests, and Gradient Boosting. We assessed execution time, resource use, latency, and trust accuracy. Results show that AdaPCA achieved a trust score prediction accuracy of 99.8 %, a resource utilization efficiency of 95 %, and reduced allocation time to 140 ms, outperforming the benchmark models across all evaluated parameters. AdaPCA had superior performance overall—expedited decision-making, optimized resource utilization, reduced latency, and the highest accuracy in trust evaluation among the evaluated models. AdaPCA is not merely another model; it represents a significant advancement towards more intelligent and safe cloud systems designed for the future.<ul><li><span>•</span><span><div>Introduces AdaPCA, a novel hybrid approach that integrates AdaBoost with PCA to optimize cloud resource allocation and improve trust-based access control.</div></span></li><li><span>•</span><span><div>Outperforms conventional techniques such as Decision Tree, Random Forest, and Gradient Boosting by attaining superior trust accuracy, expedited execution, enhanced resource utilization, and reduced latency.</div></span></li><li><span>•</span><span><div>Presents an intelligent, scalable, and adaptable architecture for secure and efficient management of cloud resources, substantiated by extensive simulation experiments.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103461"},"PeriodicalIF":1.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian spatio-temporal conditional autoregressive localized modeling techniques for socioeconomic factors and stunting in Indonesia","authors":"Aswi Aswi ,&nbsp;Septian Rahardiantoro ,&nbsp;Anang Kurnia ,&nbsp;Bagus Sartono ,&nbsp;Dian Handayani ,&nbsp;Nurwan Nurwan","doi":"10.1016/j.mex.2025.103464","DOIUrl":"10.1016/j.mex.2025.103464","url":null,"abstract":"<div><div>Stunting remains a persistent public health issue in Indonesia, exhibiting significant spatial and temporal variation. To address this, we employed a hierarchical Bayesian spatio-temporal localized Conditional Autoregressive (CAR) model that includes a clustering component to identify risk factors and estimate relative risk (RR) across 34 provinces from 2020 to 2022. A total of 480 models were evaluated, encompassing three variants of the Bayesian spatio-temporal localized CAR model, 32 covariate combinations, and five hyperprior settings. Assuming a Poisson likelihood for stunting counts, the optimal model was estimated using Markov Chain Monte Carlo methods and included two covariates, namely the poverty rate and the incidence of low birth weight, with up to five spatial clusters. Higher poverty levels and increased prevalence of low birth weight were significantly associated with elevated stunting risk among children under five. Spatio-temporal clustering patterns and the estimated relative risks of stunting varied across Indonesian provinces from 2020 to 2022. Nusa Tenggara Timur consistently ranked among the top three provinces with the highest risk (RR = 2.421 in 2020; 2.384 in 2021; 2.676 in 2022). The highest risk was observed in Sulawesi Barat in 2022 (RR = 2.768), while DKI Jakarta consistently showed the lowest (RR = 0.004).</div><div>Some key points of the article are:<ul><li><span>•</span><span><div>Bayesian spatio-temporal models facilitate the classification of distinct area groups</div></span></li><li><span>•</span><span><div>The models were employed to analyze stunting patterns in Indonesia.</div></span></li><li><span>•</span><span><div>The inclusion of covariates influenced the number of groups identified.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103464"},"PeriodicalIF":1.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP","authors":"Dr. Bharti Khemani ,&nbsp;Dr. Sachin Malave ,&nbsp;Samyukta Shinde ,&nbsp;Mandvi Shukla ,&nbsp;Razzaq Shikalgar ,&nbsp;Harshita Talwar","doi":"10.1016/j.mex.2025.103460","DOIUrl":"10.1016/j.mex.2025.103460","url":null,"abstract":"<div><div>In the healthcare industry, the ever-increasing volume of clinical trial data presents challenges for ensuring drug safety and detecting adverse drug reactions (ADRs). This study aims to address the challenge of accurately detecting Serious Adverse Events (SAEs) in pharmacovigilance, a critical component in ensuring drug safety during and after clinical trials. The key problem lies in the underreporting and delayed detection of Adverse Drug Reactions (ADRs) due to the heterogeneous nature of medical data, class imbalance, and the limited scope of traditional monitoring techniques. This study proposes a hybrid AI-driven framework that integrates structured (e.g., patient demographics, lab results) and unstructured data (e.g., clinical notes) to detect ADRs using advanced deep learning and NLP methods. The objective is to outperform traditional signal detection methods and provide interpretable predictions to aid clinicians in real-time. By leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, including Random Forests, Gradient Boosting Machines, and Convolutional Neural Networks (CNNs), our model aims to identify potential ADRs across different patient subgroups. Through meticulous feature engineering and the application of techniques to address data imbalance, our model demonstrates improved accuracy and interpretability in predicting ADRs. The CNN model achieved an accuracy of 85 %, outperforming traditional models, such as Logistic Regression (78 %) and Support Vector Machines (80 %). These findings suggest that specific demographic and clinical factors significantly influence the likelihood of adverse reactions, offering valuable insights for targeted monitoring and risk mitigation strategies[11]. This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes.<ul><li><span>•</span><span><div>The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. BERT and GPT, were also employed to provide the functionality of textual interactions over medical data.</div></span></li><li><span>•</span><span><div>Performance metrics such as accuracy, precision, recall, and F1-score were systematically applied to evaluate each model’s performance. Among the models tested, the CNN model with BERT achieved the highest accuracy, providing valuable insights into the potential of deep learning for enhancing pharmacovigilance practices.</div></span></li><li><span>•</span><span><div>These findings suggest that an inclusion of diverse clinical data when supplied to advanced ML and NLP techniques can significantly improve the detection of ADRs, leading to better alignment with the fundamental principles of Good Clinical Practice (GCP).</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103460"},"PeriodicalIF":1.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security","authors":"Harshala Shingne ,&nbsp;Diptee Chikmurge ,&nbsp;Priya Parkhi ,&nbsp;Poorva Agrawal","doi":"10.1016/j.mex.2025.103445","DOIUrl":"10.1016/j.mex.2025.103445","url":null,"abstract":"<div><div>The need for secure communication systems has driven extensive research into quantum-based security mechanisms, particularly Quantum Key Distribution (QKD). However, traditional QKD systems, within dynamic environments incorporating network fluctuation and attacks, have been relatively limited because static protocols cannot support high key generation rates and security. This work addresses these challenges by proposing the integration of AI and machine learning optimization techniques into quantum communication protocols to enhance both security and efficiency. We here propose three advanced models: first, Deep Reinforcement Learning is applied to adaptively optimize QKD protocols by dynamically adjusting the key generation parameters with respect to environmental conditions. In the state-of-the-art method, the DRL-based approach enlarges the secure key generation rate by 15–20 % and suppresses QBER 30–40 % under noisy conditions. A VAE is used for the detection of anomalies in quantum networks that effectively detects eavesdropping. By incorporating quantum-specific feature extraction and latent variable disentanglement, the VAE model detects attack detection accuracy of 85–90 % with a reduction of 25 % in false positives. Finally, it considers the optimization of cryptographic protocols in a distributed quantum network using Multi-Agent Deep Q-Networks. This multi-agent system strengthens both the security and computational efficiency by reducing attack vulnerabilities by 15–18 % and lowering the computational complexity by 20–25 %. In all, the integration of AI with machine learning methods brings far better enhancements in the field of quantum communication system security and efficiency, addressing critical limitations of conventional QKD systems and pointing to the way to more resilient adaptive quantum security solutions.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103445"},"PeriodicalIF":1.6,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Filtered aquatic small tubular mesh-bottomed containers (FAST-MC): A low-cost, efficient method for rearing zebrafish larvae in filtered water","authors":"M. Caballero,&nbsp;S. Robles,&nbsp;VP Connaughton","doi":"10.1016/j.mex.2025.103453","DOIUrl":"10.1016/j.mex.2025.103453","url":null,"abstract":"<div><div>Care for larval zebrafish (<em>Danio rerio</em>) can be taxing and time-intensive, as embryos/larvae are housed in petri dishes or well plates which require daily care. Lack of filtration in the dishes can affect overall water quality, especially in ecotoxicological exposures which can last weeks. This report describes an alternative method, Filtered Aquatic Small Tubular Mesh-bottomed Containers (FAST-MC), which separates larvae into petri dish-sized housing containers maintained in a larger volume of filtered water, mimicking the larger, recirculating systems used for adults. To validate our method, we raised zebrafish larvae in FAST-MC containers for 4-weeks and compared overall survival, behavior, and water quality with larvae reared for 4-weeks in standard deep dish petri dishes. Overall, this method provides a low-cost alternative for housing larval zebrafish that is amenable to experimental manipulation and suitable for toxicological and/or pharmacological studies.<ul><li><span>•</span><span><div>An easily constructed float can hold small mesh-bottomed (hatchery-type) containers in a larger aquarium allowing constant water filtration and improved water quality.</div></span></li><li><span>•</span><span><div>Each mesh bottom container can serve as a housing container for experimental exposures, similar to multiwell plates.</div></span></li><li><span>•</span><span><div>Zebrafish larvae raised in the mesh bottom containers had increased survival and activity compared to larvae raised in standard petri dishes.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103453"},"PeriodicalIF":1.6,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards green filtration: Thyme-based antibacterial nanofiber media as alternatives to synthetic counterparts","authors":"Zahra Shafiei ,&nbsp;Farideh Golbabei ,&nbsp;Mohammad Reza Pourmand ,&nbsp;Ensieh Masoorian ,&nbsp;Mohammad Mehdi Shirmohammadi ,&nbsp;Kamal Azam ,&nbsp;Vida Shahbazian ,&nbsp;Saba Kalantary","doi":"10.1016/j.mex.2025.103459","DOIUrl":"10.1016/j.mex.2025.103459","url":null,"abstract":"<div><div>This study presents a comparative strategy for incorporating natural and synthetic antibacterial agents into electrospun nanofiber media for face mask applications, addressing concerns over toxicity and environmental impact. Polyacrylonitrile (PAN) nanofibers containing thyme extract (5,20, 40, 60 wt %) and ZIF-8 (5,7, 10, 15 wt %) were fabricated and characterized. Morphological analysis confirmed uniform, bead-free nanofibers with diameters ranging from 207.5 to 234.2 nm and membrane thicknesses around 150 µm. FTIR and XRD results verified the successful integration and chemical interaction of additives with the PAN matrix. Filtration performance tests revealed that thyme-based nanofibers (40 wt %) achieved 96.83 % particle filtration efficiency (PFE) at 0.3 µm, 100 % bacterial filtration efficiency (BFE), and a low pressure drop of 20.33 Pa. ZIF-8 nanofibers (7 wt %) showed 96.58 % PFE and 96.06 % BFE with a slightly higher pressure drop (25 Pa). Time-kill assays indicated stronger antibacterial activity for thyme over ZIF-8 during a 4-hour period. Compared to commercial melt-blown filters, both types of nanofibers provided improved filtration with acceptable breathability. The results suggest that thyme-loaded nanofibers offer a safe, biodegradable alternative to synthetic agents like ZIF-8, though further studies on durability, scalability, and long-term performance are needed.<ul><li><span>•</span><span><div>Thyme extract demonstrated superior antibacterial and filtration performance compared to ZIF-8.</div></span></li><li><span>•</span><span><div>Electrospun nanofibers significantly outperformed melt-blown media in filtration efficiency.</div></span></li><li><span>•</span><span><div>The study supports natural compounds as sustainable, non-toxic alternatives for face mask applications.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103459"},"PeriodicalIF":1.6,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Mixed-Methods Framework for Assessing Differentiated Instruction Implementation Barriers in EFL Secondary Education Contexts","authors":"M. Jufrianto,&nbsp;Muhammad Basri,&nbsp;Iskandar iskandar","doi":"10.1016/j.mex.2025.103457","DOIUrl":"10.1016/j.mex.2025.103457","url":null,"abstract":"<div><div>This article presents a methodological framework for systematically investigating barriers to differentiated instruction implementation among English as Foreign Language (EFL) teachers. Our method combines:<ul><li><span>-</span><span><div>15-item Likert-scale questionnaire measuring teacher perceptions across three domains: teacher preparation, classroom implementation challenges, and institutional barriers</div></span></li><li><span>-</span><span><div>A structured classroom observation protocol assessing nine key indicators of differentiated instruction implementation</div></span></li><li><span>-</span><span><div>Statistical validation procedures establishing instrument reliability (Cronbach's Alpha 0.869) and observation consistency (Cohen's Kappa 0.81)</div></span></li></ul>This dual-instrument approach was applied to six EFL teachers in an Indonesian secondary school context, revealing significant discrepancies between theoretical knowledge and classroom practice. The framework effectively identified that while teachers possessed reasonable knowledge of differentiation principles (M=2.50, SD=0.98), process differentiation strategies were implemented by only 33% of participants. This methodology enables researchers to triangulate self-reported perceptions with observed practices, providing a comprehensive understanding of implementation barriers in language education contexts..</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103457"},"PeriodicalIF":1.6,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}