Traditional Methods Research Articles

Factors affecting students’ concept retention in learning science online using instructional videos

Effective science instruction in a blended learning approach is synonymous with the strategic use of instructional videos (IVs) to fill the gap in teacher support. This study aims to determine the IVs’ effectiveness in improving students' concept retention and overall learning experiences. The experimental group was exposed to instruction integrating IVs via embedded mixed-method design, whereas the control group was exposed to traditional lecture methods. The results showed that students' post-test scores and concept retention improved significantly in the experimental group, where students reported better learning experiences than in the control group. This beneficial effect of a technology-integrated approach can be attributed to various elements of IVs, such as engaging content, motion graphics, video length, the language used, and the speaker's perspective. This study recommends that IVs be used to enhance learning opportunities and results in the teaching and learning process.

Nondestructive assessment of ZnO surge arrester operating condition based on time-domain dielectric characteristics

Given the expansion of the economy and the rise in electricity demand, it is imperative to ensure the safe operation of the power system. Zinc oxide (ZnO) surge arresters play a crucial role in preventing overvoltage that can potentially harm the grid. The study presents an innovative non-destructive assessment approach for evaluating the working condition of ZnO surge arresters based on time-domain dielectric characteristics, aiming to guarantee their safe functioning within the power grid. The proposed technique provides a platform for conducting aging experiments updates the procedure for preparing piezoresistors in ZnO surge arresters and examines their performance. Furthermore, a test bed was utilized to investigate polarization/depolarization currents. The findings revealed that the pressure-sensitive voltage exhibited a minimum value of 693.09 U1mA ·cm2 using the traditional solid-phase approach while employing the chemical precipitation method with a 2:1 alcohol-to-water volume ratio resulted in a maximum value of 1028.41 U1mA ·cm2. Under the traditional solid-phase method, the voltage gradient varied from 411.04 V·mm−1 to 585.12 V·mm−1 when using the chemical precipitation method with an alcohol-to-water volume ratio of 2:1 V ·mm−1. The condition of ZnO surge arresters can be evaluated non-destructively by the polarization/depolarisation charge differential, which increases with the average service life. In conclusion, this study provides methodological criteria for assessing ZnO surge arrester conditions to ensure safe operation of power grids.

Technology's impact on language learning: Meta-analysis on variables and effectiveness

This study explores the correlation between technology utilization and language acquisition while analyzing the impact of moderating variables on this relation. Our meta-analysis approach analyzes data from 43 extracts out of 19 primary studies published between 2012 and 2021. Our data analysis employs a random-effect model utilizing a significance level of α = 0.05. Additionally, the authors examine four moderating variables: level of education, location of research, proficiency in language, and year of publication. Technology-based language acquisition outperforms traditional methods, indicating a significant and moderate impact on the learning process. This study enhances comprehension of the efficacy of technology in language acquisition by identifying various factors, such as the geographical location of research, methods of assessing language proficiency, and technology type employed. However, there is insufficient evidence to support the notion that educational level or sample size significantly impact technology-based language acquisition. This meta-analysis highlights the importance of considering nuanced factors when integrating technology into language learning. The findings emphasize the possibility of technology to transform methods of acquiring language and urge additional investigation into customized strategies that optimize its advantages.

Improved metric-based meta learning with attention mechanism for few-shot cross-domain train bearing fault diagnosis

Traction motor bearings, as a crucial component of subway trains, play a pivotal role in ensuring the safety of train operations. Therefore, intelligent diagnosis of train bearings holds significant importance. However, due to the complex and dynamic nature of bearing conditions coupled with limited fault data availability, traditional diagnostic methods fail to yield satisfactory results. To address this issue, we propose an improved metrics-based meta-learning approach for accurate few-shot cross-domain fault diagnosis of train bearings. Firstly, we introduce a 1D-signal channel attention mechanism that effectively extracts latent features and enhances recognition accuracy. Secondly, by incorporating the Adabound algorithm into our model framework, we further enhance its classification performance. Finally, through several case studies, we demonstrate the effectiveness of our proposed method in comparison to other approaches within similar settings.

Unaligned Hip Radiograph Assessment Utilizing Convolutional Neural Networks for the Assessment of Developmental Dysplasia of the Hip

Abstract Developmental dysplasia of the hip (DDH) is a condition in which the acetabular socket inadequately contains the femoral head (FH). If left untreated, DDH can result in degenerative changes in the hip joint. Several imaging techniques are used for DDH assessment. In radiographs, the acetabular index (ACIN), center-edge angle, Sharp's angle (SA), and migration percentage (MP) metrics are used to assess DDH. Determining these metrics is time-consuming and repetitive. This study uses a convolutional neural network (CNN) to identify radiographic measurements and improve traditional methods of identifying DDH. The dataset consisted of 60 subject radiographs rotated along the craniocaudal and mediolateral axes 25 times, generating 1500 images. A CNN detection algorithm was used to identify key radiographic metrics for the diagnosis of DDH. The algorithm was able to detect the metrics with reasonable accuracy in comparison to the manually computed metrics. The CNN performed well on images with high contrast margins between bone and soft tissues. In comparison, the CNN was not able to identify some critical points for metric calculation on a few images that had poor definition due to low contrast between bone and soft tissues. This study shows that CNNs can efficiently measure clinical parameters to assess DDH on radiographs with high contrast margins between bone and soft tissues with purposeful rotation away from an ideal image. Results from this study could help inform and broaden the existing bank of information on using CNNs for radiographic measurement and medical condition prediction.

HK-SPSA based performance optimization method for steam generator liquid level control

Steam generator level control systems play a crucial role in ensuring safe, economical, and stable operation of nuclear power plants. However, traditional control systems exhibit limited efficiency in commissioning and are susceptible to human error. Additionally, traditional parameter tuning methods are typically experienced-based, cumbersome, and time-consuming, making it challenging to obtain optimal parameters. To address these issues, we propose a hybrid knowledge-guided improved simultaneous perturbation stochastic approximation algorithm (HK-SPSA) to optimize the control parameters of steam generator level control systems. Firstly, the algorithm utilizes iteration point adjacency information to approximate the current optimization process status and guide the adaptive adjustment of the iteration step size. Secondly, it utilizes composite gradient information generated by the estimated historical and current gradients to guide the optimization direction and the tuning of iteration step size. Simulation experiments have shown that HK-SPSA improves optimization performance and reduces the costs of optimization and adjustment compared to traditional methods.

Embedding higher order thinking skills in Islamic history (Sirah) education in Malaysia

<span lang="EN-US">The promotion of integrating higher order thinking skills (HOTS) in the Malaysian education system is pivotal to producing analytical thinkers to keep up with the rapid globalization of this era. Thus, the implementation of HOTS in learning and facilitation (PdPc) has been emphasized. However, little is known about the integration of HOTS in Islamic history and civilization (</span><em><span lang="EN-US">Sirah</span></em><span lang="EN-US">) lessons. Furthermore, teachers continue to use traditional methods and disregard incorporating HOTS in PdPc. Therefore, the current study conducted an in-depth investigation into the components of </span><em><span lang="EN-US">Sirah</span></em><span lang="EN-US"> lessons to support the implementation of the Malaysian education development plan (PPPM) 2013-2015 by strengthening teaching techniques through the use of HOTS for a better understanding of </span><em><span lang="EN-US">Sirah</span></em><span lang="EN-US"> lessons. Using an explanatory sequential mixed method design, the findings reveal that the teaching components of </span><em><span lang="EN-US">Sirah</span></em><span lang="EN-US"> lessons with the integration of HOTS are at a moderate level in the conclusion stage of a lesson. Therefore, the current study proposes a model that integrates HOTS, i.e., the element of attitude, preparation and planning, teaching aids with relevant aspects of PAK21, knowledge of HOTS, mastery of the subject content, teaching techniques and approach, assessment, and evaluation.</span>

Critical Review on Water Quality Analysis using IoT and Machine Learning Models

Water quality and its management are the most precise concerns confronting humanity globally. This article evaluates the various sensors used for water quality monitoring and focuses on the water quality index considering the multiple physical, chemical, and biological parameters. A Review of Internet of Things (IoT) research for water quality monitoring and analysis, sensors used for water quality can help remote monitoring of the water quality parameters using various IoT-based sensors that convey the assembled estimations utilizing Low-Power Wide Area Network innovations. Overall, the IoT system was 95% accurate in measuring pH, Turbidity, TDS, and Temperature, while the traditional method was only 85% accurate. Also, this study reviewed the different A.I. techniques used to assess water quality, including conventional machine learning techniques, Support Vector Machines, Deep Neural Networks, and K-nearest neighbors. Compared to traditional methods, machine learning and deep learning can significantly increase the accuracy of measurements of groundwater quality. However, various variables, such as the caliber of the training data, the water quality metrics' complexity, and the monitoring frequency, will affect the accuracy. The geographical information system (GIS.) is used for spatial data analysis and managing water resources. The quality of its data is also reviewed in the paper. Based on these analyses, the study has forecasted the future sensors, Geospatial Technology, and machine learning techniques for water quality monitoring and analysis.

Nowhere to Hide: Online Rumor Detection Based on Retweeting Graph Neural Networks.

Online rumor detection is crucial for a healthier online environment. Traditional methods mainly rely on content understanding. However, these contents can be easily adjusted to avoid such supervision and are insufficient to improve the detection result. Compared with the content, information propagation patterns are more informative to support further performance promotion. Unfortunately, learning the propagation patterns is difficult, since the retweeting tree is more topologically complicated than linear sequences or binary trees. In light of this, we propose a novel rumor detection framework based on structure-aware retweeting graph neural networks. To capture the propagation patterns, we first design a novel conversion method to transform the complex retweeting tree as more tractable binary tree without losing the reconstruction information. Then, we serialize the retweeting tree as a corpus of meta-tree paths, where each meta-tree can preserve a basic substructure. A deep neural network is then designed to integrate all meta-trees and to generate the global structural embeddings. Furthermore, we propose to integrate content, users, and propagation patterns to enhance more reliable performance. To this end, we propose a novel self-attention-based retweeting neural network to learn individual features from both content and users. We then fuse the node-level features with our global structural embeddings via a mutual attention unit. In this way, we can generate more comprehensive representations for rumor detection. Extensive evaluations on two real-world datasets show remarkable superiorities of our model compared with existing methods.

Stochastic Economic Lot Scheduling via Self-Attention Based Deep Reinforcement Learning

The Stochastic Economic Lot Scheduling Problem (SELSP) is a difficult dynamic optimization problem with wide industrial applications. Traditional methods such as hyper-heuristics are manually designed based on substantial expert knowledge, which may limit their optimization performance. Recently, Deep Reinforcement Learning (DRL) is shown to be promising in automatically learning scheduling policies for SELSP. However, its performance is still quite far from that of hyper-heuristics, due to the lack of suitable deep models. In this paper, we propose a novel DRL method to learn dynamic scheduling policies for SELSP in an end-to-end fashion. Based on self-attention, our method can effectively extract useful features from raw state information, and is flexible in handling different numbers of products, which is not viable for previous methods. Experiments on a complex biopharmaceutical manufacturing process show that our method outperforms a recent DRL method and state-of-the-art hyper-heuristics. Moreover, the trained policy performs better in environments different from training with demand forecast errors and varying number of products, showing its strong robustness and generalization ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The Stochastic Economic Lot Scheduling Problem (SELSP) is an important problem for manufacturing enterprises, which is to optimally balance the production and inventory so as to minimize the total cost. However, SELSP is very challenging to solve due to the involvement of uncertain factors such as customer demands and machine failures. Traditional methods for solving SELSP, such as heuristic policies and hyper-heuristics, heavily rely on human experiences to design and hence the performance could be limited. This paper proposes a Deep Reinforcement Learning (DRL) based method to automatically learn scheduling policy for solving SELSP, which could alleviate the above limitation through a self-attention based feature extraction mechanism and reward based training. Experimental results on a realistic manufacturing process show that our method can deliver higher revenue than conventional manual policy and an existing DRL based method.

Emphasis on Nanostructured Lipid Carriers in the Ocular Delivery of Antibiotics.

Drug distribution to the eye is still tricky because of the eye's intricate structure. Systemic delivery, as opposed to more traditional methods like eye drops and ointments, is more effective but higher doses can be harmful. Objective- The use of solid lipid nanoparticles (SLNPs) as a method of drug delivery has been the subject of research since the 1990s. Since SLNPs are derived from naturally occurring lipids, they pose no health risks to the user. To raise the eye's absorption of hydrophilic and lipophilic drugs, SLNs can promote corneal absorption and improve the ocular bioavailability of SLNPs. Methods- To address problems related to ocular drug delivery, many forms of Nano formulation were developed. Some of the methods developed are, emulsification and ultra-sonication, High-speed stirring and ultra-sonication, Thin layer hydration, Adapted melt-emulsification, and ultrasonication techniques, hot o/w micro-emulsion techniques, etc. Results- Nanostructured lipid carriers are described in this review in terms of their ocular penetration mechanism, structural characteristic, manufacturing process, characterization, and advantages over other nanocarriers. Conclusion - Recent developments in ocular formulations with nanostructured bases, such as surface-modified attempts have been made to increase ocular bioavailability in both the anterior and posterior chambers by incorporating cationic chemicals into a wide variety of polymeric systems.

Why Motivational Interviewing Is a Person-Centered Approach

Abstract: Background: The original developers of motivational interviewing (MI) describe the foundation of their therapeutic method in the person-centered tradition of Carl Rogers. Although MI is sometimes interpreted as a cognitive-behavior therapy (CBT), it did not derive from learning theory and has from the beginning been understood and expounded as a person-centered approach (PCA). Method: Narrative and conceptual review. Results: As with Rogers’s original method, the development of MI has been guided by empirical science based on the close observation of clinical practice. It is a behavioral approach in the broad sense of behavioral science. The underlying attitude or spirit with which MI is practiced is unambiguously humanistic in emphasizing partnership, acceptance, compassion, and empowerment. MI focuses on specific observable elements of therapeutic relationship that predict better client outcomes. Various behavioral scientists have distinguished MI from CBT both conceptually and with distinct clinical procedures. The psychological content of MI overlaps substantially with PCA practices such as accurate empathy and also with therapist skills often described as common or nonspecific factors in psychotherapy. Its component processes are engaging, focusing, evoking, and planning. The evoking process of MI distinguishes it from both CBT and nondirective PCA, involving MI-specific technical skills focused on specific client language known as change talk and sustain talk. Conclusions: Like PCA more generally, MI has found broad applications in helping professions well beyond counseling and psychotherapy such as health care, education, pastoral care, conflict resolution, and leadership. Possible historic reasons for confusing MI with CBT are explored.

Plasma-fixed Nitrogen Improves Lettuce Field Holding Potential

This study shows that plasma-fixed nitrogen applied as an inorganic biostimulant can improve marketable lettuce (Lactuca sativa var. longifolia) yield following delayed harvest. Using just one-tenth of the conventional nitrogen, plasma-fixed nitrogen—which is generated by a dielectric barrier discharge over water—was field-tested against traditional fertilization methods. Although no statistically significant differences were observed in total weight of heads among treatments, plasma-fixed nitrogen–treated plants had significantly increased marketable yields of 250% compared to those grown conventionally, despite reducing applied nitrogen fertilizer.

Unperforated film-covered planting contributes to improved film recovery rates and foxtail millet grain yields in sandy soils

Plastic film mulching improves the soil water content and temperature and, thus, contributes to enhanced grain yield in arid and semi-arid areas, such as Northwest China. However, in traditional film-covering methods, plastic films are difficult to recycle, causing environmental issues. Therefore, developing cultivation methods with enhanced film recovery rates is important for sustainable agricultural development. Here, we evaluated how the planting method affected the soil temperature, water content, film recovery rate, and grain yield by applying the following three planting patterns to the traditional crop, foxtail millet, in 2021–2022: no plastic film mulching (NP), plastic film mulching (MP), and planting on the film edge (PFE); in the latter, holes were not punched in the film and crops were planted approximately 5 cm from the film edge. The results for 2021 showed that the biomass ranged from 27.69 g to 33.76 g per plant, and the biomass was increased by 21.9% and 3.2% in PFE compared with NP and MP, respectively. However, in 2022, the biomass increased in PFE by 52.5% and 17.7% compared with NP and MP, respectively. In 2021, the highest grain yield was observed in PFE (2480 kg/hm2), followed by MP (2470 kg/hm2) and NP (2343 kg/hm2); in 2022, a significantly higher grain yield was observed in PFE than that in MP and NP. Although the soil temperature increased in PFE, it was not as high as in MP. The relatively higher soil temperature increased the root growth process, stimulated the production of root exudates (acetic acid and citric acid), and increased the soil enzyme (sucrase and urease) activity. Increases in organic acids and soil enzymes assisted in the decomposition of soil organic matter and induced relatively higher levels of available nutrients, such as nitrogen and phosphorus, and the higher root reuse rates contributed to higher grain yields. In addition, fewer holes were punched in the film of the PFE model, which improved film recovery, and the recovery rate of PFE was higher than that of MP. These results indicate that the proposed unperforated film-covered planting pattern is suitable for millet production in sandy soil.

Effectiveness of Using a Self-Directed Learning Program to Teach Physical Examination and Health Assessment Skills: A Quasi-Experimental Study.

Conducting health assessments and physical examinations are essential skills for independent practice. Exploring how to teach these skills effectively is essential. This study was designed to validate the effectiveness of a self-directed learning program in improving nursing student outcomes in terms of their learning health assessment and physical examination skills. A quasi-experimental design was used, and second-year nursing students enrolled in a health assessment and physical examination course at a university of science and technology in central Taiwan were recruited using purposive sampling. Both the control and experimental groups were taught using a traditional teaching method. The experimental group additionally participated in a self-directed learning program. Structured questionnaires, including the Chinese version of the Self-Directed Learning Readiness Scale, Scales of Motivation and Learning Strategies, Interpersonal Skills Assessment Tool, the Course Satisfaction Evaluation Scale, and the Perception of Health Assessment and Physical Examination Competence Scale, were used to collect data at three time points. After controlling for the effects of pretest scores, after the intervention, the experimental group achieved significantly higher scores than the control group for self-directed learning (creative learning and love of learning), learning motivation (goal orientation, work value, expected success, and test anxiety), and cognitive strategies (total score, elaboration strategy, recitation strategy, and monitoring strategy). In addition, the health assessment exercise improved interpersonal and communication skills, and learning satisfaction was significantly higher in the experimental group than the control group. A physical examination and health assessment course designed using the self-directed learning framework can effectively improve student competence in related skills to enhance their ability to assess patient health problems in clinical settings. This study presents an alternative approach to teaching health assessment and physical examination courses and validates the positive effect of this approach on student learning outcomes.

A Physics-Guided MFL Deformed Defect Recovery Method

Magnetic flux leakage (MFL) testing and analysis is an effective non-destructive testing (NDT) method of pipeline health. The health status of pipelines is evaluated by analyzing MFL signals, especially the defect analysis. However, complex operating conditions and equipment deviations may cause deformation on the defects. Availability and integrity of the MFL defects are the keys to accurate signal processing. Due to the limited interpretability of abstract networks and insufficient feature extraction, it is hard to accurately recover the deformed defects. Considering the problems above, an MFL deformed defect recovery method with the guidance of physical features is proposed. The proposed method integrates a deep feature extraction model into a sparse autoencoder, whose parameters are guided by the combination of MSE loss and physics loss. First, the deep feature extraction model fully extracts the abstract features, which contains more information on deformed defects. Second, the sparse penalty factor in the sparse autoencoder reduces the feature redundancy of deformed defects. Third, the physical features are extracted to guide the MFL deformed defect recovery relying on the MFL mechanism for the first time, which enhances the robustness and accuracy of the network. Finally, several comparison experiments have been conducted on measured and simulated MFL defects. The results show that the proposed method is effective in deformed defect recovery. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is a practical problem of MFL deformed defect recovery. The deformation on MFL defects affects defect inversion and evaluation seriously, which causes great potential harm to pipeline safety transmission. In this paper, compared with traditional neural network-based methods, physical features based on MFL mechanism are extracted to guide the parameter update. In addition, a pre-trained model replaces regular convolutional layers to extract features sufficiently, while a sparse factor is applied to reduce redundant features. The proposed method not only utilizes deep features from deep networks, but also increases the guidance of physical features, so that the algorithm is more robust and accurate. The experiment results show that our proposed method is more effective than the comparative methods on MFL simulated and measured defects. In a word, the proposed method has strong theoretical research and practical value.

Training Demand Prediction Models by Decision Error for Two-Stage Lot-Sizing Problems

Demand prediction to support appropriate production decisions is being actively studied. Many prediction models are designed to minimize the prediction error, which is measured by determining the difference between the predicted and ground-truth demand. However, these models ignore the effect of the prediction error on downstream production decisions. This prompted our study, which focuses on demand prediction models for two-stage uncapacitated lot-sizing problems. In this paper, we present a prediction model that minimizes the decision error, which is measured by the optimization objective of lot-sizing problems. Our model mitigates the impact of prediction errors by leveraging the structure of the lot-sizing problems. To enhance the ability to accommodate imperfect data, such as data based on inaccurate information, we subsequently extend the prediction models to distributionally robust versions. We consider the worst-case formulation in the feature space to enhance the robustness of the model to data imperfection. Numerical experiments demonstrate that the proposed prediction models are significantly superior to the traditional prediction methods when the model being trained is misspecified. In addition, the robust extension enables the models to train well on imperfect datasets while requiring less training data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study is motivated by the two-stage lot-sizing problem in manufacturing systems which involves the determination of the time at which (the setup decisions) to produce before the demand is revealed, and the production quantity decisions are adjusted during production processing. Predicting the demand properly while making the set-up decisions helps to decrease the production cost. This paper proposes a novel method that aims to minimize the total production cost to train demand prediction models. Practitioners can deploy the proposed method to train popular prediction models including linear prediction, decision tree, and neural network models. It is shown that the production cost associated with the decisions based on our models is lower than that associated with the traditional prediction models. Furthermore, our method enhances the ability of the model to handle model misspecification and imperfect data. Practitioners can apply our method to overcome the problems caused by imperfect data such as those containing contaminated and inaccurate measurements.

Soft-state optical spectroscopy of the black hole MAXI J1305-704

The X-ray dipper MAXI J1305-704 is a dynamically confirmed black hole (BH) X-ray binary discovered a decade ago. While its only outburst has been studied in detail in X-rays, follow-up at other wavelengths has been scarce. We report here the results from an optical spectroscopy campaign across the outburst of MAXI J1305-704. We analysed two epochs of data obtained by the Magellan Clay Telescope during two consecutive nights, when the source was in a soft X-ray spectral state. We identified typical emission lines from outbursting low-mass X-ray binaries, such as the hydrogen Balmer series, He II 4686 Å and the Bowen blend. We focused our analysis on the prominent Hα line, which exhibits asymmetric emission and variable absorption components. We applied both traditional analytical methods and machine-learning techniques in order to explore the association of the absorption features with outflowing phenomena, and we conclude that they are best explained by broad absorption. This result is consistent with reports from other outbursting BHs, where optical outflows have predominantly been observed in the hard state. Further observations at different X-ray states are key to properly test whether this behaviour is universal and to determine the implications for the disc wind physics.

Mobile Collaborative Learning Over Opportunistic Internet of Vehicles

Machine learning models are widely applied for vehicular applications, which are essential to future intelligent transportation system (ITS). Traditional model training methods commonly employ a client-server architecture to perform local training and global iterative aggregations, which can consume significant bandwidth resources that are often absent in vehicular networks, especially in high vehicle density scenarios. Modern vehicle users naturally can collaboratively train machine learning models as they are the data owner and have strong local computing power from the onboard units (OBU). In this paper, we propose a novel collaborative learning scheme for mobile vehicles that can utilize the opportunistic vehicle-to-roadside (V2R) communication to exploit the common priors of vehicular data without interaction with a centralized coordinator. Specifically, vehicles perform local training during the driving journey, and simply upload its local model to roadside unit (RSU) encountered on the way. RSU's model will be updated accordingly and sent back to the vehicle via the V2R communication. We have theoretically shown that RSUs' models can eventually converge without a backhaul connection. Extensive experiments upon various road configurations demonstrate that the proposed scheme can efficiently train models among vehicles without dedicated Internet access and scale well with both the road range and vehicle density.

Development of a multilingual online course with the language support for teaching physics in English

&lt;span lang="EN-US"&gt;Distance education offers an alternative to traditional teaching methods, particularly online courses. During the pandemic, the role of information and communication technologies in the field of education has increased and is used to organize both synchronous and asynchronous learning. However, not all information and communication technology (ICT) tools and platforms provide the necessary functionality for developing online courses in the direction of language integration. This article discusses the issues of creating a multilingual online course of physics in English with the necessary language support and additional functions for the development of students’ academic language. The study used such methods as a survey, focus groups, analytical and descriptive methods, and empirical research. The key features of the developed course are the use of various types of content and methods for assessing both subject and language knowledge and skills. The presented survey results, data on summative assessment (SE), and trial testing show the effectiveness of the developed course modules. The conclusions and results described in the article will be useful to teachers of natural sciences in general education and private schools, especially those using international programs, deputy directors, as well as teachers who teach their subjects in a second or third language.&lt;/span&gt;