Automatic Assessment Method Research Articles

ОЦЕНКА МЕДИЦИНСКОЙ ДЕЯТЕЛЬНОСТИ НА ПРИМЕРЕ СТАЦИОНАРНОЙ ПОМОЩИ ВЗРОСЛОМУ НАСЕЛЕНИЮ ПО ПРОФИЛЮ «НЕЙРОХИРУРГИЯ»

Relevance. At the present stage of healthcare system development, automated assessment methods are being increasingly applied helping healthcare organization managers to plan and manage medical activities and improve the quality, safety and availability of healthcare. However, statistical and financial and economic indicators do not provide full data on the performance of a medical organization. One of the tools in making managerial decisions is the method of assessing medical activities using an automated technological map. Research purpose. The study aims to assess medical activities of the department of neurosurgery using key performance indicators. Materials and methods. The statistical indicators of activities of the department of neurosurgery were studied for 2020-2022, and the compliance coefficients were determined on the technological map. To assess the activities of this department, the methodology based on the existing regulatory and legal acts that are helpful in developing assessment criteria and creating a technological map were used. The data obtained when creating the technological map and the statistical indicators were used in determining the integral compliance coefficient in an automated mode at the research stages. The algorithm can be used to determine key parameters (staffing, working conditions, equipment and performance indicators), to identify problems and to make adequate management decisions based on the compliance coefficients. Results and discussion. The positive dynamics of the integral compliance coefficients was observed throughout the entire observation period. It was also revealed that the performance degradation in 2021 was due to the ongoing COVID-19 pandemic, and the compliance coefficient achieved the 2020 level only by the end of the study in 2022. Conclusion. The study has confirmed that the technological map based on the balanced indicators including the compliance coefficient is an effective method for assessing medical activities, including planning and making adequate decisions by managers of the department of neurosurgery.

Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters.

Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and variables' contributions to determine their real-life applicability. This work aims to evaluate the performance variations of a previously developed four-level physical fatigue model when alternating its inputs to have a comprehensive view of the impact of each physiological variable on the model's functioning. Data from heart rate, breathing rate, core temperature and personal characteristics from 24 firefighters during an incremental running protocol were used to develop the physical fatigue model based on an XGBoosted tree classifier. The model was trained 11 times with different input combinations resulting from alternating four groups of features. Performance measures from each case showed that heart rate is the most relevant signal for estimating physical fatigue. Breathing rate and core temperature enhanced the model when combined with heart rate but showed poor performance individually. Overall, this study highlights the advantage of using more than one physiological measure for improving physical fatigue modelling. The findings can contribute to variables and sensor selection in occupational applications and as the foundation for further field research.

Automatic quantification and assessment of grouped pig movement using the XGBoost and YOLOv5s models

The movements of grouped pigs reflect the health of the pigs to a certain extent. Monitoring pig movement is dependent upon daily patrols and the experience and judgment of breeders. This study innovatively proposes an automatic quantification and assessment method for the movements of grouped pigs using video data from the top view of a pigpen. The YOLOv5s model was used to detect the positions of grouped pigs. The mean-shift algorithm was used to identify the movement center of grouped pigs, and then the movement center was tracked, quantified, and counted. Based on the quantized values, we divided the movements of pigs into three levels. The extreme gradient boosting (XGBoost) model was used to assess the movements of grouped pigs. In the model training and position detection experiments, the P-R curve value of the YOLOv5s position detection model was 0.985, the precision was 0.97, the recall was 0.96, and the mean average precision was 0.5 (mAP_0.5) = 0.99. The XGBoost classification model was used to assess and classify the movement of grouped pigs, and the overall accuracy was 97.4%. Using this method to investigate movement over time, we found that the pigs were more active from 08:00 to 11:00 and 16:00 to 19:00 and that the movement center of grouped pigs changed over time. These results were consistent with the actual observations of grouped pigs, indicating that it is feasible to apply the method proposed in this study to large-scale farming operations.

Automatic Fluency Assessment Method for Spontaneous Speech without Reference Text

The automatic fluency assessment of spontaneous speech without reference text is a challenging task that heavily depends on the accuracy of automatic speech recognition (ASR). Considering this scenario, it is necessary to explore an assessment method that combines ASR. This is mainly due to the fact that in addition to acoustic features being essential for assessment, the text features output by ASR may also contain potentially fluency information. However, most existing studies on automatic fluency assessment of spontaneous speech are based solely on audio features, without utilizing textual information, which may lead to a limited understanding of fluency features. To address this, we propose a multimodal automatic speech fluency assessment method that combines ASR output. Specifically, we first explore the relevance of the fluency assessment task to the ASR task and fine-tune the Wav2Vec2.0 model using multi-task learning to jointly optimize the ASR task and fluency assessment task, resulting in both the fluency assessment results and the ASR output. Then, the text features and audio features obtained from the fine-tuned model are fed into the multimodal fluency assessment model, using attention mechanisms to obtain more reliable assessment results. Finally, experiments on the PSCPSF and Speechocean762 dataset suggest that our proposed method performs well in different assessment scenarios.

Comparison of End-to-End Neural Network Architectures and Data Augmentation Methods for Automatic Infant Motility Assessment Using Wearable Sensors

Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. We focus on the performance and computational burden of alternative sensor encoder and time series modeling modules and their combinations. In addition, we explore the benefits of data augmentation methods in ideal and nonideal recording conditions. The experiments are conducted using a dataset of multisensor movement recordings from 7-month-old infants, as captured by a recently proposed smart jumpsuit for infant motility assessment. Our results indicate that the choice of the encoder module has a major impact on classifier performance. For sensor encoders, the best performance was obtained with parallel two-dimensional convolutions for intrasensor channel fusion with shared weights for all sensors. The results also indicate that a relatively compact feature representation is obtainable for within-sensor feature extraction without a drastic loss to classifier performance. Comparison of time series models revealed that feedforward dilated convolutions with residual and skip connections outperformed all recurrent neural network (RNN)-based models in performance, training time, and training stability. The experiments also indicate that data augmentation improves model robustness in simulated packet loss or sensor dropout scenarios. In particular, signal- and sensor-dropout-based augmentation strategies provided considerable boosts to performance without negatively affecting the baseline performance. Overall, the results provide tangible suggestions on how to optimize end-to-end neural network training for multichannel movement sensor data.

Using gait videos to automatically assess anxiety.

In recent years, the number of people with anxiety disorders has increased worldwide. Methods for identifying anxiety through objective clues are not yet mature, and the reliability and validity of existing modeling methods have not been tested. The objective of this paper is to propose an automatic anxiety assessment model with good reliability and validity. This study collected 2D gait videos and Generalized Anxiety Disorder (GAD-7) scale data from 150 participants. We extracted static and dynamic time-domain features and frequency-domain features from the gait videos and used various machine learning approaches to build anxiety assessment models. We evaluated the reliability and validity of the models by comparing the influence of factors such as the frequency-domain feature construction method, training data size, time-frequency features, gender, and odd and even frame data on the model. The results show that the number of wavelet decomposition layers has a significant impact on the frequency-domain feature modeling, while the size of the gait training data has little impact on the modeling effect. In this study, the time-frequency features contributed to the modeling, with the dynamic features contributing more than the static features. Our model predicts anxiety significantly better in women than in men (r Male = 0.666, r Female = 0.763, p < 0.001). The best correlation coefficient between the model prediction scores and scale scores for all participants is 0.725 (p < 0.001). The correlation coefficient between the model prediction scores for odd and even frame data is 0.801~0.883 (p < 0.001). This study shows that anxiety assessment based on 2D gait video modeling is reliable and effective. Moreover, we provide a basis for the development of a real-time, convenient and non-invasive automatic anxiety assessment method.

Smart Essay Grading

Abstract: With the onset of the technological revolution, the efficiency of the current manual systems has been improved drastically and the accuracy of the products produced are also increasing. The most affected of the fields from this technological change is Education. The combination of Education with technology has been coined as a new term of edtech. Assessment in the Education system plays a significant role in judging student performance. Humans are becoming more interested in using automated tools. Consequently, in the last few years, the use of automatic assessment methods in the education system and student response evaluation has increased significantly. There is currently no adequate evaluation mechanism for grading essays and short responses; the computer-based evaluation system only works for multiple-choice questions. For the past few decades, many researchers have been working on automated essay grading and short answer scoring, but evaluating an essay while taking into account all the criteria, such as the content's relevance to the prompt, the development of ideas, cohesion, and coherence, is still a difficult task. We examined the limits of the most recent studies and research trends while studying the Artificial Intelligence and Machine Learning approaches used to assess computerized essay grading. In this project, we have studied different uses of Machine Learning and how we can improve the efficiency of the Essay Scoring using AI and other algorithms. In this project we also aim to highlight the problems faced by teachers and the possible solution which could be designed to overcome the problem.

Deep learning for collateral evaluation in ischemic stroke with imbalanced data.

Collateral evaluation is typically done using visual inspection of cerebral images and thus suffers from intra- and inter-rater variability. Large open databases of ischemic stroke patients are rare, limiting the use of deep learning methods in treatment decision-making. We adapted a pre-trained EfficientNet B0 network through transfer learning to improve collateral evaluation using slice-based and subject-level classification. Our method uses stacking and overlapping of 2D slices from a patient's 4D computed tomography angiography (CTA) and a majority voting scheme to determine a patient's final collateral grade based on all classified 2D MIPs. Class imbalance is handled in the evaluation process by using the focal loss with class weight to penalize the majority class. We evaluated our method using a nine-fold cross-validation performed with 83 subjects. Mean sensitivity of 0.71, specificity of 0.84, and a weighted F1 score of 0.71 in multi-class (good, intermediate, and poor) classification were obtained. Considering treatment effect, a dichotomized decision is also made for collateral scoring of a subject based on two classes (good/intermediate and poor) which achieves a sensitivity of 0.89 and specificity of 0.96 with a weighted F1 score of 0.95. An automatic and robust collateral assessment method that mitigates the issues with the small imbalanced dataset was developed. Computer-aided evaluation of collaterals can help decision-making of ischemic stroke treatment strategy in clinical settings.

Bone age assessment by multi-granularity and multi-attention feature encoding.

Bone age assessment (BAA) is crucial for the diagnosis of growth disorders and the optimization of treatments. However, the random error caused by different observers' experiences and the low consistency of repeated assessments harms the quality of such assessments. Thus, automated assessment methods are needed. Previous research has sought to design localization modules in a strongly or weakly supervised fashion to aggregate part regions to better recognize subtle differences. Conversely, we sought to efficiently deliver information between multi-granularity regions for fine-grained feature learning and to directly model long-distance relationships for global understanding. The proposed method has been named the "Multi-Granularity and Multi-Attention Net (2M-Net)". Specifically, we first applied the jigsaw method to generate related tasks emphasizing regions with different granularities, and we then trained the model on these tasks using a hierarchical sharing mechanism. In effect, the training signals from the extra tasks created as an inductive bias, enabling 2M-Net to discover task relatedness without the need for annotations. Next, the self-attention mechanism acted as a plug-and-play module to effectively enhance the feature representation capabilities. Finally, multi-scale features were applied for prediction. A public data set of 14,236 hand radiographs, provided by the Radiological Society of North America (RSNA), was used to develop and validate 2M-Net. In the public benchmark testing, the mean absolute error (MAE) between the bone age estimates of the model and of the reviewer was 3.98 months (3.89 months for males and 4.07 months for females). By using the jigsaw method to construct a multi-task learning strategy and inserting the self-attention module for efficient global modeling, we established 2M-Net, which is comparable to the previous best method in terms of performance.

Automated shape-independent assessment of the spatial distribution of proton density fat fraction in vertebral bone marrow

This work proposes a method for automatic standardized assessment of bone marrow volume and spatial distribution of the proton density fat fraction (PDFF) in vertebral bodies. Intra- and interindividual variability in size and shape of vertebral bodies is a challenge for comparable interindividual evaluation and monitoring of changes in the composition and distribution of bone marrow due to aging and/or intervention. Based on deep learning image segmentation, bone marrow PDFF of single vertebral bodies is mapped to a cylindrical template and corrected for the inclination with respect to the horizontal plane. The proposed technique was applied and tested in a cohort of 60 healthy (30 males, 30 females) individuals. Obtained bone marrow volumes and mean PDFF values are comparable to former manual and (semi-)automatic approaches. Moreover, the proposed method allows shape-independent characterization of the spatial PDFF distribution inside vertebral bodies.

An Artificial Intelligence Method for Automatic Assessment of Fuzzy Semantics in English Literature

An Artificial Intelligence Method for Automatic Assessment of Fuzzy Semantics in English Literature

Identifying Big Five personality traits based on facial behavior analysis.

The personality assessment is in high demand in various fields and is becoming increasingly more important in practice. In recent years, with the rapid development of machine learning technology, the integration research of machine learning and psychology has become a new trend. In addition, the technology of automatic personality identification based on facial analysis has become the most advanced research direction in large-scale personality identification technology. This study proposes a method to automatically identify the Big Five personality traits by analyzing the facial movement in ordinary videos. In this study, we collected a total of 82 sample data. First, through the correlation analysis between facial features and personality scores, we found that the points from the right jawline to the chin contour showed a significant negative correlation with agreeableness. Simultaneously, we found that the movements of the left cheek's outer contour points in the high openness group were significantly higher than those in the low openness group. This study used a variety of machine learning algorithms to build the identification model on 70 key points of the face. Among them, the CatBoost regression algorithm has the best performance in the five dimensions, and the correlation coefficients between the model prediction results and the scale evaluation results are about medium correlation (0.37–0.42). Simultaneously, we executed the Split-Half reliability test, and the results showed that the reliability of the experimental method reached a high-reliability standard (0.75–0.96). The experimental results further verify the feasibility and effectiveness of the automatic assessment method of Big Five personality traits based on individual facial video analysis.

Segmentation of human spermatozoa using improved Havrda-Chavrat entropy-based thresholding method

The assisted method of fertilization has required an identification of sperm cells with normal morphological structure. The abnormal sperm cells cannot provide successful result in artificial fertilization. Nowadays the assessment of morphology of sperm cells is subjective and error prone hence creating automatic evaluation method for morphology assessment, it will improve the success ratio in infertility treatment. The first step in our proposed system is pre-processing where noise removal process is applied on microscopic medical images. In second step, adaptive alpha valued Havrda-Chavrat entropy-based threshold technique is proposed where the maximum probability distribution of foreground pixels or background pixels is assigned to alpha value. Further, existing state-of-art threshold-based segmentation methods are implemented and obtained results on the input images. These segmentation results are compared with the proposed method in terms of supervised and unsupervised evaluation metrics, in which our proposed thresholding method has given optimum threshold value for the segmentation of spermatozoa cells. The outcome of the segmented images and their metric values are indicating better segmentation by our proposed method. Furthermore, this proposed method can be implemented in the mobile applications for diagnosis with artificial intelligence techniques.

An Automatic Assessment Method of Cyber Threat Intelligence Combined with ATT&amp;CK Matrix

With the occurrence of cyber security incidents, the value of threat intelligence is coming to the fore. Timely extracting Indicator of Compromise (IOC) from cyber threat intelligence can quickly respond to threats. However, the sparse text in public threat intelligence scatters useful information, which makes it challenging to assess unstructured threat intelligence. In this paper, we proposed Cyber Threat Intelligence Automated Assessment Model (TIAM), a method to automatically assess highly sparse threat intelligence from multiple dimensions. TIAM implemented automatic classification of threat intelligence based on feature extraction, defined assessment criteria to quantify the value of threat intelligence, and combined ATT&amp;CK to identify attack techniques related to IOC. Finally, we associated the identified IOCs, ATT&amp;CK techniques, and intelligence quantification results. The experimental results shown that TIAM could better assess threat intelligence and help security managers to obtain valuable cyber threat intelligence.

An Intelligent Athlete Signal Processing Methodology for Balance Control Ability Assessment with Multi-Headed Self-Attention Mechanism

In different kinds of sports, the balance control ability plays an important role for every athlete. Therefore, coaches and athletes need accurate and efficient assessments of the balance control ability to improve the athletes’ training performance scientifically. With the fast growth of sport technology and training devices, intelligent and automatic assessment methods have been in high demand in the past years. This paper proposes a deep-learning-based method for a balance control ability assessment involving an analysis of the time-series signals from the athletes. The proposed method directly processes the raw data and provides the assessment results, with an end-to-end structure. This straight-forward structure facilitates its practical application. A deep learning model is employed to explore the target features with a multi-headed self-attention mechanism, which is a new approach to sports assessments. In the experiments, the real athletes’ balance control ability assessment data are utilized for the validation of the proposed method. Through comparisons with different existing methods, the accuracy rate of the proposed method is shown to be more than 95% for all four tasks, which is higher than the other compared methods for tasks containing more than one athlete of each level. The results show that the proposed method works effectively and efficiently in real scenarios for athlete balance control ability evaluations. However, reducing the proposed method’s calculation costs is an important task for future studies.

Automated assessment reveals that the extinction risk of reptiles is widely underestimated across space and phylogeny.

The Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), is a crucial tool for conservation decision-making. However, despite substantial effort, numerous species remain unassessed or have insufficient data available to be assigned a Red List extinction risk category. Moreover, the Red Listing process is subject to various sources of uncertainty and bias. The development of robust automated assessment methods could serve as an efficient and highly useful tool to accelerate the assessment process and offer provisional assessments. Here, we aimed to (1) present a machine learning–based automated extinction risk assessment method that can be used on less known species; (2) offer provisional assessments for all reptiles—the only major tetrapod group without a comprehensive Red List assessment; and (3) evaluate potential effects of human decision biases on the outcome of assessments. We use the method presented here to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the IUCN. The models used in our predictions were 90% accurate in classifying species as threatened/nonthreatened, and 84% accurate in predicting specific extinction risk categories. Unassessed and Data Deficient reptiles were considerably more likely to be threatened than assessed species, adding to mounting evidence that these species warrant more conservation attention. The overall proportion of threatened species greatly increased when we included our provisional assessments. Assessor identities strongly affected prediction outcomes, suggesting that assessor effects need to be carefully considered in extinction risk assessments. Regions and taxa we identified as likely to be more threatened should be given increased attention in new assessments and conservation planning. Lastly, the method we present here can be easily implemented to help bridge the assessment gap for other less known taxa.

An Automatic Evaluation Method for Parkinson's Dyskinesia Using Finger Tapping Video for Small Samples

PurposeThe assessment of dyskinesia in Parkinson's disease (PD) based on Artificial Intelligence technology is a significant and challenging task. Based on the representative action of finger tapping, this paper designs an automatic evaluation method by deep learning technology to provide big data screening and preliminary evaluation for potential patients with PD. Presently, doctors use the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the severity of patients. This method is time-consuming and laborious, and there are subjective differences. The evaluation method based on sensor equipment is also widely used, but it is expensive and needs professional guidance, which is not suitable for remote evaluation and patient self-examination. Additionally, it is difficult to collect patient data for medical research, so it is important to find an objective and automatic assessment method for Parkinson's dyskinesia based on small samples.MethodsIn this study, we designed an automatic evaluation method combining manual features and convolutional neural network (CNN) suitable for small sample classification. Based on the finger tapping video of Parkinson's patients, we used the pose estimation model to obtain the action skeleton information and calculated the feature data. We then use the 5-folds cross validation training model to achieve an optimum trade-off between bias and variance, and finally make a multi-class prediction through fully connected network (FCN).ResultsOur proposed method achieves the current optimal accuracy of 79.7% in this research. We have compared with the latest methods of related research, and our method is superior to them in terms of accuracy, number of parameters and floating point operations (FLOPs).ConclusionThe proposed method does not require patients to wear sensor devices and has clear advantages in providing early screening and remote clinical evaluation for patients with PD. Simultaneously, the proposed method using motion feature data to train the CNN model obtains the optimal accuracy, effectively solves the problem of limited data acquisition in medicine and provides a novice proposal for a small sample size classification.

Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks.

PurposeTo standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.Materials and MethodsA dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively). The quality control standard for lumbar spine x-ray radiography in this study was defined using textbook guidelines of as a reference. An enhanced encoder-decoder fully convolutional network with U-net as the backbone was implemented to segment the anatomical structures in the x-ray images. The segmentations were used to build an automatic assessment method to detect unqualified images. The dice similarity coefficient was used to evaluate segmentation performance.ResultsThe dice similarity coefficient of the anteroposterior position images ranged from 0.82 to 0.96 (mean 0.91 ± 0.06); the dice similarity coefficient of the lateral position images ranged from 0.71 to 0.95 (mean 0.87 ± 0.10); the dice similarity coefficient of the oblique position images ranged from 0.66 to 0.93 (mean 0.80 ± 0.14). The accuracy, sensitivity, and specificity of the assessment method on the validation set were 0.971–0.990 (mean 0.98 ± 0.10), 0.714–0.933 (mean 0.86 ± 0.13), and 0.995–1.000 (mean 0.99 ± 0.12) for the three positions, respectively.ConclusionThis deep learning-based algorithm achieves accurate segmentation of lumbar spine x-ray images. It provides a reliable and efficient method to identify the shape of the lumbar spine while automatically determining the radiographic image quality.

Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma

Radiation dermatitis (RD) is a common, unpleasant side effect of patients receiving radiation therapy. In clinical practice, the severity of RD is graded manually through visual inspection, which is labor intensive and often leads to large interrater variations. To overcome these shortcomings, this study aimed to develop an automatic RD assessment based on deep learning (DL) techniques that could efficiently assist the RD severity classification in clinical application. A total of 1205 photographs of the head and neck region were collected from patients with nasopharyngeal carcinoma (NPC) undergoing radiation therapy. The severity of RD in these photographs was graded by 5 qualified assessors based on the Radiation Therapy Oncology Group guidance. An end-to-end RD grading framework was developed by combining a DL-based segmentation network and a DL-based RD severity classifier, which are used for segmenting the neck region from the camera-captured photographs and grading, respectively. U-Net was used for segmentation and another convolutional neural network classifier (DenseNet-121) was applied to RD severity classification. Dice similarity coefficient was used to evaluate the performance of segmentation. Severity classification was evaluated by several metrics, including overall accuracy, precision, recall, and F1 score. Results of segmentation showed that the averaged dice similarity coefficients were 91.2% and 90.8% for front and side view, respectively. For RD severity classification, the overall accuracy of test photographs was 83.0%. Our method accurately classified 90.5% of grade 0, 67.2% of grade 1, 93.8% of grade 2, and 100% of above grade 2 cases. The overall prediction performance was comparable with human assessors. There was no significant difference in accuracy when using manually or automatically segmented regions (P=.683). We have successfully demonstrated a DL-based method for automatic assessment of RD severity in patients with NPC. This method holds great potential for efficient and effective assessing and monitoring of RD in patients with NPC.

Automatic Assessment Method of Oral English Based on Multimodality

With the rapid development of Internet technology and educational informatization, there are more and more oral materials available on the Internet. Therefore, how to adapt to learners’ dynamic abilities and provide them with personalized learning materials has become a very important issue in educational technology. Aiming at the inefficiency of the existing automatic assessment of spoken English, a multimodal-based automatic assessment method of spoken English is proposed. The Word2Vec model is used to extract text features, and then, speech and text are input into GRU temporal structure, and encoder coding is used for multimodal fusion to realize automatic evaluation of multimodal spoken English. Simulation results show that the multimodal model proposed in this paper is superior to the traditional oral English automatic assessment model in terms of fluency, emotional expression, and sense of rhythm and can better improve learners’ oral English level.