Model For Evaluation Of Effects Research Articles

College sports offline and online mixed teaching evaluation enhanced by biomechanics and GA-BP neural network

In the process of higher education reform, physical education plays a vital role in improving students’ comprehensive quality. The online hybrid teaching mode integrates the advantages of online and traditional teaching, which has been gradually applied to various teaching scenarios. However, establishing a comprehensive and effective evaluation model for hybrid teaching remains a challenge due to its complexity. This study introduces a teaching evaluation model based on the Genetic Algorithm Optimized Back Propagation (GA-BP) neural network, incorporating the principles of biomechanics to enhance the evaluation of motor skills, movement efficiency, and physical performance. By comparing the BP and GA-BP models using sample data, results demonstrate that the GA-BP model provides higher precision, offering a feasible framework for hybrid teaching quality evaluation. This integration of computational methods and biomechanical insights not only enriches the model’s applicability but also advances the evaluation of physical education quality and athletic performance.

Evaluation model of new energy vehicle teaching effect based on artificial intelligence

Evaluation model of new energy vehicle teaching effect based on artificial intelligence

Multimodal Learning Data Analysis and Algorithmic Teaching Effectiveness Evaluation Model Construction

Introduction:A combination of internal characteristics, such as motivation, personality, beliefs, and dispositions that combine with external circumstances to affect student results is referred to as teaching efficacy. The English-speaking environment becomes a major problem as students learn by interaction. To communicate effectively, they do not acquire the speaking and listening abilities necessary in English. Objective:This study examines the impact of computer programming courses and learning analytics on student’s computer programming skills. Methods:In this study, we proposed a novel Dung Beetle Optimized Flexible Random Forest (DBO-FRF) to evaluate the teaching effectiveness. In this study, 175 students’ data were collected for teaching effectiveness. Using student data, prediction model was constructed based on the attributes of the students, their past academic records, their interactions with online resources, and their advancement in laboratory work related to programming. The proposed method is compared to other traditional algorithms. Results and conclusion:The proposed method is implemented using Python software. The expected performance in the course and, in the instance that any submitted programs failed to meet the requirements, a programming recommendation from one of the class’s top students. The result shows the proposed method achieved better performance in terms of accuracy, precision, recall, and [Formula: see text]1-score. This decreased the performance gap between students who performed lower and those who performed higher, enabling students who adjusted their programs to learn more.

Research on Evaluation Model of Physical Education Learning and Training Effect of College Students Based on Data Mining Technology

Research on Evaluation Model of Physical Education Learning and Training Effect of College Students Based on Data Mining Technology

Evaluation Model of Physical Education Teaching Effect Based on Embedded Neural Network

In response to the uniqueness of sports learning evaluation, we innovatively chose neural networks as the modeling basis and constructed a sports teaching effectiveness evaluation model based on embedded neural networks. This model achieves intelligent and automated evaluation of physical education teaching through the flexible processing capability of an efficient learning ability logic system embedded with neural networks. Specifically, embedded neural networks can deeply mine complex data during the teaching process. The embedded neural network effectively solves the uncertainty in the evaluation process and significantly improves the accuracy and adaptability of the evaluation system. In the process of model construction, we pay special attention to the adaptive processing capability of data. By introducing an adaptive neural system, the model can dynamically adjust parameters to better adapt to different teaching scenarios and individual differences among students, ensuring the objectivity and impartiality of evaluation results. This adaptability not only enhances the flexibility of the evaluation system, but also provides solid support for its application in practical teaching.

Developing an Integrated Analytical Framework for Sustainability Assessment: Focusing on Selected Projects in Riyadh

Riyadh, the capital of the Kingdom of Saudi Arabia, is now presenting itself as one of the most attractive capitals in the Middle East, with a number of ambitious expansion projects that aim to develop the city and align its urban development with the goals of Vision 2030. This urban renaissance requires researchers to adopt evaluation approaches (BSAMs) based on comprehensive sustainability criteria represented by environmental and cultural sustainability, community engagement, and economic feasibility. This research relies on the ETE methodology to determine evaluation criteria and their weights, which is a systematic and interactive method of prediction based on the opinion of a group of experts, or what is known as the Delphi method. Experts answered questionnaires to determine the weights of the criteria in three rounds where they received an anonymous summary of the experts’ predictions from the previous round with the reasons they provided for their judgments. The responses were then analyzed to identify recurring and converging themes and contradictions using the top-of-priority similarity to ideal solution (TOPSIS) technique, thus deriving an integrated evaluation model. The model was applied to evaluate architectural practices in Riyadh through three major projects: the King Abdullah Petroleum Studies and Research Center, the King Abdullah Financial District, and the King Abdullah Financial District Metro Station. Data sources included comprehensive site visits, detailed project documentation, and expert evaluation through structured questionnaires to gain a realistic view of attitudes towards architectural needs and sustainability. This adds to the knowledge on how globalization interacts with the urban renewal landscape in Riyadh and encourages us to continue proposing effective evaluation models by drawing attention to the multidimensional nature of sustainability. This in turn points to the need for continuous re-evaluation of architectural activities in Riyadh through project evaluation results that attest to their compatibility with international standards and local cultural contexts. Overall, the proposed evaluation model has proven successful in testing projects at the local level by providing a sustainable framework. The results showed that projects adhere to varying levels of sustainability requirements, but, more importantly, these evaluation models were developed to rationalize accelerated construction processes.

Construction and evaluation of a model for efficient identification of photothermal sensitivity of tobacco cultivars based on agronomic traits

The photothermal sensitivity of tobacco refers to the degree to which tobacco responds to changes in light and temperature conditions in its growth environment, which is crucial for determining the planting area of cultivars and improving tobacco yield and quality. In order to accurately and effectively evaluate the photothermal sensitivity of tobacco cultivars, this study selected five cultivars and their hybrid combinations with significant differences planted under different ecological conditions from 2021 to 2022 as materials. The experiment was conducted in two locations with significant differences in temperature and light. We measured the agronomic traits and biomass of the experimental materials, and constructed an effective tobacco photothermal sensitivity evaluation model using principal component analysis, membership function, and regression analysis. The reliability of the model was evaluated by utilizing the photosynthetic characteristics, chlorophyll content, and antioxidant enzyme system activity of the experimental materials. The results showed that tobacco biomass is the most important principal component in agricultural traits, and the comprehensive evaluation model for tobacco photothermal sensitivity is: y = 0.4571y1 + 0.2406y2 + 0.1725y3, where the fitting coefficients R2 of y1, y2, and y3 are 0.945, 0.851, and 0.977, respectively; The photothermal sensitivity of the experimental materials was calculated using this model, and the comprehensive ranking of the 11 experimental materials is: G3 < G5 < G10 < G9 < G11 < G6 < G7 < G2 < G4 < G8 < G1. Conventional identification methods have found that G2, G4, G6, G7, G8, and G11 are sensitive materials, G3, G5, and G10 are insensitive materials, and G1 and G9 are intermediate materials. The consistency rate of the evaluation results of the two methods reached 90.91%. And there is a significant correlation between the agronomic traits selected in the model and the physiological indicators selected by conventional evaluation methods, providing a scientific basis for evaluating the light temperature sensitivity of tobacco cultivars using agronomic traits in this study. The results indicate that the photothermal sensitivity evaluation model established in this study provides an efficient, convenient, and reliable method for evaluating the photothermal sensitivity of tobacco.

Evaluating the Effectiveness of Mobile Interactive Technology in University Physical Education Courses

With the rapid development of mobile interactive technology, a transformative shift in the pedagogical model of university sports courses has been initiated. Traditional teaching methods are simplistic and characterized by their lack of interactivity and personalization. However, the implementation of mobile interactive technology provides students with increased opportunities for participation and access to learning resources. Although existing research focuses on the integration of mobile interactive technology in university sports courses, a significant limitation remains in the evaluation methodologies employed, with a lack of systematic quantitative analysis and a comprehensive evaluation framework. This study proposes an effectiveness evaluation model comprising three primary components: construction of the evaluation model, calculation of indicator weights, and application of the fuzzy comprehensive evaluation method. The objective is to provide a comprehensive understanding of the effectiveness of mobile interactive technology in university sports courses through scientific evaluation methods, offering insights for future educational reforms and technological implementations.

Combat Effectiveness Evaluation of UAV Intelligent Swarm Based on AHP-Fuzzy Comprehensive Evaluation

In order to evaluate the combat effectiveness of UAV intelligent swarm, this paper puts forward the establishment of UAV intelligent swarm combat effectiveness evaluation index system based on availability, credibility and combat ability, analyzes the factors affecting the combat effectiveness of UAV intelligent swarm, establishes the effectiveness evaluation model, determines the weight of each factor by AHP, and evaluates the effectiveness index by fuzzy evaluation matrix. Using this method to evaluate the combat effectiveness of UAV intelligent swarm, the evaluation result is objective and reasonable, which provides theoretical reference for the optimal design and use of UAV intelligent swarm system.

The Teaching Effect Evaluation of Big Data Analysis in Music Education Reform

At present, the evaluation of the implementation effect of the music teaching reform project is a hot topic. This project establishes a teaching reform implementation effect evaluation model based on improving the accuracy of teaching reform plan effectiveness evaluation. This project first analyzes the current research status of the impact evaluation of the implementation of music education reform projects and builds a set of evaluation systems. The evaluation method of teaching reform plan implementation effect based on a ”big data-driven - support vector machine” was constructed. The simulation experiment verifies that this method can evaluate the implementation of the teaching reform plan well. The relative error of the evaluation results is small. It is practical in improving the effectiveness evaluation of the teaching reform plan. After verification, it is found that the research results of this project are feasible.

An adaptive operation planning and EBO-BPNN optimization method for decision support systems

Formulating a course of action (COA) before a combat is crucial for operational command. Research in command and control (C2) artificial intelligence is currently focused on using intelligent auxiliary decision-making methods to implement COA. This paper proposes a COA planning method based on line of operation (LOO) and uses planning domain definition language (PDDL) to describe combat scenarios and COA. Following the effect-based optimization (EBO) principle, an effect evaluation model for COA was constructed, and dynamic bayesian networks (DBNs) was used to determine the reasoning and calculate the results of the effect evaluation network. To further improve the execution efficiency of the effect evaluation model in practical applications, the network was optimized through a back propagation neural network (BPNN). Relevant experiments based on the coordinated distributed air defense and anti-missile scenario were carried out using the LOO model to complete the planning of COA. A BPNN evaluation model based on the DBNs evaluation model was built. After training and fine-tuning, it achieved similar evaluation results, with a mean absolute percentage error (MAPE) of less than 0.02%. Compared with the DBNs model, the BPNN model achieved an efficiency improvement of no less than 65%, effectively reducing the consumption of computing resources. This research is the first time to realize the modeled description of COA planning, automatic evaluation, and calculation optimization of COA effects. It can support the development of decision support systems (DSS) and has the potential for practical application.

How does carbon emissions trading scheme affect the investment efficiency of companies? Evidence from China

Carbon emissions trading scheme (ETS) will change the internal and external operating environment of related companies, increase new carbon reduction costs, and may affect their investment behavior and decision-making. This study constructs the policy effect evaluation model to evaluate the net effect of China's ETS pilot policy on the investment efficiency of related companies in high-carbon industries. This study indicates that: first, China's ETS pilot policy has reduced the inefficient investment of related companies by 11.40% during the sample period. Second, innovation ability plays an enhanced moderating effect in the inhibitory effect of China's ETS pilot policy on the inefficient investment of related companies in the short term, but the effect is weakened in the long-term. Third, the policy effect is relatively obvious for non-state-owned companies and companies with more institutional shareholdings. This research has important reference value for the continuous improvement of China's national carbon market, and the steady transformation from simple investment-driven to investment-efficiency-driven economic development in China.

The Regulation Mechanism and Evaluation Model of the Hydrogen Isotope Effect for Zr-Ti-Co Alloys during the Hydrogen/Deuterium Desorption Process at High Temperatures

The Regulation Mechanism and Evaluation Model of the Hydrogen Isotope Effect for Zr-Ti-Co Alloys during the Hydrogen/Deuterium Desorption Process at High Temperatures

Application of Clustering Algorithm in the Evaluation of College Students' English Reading Ability Under the Framework of Big Data

In recent years, China has accelerated the process of internationalization and made more and more achievements in transnational communication and cooperation. English learning is very important for contemporary college students. And English reading is an important means to acquire English language knowledge, understand external information and improve English language practice ability. Therefore, cultivating college students' English reading ability is a pivotal element in college English teaching. This study will focus on the application of clustering algorithm based on big data framework in the evaluation of college students' English reading ability, aiming at exploring and establishing an effective evaluation model to promote the improvement of college students' English reading ability and academic achievement.

Exploration of Issues in Online Learning for Undergraduates in Sichuan Province and Construction of an Evaluation Model

Online learning gradually becomes an important way for people to acquire knowledge. The current study aims to construct an effective online learning evaluation model to enhance online learning. In order to achieve the research aim, this study interviewed 21 respondents regarding online learning to analyze the current problems in the development of online learning among undergraduate students in Sichuan Province. Secondly, the Delphi method was used to establish a graphical model based on the feedback from 21 experts. Finally, referring to the CIPP evaluation principle, 5 experts evaluated the online learning evaluation model. Research results show that online learning suffers from low student engagement and motivation, limited teaching content and methods, inadequate personalized teaching know-how, insufficient online teaching capabilities, imperfect privacy and security, technical environment and conditions mismatched, and not available resource updates and sharing, etc. To solve the above-mentioned problems, the current study constructed an online evaluation model which comprised four core components, namely, students, teachers, technical support, and learning resources. For students, this model encourages students’ learning engagement, family support, and learning desire. At the same time, this model takes teaching method, instructional design, and teaching feedback into consideration. Moreover, this model provides support for technical support, platform availability, and technical security. Last but not least, this model generates learning resources with appropriate content, interactive tools, and managing shared resources

A Surrogate Model for the Rapid Evaluation of Electromagnetic-Thermal Effects under Humid Air Conditions

A Surrogate Model for the Rapid Evaluation of Electromagnetic-Thermal Effects under Humid Air Conditions

PDHL-MARCOS method based on cumulative prospect theory for MAGDM and its application in network security evaluation

The increasingly large and complex network brings convenience to our life and production, but it also brings potential dangers. Any minor vulnerability may lead to failure of application software configuration, software maintenance, user management and system management. Eventually, our network is interrupted, and even life and production data are leaked. Therefore, establishing a scientific and effective network security evaluation model is an effective way to supervise, manage and improve network security; It is also an effective measure to reduce the loss of life and production. Therefore, we first selected six factors closely related to network security to form a complete network security evaluation index system. Then, the utility function in the traditional measurement of alternatives and ranking according to compromise solution (MARCOS) method is improved by using the value function in cumulative prospect theory (CPT), and a PDHL-MARCOS evaluation model that can reflect the decision-makers’ (DMs’) attitudes towards gains and losses is obtained in the probabilistic double hierarchy linguistic term set (PDHLTS). In addition, the relative entropy (RE) method is also used to obtain expert weights. The combination weights of attributes are obtained using the correlation coefficient and the standard deviation (CCSD) method and the weight function in CPT.

Application of deep learning classification model for regional evaluation of roof pressure support evolution effects over time in coal mining face

Hydraulic support leg pressure serves as a crucial indicator for assessing work face support quality. Current evaluation methods for support quality primarily concentrate on static analyses-like inadequate initial support force, pressure overrun, and uneven bracket force-while neglecting dynamic column pressure changes. This paper introduces a model for assessing hydraulic support quality using deep learning techniques. Real-time data is preprocessed into a spatio-temporal pressure sub-matrix sample, which is then inputted into the model. This process assesses the support quality type and characterizes its dynamic evolution within the area. The model facilitates the identification of dynamic support quality effects in the working face area, aiding operators in making targeted adjustments to hydraulic support status. Experimental results revealed that the optimized LeNet-5 network-adjusting parameters like convolutional layer count, kernel size, and ReLU activation function-achieved the highest classification accuracy of 85.25 % for support quality, surpassed other networks. Furthermore, the improved LeNet-5 network outperformed other networks in both F1 score and recall. Additionally, the improved LeNet-5 network achieved faster convergence to the optimal solution, accelerated training speed. This highlighted its advantages in evaluating the spatio-temporal support quality of hydraulic supports in smart mining operations.

Construction of a Multimedia Instructional Effect Assessment System of College Foreign Language Translation Based on Artificial Intelligence

The teaching evaluation module is an important guarantee of teaching effectiveness, and as an important part of the multimedia teaching system, it needs special attention. This paper combines artificial intelligence and multimedia teaching effect evaluation, proposes a foreign language translation multimedia teaching effect evaluation model based on improved support vector machine (SVM) algorithm, and provides theoretical support for the construction of foreign language translation multimedia teaching effect evaluation system. The results show that the accuracy of this assessment algorithm is improved by 22.18% compared with the traditional assessment algorithm. Therefore, it is theoretically feasible to use the improved SVM to analyse the application effect of multimedia teaching mode in foreign language translation teaching. It can be found that the automatic acquisition of model data, the cumulative search of spatial knowledge and the adaptive control of the search phase of the optimised SVM algorithm have also been significantly improved, and conclusions drawn are more reliable.

Evaluation Model of Sports and Health Knowledge Dissemination Effect Based on Social Media Data and Sentiment Analysis

In the contemporary era, social media platforms serve as prominent channels for the dissemination of sports and health-related information. Understanding the effectiveness of such dissemination efforts is crucial for optimizing public health interventions and promoting well-being. This paper proposes an innovative evaluation model that harnesses social media data and sentiment analysis techniques to assess the dissemination effect of sports and health knowledge. By leveraging sentiment analysis, the proposed model delves deeper into the public's perceptions and attitudes towards sports and health-related content on social media platforms. Through the integration of advanced analytics, including machine learning algorithms, the model provides insights into the reach, engagement, and impact of information dissemination campaigns. The application of this evaluation model facilitates evidence-based decision-making for policymakers, health practitioners, and stakeholders involved in sports and health promotion initiatives. This paper outlines the theoretical framework, methodology, and practical applications of the proposed evaluation model, offering a comprehensive approach to evaluating the effectiveness of knowledge dissemination efforts in the digital age.