An AI based design of student performance prediction and evaluation system in college physical education

2020 ◽  
pp. 1-9
Author(s):  
Zhang Yangsheng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Physical Education ◽  
Student Performance ◽  
Internal Structure ◽  
Process Evaluation ◽  
Performance Prediction ◽  
Evaluation System ◽  
Evaluation Model ◽  
Good Effect

College physical education is too one-sided, which makes the teaching process evaluation meaningless. Based on this, based on neural network technology, this article combines artificial intelligence teaching system to build an artificial intelligence sports teaching evaluation model based on neural network. The artificial intelligence model starts from the process evaluation and the final evaluation. Moreover, it uses a recurrent neural network for data training and analysis, and introduces a new decoder to perform data processing, and introduces a simplified gated neural network internal structure diagram to build the internal structure of the model.In addition, this study designs a control experiment to evaluate the performance of the model constructed in this study. The research results show that the artificial intelligence model constructed in this paper has a good effect in the performance prediction and evaluation of college sports students.

An artificial intelligence data mining technology based evaluation model of education on political and ideological strategy of students

2020 ◽  
pp. 1-12
Author(s):  
Zheng Rong ◽  
Zheng Gang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
College Students ◽  
Data Mining ◽  
University Students ◽  
Evaluation System ◽  
Evaluation Model ◽  
Ideological And Political Education ◽  
Political Education ◽  
Ideological Education

The student’s political and ideological practices is a vital portion of education, and it is related to optimization of task based on fundamental scenario in establishing morality. In order to establish a scientific, reasonable and operable evaluation model for students’ ideological education, and evaluate the status of college students’ ideological education. In this paper, firstly, in view of the shortcomings of evaluation objectives, single evaluation methods, lack of pertinence of evaluation indicators and subjectivity of evaluation standards in the current evaluation system of university students’ ideological and political education, the basic principles for constructing evaluation models of university students’ ideological and political education are put forward. Secondly, in case to meet changing needs of the times, an artificial neural network algorithm based on artificial intelligence data mining and a traditional multi-layer fuzzy evaluation model are designed to evaluate the ideological and political education of college students. This newly proposed model integrates learning, association, recognition, self-adaptive and fuzzy information processing, and at the same time, it overcomes their respective shortcomings. Finally, an example analysis is carried out with a nearby university as an example. The evaluation results display that the evaluation model of students’ ideological education established in this paper is in good agreement with the previous evaluation results. It fully shows that the comprehensive evaluation model of fuzzy neural network for college students’ ideological and political education established in this paper is scientific and effective.

An artificial intelligence fuzzy system for improvement of physical education teaching method

2020 ◽  
pp. 1-10
Author(s):  
Gaobin ◽  
Cao Huan Nan ◽  
Liu Zhen Zhong
Keyword(s):  
Artificial Intelligence ◽  
Physical Education ◽  
Teaching Methods ◽  
Evaluation System ◽  
Teaching Method ◽  
Motion Vector ◽  
System Structure ◽  
Functional Requirements ◽  
Experimental Teaching ◽  
Vector Decomposition

There are certain disadvantages in the traditional physical education teaching model. In order to improve the advanced nature of physical education teaching methods, this paper builds a physical education evaluation system based on artificial intelligence fuzzy algorithm. The system uses fuzzy control instructions as the basis to combine human language and mechanical language, so that the machine can recognize human working language habits and execute commands according to the instructions. Moreover, in this study, the trapezoid function is selected as the membership function, and the improved particle optimization algorithm is used to capture the student’s motion process and the motion vector decomposition, and the system structure model is constructed based on the functional requirements analysis. In addition, this study conducts system performance analysis through experimental teaching methods. The research results show that this system can effectively promote the reform of teaching methods in physical education and has a certain practical effect.

Evaluation System of Tooth Contact Patterns of Hypoid Gears Using Artificial Intelligence

2012 ◽  
Author(s):  
Morimasa Nakamura ◽  
Keisuke Kojima ◽  
Ichiro Moriwaki
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Evaluation System ◽  
Gear Pair ◽  
Contact Pattern ◽  
Hypoid Gear ◽  
Tooth Contact ◽  
Contact Patterns ◽  
The Neural Network ◽  
Hypoid Gear Pairs

Tooth contact inspection is one of the most common methods for checking qualities of hypoid gear pairs. A change in machine setting parameters for cutting and lapping processes of a hypoid gear pair enables a tooth contact pattern of a hypoid gear pair to be varied. The deviation of the pattern from the target one is represented by a grade point. In the inspection, the qualities of hypoid gear pairs are usually classified into only two grades; OK or NG. However, in order to conduct a follow-up survey on problems of the products and to be useful to be trouble shooting tasks of the end products, finer classifications and more quantitative evaluations of tooth contact patterns could be effective. Such approaches have been tried, however, only experienced and well-trained technicians for the inspection of hypoid gear pairs can determine the point of each tooth contact pattern. And it is difficult to make this evaluation method automatic. To overcome this problem, an application of artificial intelligence system must be useful. The present paper describes a computer evaluation system using the neural network, which is a kind of the artificial intelligence systems, for tooth contact patterns of hypoid gear pairs which can evaluate the results of the inspections instead of experienced hypoid gear technicians. This system with the neural network has a capability to learn relationships between evaluation grade points of tooth contact patterns given by the hypoid gear technicians and graphics of tooth contact patterns of hypoid gear pairs. Moreover, it can return the evaluation grade points when a tooth contact pattern is input into the system. The evaluation performance of the developed system was discussed. And a quality of normative tooth contact patterns, which were used as the teacher signals for training the neural network system, greatly affected its performance. The comparison of evaluated grade points obtained from developed system with the technician’s ones showed that the correct answer ratio obtained from the developed system was about 90% in the best case.

A New Evaluation Approach to Mechanical Kinematic Concept Using Fuzzy Neural Network

2011 ◽  
Vol 421 ◽  
pp. 666-669 ◽  
Author(s):  
Jian De Wu
Keyword(s):  
Neural Network ◽  
Evaluation System ◽  
Function Analysis ◽  
Evaluation Model ◽  
Indicator System ◽  
Bp Network ◽  
Evaluation Indicator ◽  
Evaluation Approach ◽  
Fuzzy Neural ◽  
Attribute Value

In this paper, BP network is applied to structure multi-level evaluation model to implement evaluation for the kinematic concepts acquired by function analysis. Under this approach, the best concept can be selected once evaluation indicators of each candidate are fuzzily quantified, converted into evaluation attribute value, and fed into the trained network model. During the process, neural network is used to solve the bottle-neck problem of knowledge acquiring and expression, which can be viewed as knowledge base and reasoning engine for the evaluation. At the same time, it is effective in solving the problem of weight distribution in evaluation indicator system. Fuzzy logic is used to achieve the fuzzy quantization for the attribute value of evaluation indicator in evaluation system, which can be used as the I/O value for neural network.

scholarly journals Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

2021 ◽  
Vol 13 (17) ◽  
pp. 9775
Author(s):  
Bashir Khan Yousafzai ◽  
Sher Afzal ◽  
Taj Rahman ◽  
Inayat Khan ◽  
Inam Ullah ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Student Performance ◽  
Performance Prediction ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Educational Data Mining ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Superior Performance

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

scholarly journals A Multi-Attribute Fuzzy Evaluation Model for the Teaching Quality of Physical Education in Colleges and Its Implementation Strategies

2021 ◽  
Vol 16 (02) ◽  
pp. 159
Author(s):  
Yao Wang ◽  
Chunyan Sun ◽  
Ying Guo
Keyword(s):  
Physical Education ◽  
Evaluation System ◽  
Evaluation Model ◽  
Implementation Strategies ◽  
Teaching Quality ◽  
Fuzzy Evaluation ◽  
Proposed Model ◽  
Grey Relational ◽  
Fuzzy Evaluation Model

There are two major problems with teaching quality evaluation of physical education (PE) in colleges: the excessive number of evaluation factors, and the incomplete evaluation system. To solve the problems, this paper puts forward a multi-attribute fuzzy evaluation model of college PE teaching quality, and provides the strategies to implement the model. Firstly, the problems of college PE teaching were analyzed, and a novel multi-dimensional evaluation system was developed for college PE teaching quality. To quantify college PE teaching quality, an evaluation model of college PE teaching quality was established based on the Grey Relational Analysis (GRA). In addition, several strategies were presented to improve college PE teaching quality. The proposed model and strategies provide a good reference for solving similar complex system problems.

scholarly journals Teaching Evaluation System by use of Machine Learning and Artificial Intelligence Methods

2021 ◽  
Vol 16 (05) ◽  
pp. 87
Author(s):  
Jingjing Hu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Evaluation System ◽  
Learning Algorithm ◽  
Evaluation Model ◽  
Teaching Evaluation ◽  
Bp Algorithm ◽  
Machine Learning Algorithm ◽  
Existing Problems

To explore the adoption of artificial intelligence (AI) technology in the field of teacher teaching evaluation, the machine learning algorithm is proposed to construct a teaching evaluation model, which is suitable for the current educational model, and can help colleges and universities to improve the existing problems in teaching. Firstly, the existing problems in the current teaching evaluation system are put forward and a novel teaching evaluation model is designed. Then, the relevant theories and techniques required to build the model are introduced. Finally, the experiment methods and process are carried out to find out the appropriate machine learning algorithm and optimize the obtained weighted naive Bayes (WNB) algorithm, which is compared with traditional naive Bayes (NB) algorithm and back propagation (BP) algorithm. The results reveal that compared with NB algorithm, the average classification accuracy of WNB algorithm is 0.817, while that of NB algorithm is 0.751. Compared with BP algorithm, WNB algorithm has a classification accuracy of 0.800, while that of BP algorithm is 0.680. Therefore, it is proved that WNB algorithm has favorable effect in teaching evaluation model.

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