With the continuous reform and development of teaching mode, intelligent teaching has become one of the important development trends of education in the future. The core idea of intelligent teaching is to use information means to organise corresponding teaching resources, carry out teaching activities, reasonably arrange teaching activities and improve comprehensive benefits . The construction and development of intelligent teaching need dynamic changes are very important to ensure the quality of teaching . Dynamic change also means the unknown, not only the unknown of resources, but also the fundamental change of teaching mode and teaching method. Under the development needs of intelligent teaching, the construction of teaching resources also began to be gradually improved. At present, the organisation mode of intelligent teaching resources is mostly based on traditional teaching resources, which not only has a large construction time span, but also is difficult to meet the needs of dynamic teaching organisation . Many schools have made great efforts in improving the organisation mode of intelligent teaching resources, such as using the advantages of the Internet to improve the number and types of resources, specially designing the dynamic update mechanism of resources to meet the needs of dynamic management, merging, integrating and innovating teaching resources, and finally meeting the needs of users . In performance evaluation, data mining algorithms are also introduced, such as clustering algorithm, establishment of decision tree, etc. . These provide new evaluation strategies for traditional performance evaluation, but although the big data mining algorithm has developed earlier, its application in the field of teaching is still in the initial stage, and the evaluation objects are mostly limited to performance or teachers' teaching, which is relatively single.
Based on this background, this paper studies the organisation mode of intelligent teaching resources and the application performance evaluation model, which is divided into four chapters. The first chapter briefly introduces the development of intelligent teaching and the application background of data mining algorithm. The second chapter mainly introduces the teaching evaluation and big data mining algorithms at home and abroad, and summarises the main application angles and improved algorithms of algorithms at home and abroad. The third chapter constructs the intelligent teaching organisation mode, applies the performance evaluation model, uses the algorithm combining matching ant colony algorithm and association rules to realise data mining of intelligent teaching resources, uses ID3 algorithm to build a decision tree for student evaluation, judges the attributes of evaluation factors, and combines fuzzy mathematics algorithm with analytic hierarchy process to improve the accuracy of performance evaluation. In Chapter 4, the algorithm and model used in this paper are simulated and analysed, and experiments are carried out to determine the effectiveness of the algorithm by analysing the effectiveness and running time of the algorithm. The experimental results show that compared with the traditional algorithm, the maximum frequent set of the algorithm proposed in this paper is significantly lower, the number of mining is higher than the traditional algorithm, and the running time is shortened. It shows high utilisation value in student evaluation and teaching resource performance evaluation.
The innovation of this paper lies in the construction of organisation mode teaching evaluation method. On the one hand, the proposed algorithm can tap the shortcomings of intelligent teaching resources and reflect the subjectivity of students. On the other hand, in the performance evaluation, considering the shortcomings of analytic hierarchy process, combined with the method of fuzzy mathematics, the multi-source method is adopted, and the opinions of different evaluators are comprehensively considered to improve the promotion of performance evaluation on teaching work.
In the era of big data, smart teaching has begun to enter the actual teaching activities, and the data of teaching resources is also growing on a large scale. The storage and processing of these resources are very large and unchanged. Teaching evaluation has become more difficult because of the diversity of teaching subjects and the complexity of evaluation objects. Many scholars have made a lot of efforts. For example, Yz
To sum up, it can be seen that most of the current research on smart classroom is focussed on specific teaching, covering the importance of smart teaching, teaching strategies, establishment methods, etc., which can rarely be analysed from a technical perspective. In the resource construction, it is mostly described from the aspects of resource diversification and service personalisation, and there is little discussion on the evaluation method. On the other hand, although the algorithms related to data mining continue to emerge, such as classification algorithm, ant algorithm and so on, most of these algorithms belong to theoretical analysis. Their applications in teaching are mostly described from the perspective of database perfection, and they are rarely applied to teaching and teaching evaluation. Therefore, it is of great practical significance to carry out the application of performance evaluation in the organisation mode of intelligent teaching resources.
To effectively promote the development of intelligent teaching, the mining algorithm is applied to curriculum resource mining . The curriculum resources are divided into three aspects: teaching method, content and evaluation. The content input setting item is 3 and the minimum support is 0.1. The data mining algorithm is used to mine the important indicators of teaching resources.
In analysing the association between data, the data mining algorithm based on association rules is generally used, but this kind of algorithm needs to scan the database many times, and the weight may be unreasonable . Therefore, the concept of weighting is considered to be combined with ant colony algorithm. Integrating the matching ant colony algorithm and association rules, the fitness equation of ant colony algorithm is
In order to avoid infrequent itemsets, add constraints, and the equation can be expressed as
After using Eq. (3), the subsets formed by ant individuals belong to frequent itemsets. In mining association rules, ant individuals add attributes according to the rules. Randomly select the attributes that have not been added, select the attributes according to the state transition rules, and construct different association rules for the obtained attribute items . To ensure the efficiency of path selection and improve the mining effect, explore with prior knowledge and tendency. The rules are as follows:
In the performance evaluation model, students are the main body that cannot be ignored. In the evaluation model, ID3 algorithm is proposed to build a decision tree to judge the attributes of evaluation factors . In the evaluation of the organisation mode of intelligent teaching resources, the teaching situation is distinguished by classification. Each classification will produce some rules and find the factors affecting teaching .
The channel model is established. The symbol sent by the signal is represented by
Given the triple channel, the rate channel model is determined. The occurrence probability of the message constitutes the sample space, which is expressed as
There is interference signal in general channel, so it is necessary to measure this uncertain signal . The average uncertainty formula is calculated as
Now, calculate the expected value in the symbol set
Conditional entropy is expressed as
In the performance evaluation of teaching resource organisation mode, the analytic hierarchy process is used to realise and select the evaluation index and to construct the evaluation system . According to a certain logical relationship, the more complex evaluation system compares the indicators, constructs the consistency judgement matrix, obtains the feature vector, and obtains the weight of each index after normalisation . Practically, due to the influence of personal preference, the consistency test of judgement matrix cannot be carried out. Therefore, fuzzy mathematics is proposed to realise comprehensive evaluation.
The fuzzy mathematical evaluation model covers factor set, decision set and single factor decision-making. When the single factor decision-making is fuzzy mapping, there are:
Input weight classification and output comprehensive decision can be expressed as an equation, which is
The maximum and minimum method of fuzzy mathematics is used to calculate and normalise , and the equation is:
There are various factors affecting performance evaluation, so it is not possible to make an overall analysis by virtue of good or bad. Moreover, many indicators are highly subjective and difficult to be described directly by data . In analysing the application of the organisation mode of intelligent teaching resources, the investigation indicators are divided into four categories: resource scale, network performance, experience and rich content. The scale of resources reflects the richness of resources. At present, smart classroom teaching resources directly affect students, and efficiency is a factor that must be considered. In this evaluation, the selected resource scale indicators cover the total pages and links, and web analyst is used to capture data directly. The network performance reflects the technical complaints, and the evaluation indicators include integrity, link indicators and return. The experience reflects the subjective feeling of students' application, including download speed, friendly interface and connection speed. The rich content reflects the content of resources, including public content, shared content and downloadable content. The weight of each index is obtained according to the analytic hierarchy process. It is sorted by calculating the importance distance. In the construction process, consistency test is required.
Comparing the resource mining algorithm proposed in this paper with the classical Apriori algorithm, the minimum weighted support is (0.1, 0.2), the confidence is (0.2, 0.3), the running time of this algorithm is 0.625, the number of mining is 1243, the running time of classical algorithm is 17.53 and the number of mining is 1063. It shows that the running time of the algorithm is significantly shortened and the number of resource mining is also significantly improved. The stability analysis results of the algorithm are shown in Table 1. It can be seen from the data in the table that under the same minimum support conditions, compared with the basic Apriori algorithm, the maximum frequent set of the algorithm proposed in this paper is significantly lower. With the increase of minimum support, this advantage is still obvious, indicating that the stability of the algorithm in this paper is strong.
Comparison of algorithm stability
|Number of maximum frequent set||Apriori algorithm||6.65||4.45||3.82||3.26||2.36||1.47|
|Algorithm of this paper||4.03||3.17||2.00||1.42||1.24||0.75|
|Execution unit time (ms)||Apriori algorithm||5.22||4.92||3.54||2.51||1.72||1.64|
|Algorithm of this paper||3.00||2.04||1.85||1.41||0.85||0.72|
In the teaching evaluation, the teaching evaluation data of one academic year is selected as the data source, and the speciality with strong speciality is selected. The number of included samples is 50. The evaluation of teachers belongs to multi-level analysis. The evaluation indicators should select the data with important attributes. The evaluation attributes include gender, professional title, attitude, method, effect and content, with a total of six attributes. The data to be analysed is taken as the original data and sorted into the same database as the mining data source. There may be gaps in these data, so these inaccurate or correct data are proposed. In the evaluation, all numbers are required to be of continuous values, so it is necessary to classify them. According to the actual situation, the gender is divided into two grades, the professional title is divided into four grades, the attitude is divided into four grades, and the method, content and effect are divided into four grades. The calculated professional title information gain is 0.321, gender information gain is 0.103, attitude information gain is 0.576, content information gain is 0.532, method information gain is 0.514 and effect information gain is 0.428 The most far-reaching factor affecting teaching is teaching attitude, which has no obvious relationship with gender and professional title. Teaching attitude will directly affect the quality of teaching. Teachers' professional titles basically reflect their professional level. In this calculation data, it can be seen that the impact of professional titles on teaching is not very high. However, based on teaching attitude, teachers with high professional titles score better. It can be seen that through the data mining algorithm, we can deeply mine the results of students' teaching evaluation, find a variety of factors affecting the teaching quality and the correlation between them, which plays a certain role in improving the teaching quality.
Decompose the data according to the analytic hierarchy process and divide the data into different levels. Four primary indicators are used as the quasi side layers, which are resource scale, network performance, experience and rich content. There are 11 secondary indicators as the decision-making level, which are the total page of resources, link status, resource integrity, link indicators, web page return status, download speed, friendly interface, connection speed, public content, shared content and downloadable content. According to the hierarchical relationship, the upper level is the goal, and the relative importance of the lower level is the weight. Combined with the above algorithm, the indexes that are difficult to quantify are introduced into the matrix. Now select SPSS software to continue the analysis and standardise the data. All data range is from 1 to 100. Then convert the hundred point system and weigh it with the weight of secondary indicators to calculate the weight of primary indicators. Table 2 shows the score of primary indicators of performance evaluation.
Scores of primary indicators of performance evaluation
The membership matrix is established according to the weight, the closeness between excellent performance and ideal performance is obtained, and then the ranking result is obtained. It can be seen from the data in Table 2 that among the primary indicators, the resource scale has the highest score and the network performance has the lowest score. These data show that the current smart teaching website scale construction basically meets the needs of students. Although the network performance develops rapidly, it also has its own shortcomings. The content and resource experience are good, and the resource recommendation score is low, which needs to be improved. The weight of website content is not the largest, indicating that the content needs to be further enriched, which is also the material basis to meet the needs of intelligent teaching.
In the context of big data, smart teaching has become one of the inevitable development directions of teaching reforms. At present, many colleges and universities have reformed based on previous information-based teaching, and teaching resources are constantly enriched. However, due to algorithm reasons, most of the previous teaching performance evaluation started from a single point of view, and most of them were teachers' evaluation of students. Based on this, this paper studies the application performance evaluation model of intelligent teaching resource organisation model. Based on using big data mining algorithm to mine resource information, taking students as the main body and using decision tree model to establish student evaluation method, the classical analytic hierarchy process is selected for performance evaluation. Considering that some indicators are difficult to describe accurately, the comprehensive evaluation is carried out in combination with the concept of fuzzy mathematics. The experimental analysis proves the effectiveness and stability of the proposed algorithm, and it can shorten the running time. Through the weight calculation, the main factors affecting the performance can be obtained, and the deficiencies in teaching can be found. It should be pointed out that smart teaching is still in the development stage, and will continue to develop in the future. There will be a series of changes and new contents in resource construction. With the continuous progress and maturity of big technology, more technologies will be applied to the performance evaluation of intelligent teaching. Moreover, the data mined in this paper is not many, and the more in-depth data related to teaching needs further research.
Scores of primary indicators of performance evaluation
Comparison of algorithm stability
|Number of maximum frequent set||Apriori algorithm||6.65||4.45||3.82||3.26||2.36||1.47|
|Algorithm of this paper||4.03||3.17||2.00||1.42||1.24||0.75|
|Execution unit time (ms)||Apriori algorithm||5.22||4.92||3.54||2.51||1.72||1.64|
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prediction of e-commerce customer based on data mining Algebraic Equations in Educational Model of College Physical Education Course Education Professional English Translation Corpus Under the Binomial Theorem Coefficient Geometric Tolerance Control Method for Precision Machinery Based on Image Modeling and Novel Saturation Function Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation Semiparametric Spatial Econometric Analysis of Household Consumption Based on Ordinary Linear Regression Model Video adaptive watermark embedding and detection algorithm based on phase function equation English Learning Motivation of College Students Based on probability Distribution Scientific Model of Vocational Education Teaching Method in Differential Nonlinearity Research on mobile Awareness service and data privacy Protection based on Linear Equations computing protocol Vocal Music Teaching Model Based on Finite Element Differential Mathematical Equations Studying a matching method combining distance proximity and buffer constraints The trend and influence of media information Propagation based on nonlinear Differential equation Research on the construction of early warning model of customer churn on e-commerce platform Evaluation and prediction of regional human capital based on optimised BP neural network Study on inefficient land use determination method for cities and towns from a city examination perspective A study of local smoothness-informed convolutional neural network models for image inpainting Mathematical Calculus Modeling in Improving the Teaching Performance of Shot Put Application of Nonlinear Differential Equation in Electric Automation Control System Higher Mathematics Teaching Curriculum Model Based on Lagrangian Mathematical Model Computational Algorithm to Solve Two–Body Problem Using Power Series in Geocentric System Decisions of competing supply chain with altruistic retailer under risk aversion Optimization of Color Matching Technology in Cultural Industry by Fractional Differential Equations The Marketing of Cross-border E-commerce Enterprises in Foreign Trade Based on the Statistics of Mathematical Probability Theory Application of Linear Partial Differential Equation Theory in Guiding Football Scientific Training Nonlinear Channel Estimation for Internet of Vehicles Some Necessary Conditions for Feedback Functions of de Bruijn Sequences The Evolution Model of Regional Tourism Economic Development Difference Based on Spatial Variation Function System Model of Shipping Enterprise Safety Culture Based on Dynamic Calculation Matrix Model An empirical research on economic growth from industrial structure optimisation in the Three Gorges Reservoir area The Inner Relationship between Students' Psychological Factors and Physical Exercise Based on Structural Equation Model (SEM) Analysis and Research on Influencing Factors of Ideological and Political Education Teaching Effectiveness Based on Linear Equation Study of agricultural finance policy information extraction based on ELECTRA-BiLSTM-CRF Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Innovative research of vertical video creation under the background of mobile communication Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model Chinese-English Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model Genetic algorithm-based congestion control optimisation for mobile data network