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Research on an early warning model of effectiveness evaluation in ideological and political teaching based on big data

Publicado en línea: 24 Aug 2022
Volumen & Edición: AHEAD OF PRINT
Páginas: -
Recibido: 29 Mar 2022
Aceptado: 30 May 2022
Detalles de la revista
License
Formato
Revista
eISSN
2444-8656
Primera edición
01 Jan 2016
Calendario de la edición
2 veces al año
Idiomas
Inglés
Introduction

Ideological and political (IAP) education is an important method and means for the party and the country to carry out mainstream ideological propaganda, improve people's ideological, political and moral literacy and consciousness and promote people's free, comprehensive and sustainable development. How to improve its educational effect is an important topic that experts and scholars have been devoted to studying. They always combine IAP education with the latest scientific and technological achievements to obtain the latest research ideas. For example, since the birth of computer, there have been some new words [1,2,3] such as network IAP education, “Internet + IAP education”, massive open online course, flipping classroom, etc. Now, in the era of big data, the research upsurge of the new model of “big data + IAP education” will inevitably be formed. In terms of policy, it is also at the forefront to support the combination of IAP education and the latest technology.

As the core function of big data, prediction can analyse the ideological trends and predict the behaviours of the educated objects through quantitative data so as to formulate the corresponding innovation mechanism and education model and serve to better improve the effect of IAP education. The difference between it and the traditional thought prediction lies in the means of data information acquisition, the intelligence of analysis process and the reliability of analysis results [4, 5]. Therefore, the function and effect of the traditional IAP education prediction provides ideas for the application of big data prediction function in IAP education. In addition, IAP education in higher vocational colleges involves a wide range, diverse audience levels, and has not formed a unified standard model. It belongs to a complex system engineering, which must be analysed by systematic methods and learnt using collaborative theory. Building a collaborative platform, perfecting the collaborative mechanism, creating a collaborative culture and establishing a dynamic early warning model are the only way to realise the overall collaborative control of IAP education in higher vocational colleges [6]. IAP education is influenced by many complicated factors, and the lack of timely and accurate prevention of abnormal changes in any one of them may lead to serious deviations from or even contrary to the expected target, triggering various crisis events.

At present, the early warning of IAP education crisis has the characteristics of nonlinear development path, incomplete information collection, irregular data distribution and difficult quantification of index design [7,8,9] As an organisational means to monitor, diagnose and pre-control the changes of influencing factors in the process of IAP education, if the early warning method is not used properly, it will not only achieve the expected effect; on the contrary, it will waste a lot of manpower, material resources and financial resources due to false alarm and even lose the right to speak in IAP education. Therefore, how to take active and effective measures to educate and guide college students to consciously establish a national security concept, build a dynamic early warning mechanism of IAP education for college students and strengthen the IAP defence line in higher vocational colleges is a new challenge to carry out IAP education in higher vocational colleges.

Application basis of big data in IAP teaching evaluation
Social computing theory

“Social computing” has two meanings [10]: one is to serve social work as a tool of “social software”, which means to apply the latest information computing technology to social activities; the second one is to embed humanities and social sciences into computer or computing technology so as to realise in-depth research in social and humanities fields.

Although the concepts of social computing and big data are contemporary words, in recent years, with the rise of big data, both of them are based on computing technology and gradually blend with each other and complement each other. It can even be redefined as follows: scientific computing theories and technologies such as system theory, cloud computing, artificial intelligence and data analysis are used as methods and means in social science theory, which effectively solves the multifarious problems in the field of humanities and social sciences. Social computing in the era of big data is based on massive data, by continuously recording, mining and analysing personal thought and behaviour data information, it is possible for scientific, quantitative analysis and accurate research and calculation of humanities and social sciences, interpersonal interaction and social complex adaptive system.

The assumption and possibility of applying big data prediction function in the field of IAP education are based on social computing theory. The guidance of social computing ideas provides a new opportunity for big data technicians and researchers of IAP education to explore deeply, that is, using big data prediction technology and cloud computing technology to explore the IAP education in the field of humanities and social sciences.

Social computing theory

As shown in Figure 1, at present, there are two explanations of educational informatization in academic circles: “technology theory” and “process theory”.

Fig. 1

Connotation of social computing theory

Technology theory takes information as a scientific and technological means and applies modern information technologies such as computer, Internet, mobile terminal, big data and cloud computing to the process of education, thus promoting the comprehensive reform of education and enhancing its effectiveness. This theory holds that information technology assumes a significant part in the advancement of education, which is an indispensable tool and means to realise educational informatization; so, it implies the attributes of “technology” and “education” [11].

Process theory holds that educational informatization is a process of improving the effectiveness of teaching methods by modern information technology so as to cultivate the information literacy of educational objects and realise the modernisation of education. Specifically, it refers to taking the educational thought as the guidance in all categories; by using information technology, we can effectively develop and make full use of information resources at all levels and train high-tech talents for the society, thus accelerating the process of educational modernisation.

Methodology of IAP education

The method of IAP education is accompanied by educational practice, which is attached to the different stages of the process of IAP education and the constant renewal of its activity forms; so, there are various concrete forms, such as indoctrination education method, practice exercise method, example demonstration method, self-education method, psychological guidance method, etc. [12, 13]. The method of IAP education follows the principle of creativity, that is to say, educators should constantly study new situations, creatively use traditional educational methods or summarise and explore new educational methods. The most important innovative way is to innovate IAP education methods by absorbing and applying research results of related disciplines or to use modern scientific and technological achievements to realise the modernisation of educational means.

With the advent of the era of big data, big data thinking and technology not only participate in the whole process of IAP education research and innovate the practical activities of IAP education but also promote its traditional process, that is, the process of making, implementing and evaluating educational programmes can be realised by new ways of thinking. Meanwhile, under the background of big data prediction function, the IAP education modes such as intelligent calculation, precise customisation and immediate prevention have been born, which also provide new educational methods for educators.

Research on the early warning model of IAP teaching effectiveness
Education data mining

It is essentially a user learning behaviour collection system based on the computer system, which is used to collect user learning behaviour [14]. As shown in Figure 2, it has the functions of interactive, analogue and management platform.

Fig. 2

Steps of educational data mining

With the high-level development of quality-oriented education, educational data mining is gradually becoming a mainstream research trend. In recent years, the rapid development has attracted many educational scholars and research institutions to invest in this research. At present, education data mining mainly includes the following processes and steps:

Data preprocessing

Pretreatment technology is an important part of data mining. Especially for data with particularly complex data sources, it is necessary to improve the quality of data in data mining. There are often incomplete data information or even inconsistent data, and so, data preprocessing is needed [15]. For this study, because of the diversity of data sources, data preprocessing is essential in this study; the quality of data set through data preprocessing can be improved.

Data mining

The research topic is clarified. After the data preprocessing is completed, the mining algorithms are filtered according to the research objectives. The preprocessed data are trained by feature extraction, and the effective experimental results [16] are obtained. In this stage, the selection and optimisation of algorithms are extremely important, which will have a far-reaching impact on the quality of data mining. Data mining is also a process of discovering new knowledge, and its technology has powerful computing and processing capabilities, which can further improve the experimental efficiency.

Model evaluation and application

After obtaining the results of data mining, it is necessary to evaluate the results so as to eliminate possible redundant problems or irrelevant patterns and finally determine whether the test results are accurate. If the user's demand cannot be met, it is necessary to return to the previous step, such as re-selecting feature parameters or data preprocessing [17]. To put it simply, the whole mining process needs to be constantly adjusted according to the feedback of the results, and finally, the model that meets the research objectives is obtained and used in new data mining, and the mining results will be used to guide the actual work or serve as the reference basis for decision-making.

Logic design based on the SVM algorithm

The dynamic early warning system of IAP education based on the improved SVM algorithm mainly realises the data collection and potential law mining of the courses, effect evaluation, ideological dynamic control, early warning feedback and other links of IAP education for students in higher vocational colleges with obvious potential ideological dynamic differences [18] so as to realise the complete life cycle information management and control of IAP education in higher vocational colleges. This is shown in Figure 3.

Fig. 3

Logic design of early warning model

As shown in Figure 4, corresponding to the dynamic early warning model of IAP education based on the improved SVM algorithm, the internal workflow flow order is as follows:

Step 1: Enter the important speech made by General Secretary Xi Jinping under the new situation and relevant national policies and regulations as the general basis for higher vocational colleges to formulate individualised IAP education programmes;

Step 2: Carry out standardised inspection and small-scale experiments on the individualised IAP education plan formulated by higher vocational colleges;

Step 3: Decide whether to popularise according to the experimental results. If the experimental results support popularisation, start the IAP education effect analysis submodule and evaluate the personalised IAP education scheme formulated by higher vocational colleges from multi-dimensional effect evaluation; otherwise, return to the first step;

Step 4: Start the dynamic early warning submodule of IAP education, find out the students with dangerous ideological trends in advance and give early warning tips and take timely and active intervention measures to ensure that the IAP education in higher vocational colleges plays an effective role.

Fig. 4

Internal working plan of early warning model

Decision analysis based on the BP neural network

To make comprehensive fuzzy evaluation on the quantitative data of users' invisible early warning, it is integrated into the decision analysis system mechanism, as shown in Figure 5.

Fig. 5

Decision analysis mechanism

Fuzzy neural network is introduced for decision evaluation and adaptive training of fuzzy rules. Structurally, the user's invisible interest input layer, membership function fuzzification layer [19], fuzzy rule adaptive training layer, output variable fuzziness division layer and user's invisible interest decision analysis layer are formed. Functionally, the BP neural network is used as the pattern memory. The self-learning ability is used to continuously update the optimisation weight coefficient, which makes the fuzzy system have generalisation ability, realises the comprehensive evaluation of personalised service and feeds back the results to the improved support vector machine algorithm, thus realising a benign closed loop.

Algorithm flow

Support vector machine (SVM) algorithm essentially belongs to an efficient, limited and generalised classifier with supervised and extensible classifiers, which can overcome linear and nonlinear obstacles. In order to realise nonlinear multi-core data mining and clustering effect, improve the memory consumption ratio, balance generalisation ability and learning ability, improve the interpretability of data sets and strengthen the adaptability of kernel functions and so on, introducing cold and hot data separation factor and random gradient descent factor to improve the SVM algorithm [20]. The specific steps are as follows.

It can be seen from Figure 3 that the dynamic early warning model of IAP education oriented to multidimensional application is transformed into an unlimited experience loss minimisation problem with penalty factors, and the objective function is defined as Eq. (1). f(ω)=minωλ2||ω||2+1ml(ω,(x,y)) f(\omega ) = \mathop {\min }\limits_\omega {\lambda \over 2}{\left| {\left| \omega \right|} \right|^2} + {1 \over m}\sum l(\omega ,(x,y)) (ω, (x, y)) in Eq. (1) can be calculated using Eq. (2). l(ω,(x,y))=m{0,1y<ω,xω} l(\omega ,(x,y)) = {\bf m}\left\{ {0,1 - y < \omega ,x \gg \omega } \right\}

Using random gradient descent to solve the objective function, in each iteration period, randomly select training samples and map the corresponding objective function gradient and select the gradient step length on the opposite side of the iteration direction to ensure that the running time of the algorithm is satisfied O(nλg) O\left( {{n \over {{\lambda _g}}}} \right) , among them, n is the sum of dimensions in the constraint space [21] of ω and x. To solve the duality problem of nonlinear correspondence, the following mapping transformation is carried out as shown in Eq. (3). αiyixi(0isaninteger) \sum {\alpha _i}{y_i}{x_i} \to \left( { \ne 0\;{\rm{is}}\;{\rm{an}}\;{\rm{integer}}} \right)

After the mapping of Eq. (3), the problem of minimising unlimited experience loss with penalty factors under the multidimensional constraint given in Eq. (1) is transformed into the problem of solving extreme value under the single constraint. Further, smooth loss functions are used to replace hinge loss to further transform the problem into a smooth and unconstrained optimisation problem under the hyperplane. The specific solution process is as follows:

A training sample it is randomly selected in the hyperplane constraint space, among them, i characterises intrinsic property of the sample, t characterises the external activity (iteration times) of the sample, and these are integrated into Eq. (1), such as Eq. (4). f(ω,it)=λ2||ω||2+l(ω,(xii,yit)) f(\omega ,{i_t}) = {\lambda \over 2}{\left| {\left| \omega \right|} \right|^2} + l\left( {\omega ,\left( {{x_{{i_i}}},{y_{{i_t}}}} \right)} \right)

Sub-gradients are used to solve Eq. (4), such as Eq. (5). t=λωtI[yit{ωt,xii}<1]yitxii {\nabla _t} = \lambda {\omega _t} - I\left[ {{y_{{i_t}}}\left\{ {{\omega _t},{x_{{i_i}}}} \right\} < 1} \right]{y_{{i_t}}}{x_{{i_i}}}

In Eq. (5), I [yit {ωt, xii} < 1] is the indicator function, with a range of two values; if it is true, it is 1; otherwise, it is 0. Based on Eq. (5), input the data set of user interest points S, regularization factor λ, and sample external activity (number of iterations) T, the iteration of one cycle can be expressed as Eq. (6). ωt+1ωtβtt {\omega _{t + 1}} \le {\omega _t} - {\beta _t}{\nabla _t}

In Eq. (6), βt=1λt {\beta _t} = {1 \over {{\lambda _t}}} is an adaptive step factor, which is negatively correlated with the number of iterations, and Eq. (7) can be obtained by bringing Eq. (5) into Eq. (6). ωt+1ωtβλωtI[yit{ωt,xii}<1]yitxii {\omega _{t + 1}} \le {\omega _t} - {\beta _\lambda }{\omega _t} - I\left[ {{y_{{i_t}}}\left\{ {{\omega _t},{x_{{i_i}}}} \right\} < 1} \right]{y_{{i_t}}}{x_{{i_i}}}

Further, simplify the Eq. (7) and backward deduce the available Eq. (8). ωt+1(11t)ωt+βtI[yit{ωt,xii}<1]yitxii {\omega _{t + 1}} \le \left( {1 - {1 \over t}} \right){\omega _t} + {\beta _t}I\left[ {{y_{{i_t}}}\left\{ {{\omega _t},{x_{{i_i}}}} \right\} < 1} \right]{y_{{i_t}}}{x_{{i_i}}}

Based on Eq. (8), if the indicator function is true, it is converted to Eq. (9). ωt+1(11t)ωt+βtyitxii {\omega _{t + 1}} \le \left( {1 - {1 \over t}} \right){\omega _t} + {\beta _t}{y_{{i_t}}}{x_{{i_i}}}

Based on Eq. (8), if the indicator function is false, it is converted to Eq. (10). ωt+1(11t)ωt {\omega _{t + 1}} \le \left( {1 - {1 \over t}} \right){\omega _t}

It can be seen from Eq. (8) that the cold and hot data can be separated by setting the comprehensive bias term. Furthermore, by introducing the online learning mechanism, the predictor with low generalisation error can be obtained, which balances the generalisation ability and learning ability.

Early warning evaluation of IAP teaching effectiveness
Construction of the index system

The construction of the early warning index system is the basis of whether its evaluation method is effective or not. The basic way is to decompose the target layer by layer according to certain standards to form an index system. Through interviews and investigations with educational scholars and experts from industrial enterprises in higher vocational colleges, the early warning evaluation index system of IAP teaching effectiveness as shown in Table 1 is adopted.

Early warning evaluation index system

Primary index Secondary index Quantitative index (%)

Teaching staff of IAP education Doctoral proportion of teachers 0–100
Teaching level 0–100
Professional quality 0–100 (poor–good)
Proportion of senior titles 0–100
Ideological expression National pride 0–100 (poor–good)
Social responsibility 0–100 (very weak–strong)
Career ideal 0–100 (poor–good)
Socialist identity 0–100 (very low–high)
Behaviour expression Attendance rate of IAP class 0–100
Social practice ratio 0–100
Failure rate of IAP course 0–100
employment rate 0–100
Value of early warning evaluation

The early warning evaluation value of higher vocational education quality is directly related to the determination of early warning limits. In view of its fuzziness and subjectivity, this paper uses the fuzzy comprehensive evaluation method [22], which combines fuzzy mathematics with analytic hierarchy process to calculate.

Determine the comment set

Assign each index 1, 2, 3, 4 and 5 points, respectively. Namely: U=(u1,u2,u3,u4,u5)={1,2,3,4,5} U = \left( {{u_1},{u_2},{u_3},{u_4},{u_5}} \right) = \left\{ {1,2,3,4,5} \right\}

Establish a membership matrix R

There is a certain degree of membership between the elements in the fuzzy set and the set, that is, the degree of membership, whose value is [0,1]. In this paper, the membership degree is determined by the data of experts' scoring. As shown in Eq. (11), R={r11,r12,,r1nr21,r22,,r2nrn1,rn2,,rnn} R = \left\{ {\matrix{ {{r_{11}},} & {{r_{12}},} & { \cdots ,} & {{r_{1n}}} \cr {{r_{21}},} & {{r_{22}},} & { \cdots ,} & {{r_{2n}}} \cr \cdots & \cdots & \cdots & \cdots \cr {{r_{n1}},} & {{r_{n2}},} & { \cdots ,} & {{r_{nn}}} \cr {} & {} & {} & {} \cr } } \right\} where rij refers to the degree to which the i-th indicator of the early warning indicator system belongs to the j-th comment and j = 3.

Calculate the comprehensive evaluation value of each index early warning

Use f to represent the collection of early warning evaluation values of each index, and the value of f can be obtained by the following Eq. (12): f=R×U={r11,r12,,r1nr21,r22,,r2nrn1,rn2,,rnn}×{u1u2un}={f1f2fn} f = R \times U = \left\{ {\matrix{ {{r_{11}},} & {{r_{12}},} & { \cdots ,} & {{r_{1n}}} \cr {{r_{21}},} & {{r_{22}},} & { \cdots ,} & {{r_{2n}}} \cr \cdots & \cdots & \cdots & \cdots \cr {{r_{n1}},} & {{r_{n2}},} & { \cdots ,} & {{r_{nn}}} \cr } } \right\} \times \left\{ {\matrix{ {{u_1}} \cr {{u_2}} \cr \cdots \cr {{u_n}} \cr } } \right\} = \left\{ {\matrix{ {{f_1}} \cr {{f_2}} \cr \cdots \cr {{f_n}} \cr } } \right\}

The calculation of comprehensive early warning value is to combine the early warning value of individual indicators with the weight of indicators for comprehensive consideration. Therefore, the comprehensive early warning evaluation value of warning indicators can be determined by multiplying the early warning evaluation value f = (f1, f2, . . . , fn) of each indicator and the weight of each indicator w = (w1,w2, . . . , wn). F=w×f={w1,w2,,wn}×{f1f2fn} F = w \times f = \left\{ {{w_1},{w_2}, \ldots ,{w_n}} \right\} \times \left\{ {\matrix{ {{f_1}} \cr {{f_2}} \cr \cdots \cr {{f_n}} \cr } } \right\}

Value of warning limit

In early warning, the early warning limit is determined by the range of early warning value, and different early warning signals need to be determined according to the early warning limit so as to send a warning to the manager with an intuitive image. So, the rationality of early warning limit determination is an important factor to determine the accuracy of early warning. This paper uses the theory of statistics 3σ to determine the early warning limit [23] σi=j=1N(x¯ixij')2m1 {\sigma _i} = \sqrt {{{\sum\nolimits_{j = 1}^N {{\left( {{{\overline x }_i} - x_{ij}^\prime} \right)}^2}} \over {m - 1}}} where σi is the standard deviation of the i-th index. Then, the comprehensive standard deviation σ of the warning sign early warning evaluation value is calculated using Eq. (15). σ=i=1m(x¯xi')211 \sigma = \sqrt {{{\sum\nolimits_{i = 1}^m {{\left( {\overline x - x_i^\prime} \right)}^2}} \over {11}}}

According to the principle of normal distribution in statistics, when the early warning evaluation value of each index is within the interval of (−σ,+σ], it shows that most of the early warnings are in a loose state, which needs to be analysed according to the specific situation and belongs to the benign warning situation. If the early warning evaluation value is within the interval of (−2σ, −σ], it shows that a small number of warning signs are in a state of tension, which belongs to mild warning situation. If the early warning evaluation value is within the interval of (−3σ, −2σ], it means that more than half of the warning signs are in a state of tension, which is a moderate warning situation. If the early warning evaluation value is within the interval of (−∞,−3σ, it means that most or even all warning signs are in a state of tension, which is a serious warning situation.

After dividing the warning interval, it is necessary to determine the warning level and output the corresponding warning status by signal light method. In the design of warning signal system, ★ indicates the first-level warning, ★★ indicates the second-level warning, ★★★ indicates the third-level warning and ★★★★ indicates the crisis warning. The more serious the warning situation, the fewer stars, as shown in Table 2.

Classification of alert status

★★★★ ★★★ ★★

No police area Light police area Middle police area Heavy police area
Good Common Danger Crisis
( − σ, +σ] ( − 2σ, − σ] ( − 3σ, − 2σ] (−∞, − 3σ
Conclusion

In order to better meet the needs of IAP education in higher vocational colleges under the new situation and overcome the problems existing in IAP education in higher vocational colleges, such as lagging information feedback, imperfect early warning intervention mechanism, weak risk management and control ability and so on, an early warning model of IAP teaching effectiveness based on the SVM algorithm logic design and BP neural network decision analysis is proposed. In this paper, the fuzzy comprehensive evaluation method is used to determine the early warning evaluation value of warning indicators. Based on the statistical 3σ theory, the early warning boundary is calculated, the early warning interval is divided and the early warning signal system is designed and the early warning model of education quality is constructed. It effectively enhances the ability to monitor and predict the crisis of IAP education in colleges and universities so as to improve the comprehensive evaluation and dynamic analysis theory of IAP education quality.

Fig. 1

Connotation of social computing theory
Connotation of social computing theory

Fig. 2

Steps of educational data mining
Steps of educational data mining

Fig. 3

Logic design of early warning model
Logic design of early warning model

Fig. 4

Internal working plan of early warning model
Internal working plan of early warning model

Fig. 5

Decision analysis mechanism
Decision analysis mechanism

Classification of alert status

★★★★ ★★★ ★★

No police area Light police area Middle police area Heavy police area
Good Common Danger Crisis
( − σ, +σ] ( − 2σ, − σ] ( − 3σ, − 2σ] (−∞, − 3σ

Early warning evaluation index system

Primary index Secondary index Quantitative index (%)

Teaching staff of IAP education Doctoral proportion of teachers 0–100
Teaching level 0–100
Professional quality 0–100 (poor–good)
Proportion of senior titles 0–100
Ideological expression National pride 0–100 (poor–good)
Social responsibility 0–100 (very weak–strong)
Career ideal 0–100 (poor–good)
Socialist identity 0–100 (very low–high)
Behaviour expression Attendance rate of IAP class 0–100
Social practice ratio 0–100
Failure rate of IAP course 0–100
employment rate 0–100

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