Development and Training Strategies of College Teachers Based on Data Mining Technology

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Introduction
When facing the problems existing in the teaching ability of newly recruited teachers and needing to adopt methods and means, after comparing and analyzing the advantages and disadvantages of a single training mode, a series of intervention measures have been taken, such as integrating the methods of teacher training.Te birth of data mining technology is signifcance, and its scope is inseparable in the high-tech feld.Te high-tech feld refers to the process of human secret discovery, which is exactly consistent.Terefore, it is an important subject to study and explore the establishment of university management information system suitable for China's national conditions.Tis is a way to achieve the school's requirements for educating people and cultivating talents.Terefore, we should understand the structure of the teaching staf, the quality of teachers, and the working conditions, identify problems and defciencies, adjust the teaching staf in a targeted manner, and analyze the academic performance of students and the quality of the test papers, so as to obtain a variety of valuable information in order to better formulate teaching objectives and training methods suitable for students, and promote the comprehensive and healthy development of students.Education is the cornerstone of social progress and national rejuvenation, and higher education is the booster of social development.College teachers are the implementers of higher education, playing multiple roles such as teachers, scholars, and service providers, which fundamentally determine the level of education, teaching, and scientifc research in colleges and universities [1].
Te research on the cultivation and improvement of teachers' teaching ability is mostly based on experience, showing the characteristics of individuality and looseness.Te proposed solutions can no longer fully cover the current development of education.Since the establishment of the teacher development center, various aspects conditions have improved and levels have been raised.At present, the most urgent thing is that it can better serve the data resources of colleges.How to use these data has become the top priority, fnd its potential law, let the senior managers of colleges and universities provide a feasible way to integrate the data, and use data mining technology to fnd out the potential law and association, for the future of colleges and universities in various felds to make correct decision-making judgment.Data mining technology can also be applied to the teaching quality management in colleges and universities.Using data mining technology to fnd useful information in massive data has far-reaching signifcance for improving the teaching quality and management level of schools.
Buczak A believes that focused literature surveys on machine learning ML [2].Lu et al. believes that of discovering symbol classifcation rules using neural networks, his research lacks data [3].Zhu et al. can discover their interrelationships and coupling efects.Although he proposed numerical simulation data mining, his research lacks data [4].Figueiredo et al. studied the use of data mining tools, with the purpose of examining the impact of the methodology used in the chemistry laboratory course on students attributable to laboratory work on learning and their motivation.Response frequency analysis cannot distinguish the interviewee's views based on the types of teaching methods used in the experimental class.Although he used the kmeans clustering method in the process of data mining, his research is not novel enough [5].In order to help new teachers solve their confusion and build up their confdence in teaching, a platform for solving confusion or providing consultation is needed, that is, the teacher development center comes into play.
With the advancement of technology, data mining is continuously applied in diferent felds.Among them, education data mining is an interdisciplinary feld that results from the application of data mining in the feld of education.Tis paper chooses an unsupervised classifcation method: cluster analysis.In addition to obtaining reasonable classifcation results, this method can also give reasonable development suggestions for each employee through the classifcation results.Te recruitment management module is often to collect applicant information, register in the database, and then perform a series of personnel screening according to the employment criteria, and fnally determine the possible candidate teachers, and then conduct a series of comprehensive assessments such as written examinations and interviews, and fnally according to the applicants comprehensive results of the students will be selected for admission.Te algorithm has high computational efciency and is suitable for large-capacity data operationsTthrough the mutual cooperation of national and regional colleges and universities, by formulating high-quality college teacher development and strategic plans, focusing on personal development, so as to promote the efectiveness of college teacher development and training.

Recruitment and Introduction of University Teachers
2.1.Recruitment of College Teachers.Te recruitment of teachers in China's universities is the needs of China's universities for their development.Based on the status of universities and academic research capabilities, to a large extent, they are directly determined by the quality of the university's faculty.Terefore, all universities attach great importance to the recruitment of teachers.At the same time, the national higher education management department also realizes that talents play a nonnegligible role in the development of education, and has formulated and implemented a series of policies to support and guide the recruitment of teachers in major universities.Tis includes the introduction of a system for rewarding academic leaders at the end of the last century; in recent years, a series of preferential policies for Chinese students returning to work in the country have been formulated; in the recent period, increased fnancial investment in higher education, improved college teachers treatment, and the formulation of policies for vigorously developing higher education.[6,7].Te defnition of abnormal data is very important.Te result of fnding abnormal data depends on the defnition of abnormal data [8].Te relative information entropy of object x relative to A is defned as follows: Among them, H(A) is the information entropy of A [9].Let IS � (U, A, V, f ) be the information system, and the abnormality of object x is defned as follows [10,11]: 2.2.Personnel Management in Universities.Te functional requirements of the university personnel management system must be determined closely in conjunction with the actual needs [12].Te staf involved in the personnel management system of colleges mainly include staf personnel department, teaching afairs, fnance department, scientifc research department, and other departments, and also include all teachers and leaders of the school.Te personnel department is the management department of 2 Mobile Information Systems human resources and corporate public afairs of an enterprise or group [13].Tey mainly use this system to complete some daily management or statistical inquiries and other tasks.To this end, the system must meet the following requirements: (1) Dynamic personnel management.In order to improve the efciency and quality of personnel management, teachers can devote themselves to the education of the school.Te information system required to manage it must be able to refect the relevant data in the system in real-time and accurately according to the actual work situation of the teacher [14,15].(2) Complete statistical analysis function.In order to provide valuable data and parameter basis for school leaders or relevant decision-making departments.It is required that the personnel management system of colleges and universities realizes the dynamic management of personnel work, and can also perform a certain intelligent analysis of the stored personnel-related data according to the needs of the school and provide scientifc, reasonable, accurate, and comprehensive statistical analysis reports in order to better serve the school leaders and the management of the personnel department, to make scientifc decisions.According to the abovementioned system architecture design and analysis, the system each corresponding to a layered model, combined with Visual Stu-dio2010 development tools, the structure of the personnel management system is shown in Te fuzzy relationship matrix from evaluation factor F to evaluation grade C is the factor evaluation matrix [16]: (3) Among them, V ∈ (0, 1).In the teacher performance appraisal management module, the degree of membership refers to the weight of each evaluation index of teacher performance [17].Evaluation weight is defned as follows: Evaluation index collection [18]: Evaluation level collection [19]: Te evaluation index weight set B and the relation set R are combined into operation [20].

Data Mining.
Similar synonyms with data mining include data fusion, data analysis, and decision support.[22].
Te scope of clustering data mining RAND is defned as follows: If A is used as the test attribute, the information entropy of the divided subsets is shown in the following formula: Information entropy is a rather abstract concept in mathematics.Here, information entropy may be understood as the probability of occurrence of certain information.Te information entropy of a given sample classifcation is defned as follows: Among them, X 1 , X 2 , . . ., X m is a data collection.
Among them, if the new value interval is [0, 1], the formula can be simplifed to

Experiments on the Recruitment and Training of College Teachers
3.1.Overall Design of the System.Te overall function is subdivided into the following functional modules: college management module, personnel management module, recruitment management module, performance appraisal module, and promotion management module.

Design of the College Management Module.
Corresponding college management module is relatively simple, maintain manage information unit and department, Mobile Information Systems including a series of information such as department name, department address, department telephone, and employees.After sorting and summarizing the common operations of this module, it mainly includes several types: add department information, delete department information, and modify department information.All operations of the above college management module are assigned diferent operation permissions based on diferent user permissions.All users can query department information, but only the college administrator can add, delete, and modify department information.

Design of the Personnel Management Module.
Te personnel management module is mainly used to maintain and manage the teacher information of the unit.Tis kind of information mainly includes the teacher's family information, identity information, academic information, title information, and postinformation.In view of the abovementioned series of imported teacher information, the functions that the personnel management module should provide mainly include the following categories: teacher information management, contract information management, resignation information management, and querying teacher personnel information.Te operation of this module should also be given diferent user rights according to diferent user roles.For general users, only part of the personal information can be viewed and modifed.If you want to modify some of the more important personal information, you must modify it through the personnel management specialist with greater authority; for the personnel management commissioner, the authority is relatively large, and the teacher will be introduced.All functional querying information should have the authority to execute.Te function of the personnel management module is trivial, and there are more teachers' personal information that needs to be maintained, but it is often repetitive and organized and revised work.

Recruitment Management Module Design.
Realizes the management and maintenance of school recruitment and employment information.In the normal recruitment process of a unit, generally, the employing department frst proposes recruitment requirements based on the actual work tasks of the department, and then after confrmation by the supervisor, it is sent to the human resources recruitment department to form the actual recruitment plan.In the initial stage, it is often to collect applicant information, register in the database, and then conduct a series of personnel screening according to the employment criteria, and fnally determine the possible candidate teachers, and then conduct a series of comprehensive assessments such as written examinations and interviews, and fnally according to the applicants Te overall results of the candidates will be selected for admission.In response to the above work requirements, the functions that the recruitment management module should provide mainly include the following types: recruitment plan management, teacher application management, teacher performance management, teacher recruitment management and other modules.Te teachers who are fnally recruited into the unit will be recruited through personnel In the management module, the personnel management commissioner enters other personal information related to it.And persuasive.In the K-means clustering algorithm, K-means to cluster the data into K clusters, and means that the mean of the data in each cluster is used as the center of the cluster, also known as the centroid.In the yearend assessment, the index score of each introduced teacher of the unit is scored by the assessment team composed of teacher leaders and selected teachers.For the calculation process of the performance appraisal module, the K-means in the clustering analysis is adopted.Te algorithm has high computational efciency and is suitable for large-capacity data operations.Trough the clustering algorithm, a reasonable assessment method is formulated, and it is efectively used in this article.Te training of new teachers' 3.1.6.Attendance Management Module.Tis module can meet the diverse attendance management systems of colleges and universities, mainly to manage the daily attendance of faculty and staf, including information such as lateness, early leaving, absenteeism, leave, and actual attendance days.You can also set the parameters according to the attendance, and analyze and count the attendance results of all faculty and staf.

System Login Module Design.
Te system login module is often the direct entrance of the entire system, and the corresponding execution function is relatively simple.On the one hand, it is necessary to verify the user's identity information, and on the other hand, according to the execution authority granted to the user by the administrator.Te login interface is shown in Figure 2. Use data mining technology to build a predictive model, and conduct indepth mining and learning of the data provided by the university to predict student performance and teaching quality.

Database Design.
Database management systems can be simply divided into three categories.Relational database systems have good data organization capabilities and fast data query capabilities.Terefore, modern large-capacity data storage systems often choose relational databases.Te more representative ones are Oracle, MySql, SqlServer, and other high-performance database management systems.MySQL is a relational database management system and one of the most popular relational database management systems.
Te frst stage of database design is often to use entityrelationship diagram (E-R diagram for short) to abstract the relational patterns, and then design the table structure in the database.Te entity-connection graph is represented by three basic elements: entity, attribute and connection.In addition, the connection can also be accompanied by its own attribute information.After analysis, the following entity information exists in the human resources assessment system: department information, employee information, recruitment plan, applicants, and promotion information.Te connection between these entities is more complicated, for example, the relationship between employees and departments is 1:1.Contact information, 1:n contact information between recruitment plan and applicants, etc.

Including employee information table, contract information table, department information table, resignation information table, assessment information table, promotion information table, applicant information table, and recruitment plan information table.
Te employee information table is composed of employee ID, name, gender, ethnicity, native place, education, job title, ID number, home address, department ID, job name, recruitment source, login password, entry time, and remarks.It is used for employee information management Data storage basis, the specifc structure is shown in Table 1.

Teacher Assessment Index.
Tis paper chooses an unsupervised classifcation method: a cluster analysis.Tis method does not require human supervision and classifcation, but is automatically classifed by algorithms.It only needs to set the number of classifcations.Te results obtained have no human participation and have high credibility.And the principle of cluster analysis is the nearest neighbor principle, which can classify objects with relatively close development of various factors into one category.In addition to obtaining reasonable classifcation results, it can also take into account the comprehensiveness of employee development and give it through classifcation results.Reasonable development suggestions for each employee.Teachers are the main body of colleges and universities, the Mobile Information Systems driving force of college development, and the leader of social progress.Te world's higher education is undergoing profound changes, and the development of university teachers is highly valued by governments, societies, scholars, and university administrators.

Results and Discussion
Operations that this module needs to perform are mainly, querying and deleting database department information.Terefore, in the development process of this module, the C++ language is mainly used to call a series of SQL statements that perform the reservation function to achieve maintenance function of department information.For the above four diferent operations, the corresponding calling process is similar, and the only diference is the SQL statement with diferent functions.Te fnal implementation efect is shown in Figure 2. All the functions of the college management module are only valid for the administrator's authority.For other user's authority, the module will be automatically hidden and cannot be executed.Te college management module displays all existing department information in the system by default.Te user enters the corresponding parameter information in the query box at the top, then you can query the specifed department information, and place "add department information," "delete department information," and "modify" below.Tere are three function buttons for "department rest."Click "add department information" to add a new department.If the user selects a piece of department information, he can "delete department information" and "modify department information."Before "delete department information," the system will throw out the corresponding prompt information to remind the user whether to delete the department information.Te college management module is shown in Figure 3. Full score for each item is 10 points, and the minimum is 0 points.Part of the scoring results is shown in Table 2.
On the surface, it seems that R9 is better, but the results of the analysis data are shown in Table 3. Te total score of R9 is higher than R3, but its scores vary greatly.Among them, the score of innovation is 9 and the score of communication and coordination is only 3. Te score of R3 is lower than that of R9, but its scores are more diferent.Te overall score is relatively average, which is the main difference between the traditional classifcation method and the unsupervised clustering method proposed in this article.Te score classifcation method only looks at the overall score and ignores the balance of the overall development of the individual.
Competent object data results are shown in Table 4. Competent objects are: clustering method competent persons are R9, R11, and R12; scoring method competent persons are R3, R5, R11, and R12.In the frst type of analysis, it is known that R3 is classifed as an excellent grade, and comparing R9 and R5, it can be seen that the overall score of R9 has a clear advantage over R5, and R9 is better than R5 in the classifcation of the scoring method itself.For the results obtained by the clustering method, it is derived from the abnormal data of R5.R5's morality, words, and deeds score only 2 points, which has a greater impact on the overall index, while the overall score of R9 is much better than that of R5.
Te clustering method is R5, R6, and R7, the scoring method is R6 and R7, and the main diference is the object R5.For R6 and R7, obviously, the overall score is low, and there are more diferent data.Part of the division results is shown in Table 5.In R6's score, the two scores of morality and deeds and innovative spirit are 1 and 3, both of which are low scores.Tey are diferential data in the overall data, and the scores of other items except these two are overall in the middle; R7's diferential data.For cost awareness and innovation, the scores are 2 and 3 points, respectively.Te higher score for the sense of responsibility is 9 points, and the scores for the remaining items are between 4-6 points, which belong to the middle deviation score situation, so these two objects were clearly classifed as incompetent.
Te data tested by the data mining tool contains 1,400 rows of data.Te minimum support is 5% and the minimum confdence is 90%.Te fnal generated results are shown in Table 6.Support is used to measure the frequency of clustering in the entire data set, and confdence is used to measure the credibility of clustering.Te clustering in the table can be visually analyzed in this way.Among the 50-60year-old, the number of professors with a doctorate degree is the highest.Although there are some professors with a master's degree, it has not yet reached the minimum support of 5%.For doctoral students under the age of 35, although some teachers are already associate professors, the proportion is relatively small and does not reach the minimum support of 5%.
Te results of the credibility analysis are shown in Figure 4. From the data mining results obtained, the number of teachers in the teaching and research positions of the school is the largest among doctors between the ages of 45-49 and 50-60.Te number of PhDs is the largest number of year-olds; the number of PhDs with lecturers under 35 and the number of masters aged 35-39 is the largest.It can be seen that most of the professors are doctoral students between the ages of 45-49 while most of the young teachers   Mobile Information Systems have doctoral degrees and there are no doctoral degree teachers.Tis analysis result is consistent with the school's title review and talent introduction in recent years, so the data excavated is reasonable and efective.Security is very important for a qualifed application.Adopting appropriate methods and means is conducive to improving the security of data and efectively preventing illegal login to the system.Of course, there are many aspects of security measures to be considered.From the user's perspective, some simple security performance tests are carried out on the system.Te test conditions are shown in Table 7.
Te friendly and intuitive program interface can bring users a good experience efect, and can efectively reduce the difculty of user learning and operation.Te following shows some test situations of the system in the interface test are shown in Figure 5.
In the correlation analysis of teacher information, data mining can reveal the hidden internal connection between the teacher's personal situation and the student's academic performance, and then, through targeted measures teacher's quality, thus promoting the development of school teaching and scientifc research.Te confdence level is 66% and the support level is 46%.It shows that the higher the professional title and educational background, the higher the average grade of the students taught.Terefore, it is necessary to actively improve the professional title and educational background of the teacher, encourage young teachers actively participate in learning, and improve their own educational background.Te age of teachers and working years in colleges and universities can afect students' academic performance.Te older the teacher and the longer the working years in colleges and universities, the higher the student's performance.Terefore, we must pay attention to the role of old teachers in teaching, so that young teachers can learn from old teachers in teaching methods, to improve the level of lectures.Teachers' working years and students' academic performance are shown in Figure 6.
Te professional title of the introduced teachers is orthometric height level, the highest degree is a doctorate, and the number of SCI-published teachers accounted for 16% of the total number.From these data, we can fnd the intrinsic relationship between some attributes, such as: the higher the professional title, the higher the level of the paper; the level of a teacher's published papers has a lot to do with their academic qualifcations and professional titles, and the professional title is also afected by academic qualifcations and papers' level of infuence.Trough these associations, it can help school leaders to make decisions to improve the overall level of scientifc research, such as introducing highly educated talents, or sending existing faculty members of the school for further training, and can also encourage teachers to take the initiative to study and publish high-quality papers.Te papers published by the introduced teachers are shown in Figure 7. Mobile Information Systems

Conclusion
With the informatization of higher education, the continuous development of various management information systems, and the continuous progress of improving the accumulation and construction of teaching materials, data mining technology will be more in-depth and extensive in colleges and universities.Data mining technology can also provide some data in teacher training, especially in the quality evaluation system of teachers, to support university managers to help managers make efective and reasonable decisions.Based on data mining technology, designed and implemented a teaching quality and evaluation system of a university, obtained a large number of evaluation data ideas, based on the use of this system and its related data bits, established a data warehouse by applying association rules mining the design of technology and the data mining module of the teaching quality monitoring and evaluation system, using the module's analysis data warehouse, some association rules.Tis paper chooses an unsupervised classifcation method: cluster analysis.In addition to obtaining reasonable classifcation results, this method can also take into account the comprehensiveness of employee development.Te recruitment management module is often to collect applicant information, register in the database, and then perform a series of personnel screening according to the employment criteria, and fnally determine the possible candidate teachers, and then conduct a series of comprehensive assessments such as written examinations and interviews, and fnally according to the applicants comprehensive results of the students will be selected for admission.Tis research contributes to the reasonable planning of human resources in universities and enhances the comprehensive competitiveness of universities.In the future introduction of talents from colleges, after clarifying the development positioning of colleges, we can understand the type and quantity of talents needed, and avoid the recruitment risks and school costs caused by blindly pursuing high academic qualifcations and high professional titles.Of course, for the introduction of certain high-level talents or special talents, colleges and universities can also consider simplifying the recruitment procedures appropriately and adopting some relatively fexible policies.Although the selection of research samples meets the requirements of institutional research, it still has certain limitations.Te representativeness of local undergraduate colleges and universities is not enough, and the characteristics of teachers are not obvious enough.Further research and empirical evidence are needed.

Fig- ure 1 .
Visual Studio 2010 Chinese version is a professional development environment used by software design and development workers in their daily work.

Figure 1 :
Figure 1: Te structure of the personnel management system.

Table 1 :
Employee information management.

Table 2 :
Part of the scoring results.

Table 3 :
Analysis data results.

Table 4 :
Competent object data results.

Table 6 :
Final generated results.

Table 7 :
Test situation.Whether legal users can log in smoothly.② Whether illegal users can successfully log in.③ Whether they can successfully enter the system Can the user login information be recorded correctly.② Can the user operation log be completely saved.③ Can the system operation alarm information be saved