Research on the education salary resources based on computer incentive mechanism mathematical model.
The application of incentive in education involves two aspects. One is student incentive, which means how teachers use incentive theories to arouse students' enthusiasm of studying; the other is teacher incentive, which means how to use incentive theories to arouse teachers' enthusiasm of teaching. Incentive theory plays an important role in the working enthusiasm of teachers. Based on different teaching periods of teachers, the research on teacher incentive can be divided into college teacher incentive, middle school teacher incentive and primary school teacher incentive. There are a lot of essays regarding "teacher incentive" on CNKI (China National Knowledge Infrastructure). When typing "college teacher incentive", there are 9089 essays. When typing "middle school teacher incentive", there are only 2455 essays. When typing "primary school teacher incentive", there are only 2313 essays. It shows that the researches on college teacher incentive are much more than that of middle school teacher incentive and primary school teacher incentive (Ualikhanova, B. S., Rumbeshta, E. A., Baizak, U. A., Turmambekov, T. A., Sarybaeva, A. H., & Kurbanbekov, B. A., 2015).
Specifically, the research is about the discussion of incentive theory, and the incentive measures taken on teachers and students based on western incentive theories. Involved with what incentive should be used and how to use the incentive, incentive theory consists of two aspects. The first one is the content-based incentive, which focuses on what incentive is used, including Maslow's Hierarchy of Needs, Frederick Herzberg's Two-Factor Theory (Hygiene-motivational Factors) and McClelland's Achievement Need Theory; the second one is the process-oriented incentive, which focuses on the method that the incentive used, including Vroom's Expectancy Theory, Adams' Equity Theory and the famous American managerialist Drucker's Management by Objective, etc. To effectively promote the working enthusiasm of the teachers, the most important is to eliminate the unfair perceptions of them. Adams' Equity Theory can be conducted to assure the incentives on college teachers are just and equitable (Chaabia, R., Bounouala, M., & Boukelloul, M. L., 2015). The research on teacher incentive mechanism mainly include the incentive measures of incenting teachers and how to innovate based on current incentive mechanism (Galvan, J. B., Recarte, L., & Perez-Ilzarbe, M. J., 2014).
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This paper mainly discussed the strategy of promoting the overall utilization of regional education salary resources from the perspective of user incentive. Based on consumer psychology, the study started from the analysis of the features of education salary resource and the process of users employing education salary resources, and proposed to use regional education salary resources with "Education salary vouchers" (Bondarenko, O. I., 2015); then applied the accumulative credit method for the information resource utilization of the users, settled regularly (one year or one term) according to the accumulative credit exchange rate and allocated related funds to the school; it studied and made full use of mathematical model's bridge advantage of bringing the theoretical perception to practice, preliminarily constructed the mathematical model of school's obtaining incentive funds through education salary resources. Meanwhile, the paper verified the feasibility and operability of the mathematical model by conducting computer stochastic simulation so that to provide certain referential value for the improvement of education salary resource utilization performance (Lal'Arya, M., 2015).
2. Researches on Education Incentive Theories
The sharing of education salary resources is very important in promoting the application of them.It is one of the important ways of realizing the fairness of education salary resources and narrowing the digital gap of them. The urgency of the sharing of education salary resources is being more and more concerned. Some places such as Wu Xi already attempted to share the education salary resources (Benneaser, J. O. H. N., Thavavel, V., JAYARAJ, J., Muthukumar, A., & JEEVANANDAM, P. K., 2016). However, the con-construction and sharing of education salary resources are also influenced by multiple factors. First, the idea factor is one of the core factors. Idea plays an important role in the sharing of resources because real actions are based on the change of idea. The idea factor mainly refers that the sharing consciousness of education salary resources is weak and people are used to the self-sufficient way; the narrow-minded egalitarianism idea makes people not willing to share resources; people have weak consciousness who pay more attention to the construction of resources but seldom make full use of them, and the owners with abundant resources are not willing to share their resources with others. Secondly, management factor is also a significant factor. For example, the management mechanism needs to be improved to further promote the sharing mechanism, and the policies on resource sharing are also needed to lead the sharing with detailed execution strategies (Stavnas, C. C. M., & Nielsen, L. M., 2015). Besides, problems as imperfect sharing mode and lack of principal functions of sharing parties also existed in the sharing of resources. Hence, it is important to construct a reasonable sharing mechanism of education salary resources. The construction of a perfect resource sharing mechanism is a systematic and complicated process. (Oliveira, J. A., Ferreira, J., Figueiredo, M., Dias, L., & Pereira, G., 2014) The completeness and reasonableness of the system planning is crucial. The sub-mechanisms include funds protection mechanism, standard mechanism, incentive reward mechanism, resource access mechanism, evaluation feedback mechanism, management mechanism and innovation mechanism, etc. With these submechanisms, the effective and reasonable operation of the sharing mechanism can be assured.Based on the information ecological ideas, the education salary resource sharing model can be constructed from the resource, user and service perspectives, through which to discover the relationship between each other and lay the basis for the resource sharing practice. The education salary resource sharing mode is to construct regional education salary source base (Lunga, W., & Musarurwa, C., 2016). One region (a county or a city) shares one central resource base. All the teachers and students in the region have the change to enjoy the high-quality education salary resources. It is recommended for multiple parties as the government, schools, enterprises and experts to attend the construction of source base. As the executor of the education salary resource sharing, the government is responsible for the plans of the construction and utilization of the education salary resources in the region, asks enterprises to development high-quality education salary resources and encourages schools to use the resources. The enterprises develop high-quality education salary resources according to the demands of the users. Schools become the simple users instead of both the users and the constructers. They bring up feedback and evaluations to resource constructers when using the resources, and make the developers constantly improve the quality of education salary resources. As a result, a government-oriented, enterprise-developed and school-used sharing mode is created (Xiong Caiping), which maximized the effect of the resource allocation and the fairness of education, and accelerated the information pace, with the original education investments keeping unchanged (Quintana, M. G. B., & Zelaya, D. S., 2015).
3. Construction of User Education Salary Resource Incentive Mechanism Mathematical Model
3.1. The Idea of Construction of Incentive Mechanism Mathematical Model
The construction idea of education salary resource user incentive mechanism mathematical model is: to use with vouchers; to accumulate credits; to settle accounts regularly, credits exchange and incentive basis. The forms of user accumulated credits mainly refer to the use frequency, including the visit frequency and use duration. When the user is extremely interested in a certain resource or think it is necessary to learn more about the part, he/she can choose to download the source for use (Lima, P. C., & Piacentini, V. D. Q., 2015). Download for use is considered as the saturation use of a certain resource. One download can obtain the maximum credit value of the resource. "Visit" is a browsing way adopted when the user wants to know about the quality of a certain resource or whether the resource is important or not, or when the user's browsing duration has not reached the time limit (e.g. 1 minute) due to his/her misoperation or other reasons. It will be counted as one visit with the default value (e.g. 1 credit) to accumulate credits; use duration refers to the online study of the user. When the user chooses to study online, usually the credits are accumulated by the ratio of the real time used and the total time of finishing the study of the resource in one time. "To settle accounts regularly" setup a certain settlement period (a quarter, half a year, or yearly) according to the real education or study. The resources in the information resource base are designed and developed according to a unified standard. Different resources have different functions and purposes. The users in the region are also from different grades or subjects with different demands and preferences of the resources. In a certain period, the users may obtain different accumulated credits. "To receive rewards" means the incentive funds that the school obtained. Usually in a statistic settlement cycle, the funds are obtained by the credits exchange rate of incentive funds. The exchange rate is the ratio between the total incentive funds that the authorities allocated the school and the total accumulated credits of the user (Youn, M., 2015). Different schools are given different incentive funds. The more the education funds resources, the more the incentive funds and the higher the expenses used for improving teacher salary level and training of users. The higher the education level of the school is, the higher the "consuming" potential in the school education salary resources will be, which will be a virtuous circle (Schilder, J. D., Brusselaers, M. B., & Bogaerts, S., 2015).
3.2. Establishment of Incentive Mechanism Mathematical Model
The utilization of education salary resources is a complex dynamic process. In order to highlight the conciseness and accuracy of mathematical model and to fully illustrate the user incentive mechanism of the regional education salary resources, the following assumptions are made during the establishment of the mathematical model: (1) In the administrative region of this study, different schools have different numbers of users (teachers and students); the total users number within a certain region will not have significant variations in a given settlement cycle, and it is set as a constant (Cebotari, V., & Mazzucato, V., 2015). (2) During the co-constructive and shared resource design development process, new education salary resources will be developed and utilized in batches; both the quality and quantity of the resources invested in this region have no significant changes within a school year. It is also assumed that in the settlement cycle, the quality and quantity of the resources in the education salary resource base of this administrative region have no big changes. And generally, it has no influence on users' utilization frequency of the education salary resources. (3) To spend the funds, schools should actively improve their information network environment and carry out various incentive measures to encourage users to frequently utilize their education salary resources; the incentive measures include spiritual encouragement, emotional concern, and financial rewards and so on (Buysse, V., Peisner-Feinberg, E., Soukakou, E., Fettig, A., Schaaf, J., & Burchinal, M., 2016). (4) Users can be affected by both internal and external factors when using the education salary resources. In a certain time period, users' utilization of education salary resources is a dynamic and randomized process, which is reflected in variable accumulated credits obtained by the users; for this reason, it is set as the random variable (Botha, J., & Kourkoutas, E., 2015). The basic symbols used in the establishment of the model are listed in Table 1.
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In the administrative region, the incentive funds a school receives are determined by the number of users, the accumulated credits of the resources and its exchange rate:
F = f (Q, E, C) (1)
In this equation, E is the exchange rate of user accumulated credits; it is the ratio of P (the total allocation the government invests in education salary resources) to [I.sub.u] (the total accumulated credits of all the users):
E = P / [I.sub.u] = P / [U.summation over (k=1)] [C.sub.k] (2)
Where, C stands for the accumulated credits of a single user within a settlement cycle, namely, a school year. All the resources in the resource base are divided according to the same technical standard. The user will able to acquire the maximum accumulative value M as long as he (or she) can make the best use of the resources no matter which kind of resources he (or she) is using. The accumulated credits of a single user are composed of three parts: credits accumulated by downloading the resources (CD); credits accumulated by using the resources online (CT); credits accumulated via the access to the resources (Cv). It can be expressed as following:
C= [C.sub.D] + [C.sub.T] + [C.sub.V] (3)
Put Equation (2) and (3) into Equation (1) to form Equation (4) to calculate the incentive allocation of each school obtained through the utilization of education salary resources can be calculated:
F = I * E = [Q.summation over (j=1)] [C.sub.j] * (P/[U.summation over (k=1)] [C.sub.k]) (4)
According to Equation (4), under the condition of the same exchange rate of accumulated credits, the incentive funds a school receives are closely related to its total accumulated credits. The total accumulated credits are determined by the number of education salary resource users as well as their utilization frequency and duration of the resources (Greco, P. M., 2015).
The optimized annual salary model is made up of three parts: annual salary income model C(t), variable annual salary deferring mechanism D(t); annual salary payment model C(t). In this model, the annual salary income model C(t) is not equivalent to the annual salary payment model C(t). Generally, C(t)<C(t).
The optimized annual salary model is:
C(t) = B(t) + P(t) + E(t) + S(t) (5)
It is the annual salary income model, including the relatively fixed income and the variable part.
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4. Data Analysis and Simulation of the Mathematical Model of Education Salary Resource Incentive Mechanism Adopted by the School
The simulation diagram of school accumulated credits and incentive funds' change can be obtained by analyzing the above data (see Figure 4).
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According to the above incentive mechanism, it can be assumed that the limit of each user's education salary voucher issued by the government is 400 yuan, and the user can get 1 credit when he (or she) "spends" 1 yuan of his (or her) education salary voucher. As shown in Table 2, the number of users in school S2 is 1838, and its pre-obtained education salary voucher is 735,200 yuan; the number of users in school S2 is 1692, and its pre-obtained education salary voucher is 675,800 yuan. However, the number of education information voucher of a school does not mean its "consumption" capacity. The usage of the resources can only be reflected by the total accumulated credits the users get after using their education salary resources. In the end of the school year, school S2 accumulated 180,800 credits in total; the pre-obtained education salary voucher of school S259 is 421,600 yuan, but S259 accumulated 693,000 credits. It indicates that the resource utilization frequency of S259 is much higher than that of S2.
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According to the mathematical model of incentive mechanism, the actual amount of incentive funds a school receives equals to the product of the total accumulated credits that a school obtains by spending its education salary resources and the exchange rate of the accumulated credits. The intra-regional average credits stand for the average credits every user obtains by spending education salary resources within a settlement cycle; the intra-regional average credits is calculated by dividing the total accumulated credits of the region by the total number of users in this region. If other conditions are the same, theoretically, the amount of incentive funds a school receives is closely related to the number of its users. In other words, a school can receive more incentive funds if its number of users increases. Based on the above simulations, the total accumulated credits the school gathers and its exchange rate can be finally obtained; then, the actual incentive funds that the school will receive can be calculated. In Figure 5, the variations of these 5 schools' actual incentive funds and theoretical funds are illustrated.
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It can be concluded from the above figure that different schools receive different amount of incentive funds. The more the education salary resources a school uses, the more incentive funds the school will receive. In this case, the school will have more money to spend on teacher's salary and users' training. Hence, the education level of this school will be improved, and the "consumption" potentiality of this school's education salary resources will be higher, thus forming a positive cycle.
As an important part of education industry's incentive mechanism, the allocation of education salary resources should be given great attention. The computer incentive mechanism has been widely applied to many industries; its merits of being safe and fair have been well presented. In this study, an education salary resource model is designed based on the analysis of the education salary resources by using the computer incentive mechanism. And the validity of this model is has been verified through computer simulations, in which the salary resources of education industry is simulated based on the fairness and reliability of computer incentive mechanism. The analysis of a college's input in its education salary resources revealed that the education incentive funds vary from school to school. The more the education salary resources are, the more incentive funds will be. In this case, the school will receive more funds on teacher's salary and users' training funds. Hence, the education level of this school will be improved, and the "consumption" potentiality of this school's education salary resources will be higher, thus forming a positive cycle. This way, the feasibility and operability of the model in practical application are proved, providing a reliable strategy to improve the utilization efficiency of education salary resources and offering corresponding reference for the adjustment of the traditional education funds' allocation approach.
Benneaser, J. O. H. N., Thavavel, V., JAYARAJ, J., Muthukumar, A., & JEEVANANDAM, P. K. (2016). Design of Open Content Social Learning that Increases Learning Efficiency and Engagement Based on Open Pedagogy. Turkish Online Journal of Educational Technology, 15(1), 20.
Bondarenko, O. I. (2015). Options of sustainable development of region's territory. Natsional'nyi Hirnychyi Universytet. Naukovyi Visnyk, (4), 157.
Botha, J., & Kourkoutas, E. (2015). A community of practice as an inclusive model to support children with social, emotional and behavioural difficulties in school contexts. International Journal of Inclusive Education, 1-16.
Buysse, V., Peisner-Feinberg, E., Soukakou, E., Fettig, A., Schaaf, J., & Burchinal, M. (2016). Using Recognition & Response (R&R) to improve children's language and literacy skills: Findings from two studies. Early Childhood Research Quarterly, 36, 11-20.
Cebotari, V., & Mazzucato, V. (2015). Educational performance of children of migrant parents in Ghana, Nigeria and Angola. Journal of Ethnic and Migration Studies, 1-23.
Chaabia, R., Bounouala, M., & Boukelloul, M. L. (2015). A preliminary study on anini deposit iron ore enrichment (algeria) in order to use it in metallurgical industry. Natsional'nyi Hirnychyi Universytet. Naukovyi Visnyk, (4), 44.
Galvan, J. B., Recarte, L., & Perez-Ilzarbe, M. J. (2014). Desarrollo de un Sistema de Decision basado en Logica Borrosa para el uso de Bombas de Insulina. RISTI Revista Iberica de Sistemas e Tecnologias de Informacao, 2014(13), 1-15.
Greco, P. M. (2015). Can you do it all?. American Journal of Orthodontics and Dentofacial Orthopedics, 148(6), 896.
Lal'Arya, M. (2015). Role of ict in teaching of social science/studies. Global Journal of Multidisciplinary Studies, 5(1).
Lima, P. C., & Piacentini, V. D. Q. (2015). Obituary: Rolf Karl Heinz Grantsau (1928-2015). Revista Brasileira de Ornitologia-Brazilian Journal of Ornithology, 23(2), 87-89.
Lunga, W., & Musarurwa, C. (2016). Exploiting indigenous knowledge commonwealth to mitigate disasters: from the archives of vulnerable communities in Zimbabwe. Indian Journal of Traditional Knowledge, 15(1), 22-29.
Oliveira, J. A., Ferreira, J., Figueiredo, M., Dias, L., & Pereira, G. (2014). Decision Support System for Not Urgent Transportation of Patients in Shared Vehicle. RISTI-Revista Iberica de Sistemas e Tecnologias de Informacao, 2014(13), 17-33.
Quintana, M. G. B., & Zelaya, D. S. (2015). The TPACK model to prepare and evaluate lesson plans. An experience with pre-service teachers using social networks and digital resources. Journal of Mobile Multimedia, 11(1-2), 134-146.
Schilder, J. D., Brusselaers, M. B., & Bogaerts, S. (2015). The Effectiveness of an Intervention to Promote Awareness and Reduce Online Risk Behavior in Early Adolescence. Journal of youth and adolescence, 1-15.
Stavnas, C. C. M., & Nielsen, L. M. (2015). Drawing and terminology-A critical look at textbooks in drawing used in specialised teacher education. FORMakademisk, 8(3).
Ualikhanova, B. S., Rumbeshta, E. A., Baizak, U. A., Turmambekov, T. A., Sarybaeva, A. H., & Kurbanbekov, B. A. (2015). Formation of Medical Students' Competences in the Republic of Kazakhstan. Indian Journal of Science and Technology, 8(s (10)).
Youn, M. (2015). Inequality from the first day of school: The role of teachers' academic intensity and sense of responsibility in moderating the learning growth gap. The Journal of Educational Research, 1-18.
Pang Nan (1) *, Yan Feng (2)
(1) North China University of Science and Technology, 063000, Tangshan, Hebei, China
(2) XingTai University, 054001, Xingtai, Hebei, China
Table 1--Simulation of School Accumulated Credits and Incentive Funds' Variations School Users Education Accumulated number pay securities integral S1 1838 73.52 402.79 S2 1692 67.58 18.08 S3 1981 79.24 306.74 ... ... ... ... S257 1457 58.28 11.74 S258 968 38.72 211.54 S259 1054 42.16 69.30 School The exchange Funding number rate S1 0.06 24.16 S2 0.06 1.08 S3 0.06 18.41 ... ... ... S257 0.06 0.70 S258 0.06 12.69 S259 0.06 4.15 Table 2--Variation Range of a Single Resource's Accumulated Credits Users Integral range Average 5000 10472-13130 11698 5500 11422-14131 12773 6000 12388-15216 13959 6500 13817-16156 15171 7000 14825-17750 16276 7500 16169-18778 17576 8000 17467-19872 18628 Table 3--Statistical Table of a School's Funds Resources and Funds Income from 2012 to 2014 Year 2012 Project Amount Amount Education funds 56,332 55,573 Scientific 6,000 8,740 research funds Education 31,650 43,858 business income Scientific 10,000 11,500 enterprise income Subordinate 3,000 3,500 unit payment Operating income 12,000 20,000 Other income 4,900 6,450 Totals 123,882 149,621 Year 2013 2014 Project The growth Amount The growth rate rate Education funds -1.35% 61,254 10.22% Scientific 45.67% 11,800 35.01% research funds Education 38.57% 52,474 19.65% business income Scientific 15% 14,700 27.83 enterprise income Subordinate 16.67% 3,350 -4.29% unit payment Operating income 66.67% 25,817 29.09% Other income 31.63% 8,650 34.11% Totals 20.78% 178,045 19%
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|Author:||Nan, Pang; Feng, Yan|
|Publication:||RISTI (Revista Iberica de Sistemas e Tecnologias de Informacao)|
|Date:||Mar 30, 2016|
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