Orientation Model of Elite Education and Mass Education

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1 Proceedngs of the 8th Internatonal Conference on Innovaton & Management 723 Orentaton Model of Elte Educaton and Mass Educaton Ye Peng Huanggang Normal Unversty, Huanggang, P.R.Chna, 438 (E-mal: Abstract: Ths paper studes the problem of how to orentate the educatonal mode between elte educaton and mass educaton for the unverstes. Frstly, the major factors whch nfluence on educaton development level are dscussed. Then, an orentaton model of educatonal mode s establshed based on grey relatonal analyss method. Fnally, the orentaton model s appled to orentate the educatonal mode between elte educaton and mass educaton for Central Chna Normal Unversty and Huanggang Normal Unversty Key words: Elte educaton; Mass educaton; Educaton orentaton; Grey relatonal analyss Introducton The educatonal mode of unverstes can be dvded nto two nds,.e., elte educaton and mass educaton [-]. For a unversty, ts educatonal mode should orentate the format of elte educaton or mass educaton? What ey aspects should strve to develop to enhance educaton levels? These problems are very mportant to study for the decson maers n unverstes. Amng at these problems, ths paper dscusses the major factors whch nfluence on educaton development level, and establshes an orentaton model of educatonal mode based on the method of grey relatonal analyss [6-8]. Ths model provdes the decson maers n unverstes wth useful method to orentate the educatonal mode between elte educaton and mass educaton. 2 The Orentaton Model Grey relatonal analyss s one ey theory of grey system theory, and s also the base of grey system analyss, modelng, forecast and decson. Grey relatonal analyss provdes the modelng of complex system wth mportant techncal analyss method by establsh grey relatonal analyss method [6-8]. Its ultmate prncple s to clear the relevance ln degree to multple factors by comparng multple data sequences. The geometrc shape of two sequences s closer, the grey relatonal degree s greater. Ths prncple s one of two mportant prncples n the grey system theory. 2. Orgnal data processng To ensure hgh qualty of the modelng and the rght results of system analyss, the collected orgnal data must be to mae data transformaton and processng to elmnate dmenson. Defnton. Let the orgnal data sequence be x = ( x(), x(2),, x( n)), then the f x = y, =, 2,, n s called the data transformaton from sequence x to transformaton ( ( )) ( ) y, where y = ( y(), y(2),, y( n)). Accordng to the dfferent types of orgnal data, the followng data transform forms are usually used n grey modelng. Transformaton of ntal value: x( ) f ( x( ) = = y( ), x() x() Mean transformaton: n x( ) f ( x( ) = = y( ), x = x( ) x n = Percentage transformaton: Ths paper s supported by the Internatonal exchanges and cooperaton project of Hube Provncal Scence and Technology Department.

2 724 Proceedngs of the 8th Internatonal Conference on Innovaton & Management x ( ) f ( x ( )) = = y ( ) x ( ) Magnfcaton transformaton: x ( ) f( x( )) = = y( ), mn x( ) mn x ( ) Normalzaton transformaton: x ( ) f ( x ( )) = = y ( ), x where x s a constant greater than zero. Maxmum transformaton: x ( ) mn x ( ) f ( x ( )) = = y ( ) x ( ) Interval threshold transformaton: x ( ) mn x ( ) f ( x ( )) = = y ( ) x ( ) mn x ( ) Defnton 2. Suppose that x = ( x(), x(2),, x( n)), x2 = ( x2(), x2(2),, x2 ( n)),, xm = ( xm(), xm(2),, xm( n)), then f ( x( )) = y( ) s called the data transformaton from sequence x to y. The data transform methods wth multple factors and multple ndexes manly depend on the attrbute types. At present the most commonly used types are cost-type and beneft-type. For the beneft-type ndex and cost-type ndex, the data transformaton methods n grey relatonal analyss are gven as follows. x( ) mn x( ) y ( ) = =, 2,, m x ( ) mn x ( ) x( ) x( ) y ( ) = =, 2,, m x ( ) mn x ( ) 2.2 Grey relatonal degree Defnton 3. Let X = { x, x,, x m } be the grey relatonal factor set, x be the reference sequence, x be the compared sequences, =, 2,, m, x ( ) and ( ) x are the -th number of x and x respectvely, and x = ( x(), x(2),, x ( n)), x = ( x(), x(2),, x( n)), =, 2,, m. Let ( ), x ( )) be a real number, and ω s the weght of the -th number, and satsfes ω, ω =. If n = ( ), x( )) + ρ n mn =, = ( ) ( ) + ρ ω =, x), x( )) where () = x() x() s absolute dfference, mn = mnmn ( ) s mnmum dfference, = ( ) s mum dfference, ρ s resoluton rato, ρ (,), and the followng condtons are satsfed:

3 Proceedngs of the 8th Internatonal Conference on Innovaton & Management 72 ) Standardablty:, x ),, x) = x, x,, x ) = x = x ; 2) Symplectc symmetry: x, y X, γ ( x, y) = γ ( y, x) X = { x, y} ; 3) Wholeness: x j, x X = { xσ σ =,,, n}, n 2, r( xj, x) r( x, xj) ; 4) Accessblty: The value of x ( ) x ( ) s smaller, the value of ( ), x ( )) s greater. Then ( ), x ( )) s called grey relatonal coeffcent,, x ) s called the grey relatonal degree between x and x. 2.3 The steps of grey relatonal analyss In practce, grey relatonal analyss method ncludes the followng basc steps: () Collect the orgnal data, and determne the reference sequence and compared sequences. (2) Mae the data transformaton for the orgnal data sequence. (3) Calculate the absolute dfference. (4) Calculate the grey relatonal coeffcent. () Calculate the grey relatonal degree. (6) Ran order accordng to the grey relatonal degree. (7) Strength analyss accordng to the grey relatonal degree. 3 Applcaton Analyss of Orentaton Model In ths secton, we select Chnese Unversty Ranngs of Chna WangDa over the years, and choose the followng sx one-class ndexes as the ey nfluencng factors whch nfluence on the development level of unverstes: X: Reputaton; X2: Academc resources; X3: Academc Achevements; X4: Students stuaton; X: Faculty resources; X6: Materel resources Now we use the grey relatonal analyss method to analyze the nfluence degree of all ey nfluencng factors whch nfluence on the development level of unverstes. Then, we can determne the educatonal mode of a certan unversty s elte educaton or mass educaton accordng to the results of grey relatonal analyss. Frstly, we select the data of Central Chna Normal Unversty from Chnese Unversty Ranngs of Chna WangDa from 23 to 28. All statstcal data of above sx factors are lsted n Table. Table Chnese Unversty Ranngs of Chna WangDa from 23 to 28 Year Ranng X X X X X X Table 2 The Processed Data Year Ranng.4286 X X X X X X () Let the ranng value of Central Chna Normal Unversty be the reference sequence x ( ), =,2,,, and the values of the other sx nfluencng factors be compared sequences x ( ), =,2,,6; =,2,,. Obvously, the ranng value s a cost-type ndex, and X, X 2,,X 6 often

4 726 Proceedngs of the 8th Internatonal Conference on Innovaton & Management are all beneft-type ndexes. To elmnate the nfluence of dmensonal, we use the method of nterval threshold transformaton to process the orgnal data, the transformed data s denoted as y ( ), =,2,,6; =,2,,, the detal results are lst n Table 2. (2) Calculate the absolute dfference ( ) = y( ) y( ) accordng to the data n Table 2, the results are as follows. = (,.7869,.4283,.387,.74) ; 2 = (,.89,.3,.2636,.74) ; 3 = (,.8282,,.798,.94) ; 4 = (,.987,.726,.3744,.74) ; = (,,.23,.43,.497) ; 6 = (,.944,.644,,.443) ; So we get mn =, =. (3) Calculate the grey relatonal coeffcent. We set ρ =., then mn ( ), x ( )) = + ρ ( ) + ρ +. = ( ) +.. = ( ) +. Substtutng (2) nto above computatonal formula, we have r = (,.388,.386,.67,.4667) ; r 2 = (,.8946,.946,.648,.4667) ; r 3 = (.3333,.3764,.3333,.3873,.799) ; r 4 = (,.3377,.478,.78,.4667) ; r = (,,.7997,.777,.496) ; r 6 = (,.469,.437,,.776) ; (4) Calculate the grey relatonal degree We set ω = ω2 = ω3 = ω4 = ω =, then the grey relatonal degree between x and x are as follows., x) = r( ) =.69 ;, x2) = r2( ) =.79 ; = 3 3 = =, x) = r( ) =.43;, x4) = r4( ) =.68 ;, x) = r( ) =.822;, x6) = 6( ).734 ξ = ; = = From ths result, we obtan, x) >, x2) >, x6) >, x) >, x4) >, x3). The result shows that the nfluence degree of ey nfluencng factors whch nfluence on the development level of Central Chna Normal Unversty from hgh to low n turn s Faculty resources, Academc resources, Materel resources, Reputaton, Students stuaton, Academc Achevements. Accordng to the ranng results of Central Chna Normal Unversty snce 23, the ranng order of Central Chna Normal Unversty s n 3 to 4 fluctuatons around, and the ran tend s to be stable. The ranng results show that Central Chna Normal Unversty develops from teachng unversty turn to research unverstes. In addton, from the ey nfluencng factors whch nfluence on the development level of Central Chna Normal Unversty, the contrbuton of Faculty resources and Academc resources are hgher. Ths s because they vgorously develop teachers (such as vgorously ntroduce Doctor overseas and Changjang Scholars), efforts to mprove staff and student rato and senor professonal rato, mang good efforts to buld Master's degree program and Key natonal dscplnes, natonal laboratores and natonal engneerng centers and natonal humantes socal scences research base and so on. From above analyss, we can conclude that Central Chna Normal Unversty should go the development road of elte educaton. In addton to mantanng and strengthenng faculty =

5 Proceedngs of the 8th Internatonal Conference on Innovaton & Management 727 resources, academc resources, materal resources and other aspects of development benefts, t should also focus on the school s reputaton, the students stuaton, ncrease academc achevement n the development efforts, such as the creaton of a better research envronment to actvely ncrease the SCI, SSCI, EI, CSSCI and other hgh-level academc achevement, to further mprove qualty of new students, ncrease the proporton of graduate students, and actvely ntroduce academcans and renowned academcs and so on. Secondly, we select the data of Huanggang Normal Unversty from Chnese Unversty Ranngs of Chna WangDa from 23 to 28. All statstcal data of above sx factors are lsted n Table 3. Let the ranng value of Huanggang Normal Unversty be the reference sequence x ( ), =,2,,, and the values of the other sx nfluencng factors be compared sequences x ( ), =,2,,6; =,2,,. Accordng to the same computng method as above example, we set ω = ω2 = ω3 = ω4 = ω =, and the grey relatonal degree between x and x are as follows., x) = r( ) =.783 ;, x2) = r2( ) =.783 ; = = r( x, x3) = r3( ) =.683 ;, x4) = r4( ) =.662 ; = =, x) = r( ) =.46;, x6) = 6( ).838 ξ = ; = = So we have, x) =, x2) >, x3) >, x4) >, x6) >, x) Table 3 Chnese Unversty Ranngs of Chna WangDa from 23 to 28 of Huanggang Normal Unversty Year Ranng X X2 X X X X The result shows that the nfluence degree of sx ey nfluencng factors whch nfluence on the development level of Huanggang Normal Unversty from hgh to low n turn s reputaton, academc resources, academc achevements, students stuaton, materel resources, faculty resources. Accordng to the ranng results of Huanggang Normal Unversty snce 23, the ranng order of Central Chna Normal Unversty s n fluctuatons around, and the ran tend s to be stable. The ranng results show that the comprehensve level of Huanggang Normal Unversty s n low medate level wthn the natonal ordnary unverstes. In addton, from the ey nfluencng factors whch nfluence on the development level of Huanggang Normal Unversty, the contrbuton of reputaton and academc resources are hgher. Ths s because the scores of reputaton and academc resources are all zero. The score results of these two aspects are derved from two basc statuses,.e., () the academcans and Yangtze scholars are scarce. () Huanggang Normal Unversty was upgraded n 999 from college to undergraduate unversty. There s no doctoral pont, master's degree, natonal ey dscplnes, laboratory-scale natonal engneerng centers, natonal ey research base of humantes and socal scences and other advanced platform. In addton, the hgh level of academc achevement s lac due to lac of senor talent and senor platform. Moreover, Huanggang Normal Unversty s the second batch of the undergraduate unversty, so the student qualty of College Entrance Examnaton s not hgh. Based on the present stuaton, we can conclude that Huanggang Normal Unversty should go the development road of mass educaton. As a young local unversty, t should adhere to the "local flavor", and actvely serve the local economc and socal development, tran hgh-qualty, hgh-grade avalablty, applcaton-orented talents and promote ther development as a power; t should be

6 728 Proceedngs of the 8th Internatonal Conference on Innovaton & Management people-orented, and actvely ntroduce and develop top talent, efforts to mprove teacher-student rato and the proporton of senor professonal ttles, and actvely create a good research envronment to ncrease the output of a hgh level of academc achevement. 4 Conclusons In ths paper, an orentaton model of educatonal mode based on grey relatonal analyss method s establshed to orentate the educatonal mode between elte educaton and mass educaton. In ths model, the major factors whch nfluence educaton development level are dscussed, and then the advantages and dsadvantages of each unversty can be obtaned. Ths result can provde reasonable reference for one unversty to orentate the format of elte educaton or mass educaton. References [] Fnnegan D.E. Structurng Mass Hgher Educaton: The Role of Elte Insttutons[J]. Revew of Hgher Educaton, 2, 34: [2] Deresewcz W. The Dsadvantages of an Elte Educaton[J]. Amercan Scholar, 28, 3: [3] Rybaova M.V. Integraton of Scence and Educaton as a Bass of Elte Scentsts Tranng[J]. Russan Educaton & Socety, 28, : [4] Huang S., Huang C. Control of an Inverted Pendulum Usng Grey Predcton Model[J]. IEEE Transon Industry Applcaton, 2, 36: [] Amercan Councl on Educaton. Fact Boo on Hgher Educaton[M]. New Yor: N.Y, Macmllan, 989 [6] Deng Julong. The Foundaton of Grey Theory[M]. Wuhan: Huazhong Unversty of Scence and Technology Press, 22 (In Chnese) [7] Deng Julong. Grey Forecastng and Grey Decson-mang[M]. Wuhan: Huazhong Unversty of Scence and Technology Press, 22 (In Chnese) [8] Xao Xngpng. The Foundaton and Applcaton of Grey Technology[M]. Bejng: Scence Press 2 (In Chnese)