GRİ İLİŞKİ ANALİZİ YÖNTEMİYLE ÖĞRETİM ELEMANI PERFORMANS DEĞERLENDİRMESİ *

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1 The Journal of Academc Socal Scence Studes Internatonal Journal of Socal Scence Do number: Number: 23, p , Sprng 2014 GRİ İLİŞKİ ANALİZİ YÖNTEMİYLE ÖĞRETİM ELEMANI PERFORMANS DEĞERLENDİRMESİ * PERFORMANCE EVALUATION OF INSTRUCTORS THROUGH GREY RELATIONAL ANALYSIS METHOD Yrd. Doç. Dr. Necla TEKTAŞ Marmara Ünverstes (Sosyal) Teknk Blmler Meslek Yüksekokulu Özet Bu çalışma, öğretm elemanının performansının değerlendrlmes çn geleneksel yöntemlerden farklı olarak Gr İlşk Analz yöntem kullanılan lk çalışmalardandır. Yükseköğretm kurumlarının öğretm elemanı ntelğ, eğtmn kaltesn doğrudan etkleyen faktörlern başında gelmekte ve öğretm elemanlarının performanslarının değerlendrlmes, kalte kontrol güvencelernn en öneml göstergelernden br olarak görülmektedr. Öğretm elemanının performansı, benzer çalışmalarda, öğretm performansı ve danışmanlık performansı olarak ayrı ayrı ele alınmasına rağmen, bu çalışmada brlkte ele alınarak öğretm elemanının performansı olarak adlandırılmıştır. Öğretm elemanının performansının değerlendrlmes amacıyla Marmara Ünverstes Teknk Blmler Meslek Yüksekokulu Endüstryel Elektronk Bölümünde öğrenm gören öğrenclere anket uygulaması yapılmıştır. Yapılan ankette öğrenclere; demografk sorularının yanı sıra öğretm elemanının performansı le lgl sorular yönetlmştr. Anketn değerlendrlmesnde kullanılan Gr lşk analznde; cevapların frekanslarından zyade öğrenclern fadelere verdkler yanıtlar kullanılmaktadır. Ver kümesnn kısıtlı ve az sayıda olması, değşkenlern keskl ve sürekl rasgele değşkenlerden oluşması, herhang br olasılık dağılımına sahp olma şartı aranmaksızın belrsz durumlara uygulanablr olması, fonksyonel br dz şlemlern yerne bast, belrl ve net hesaplama adımlarıyla brlkte verler arasındak brebr lşk * Bu makale Crosscheck sstem tarafından taranmış ve bu sstem sonuçlarına göre orjnal br makale olduğu tespt edlmştr.

2 466 Necla TEKTAŞ derecesn sayısal olarak analz edeblyor olması, varsayımlarının olmaması, kolay, pratk ve kullanışlı olması gb özellkler, Gr lşk analzn dğer yöntemlerden ayıran ve üstün kılan özellklerdr. Bu nedenle, anket sonucunda elde edlen verler yen br statstk yöntem olan Gr İlşk Analz yöntem le değerlendrlmş ve sonuçları bu doğrultuda yorumlanmıştır. Anahtar kelmeler: Gr lşk analz, öğretm elemanı performansı, performans değerlendrme Abstract As opposed to tradtonal methods, ths study s among the frst ones that use Grey Relatonal Analyss method for evaluaton of nstructors' performances. The qualfcatons of an nstructor n a hgher educatonal nsttuton s one of the top factors that drectly affect the qualty of educaton, and hence, the evaluaton of nstructors' performances s among the most sgnfcant ndcators of qualty control assurance. Even though the performance of an nstructor s evaluated separately n smlar studes as teachng performance and advsng performance, ths study handles these two together and calls t the performance of an nstructor. To be able to evaluate the performance of the nstructor, students of Marmara Unversty Techncal Scences Vocatonal School Industral Electroncs department are surveyed. In the survey mplemented, students are asked demographc questons n addton to the questons related to the performance of the nstructor. In Grey relatonal analyss appled for assessment of the survey, the responses of the students are used nstead of the frequency of the responses. The fact that ts data set s lmted and low n number; the varables are ntermttent and composed of contnuous random varables; can be appled n uncertan stuatons wthout necesstatng a probablty dstrbuton; provdes an ablty to analyze one-toone nterrelatons of data n a numercal manner wth smple, specfc and net calculaton steps nstead of a number of functonal processes; contans no assumptons, and s easy to use and practcal makes Grey relatonal analyss a method superor to others. Therefore, the data obtaned as a result of the survey are assessed and the outcomes are nterpreted wth ths new statstcal method called Grey Relatonal Analyss. Key words: Grey Relatonal Analyss, nstructor performance, performance evaluaton Introducton As nsttutons provdng hgher educaton, unverstes need a unversal level of qualty necesstated by the modern era and effectve evaluaton systems to be able to renovate themselves. The qualfcatons of an nstructor n a hgher educatonal nsttuton s one of the top factors that drectly affect the qualty of educaton, and hence, the evaluaton of nstructors' performances s among the most sgnfcant ndcators of qualty control assurance.

3 Gr İlşk Analz Yöntemyle Öğretm Elemanı Performans Değerlendrmes 467 Ths qualfcaton can only become meanngful by evaluatng the nstructor's performance both wthn and outsde the classroom. Whle the n-class effectveness of an nstructor s determned by factors lke havng a good command of the course, the way subjects are taught, reassurng the students, knowledge-based authorty n the class, the ablty to establsh a dalogue wth the students, and sncerty towards the students, effectveness outsde the classroom s determned by the help and gudance the nstructor provdes to the advsee students about the level of knowledge on the hgher educaton nsttuton they attend, learnng ts rules, adaptaton to ts socal and academc envronment, personal development, awareness, self-confdence, preferences and launchng ther careers equpped wth necessary knowledge. Performance Evaluaton Performance evaluaton s the systematc dentfcaton of strengths and weaknesses of an ndvdual or a group n a gven task (Kepr-Snangl,H. 1998). Barutçugl (2002) defnes performance evaluaton as a planned tool that completes the success of an ndvdual n a gven task, hs/her atttude and behavor, moralty and qualfcatons n the job and assesses the contrbuton of the ndvdual to the success of an organzaton. As a tool used extensvely n many organzatons, performance evaluaton s now beng wdely used n educatonal nsttutons as well. Unversty s an educatonal nsttuton that provdes hgher level of educaton, tranng and scentfc research. Composed of academc and admnstratve personnel and students, a unversty exhbts a heterogeneous structure n terms of ts components ( Basbug & Unsal, 2009). The exstence of dfferent groups wthn a unversty necesstates consderaton of nterest and requrements of all these groups by the admnstraton. Especally n terms of the performance evaluaton of the unversty's academc personnel, the purpose of the evaluaton, what should be evaluated and who wll be responsble are sgnfcant ssues that need to be addressed (Rutheford, 1988). The fact that unverstes are knowledge-based organzatons ndcates that performance evaluatons n these nsttutons should be handled wth specal care (Smmons, 2002; Basbug & Unsal, 2009) Ths s why the evaluaton method that compensates shortcomngs and ams development by dentfyng the level of teachng performance of the academcans s called formatve evaluaton. In formatve evaluaton, the nstructor and the course are evaluated wth data obtaned from dfferent sources. Summatve evaluaton on the other hand s the evaluaton of the nstructor and the course for the purposes of determnaton of the level and decson makng based on data obtaned from varous sources. The methods used n evaluatng the teachng performances of the academcans are examned under ten headngs (Kalaycı, 2009). These methods are student assessment survey, evaluaton performed by experenced colleagues wthn the

4 468 Necla TEKTAŞ faculty or department, self-evaluaton, evaluaton by admnstrators, specalst evaluaton from outsde the unversty, evaluaton by graduated students, awards gven to most successful nstructors, vdeo shoots, the vews of employers on graduates and the level of students' learnng. In evaluatng the teachng performance of the nstructors, havng a good command of the course, the way subjects are taught, reassurng the students, knowledge-based authorty n the class, the ablty to establsh a dalogue wth the students, and the sncerty towards the students n addton to the actvtes outsde the classroom called academc advsng as one of the tasks of a lecturer are among the sgnfcant factors that nfluence the success of the students. Academc advsng contans provdng help and gudance to the students n learnng about the hgher educaton nsttuton they attend, learnng ts rules, adaptaton to ts socal and academc envronment, personal development, awareness, self-confdence, preferences and launchng ther careers equpped wth necessary knowledge. In evaluatng the performances of nstructors, no method s adequate by tself. However, these methods may provde vald and relable outcomes when assessed together (Kalaycı, & Çmen, 2011) Ths study, as opposed to the tradtonal methods of performance evaluaton, uses grey relatonal analyss that has been ganng sgnfcance n recent years. Grey Relaton Analyss The valdty of tradtonal statstcal analyss technques s based on assumptons such as the dstrbuton of populaton and varances of samples. Nevertheless sample sze wll also affect the relablty and precson of the results produced by tradtonal statstcal analyss technques. J. Deng argued that many decson stuatons n real lfe do not conform to those assumptons, and may not be fnancally or pragmatcally justfed for the requred sample sze. Makng decsons under uncertanty and wth nsuffcent or lmted data avalable for analyss s actually a norm for managers n ether publc or prvate sectors (Deng, 1982; Deng, 1989). To address ths problem, J. Deng developed the grey system theory, whch has been wdely adopted for data analyss n varous felds. The Grey Relatonal Analyss ntroduced n the followng s a method n Grey System Theory for analyzng dscrete data seres. A procedure for the Grey Relatonal Analyss, whch s approprate for Lkert scale data analyss, conssts of the followng steps. 1. Generate reference data seres x0. x0 = (d01, d02,..., d0m) where m s the number of respondents. In general, the x0 reference data seres conssts of m values representng the most favoured responses. 2. Generate comparson data seres x. x = (d1, d2,..., dm)

5 Gr İlşk Analz Yöntemyle Öğretm Elemanı Performans Değerlendrmes 469 where = 1,..., k. k s the number of scale tems. So there wll be k comparson data seres and each comparson data seres contans m values. 3. Compute the dfference data seres Δ. Δ=( d01 d1, d02 d2,..., d0m dm ) 4. Fnd the global maxmum value Δmax and mnmum value Δmn n the dfference data seres. max max max mn mn mn 5. Transform each data pont n each dfference data seres to grey relatonal coeffcent. Let γ(j) represents the grey relatonal coeffcent of the jth data pont n the th dfference data seres, then mn max ( j) ( j) max where Δ (j) s the jth value n Δ dfference data seres. ς s a value between 0 and 1. The coeffcent ς s used to compensate the effect of Δmax should Δmax be an extreme value n the data seres. In general the value of ς can be set to Compute grey relatonal grade for each dfference data seres. Let Γ represent the grey relatonal grade for the th scale tem and assume that data ponts n the seres are of the same weghts 1, then m 1 ( n) m 1 n The magntude of Γ reflects the overall degree of standardzed devance of the th orgnal data seres from the reference data seres. In general, a scale tem wth a hgh value of Γ ndcates that the respondents, as a whole, have a hgh degree of favoured consensus on the partcular tem. 7. Sort Γ values nto ether descendng or ascendng order to facltate the manageral nterpretaton of the results (Chen-Ho Wu, 2007) Methods and Fndngs The unverse of the study s composed of assocate degree students attendng to Marmara Unversty n term. The samplng frame of the study s composed of students attendng to Marmara Unversty Techncal Scences Vocatonal School Industral Electroncs department. Convenence samplng s used as the method of samplng. Method of Data Collecton Surveyng s chosen as the tool for data collecton and the data are collected ether through face to face ntervews or n paper form dstrbuted n classrooms. A total of 178 surveys are completed, and all of these are examned n terms of

6 470 Necla TEKTAŞ ncomplete or naccurate nformaton. After elmnatng the ncorrect surveys, 170 of these are found to be usable and analyses are conducted on these 170 surveys. The survey form used n the study s composed of two sectons. In the frst part of the survey form, students are asked questons amed at dscoverng the demographc characterstcs of the students. These questons were related to gender, program, educaton style and class. In the second part of the survey, there was an Instructor Performance Evaluaton Survey (IPES) composed of 15 questons amed at determnng the performance of the nstructor. The questons of the survey were created wth the ad of surveys ( İçl,& Vural, 2010; Tektaş, et.al., 2010; Gökmen, et.al, 2010) conducted prevously on the same subject. A fve level Lkert type scale s used n IPES (1=Strongly Dsagree, 5=Strongly Agree). Demographc Characterstcs of the Responders Gender Department Type of Educaton Class Table 1. Profle of the Responders N % Female Male Total Electroncs Bomedcal Communcatons Total Formal Evenng Total Freshmen Sophomore Total The profle of the responders s gven n Table 1. As seen n Table 1, there were a total of 170 partcpants n the survey. Whle 21% of the responders were female, 79% were male; and 36% of the responders were students n Electroncs, 29% Bomedcal and 35% were students n Communcatons department. Accordng to Table 1, whle majorty of the responders attended formal educaton 59%, 41% of the students attended evenng educaton classes. 49% of the students were freshmen and 51% of them were sophomores.

7 Gr İlşk Analz Yöntemyle Öğretm Elemanı Performans Değerlendrmes 471 Results of Grey Relatonal Analyss Calculaton and assessment of grey relatonal coeffcents: The raw data used n soluton of Grey Relatonal Analyss are converted nto a chart for the frst fve people and 170 th person and gven n Table 1. (Because of space lmtatons, not all raw data are gven.) The fgure n the last column of the table s the S0 reference number,.e. grey relaton element. Table 2: Raw Data Used n Grey Relatonal Analyss S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S0 K K K K K K Table 3 gves the dfference data of the data n Table 2. The questons of the survey are prepared based on Lkert scale (1: Strongly Dsagree; 5: Strongly Agree). Dfference data of the analyss are obtaned by subtractng the sequence number of the answer from representaton value of each responder (S0-SS). For nstance, when creatng the dfference sequence: S 0 S Usng the dfferences, maxmum (max(s0-ss)) and mnmum(mn(s0- SS)) values are obtaned to create Table 3. Table 3: Dfference Data used n Grey Relaton Soluton S0-S1 S0-S2 S0-S3 S0-S4 S0-S5 S0-S6. S0-S13 S0-S14 S0-S15 K K K K K

8 472 Necla TEKTAŞ K Mak Mn For each responder and varable, grey relaton grades of varables are obtaned by takng the average of grey relaton coeffcents calculated by the grey relaton coeffcent formula. Table 4: Grey Relaton Coeffcents and Grey Relaton Grades of Varables ( k s, Ss ) S1 S2 S3 S4 S5 S15 K1 0,33 0,33 0,40 0,40 0,40 0,33 K2 0,67 0,40 0,50 0,67 0,67 0,67 K3 1,00 1,00 0,67 1,00 1,00 1,00 K4 0,40 0,33 1,00 0,50 0,33 0,67 K5 1,00 1,00 1,00 1,00 1,00 0,50 Average Grey Relaton Coeffcents=Grey Relaton Grades S S 0,64 0,65 0,69 0,67 0,65 0,64 Grey relaton grades shown wth Γ n Table 5 are arranged n order of magntude. Table 5: Order of Magntude of Grey Relaton Grades of Varables 0,73 0,68 0,67 0,65 0,65 0,65 0,64 0,64 0,63 0,63 0,63 0,62 0,61 0,60 0,59 S11 S3 S10 S4 S15 S12 S8 S5 S7 S6 S1 S13 S2 S14 S9 When grey relaton grades are ordered by magntude, the responses to the statements below can be sad to move away from strongly agree and move towards dsagree: S11) My nstructor provdes adequate gudance durng course regstratons. S3) Instructors are competent n ther own feld, have comprehensve knowledge. S10) My advsor gves me suffcent tme and helps me n solvng varous problems.

9 Gr İlşk Analz Yöntemyle Öğretm Elemanı Performans Değerlendrmes 473 S4) Instructors are usng class hours effectvely. S15) Instructors gve us opportunty to express our opnons and thoughts n classroom. S12) When I frst arrve school, my advsor nforms me about the school, lecturers and the rules I should obey. S8) Instructors' approach and communcaton wth the students are good. S5) Instructors follow the recent developments n ther feld and share these wth the students. S7) Instructors are far and mpartal towards the students. S6) Instructors provde nformaton about the students' status (attendance, performance, partcpaton to the lecture, exam results) durng the class. S1) Instructors take ther classes serously and prepares well. S13) Our advsors gve us adequate nformaton about job opportuntes. S2) Instructors arrve class on tme and look presentable. S14) Instructors brng speakers from the sector, organze feld trps and provde nformaton on relevant matters (career days, varous meetngs, job postngs). S9) It s possble to reach nstructors outsde the class hours. Concluson and Dscusson In Grey Relatonal Analyss used for assessng the nstructor performance evaluaton survey, students' answers to the statements are used drectly nstead of frequences. Also, an analyss s conducted for 1 as Strongly Dsagree and 5 as Strongly Agree as Grey Relaton Value. In performance evaluaton of nstructor, the frst tem s the one on academc advsng. Item on teachng performance comes n second. It becomes evdent from ths fact that when evaluatng the performance of an nstructor, actvtes both nsde and outsde the classroom are taken nto consderaton. The tems that come last ndcate that nstructors are not able to allocate adequate tme to ther students because of mmense course load n addton to hgh number of advsee students. As a concluson, when the data set s lmted and low n number, n other words when the sample sze s small, varables are ntermttent and composed of contnuous random varables, Grey Relatonal Analyss s proven to be an applcable and advantageous technque that can be appled n uncertan stuatons wthout necesstatng a probablty dstrbuton. In analyss of the data, usng drect answers of the responders nstead of frequences, smple, specfc and net calculaton steps nstead of a number of functonal processes and ablty to analyze one-to-one nterrelatons of

10 474 Necla TEKTAŞ data n a numercal manner make Grey Relatonal Analyss an advantageous one. When the advantages of the Grey Relatonal Analyss s consdered together wth the fact that t s of a determnstc type, contans no assumptons, and s a method that s easy to use and practcal allows us to regard the analyss as a non-parametrc technque. REFERENCES BARUTÇUGIL, İ. (2002). Performans Yönetm. İstanbul: Karyer Yayıncılık. BASBUG, G., UNSAL, P.(2009) Kurulacak Br Performans Değerlendrmes Hakkında Akademk Personeln Görüşler: Br Kamu Ünverstesnde Yürütülen Anket Çalışması, İstanbul Ünverstes Pskoloj Çalışmaları Dergs, pp.1-24 DENG,J. (1982). Control Problems of Grey System, System and Control Letters,1(5), pp DENG, J. (1989) Introducton to Grey System Theory, Journal of Grey System, 1(1), pp CHIEN-HO WU (2007). On the Applcaton of Grey Relatonal Analyss and RIDIT Analyss to Lkert Scale Surveys Internatonal Mathematcal Forum, 2, no. 14, pp GOKMEN, G., AKINCI,T.Ç., TEKTAŞ, M., ONAT,N., KOCYIGIT, G., TEKTAŞ, M., TEKTAS, N.(2010) Evaluaton of student performance n laboratory applcatons usng fuzzy logc Proceda Socal and Behavoral Scences 2, pp IÇLI,G.E.& VURAL, B.B.(2010) Toplam Kalte Yönetm ve Uygulamaları Çerçevesnde Kırklarel Ünverstes Meslek Yüksekokulları Öğrenc Memnunyet Araştırması, M.Ü. İ.İ.B.F.Dergs, 2010, Clt XXVIII, sayı 1, p KALAYCI, N. (2009). Yükseköğretm kurumlarında akademsyenlern öğretm performansını değerlendrme sürecnde kullanılan yöntemler. Kuram ve Uygulamada Eğtm Yönetm. 15 (60), pp KALAYCI, N. & ÇIMEN, O. (2011) Öğretm Elemanı ve Ders Değerlendrme (ÖEDD)Anketlerne Verlen Puanların Bazı Değşkenlere Göre İncelenmes Uluslararası Yükseköğretm Kongres: Yen Yönelşler ve Sorunlar (UYK-2011), Mayıs, İstanbul. KEPIR-SINANGIL, H. (1998). Örgütsel Performans: Değerlendrmes ve Yönetm. S. Tevrüz (Ed.), Endüstr ve Örgüt Pskolojs 2 çnde (pp ). Ankara: Türk Pskologlar Derneğ Yayınları. RUTHERFORD, D. (1988). Performance apprasal: A Survey of Academc Staff Opnon. Studes n Hgher Educaton, 13, pp

11 Gr İlşk Analz Yöntemyle Öğretm Elemanı Performans Değerlendrmes 475 SIMMONS, J. (2002). An "Expert Wtness" Perspectve on Performance Apprasal n Unverstes and Colleges. Employee Relatons, 24, pp TEKTAŞ, N., TEKTAŞ, M., POLAT,Z., TOPUZ, A.S.(2010) Comparng the expectatons of undergraduate and graduate degree students, Proceda Socal and Behavoral Scences 2, pp WU CHIEN-HO. (2007), On the Applcaton of Grey Relatonal Analyss and RIDIT Analyss to Lkert Scale Surveys, Internatonal Mathematcal Forum, 2, no.14, pp