Classification of individuals according to their opinions on acquiring necessary competencies within their studies

31 ACC JOURNAL 2016, Volume 22, Issue 3 DOI: 10.15240/ ul/004/2016-3-004 CLASSIFICATION OF INDIVIDUALS ACCORDING TO THEIR OPINIONS ON ACQUIRING NECESSARY COMPETENCIES WITHIN THEIR STUDIES Ma a Žambocho á1; Ane a Kulhano á2 Jan E angelis a Pu kyně Uni e si y in Ús í nad Labem, Facul y o Social and Economic S udies, Depa men o Ma hema ics and Compu e Science, Moske ská 54, 400 96 Ús í nad Labem, Czech Republic e-mail: 1ma a.zambocho [email protected]; 2[email p o ec ed] Abs ak New imes equi e new skills in new a eas. This is he eason why he s uc u e and syllabus a e becoming obsole e. Many eams o eache s s udy new con en o eaching ma e ials and new ways o eaching. These e o s a e suppo ed by di e en o ganiza ions, beginning wi h he leade ship o schools ac oss he s a e au ho i ies o he bodies o he Eu opean Union. In ou s udy, s uden s’ poin o iew is examined. The g oup o esponden s consis s o bo h po en ial and cu en s uden s, as well as he o me ones. The emphasis is laid on inding skills which "o dina y people", acco ding o hei opinion, need o hei pe sonal and p o essional li e. The aim o he esea ch was o de e mine he knowledge and skills ha should be pa o he s udies acco ding o hese people, and on he o he hand, o poin ou hings which hey would gladly gi e up. The su ey was conduc ed among schools a all le els, al hough he p io i y was he college le el s udies. Ou expec a ions we e con i med in many ways. Howe e , many o he equi emen s we e inconsis en wi h he ac ual s a e o educa ion. The knowledge gained in ou esea ch could help in c ea ing ack objec s, hei con en and he o m o eaching. Keywo ds P o essional compe ence; Unde g adua e s uden s; Explo a ion; Tes s o hypo hesis; Decision ees. In oduc ion New ime equi es new knowledge in new ields. Tha is he eason why he s uc u e and syllabi o eaching become obsole e. The p oblem wi h imp o ing necessa y compe ences wi hin eaching a di e en school le els has been sol ed by p o essionals om many coun ies (e.g. Russia, Belgium, Spain, USA, I an, he Ne he lands, o Cyp us). I is e y impo an o adap o inc easing specializa ion and di e en ia ion o knowledge and o use p ope me hod o eaching, especially a uni e si ies [15]. The e o e, he me hodology o app op ia e p o essional compe ences has o be explo ed [5], [11], [22]. Nowadays, he p oblem wi h IT compe ences is ele an . Se e al concep ual amewo ks o he de elopmen o p o essional compe ences based on hei own empi ic esea ch we e p esen ed [1]. Mo eo e , e hinking he ield o o eign languages educa ion is also cu en [16]. The c i icism o changes in syllabi and concep s o app oach o pupils in he ield o language educa ion is p esen [7]. Th ough he esea ch among s uden s i was concluded ha he ealized changes had no b ough expec ed imp o emen , pa icula ly conce ning communica ion abili ies. Acco ding o he opinion o esponden s, schools w ongly p ima ily ocus on imp o emen o g amma , whe eas he communica ion compe ence is neglec ed. 32 The esea ch among Japanese s uden s a Ame ican uni e si ies showed ha he g ea es sho age is in he ield o communica ion and in e ac i e and p esen a ion compe ences [16]. The e is a demand o s eng hening he ole o expe ience and p ac ical knowledge du ing o eign language s udies as well as emphasis on communica ion abili ies [10]. Finding new app oaches, me hods and means o c ea ion o o eign language communica i e compe ences o u u e uni e si y o eign language eache s is impo an [6]. Excep o IT and o eign language ields, he e a e also o he ields o co e ed educa ion, e.g. compe ence in na u al science [14], [17] o en ep eneu ial educa ion [3], [9], [12]. 1 Resea ch Objec i es Resea ch was epea edly pe o med wi hin s uden ac i i ies a he Facul y o Social and Economic S udies o Jan E angelis a Pu kyně Uni e si y in Ús í nad Labem be ween he yea s 2012 – 2014. The i s phase o p ocessing is men ioned in a icle [28]. By using a ques ionnai e su ey, his esea ch moni o ed opinions on he le el o in o ma ion acqui ed a school, on demands, and on eali y. The esea ch ocused on people esiding in he Ús í Region o he Czech Republic. The esponden s we e people o e 18 yea s old who inished hei s udies no mo e han 15 yea s be o e hei pa icipa ion in he esea ch. Wi hin p ocessing his esea ch, esponden s we e classi ied om di e en poin s o iew, namely by he age, gende , le el o educa ion, he ield o s udy and also by hei opinions on he quali y o educa ion and acqui ing di e en kinds o compe ences. Two basic ypes o classi ica ion we e used – a clus e analysis and classi ica ion ees. The objec i e o his classi ica ion was o analyse subjec i e esponden s’ iew o compe ences ha school sys em p o ided hem. The objec i e was also o analyse esponden s’ iew o compe ences ha we e, acco ding o hei opinion, impo an du ing discha ge o hei occupa ion. The ocus was pa icula ly pu on he s uc u e o hese compe ences and hei le el. 2 Da a The ques ionnai e was answe ed by 954 esponden s olde han 15 yea s. 181 o hese ques ionnai es we e answe ed in 2012, 284 in 2013, and 489 in 2014. Men ep esen ed 47% o esponden s. The a e age age o esponden s was 33. Sou ce: Own esea ch Fig. 1: S uc u e o he esponden s in e ms o educa ion a ained Sou ce: Own esea ch Fig. 2: S uc u e o he esponden s in e ms o numbe o p e ious jobs Thei educa ion s uc u e is appa en in Fig. 1. I is ob ious ha seconda y school g adua es p edomina ed in he esea ch. The s uc u e o esponden s in e ms o he numbe o p e ious employmen s is appa en in Fig. 2.The educa ion s uc u e is appa en in Fig. 3. I is ob ious ha business school g adua es p edomina ed in he esea ch. The second mos equen ield is he echnical one. I is ob ious in Fig. 4 ha employees p edomina ed in he esea ch in e ms 0% 10% 20% 30% 40% 50% 60% p ima y educa ion seconda y educa ion bachelo 's deg ee mas e 's deg ee Ph.D.deg e 0% 5% 10% 15% 20% 25% 0 1 2 3 4 5 6 7 mo e 33 o cu en job ocus. Hal o all add essed employees we e ep esen ed by adminis a i e employees. Almos a qua e o esponden s we e ep esen ed by s uden s. Sou ce: Own esea ch Fig. 3: S uc u e o he esponden s in e ms o s udy ocus Sou ce: Own esea ch Fig. 4: S uc u e o he esponden s in e ms o cu en job ocus I was also ound ou ha 74% o esponden s exp essed sa is ac ion wi h hei ield o educa ion, 35% o esponden s a e s udying a uni e si y a he momen (ei he ull- ime o pa - ime apa om being employed), and 38% o esponden s plan u he educa ion. 3 Me hods The ques ionnai e mos ly consis ed o closed ques ions. Tha means ha esponden s could choose om p o ided answe s. They we e o en answe s assigned o a i e-g ade scale. Only a mino i y o ques ions was open ques ions ha we e o speci y he answe s om p e ious closed ques ions. Acco ding o he cha ac e o ques ions quan i ies o di e en ypes we e o med, nominal ca ego ical quan i ies, o dinal quan i ies as well as ca dinal con inuous ones. While p ocessing he da a, di e en kinds o s a is ical hypo hesis es ing we e used. Di e se g oups o esponden s we e compa ed om di e en poin s o iew. In he case o ca ego ical non-scale quan i ies he chi-squa e es o independence was used. In he case o o dinal a iables (a scale ques ions) he F iedman es was used o assess he le el o answe s in di e en g oups. In he case o con inuous quan i ies a pai o es s was used: he F- es o he equali y o wo a iances and hen he S uden ’s - es o compa e mean alues (based on he esul o he F- es , he - es was de e mined wi h compa able a iances, in he case o he - es wi h incompa able a iances). See he usage desc ip ion in [18]. All es s ha e been done a a 5% le el o signi icance. The null hypo hesis was he equali y o obse ed quan i ies ( a iances, popula ion means) o indi idual se s. One o he main s eps o ou p ocessing was he classi ica ion o esponden s in e ms o hei opinions wi h ega d o educa ion quali y wi hin he meaning o acqui ing knowledge necessa y o being success ul a wo k. Fo his pu pose, wo basic classi ica ion me hods we e used: he clus e analysis and decision ees. The wo k uses he me hod o he ques ionnai e-based su eys and da a p ocessing wi h he use o ele an su ey me hods desc ibed, o example, by [8] o [2]. 3.1 Clus e Analysis The clus e analysis is one o he me hods o educa ion wi hou a eache [4], [20]. I deals wi h simila i y o da a subjec s. I add esses di ision o he g oup o subjec s in o se e al p e iously non-speci ied g oups (clus e s) so ha he subjec s wi hin indi idual clus e s a e as 34 mos simila as possible and subjec s om di e en clus e s a e as leas simila as possible. This happens ia minimiza ion o he selec ed pu pose unc ion. The clus e analysis may be pe o med by many di e en me hods. Indi idual me hods di e by a ious me hods o de e mining simila i y o subjec s (simila i y a es) and also by clus e ing me hods (e.g. hie a chical and non-hie a chical). While selec ing he clus e analysis me hod i depends on whe he he sou ce da a o agg ega ed da a is di ec ly a ailable (e.g. he able o equencies o simila i y ma ix). I he sou ce da a a e a ailable, he selec ion me hod depends on he ype o a iables (nominal, o dinal, quan i a i e a iables). In o de o p ocess ou da a, he selec ion o he hie a chical me hod was no sui able because o a ela i ely la ge numbe o subjec s. The k-means algo i hm is in ended o clus e ing subjec s desc ibed by quan i a i e a iables, which was no ou case. In o de o use his me hod, i would be necessa y o p e-p ocess he da a by bina iza ion, which means by a ans e o each ca ego ical a iable o se e al bina y a iables (a a iable acqui ing only 0 and 1 alues). The mos sui able me hod o p ocessing he da a was he wo-s ep me hod. The p inciples o he wo-s ep clus e analysis a e desc ibed o example in [19]. This me hod uses he BIRCH (Balanced I e a i e Reducing and Clus e ing using Hie a chies) algo i hm which is desc ibed in de ail in [26], o [27]. The algo i hm c ea es a so-called CF- ee o which con inuously incoming da a a e assigned. The ad an age o his p ocedu e is ha i goes h ough he da a se only once. The disad an age is sensi i i y o he o de o incoming da a poin s. 3.2 Decision T ees Decision ees belong o he g oup o me hods o educa ion wi h a eache . In his case he decision-making ules o assigning subjec s o classes a e c ea ed on he basis o he eaching ( aining) g oup. Va ious ypes o decision ees a e a widely used g oup o ees u ilized in da a models. Decision ees a e s uc u es ha ecu si ely so ou obse ed da a acco ding o ce ain decision-making c i e ia. The ee oo ep esen s he en i e popula ion se . The in e nal nodes o he ee ep esen subse s o he popula ion se . The alues o he explained a iable can be ead in ee lea es. Two ypes o decision ees a e used, classi ica ion ees (each lea has a class assigned) and eg ess ees (each lea has a cons an assigned – es ima e o he explained a iable alue). The decision ee is c ea ed ecu si ely by spli ing he space o p edic o alues (explaining a iables) on he basis o inding a ques ion (b anching condi ion) ha spli s he space o obse ed da a in o subse s as well as possible, ha means i maximizes he c i e ion o spli ing quali y (so-called spli ing c i e ion). The spli ing p ocess s ops i he so-called s opping ule is me . Ano he s ep o he algo i hm is ee p uning. I is necessa y o de e mine he “p ope ” ee size ( oo small ees do no su icien ly cha ac e ize all ela ions wi hin da a; oo big ees include also a bi a y da a p ope ies in he desc ip ion). Sub- ees o he ee ha a e c ea ed by he cons uc ion algo i hm a e gene a ed and he quali y o sub- ees gene aliza ion is compa ed (how well hey depic da a). The p ocedu e may be ha decision ees a e ini ially c ea ed based on so-called aining da a and hen hei quali y is alida ed on so-called es ing da a. Ano he me hod is c oss alida ion when all da a a e used o c ea e a ee and i s sub- ees. A e wa ds he da a a e di ided in o se e al disjunc i e pa s o a simila size and g adually one da a pa is emo ed om he se . By using he o med da a se s he quali y o a ee and i s sub- ees is alida ed. Such a sub- 35 ee which has he lowes es ima e o an ac ual e o is selec ed. I mo e sub- ees wi h a compa able es ima e o an ac ual e o exis , he smalles sub- ee is selec ed. A huge amoun o algo i hms was de eloped in o de o c ea e decision ees. The mos used a e CART, ID3, C4.5, AID, CHAID, and QUEST. Th ee ypes we e used in ou wo k. Thei algo i hms a e implemen ed in he SPSS s a is ical sys em. The CART algo i hm (Classi ica ion and Reg ession ees) [21], [25] can be used in he case whe e he e is one o mo e explaining a iables a ailable. These a iables may be ei he con inuous o ca ego ical (o dinal and nominal). Fu he mo e, in ou wo k a a iable which may be also ca ego ical (nominal and o dinal) o con inuous was once explained. The CHAID algo i hm (Chi-squa ed Au oma ic In e ac ion De ec o ) [25] is a modi ica ion o he AID me hod o a ca ego ical dependen a iable. The esul is non-bina y ees. Fo es ing, he me hod uses he chi-squa e es . The QUEST algo i hm [13] is usable only o he nominal dependen a iable. Simila ly o he CART algo i hm, only bina y ees a e c ea ed. Unlike he CART me hod, which pe o ms he selec ion o he a iable o spli ing he node and selec ion o he spli ing poin du ing he ee c ea ion a he same ime, he QUEST me hod pe o ms hese asks sepa a ely. The QUEST ( o Quick, Unbiased, E icien , S a is ical T ee) me hod elimina es se e al disad an ages o algo i hms using he exhaus i e inding (e.g. CART), such as ime expendi u e o p ocessing, dec ease in esul gene ali y. 4 Resul s and Discussion While compa ing he da a se s om di e en yea s, no s a is ically impo an di e ence in indi idual se s was ound in mos cases ( he esul ing p- alues we e conside ably highe han 5%, hey we e wi hin a ange o 0.26 o 0.74). Then, i was possible o con inue in wo king wi h all se s in summa y. The only excep ion was he issue o uni e si y cu en ly s udied a . This ques ion had a wide ange o answe s and he composi ion in indi idual yea s was e y di e en . Fo his eason, his ques ion was no included in he main p ocessing. One o ou main esea ch ques ions was he impo ance o acqui ing compe ences in IT and o eign language ields. The e o e he ocus o his esea ch is on hese subjec s in mo e de ail in se e al de ailed ques ions. A e pe o ming he s a is ical e alua ion he conclusion was ha imp o ing abili ies in hese wo ields would be p esumed by he majo i y o people (almos 95%) e en beyond school educa ion. The es s p o ed ha his opinion was sha ed by all g oups o esponden s. Only he p- alue 0.12 poin ed ou sligh di e ence o unemployed people. This ca ego y o people p e e s ex acu icula imp o emen in 98%. Howe e , he e was a con adic ion ound in equi emen s o he ype o compe ences in hese ields. The esponden s we e sa is ied wi h he ex en o hou s allo men o subjec s ela ed o IT and o eign languages bu , con a ily, hey we e dissa is ied wi h he amoun and kind o knowledge acqui ed. This ac was mos e iden a uni e si y educa ion (p- alue o IT was 0.032, o o eign languages i was 0.028). By a close esea ch i was ound ha jus hese esponden s eel insu icien connec ion o school and p ac ice. The e o e, i is possible o conclude ha hese subjec s a e augh a school a he heo e ically and less p ac ically han he esponden s would need in hei u he employmen . Ano he pa o ques ions was ocused on imp o ing compe ences ha do no di ec ly belong o speci ic subjec s. An example o hese compe ences is an abili y o lea n, logic, s ess esis ance, eliabili y, adap a ion o condi ions, wo king wi h people, sel -p esen a ion, e c. Wi hin he en i e in es iga ed se , su p isingly logic had he highes s a is ical di e ence (p alue 0.017) he imp o emen o which would be equi ed by 85% o people ac oss educa ion 36 le els and ields. Ano he conside able g oup was he compe ence o wo king wi h people, eliabili y, abili y o lea n and adap o speci ic condi ions and sel -p esen a ion (p alue wi hin 0.028 – 0.039). All hese compe ences we e suppo ed by app oxima ely 60% o he esponden s. A some o hese compe ences, s a is ical es s showed di e ences wi hin indi idual g oups o esponden s. The abili y o adap is, o example, s ongly suppo ed by people wi hou he seconda y school-lea ing exam. The abili y o wo k wi h people is conside ably suppo ed by adminis a i e employees. People o a is ic specializa ion and execu i es would like o de elop he abili y o sel -p esen . As al eady s a ed abo e, one o ou main s eps o ou p ocessing was he classi ica ion o he esponden s in e ms o hei opinions wi h ega d o educa ion quali y wi hin he meaning o acqui ing knowledge necessa y o being success ul a wo k. In he i s classi ica ion s age, he clus e analysis was ca ied ou in o de o di ide he esponden s in o g oups wi h simila opinions. As a classi ica ion a iable a iad was chosen, desc ibing he a e o sa is ac ion wi h he school s udied a , wi h he a e o willingness o be educa ed in IT and o eign languages e en ou side school. The wo-s ep me hod di ided he esponden s in o i e g oups wi h qui e a good quali y o esul ing clus e ing. This is appa en in Fig. 5. Sou ce: Own esea ch Fig. 5: Clus e quali y o model wi h 3 inpu s and 5 clus e s Fu he mo e, h ee algo i hms we e used o c ea e decision ees, namely CRT, CHAID, and QUEST. The aim o hese ees was o ind he desc ip ion o c ea ed clus e s in e ms o he obse ed esponden s’ opinions. The second objec i e was o ind he desc ip ion o c ea ed clus e s in e ms o he esponden s’ iden i ica ion. In bo h cases he esponden ’s a ilia ion o indi idual clus e s was chosen as he explained a iable. In he i s case, he explained a iables we e he iad o a iables iden ical o classi ying a iables o he clus e analysis. In he second case, he explained a iables we e iden i ica ion da a o he esponden s, namely gende , age, educa ion deg ee and ield, cu en employmen , and da a on cu en o u u e educa ion. The quali y o he second g oup o decision ees was e y high. Risk es ima e was wi hin 0.080 o 0.177. This means ha he success ulness o classi ica ion was wi hin 82.3% o 92.0%. In his case, he ee c ea ed by using he QUEST algo i hm was o he highes quali y. Un o una ely, he quali y o he i s g oup o decision ees was a he low. Risk es ima e was wi hin 0.502 o 0.556. This means ha he success ulness o classi ica ion was a low le el wi hin 44.4% o 49.8%. Despi e his, i is possible o come o se e al in e es ing conclusions abou ou esponden s. En ep eneu s, execu i es, and non-adminis a i e employees a e mo e o less sa is ied wi h he le el o he school hey s udied a in e ms o he acqui ed knowledge and hey a e s ongly willing o be educa ed wi hin IT e en ou side school educa ion. S uden s o seconda y gene al educa ion and bachelo s o humanis ic and law s udies a e no sa is ied a 37 all wi h he le el o he school hey s udied a in e ms o he acqui ed knowledge bu hey a e no e y willing o be educa ed ou side school, no e en wi hin IT o o eign languages. G adua es om lowe school educa ion o pedagogical, medical o IT ields a e conside ably sa is ied wi h he le el o he school hey s udied a and hey a e willing o be u he educa ed wi hin IT and o eign languages. G adua es om seconda y p o essional schools, economy and IT bachelo s udies a e s ongly willing o be u he educa ed wi hin IT. In he second classi ica ion s age, he clus e analysis was ca ied ou in o de o di ide he esponden s in o g oups wi h simila opinions on di e en s udied subjec s. As he classi ying a iables, he e we e wen y a iables selec ed, exp essing de ailed esponden s’ opinions on eaching hese selec ed subjec s (among hem o example mo he ongue, ma hs, o eign languages, PC wo k, psychology, law basics, inancial li e acy bu also gene al o e iew). The wo-s ep me hod di ided he esponden s in o wo g oups. The quali y o he esul ing clus e ing was somehow lowe han in he p e ious case. Ye i is s ill on he usabili y h eshold. Fu he mo e, h ee algo i hms we e used again o c ea e decision ees, namely CRT, CHAID, and QUEST. The aim o c ea ing hese ees was again o ind he desc ip ion o he c ea ed clus e s. A i s , he aim was o ind he desc ip ion o he c ea ed clus e s in e ms o he esponden s’ iden i ica ion. The second desc ip ion was in e ms o obse ed esponden s’ opinions. In bo h cases, he esponden s’ a ilia ion o one o he wo esul ing clus e s was chosen as he explained a iable. In he i s case, he explained a iables we e iden i ica ion da a o he esponden s, namely gende , age, educa ion deg ee and ield, cu en employmen , and da a on cu en o u u e educa ion. In he second case, he e we e chosen wen y a iables iden ical o classi ying a iables o he clus e analysis as he explained a iables. The quali y o he g oup o decision ees was balanced and e y high. Risk es ima e was wi hin 0.088 o 0.116. This means ha he success ulness o classi ica ion was wi hin 88.4% o 91.2%. In his case, he ee c ea ed by using he CRT algo i hm was o he highes quali y. This ee is shown in Fig. 6. The quali y o he second g oup o decision ees was highe han in he case o he p e ious clus e ing. Risk es ima e was wi hin 0.275 o 0.354. This means ha he success ulness o classi ica ion was a well-usable le el, exac ly wi hin 64.6% o 72.6%. The mos dis inc esul s o his classi ica ion can be summa ized in o he ollowing conclusions. G adua es o lowe schools o humanis ic, pedagogical, na u e-science s udies and schools o ien ed on IT, who g adua ed a he u n o he cen u y, ha e he opinion ha mo he ongue and PC wo k should be augh o he whole du a ion o educa ion. They conside he abili y o adap o ex e nal condi ions o be an impo an p ope y ha should be suppo ed by he educa ion p ocess. Young esponden s o he age up o 24 yea s who g adua ed a gene al, economic and a is ic schools o a seconda y o bachelo le el belong o a g oup ha would p e e o be educa ed in o eign languages o he whole du a ion o hei s udies. On he con a y, hey do no conside mo he ongue educa ion o he whole du a ion o hei s udies o be bene icial. They do no ha e any clea opinion on eaching exac subjec s and IT. In he hi d, he las s age o p ocessing, selec ed esponden s’ answe s o indi idual ques ions ega ding hei sa is ac ion we e chosen as he classi ying a iables. The wo-s ep me hod c ea ed h ee clus e s. The in o ma ion abou he esponden s’ a ilia ion o he clus e in he p ocessed a iables was included. The quali y o he esul ing clus e ing was e y high. 38 Sou ce: Own esea ch Fig. 6: Classi ica ion ee c ea ed using he algo i hm CRT 39 In ano he s ep o p ocessing, all h ee ypes o classi ica ion ees we e c ea ed again. The a ilia ion o he clus e was chosen as he explained a iable; and he answe s o indi idual ques ions ega ding he esponden ’s opinions as explaining a iables. Risk es ima e is anged om 0.142 o 0.235 o all he c ea ed ees. This means ha he success a e o classi ica ion o objec s anged om 85.8% o 76.5%. The quali y o models was adequa e. Fu he mo e, he second iad o decision ees was c ea ed. The a ilia ion o a clus e as he explained a iable was chosen again. Unlike in he p e ious p ocessing, he answe s o ques ions ega ding he ype o esponden as he explaining a iables we e chosen. Risk es ima e is anged om 0.348 o 0.483 o all he c ea ed ees. This means ha he success a e o classi ica ion o objec s anged om 65.2% o 51.7%. The e o e, he quali y o models was no e y high. Bu his quali y can be conside ed su icien . The esul o ou classi ica ion is he ollowing indings. The mos sa is ied esponden is o e 35 yea s old, o economic and echnical specializa ion. These esponden s mos ly miss educa ion wi hin o eign languages and ICT. People wi h pedagogical specializa ion a e less sa is ied bu hey ha e no speci ic wishes. On he con a y, he leas sa is ied ones a e he esponden s wi h seconda y economic educa ion wo king in adminis a ion, o he age be ween 23 and 35. They mos ly miss highe connec ion o school and p ac ice as well as highe quali y o eache s. Apa om o eign languages and ICT hey would p e e mo e educa ion in he ield o en ep eneu ial compe ences, logic bu also psychology and gene al o e iew. They would also p e e educa ion in he o m o discussion and assis ance wi h imp o ing sel -p esen a ion abili y. The esponden s o a is ic specializa ion ha e no conside able objec ions o he quali y o he school sys em. They would p e e mo e c ea i i y. I would be e en p e e ed by medical s uden s. Apa om his, hey would p e e be e connec ion o p ac ice and imp o emen s wi hin wo king wi h people. Conclusion Ou esea ch shows ha , despi e umou s, people a e gene ally mo e o less sa is ied wi h he quali y o he school sys em in he Czech Republic. This issue is mos c i ically pe cei ed by people o seconda y economic educa ion who wo k in adminis a ion. The bigges p oblem o he Czech school sys em is seen in he connec ion o school and p ac ice. This con i ms he iews o he au ho s o he a icle [23]. The example o he de elopmen model o compe encies which g adua es need in he labou ma ke is also desc ibed in he a icle [24]. The e is also dissa is ac ion wi h he compe encies o eache s. Ob iously, he e is a demand o mo e in ensi e o eign language and ICT educa ion ac oss all esponden g oups. These subjec s should be augh o he whole du a ion o educa ion. In his ield, he e is also dissa is ac ion wi h he quali y o eache s and eaching me hods. In bo h ields he esponden s would p e e less heo y and mo e p ac ical expe ience. This conclusion is in compliance wi h conclusions o se e al in e na ional s udies, such as [7], [16], [11], o [6]. Un o una ely, ou esea ch also implies ha no all hose who pe cei e insu icien compe ences in hese ields also ha e a need o be u he educa ed ou side he school sys em i sel . This mainly applies o g adua es om seconda y gene al educa ion and humanis ic and law bachelo s udies. Howe e , he iew o o he educa ion o ien a ion is mo e di e en ia ed acco ding o esponden g oups. Technically o ien ed esponden s pe cei e insu icien quali y o ma hs educa ion in he Czech school sys em. Humanis ic- and economic-o ien ed esponden s would p e e o ha e mo e psychology, logic, en ep eneu ial educa ion and gene al o e iew. Responden s wi h medical and a is ic specializa ion would like o ge highe suppo o c ea i i y. Responden s o he wo la e g oups would p e e educa ion in he ield o wo king wi h people. I is he e o e ob ious ha highe di e en ia ion o subjec s a di e en school