TAP CHi GONG THlfdNG
MO HINH MO PHONG
PHAN TICH DlJ BAO P H U ^ N G PHAP DAY HOC TRAI NGHIEM
• N G U Y I N NAM THANG
TOM TAT:
Bdi vidt nghien cffu phffdng phap day hpc trai nghiem cua David Kolb tich hdp phffdng phap day hpc hieu qua cua Dai hpc Main ffng dung md hinh md phdng Monte Carlo vdi sff trd giup cua ph^n mdm phan tich dff lieu Oracle Crystall Ball dff bao ket qua day hpc trai nghidm theo chuan dau ra cua Bloom md rdng giup ngffdi day, cdc cd sd ddo tao va nha hoach dinh chinh sdch co them mdt cdng cu phdn tich dff bao ket qua day hpc trai nghiem, tff dd dieu chinh cdch thtfc day hoe tffdng ffng dat ky vpng mong mudn xdy dffng chien Iffdc giao due ddo tao.
Tiif khda: Day hpc tr^i nghidm, day hoc hi§u qua, md hinh dff bdo kdt qua day hoc trdi nghiem.
1. Gidi thieu
Thd gidi da di nhffng bffdc dai trong sff phat tridn cdc phffdng phdp day hpc, trong dd cd phffdng phdp day hpc trai nghiem the hien mdi quan he giffa ngffdi day va hpc la mdt trong nhffng ly thuyet dffdc ffng dung rong rai. Tren ed sd chffdng trinh dao tao va gido trinh da dffdc xay dffng theo chudn dau ra, giang vien phai chpn phffdng phdp giang day, con viec day cai gi (What) khdng quan trpng ma laphaiday nhffthe nao(How).Dovay,ddingu giang vien can phai dffdc tap hud'n, trang bi cac ky nang ve phffdng phap day hoc trai nghiem trffdc khi lien hanh day.
2. Cd sof ly thuyet
Nacy Tobler (2009), cho tha'y ket qua khao sat hieu qua cua cdc chffdng tnnh dao lao mang tinh trdi nghiem cao ve ca kien thffc, tff duy va thai dp trong nhffng chffdng trinh trai nghiem deu co sff thay ddi manh me so vdi chffdng trinh khdng trai nghiem nhd't la vd ky nang.
Dai hpc Harvard, co nhieu phffdng phap day hpc, nhidu cdch tidp can khde nhau, ve cd ban cd 2
Hinh 1: Hieu qua chUdng trinh dao tao co trai nghiem so vdi khong co trai nghiem
phffdng phdp dd la: phffdng phdp day hpc chd ddng bao gdm phffdng phap dpng nao (Osbom, 1963), phffdng phdp tffng ddi chia s^ suy nghi (Lyman, 1987), phffdng phdp chu de (Hmelo-Silver, 2004), phffdng phap nhom, phffdng phap ddng vai (Kriizerow, 1990) va phffdng phap day hoc trai nghiem bao gdm phffdng phap dff an (Bransford v^
Stein,1993), phffdng phdp tinh hudng (Scholz vS 112 Sd 10-Thang 5/2019
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Olaf, 2002), phffdng phap cdng ddng (Jacoby,1996), phffdng phap mo phdng (Robert vd Casella, 2004).
Nhff vay, cd nhidu phffdng phap day hpc khac nhau tren the gidi, tac gia chpn phffdng phap day hpc trdi nghiem cua David Kolb tich hdp vdi phffdng phdp xac dinh ket qua day hpc cua dai hpc Main USA cho nghien cffu nay.
2.1. PhUcfng phdp day hoc trdi nghiem cua David Kolb
David Kolb (1939) vd cdc nha tdm ly hpc, giao due hpc da dffa ra hpc thuyet ve phffdng phdp day ngffdi hpc trdi nghiem (Experimental Teaching Methods) vdi quan diem viec hpc la qud trinh tao ra tri thffc mdi tren ed sd trai nghiem thffc td ke thffa nhffng kien thffc, ky nang, phdn tich, danh gid, kinh nghidm da co tff trffdc. Phffdng phap day hpc trai nghiem thffdng dffdc cho la dd'i ngffdc vdi phffdng phdp day hpc ban Ian (Academic learning) la qua trinh dat dffde thong tin thdng qua nghien cffu mdt van dd ma khdng can kinh nghiem trffc tiep (Direct experience). David Kolb (2013), ly thuye't day ngffdi hpc thdng qua trai nghifem dffdc dp dung trong rat nhieu nganh va Iinh vffc nhff cdc chffdng trinh ddo tao chuyen nghiep (Reese, 1998; Kolb, 1995) va gido due dai hpc (Menlkowski, 2000).
Ndi dung chu ydu cua nghien cffu de cap tdi vai tro cua ngffdi day, mdi quan h^ giffa ngffdi day va ngffdi hpc trong viec trien khai hoc thuyet ve phffdng phdp day hpc trai nghiem la mpl qua trinh ham chffa nhidu md'i lien he phffc tap, gdm sff cdn bang sff tdp trung eua ngffdi hpc dd'i vdi vd'n de chuydn mdn (subject matter), vffa can bang dffdc kha nang phan tich nhffng y nghTa sdu xa cua cdc quan mem (ideas) vdi ky nang van dung chiing ma ngffdi day can thffc hien la: Ngffdi hd trd (facihtator); Chuyen gia mdn hpc (subject expert);
Ngffdi thidt lap va danh gia tieu chuan (standard- seller/evaluator); Ngffdi hua'n luyen (coach), cu thd:
1. Ngffdi hd trd (Facilitator): Ngffdi day giiip ngffdi hpc bam sat kinh nghidm ca nhan cua ho va Iff phdn tich. Sff dung mdt cdch quyet doan nhffng thdn thien, d'm ap de Idi cuon sff yeu thich cua ngffdi hpc, dpng Iffc ben trong va kien thffc ban than (self-knowledge) bdng hpi thoai nhdm nhd, tao ra mdi quan he ca nhan vdi ngffdi hoc.
2. Vai trd chuydn gia mdn hpc (Subject Expert):
Trong vai ff-o nay, ngffdi day giup ngffdi hpc td chffc va kdt nd'i nhffng phdn tich cua hp ve kien thffe dffa CrSn cdc vd^n de cfla mdn hpc. Cach cffa ngffdi day d
day la co tham quyen, phan anh dd la ngffdi day, day bang vi du, lam mau vd khuyen khich ngffdi hpc tff duy phan bien khi td chffc va phan tich mdt each hd thd'ng kien thffc mdn hoc. Cdc kien thffc ndy dffdc truydn thdng qua bai giang kem gido trinh.
3. Vai trd thiet lap va ddnh gid tieu chuan (Standard-selter/evaluator): Ddng vai trd la ngffdi thidt lap va danh gid tidu chuin, ngffdi day giup ngffdi hoc nam vffng dffdc kidn thffc va ky nang de dap ffng cae yeu cau hpc. Hp sff dung mot phong cdch dinh hffdng ke't qua muc lieu, lao ra cac boat ddng eho ngffdi hpc de danh gia viec hpc.
4. Vai trd ngffdi hua'n luyen (Coach): Trong vai trd la ngffdi huan luyen, ngffdi day giup ngffdi hpc dp dung kidn thffc dd dat dffdc cdc muc lieu cua minh. Hp sff dung each khuyen khich, hdp tac, thffdng lam viec vdi tffng cd nhan dd giup hoc tff nhffng trai nghiem trong thffc te cung nhff hd trd ngffdi hpc trong viec Idp ra cdc ke hoach phat tnen ca nhan vd cung ca'p cac each thffc nhan phai hdi tff phan vffa thffc hien.
Day hpc bang drai nghidm doi hdi ngffdi day phai tudn theo each thffc la ngffdi hd trd chff khdng hffdng dan (non-directive facilitator) de giup ngffdi hpc ITnh hpi dffdc kid^n thffc tff nhffng kinh nghiem thffc td, ddng thdi phai phu hdp vdi each thffc cua ngffdi hpc. Vay cau hdi ddt ra la each hpc the ndo?
Cdch hpc khdng cd dinh ma gidng vdi thdi quen hpc tap, dffdc hinh thanh tff kinh nghiem va cd chon loc.
David Kolb (2013), da phdt trien phien ban kham phd cdch thffc hpc (Kolb Learning Style Inventory 4.0), trong do tich hdp 9 cdch thffc hoc, theo dd, ngffdi hpc co the thich nghi each thffc hpc phu hdp vdi yeu cau cua tffng tinh hud'ng, eu thd:
Hinh 2: M6 hinh day hpc trai nghiem
TAP CHi CONG THirifNG
* Cdch khdi tao: Nang Iffc khdi tao hanh ddng dd giai quydt linh hudng.
* Cdch trai nghiem: Ndng Iffc tim kidm y nghTa khi tham gia vao trai nghidm.
* Cdch tffdng tffdng: Nang Iffc tffdng tffdng ra tinh kha thi bang cdch quan sal.
* Cach dffa vao kinh nghiem: Ndng life sff dung kinh nghiem ket ndi y tffdng.
* Cdch phdn tich: Ndng Iffc tich hdp va he thdng hda cdc y tffdng
* Cach tff duy: Nang Iffc tham gia vao tranh ludn logic va trffu tffdng.
* Cach quydt dinh: Nang Iffc vdn dung ly thuyet va md hinh de dffa ra quyet dinh Iffa chon cac giai phdp va cdc hanh ddng.
* Cdch hanh dpng: Nang Iffc tao ra dpng Iffc manh me hffdng ddn muc tieu djnh hffdng eho hanh ddng tich hdp giffa con ngffdi va cdng viec.
* Cach cdn bang: Nang Iffc thich ffng, can bang cdc kinh nghiem rdi rac, khai niem hoa tritu tffdng, thi nghiem tich cffc va quan sdt.
Sff dung phffdng phap nay d mdt pham vi nhd't dinh, ngffdi day cd xu hffdng sff dung vai trd lam ngffdi hd trd thay vi vai tro lam cbuySn gia hay ngffdi danh gia nhff trong each day hpc la'y viSc hpc lam trung tam (subject-centered).
2.2. PhidMgphdp xdc djnh ke't qud day hpc ciia dai hoc Main USA
Hinh 3: Md hinh hieu qua hoc tap cuo Dgi hoc Main USA
ngffdi day theo thang diem 10 vdi mffc y nghia a = 5%. Tham chieu ke't qua dau vao 6,4 diem, qui trinh hpc 8,5 diem va didm thi bet hpc phan 6,6 diem cua 108 sinh vien hoc CTDT nganh TCNH trung binh 7,2 diem, nhff sau:
Bang 1. Ket qua day hoc trai nghiem
a 5%
-10 7,45
Nghe 5%
2 1,9
Doc 10%
3 2,7
TTiay 20%
4,5 3,9
Lim 30%
1,5 1,2
L u ^ 50%
2,5 2,0
Hanh 75%
1,5 1,1
Dw
90% 6,5 4,6
Tff md hinh hieu qua hpc tap tien hanh tinh todn dff bao ket qua day hpc trai nghiem tffdng lai quy vd thdi diem hien lai eua ngffdi hpc long ghep vdi
Nhff vay, kel qua tinh todn theo phffdng phap day hpc trai nghiem ddi vdi CTDT nganh TCNH trung binh la 7,45 diem kha phii hdp.
3. Md hinh ngliien cihi mo phong phan tich dif bao day hpc trai nghi$m cua monte carlo
3.1. Nguyin tdc phdn tich dif bdo day hpc trdi nghiem
Robert, C P and Casella, G (2004), khac vdi mo hinh phan tich td't dinh, nhffng gid tri hay nhffng tinh hud'ng dffdc xac dmh trffdc dd trd Idi cdu hdi "neu, thi" trong phan tich md phdng cdn gpi la phdn tich xde sua't, moi chuyen se la hoan loan ngau nhien.
Phdn tich ba't dinh cdn gpi la phan tich xac suat (Probabihstic) hay md phong (Simulation), nhffng gid tri cua nhan to rui ro se xua't hidn mot each bat dinh va ngdu nhien khdng dinh trffdc. Va td't nhien nhffng ket qua ffdc Iffdng dffdi hinh thffc gia tri cung mang tinh ngdu nhien gid'ng nhff trd chdi quay sdfl cac sdng bac (Casino) d Lasvegas do la phifdng phap md phdng cua Monte Carlo dffdc sff dung ph^
bien trong phan tich dff bao cdc hien tffdng kinh te xd hdi. Vdn dung phffdng phap mo phong nay de phan tich dff bdo kdt qua day hpc trai nghiem theo chuan dau ra dffdi sff tdc dpng ddng thdi cua cac yd^u Id' trong cdc tinh hud'ng khde nhau ed tinh tdi phdn bd xdc sud't va gid tri co the eua ede bidn so nhan td vi cd ffu diem hdn eac phffdng phdp khde la xem xet sff kel hdp cua cac nhan td ddng thQi co tinh tdi md'i quan he cua chung.
VI vdy, day la phffdng phdp khd phffc tap doi hoi ngffdi phdn tich phai cd kinh nghiem ve ly thuydt xac sud't thd'ng ke todn kinh te va ky nang thffc hien tdt vdi sff trd giup ky thuat cua mdy tinh dffdc tien hanh theo tnnh tff nhff sau:
* Lffa chpn cac bien quan trpng dffa vao mo
114 Sd 10-Thang 5/2019
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hinh phan tich dffa tren cd sd phdn lich dp nhay dd dffa vao cdc ydu toed anh hffdng Idn tdi day hpc.
* Xdc dinh md hinh bien dpng cua cac yeu td' anh hffdng trong md'i quan he cua chung vdi bidn ngau nhien.
* Xac dinh cac xdc sua't.
* Sff dung mo hinh md phong xac dinh cac ket qua phdn tich giup cho viec dff bdo ket qua day hpc dffdc chinh xac.
* Can cff ket qua dff bao day hpc, tien hanh xdc dinh ngtfdc lai cac bidn.
Tuy nhien, phffdng phap nay co mot sd mat ban chd do la khd ffdc Iffdng ve xac sud't xay ra, dieu nay ddn den kdt qua la vi6c sff dung eac xdc sud't chu quan Id khd cd thd tranh dffdc va mdi quan hd giffa cdc bidn cd the ra't phffc lap. Mac dii day la phtfdng phdp hay nhffng nd ddi hdi mot Iffdng thdng tin rd't Idn ctia ngffdi day, ngffdi hpc vd cdc dd'i tffdng khac co Ii8n quan. Bdi vdy, ndu sff dung phffdng phdp nay phai tdn rd't nhieu chi phi va thdi gian. Song, vide chpn lffa phffdng phdp day hpc hieu qud c^n phdi sff dung md hinh m6 phong Monte Carlo dd tao ra cdc kich ban cho phffdng phdp td'i ffu. Sau nhidu lan thff, cac nhd nghien cffu ffng dung tha'y dffde cac quy ludt phdn phd'i xac sua't. Theo dd, cd bao nhieu phan tram kdt qua rdi vdo khoang nao dd tffdng Iff nhff trong kinh te Iffdng dffdc gpi la khoang tin cdy 1 - a vdi mffc y nghia a thffdng dffdc chpn la 5%. Trong khudn khd nghien cffu nay, cdc kien thffc ed ban ve thdng ke todn mac nhi§n ngffdi day va ngffdi hpc da bie't Vi vdy, chi cIn nhdn dien cac bien riii ro va cac bidn ket qua, cdng viec con lai se do phan mem phan tich dff lieu OCB trong Excel giup xem xet cac bidn sd' de dffa ra quydt dinh lffa chon each day hpc to'l hdn.
3.2. Mo hinh mo phong phdn tich dU bdo ket qua dq.y hgc trai nghifm
Chuydn sd lieu Bang 1 vao bang tinh Excel, cd the diing ham xac dinh chuan dau ra theo phffdng phdp day hpc trai nghiem eho ket qua cung bang 7,45 diem.
Tnnh tff phan tich dff bao ket qua day hpc trai nghiem thdng qua md hinh md phong Monte Carlo kdt hdp vdi phan mem phan tich dff lieu OCB dffdc tidn hanh thffc hien trinh tff nhff sau:
Bffdc I. Khai bao bien rui ro (bien nguyen nhan hay bie'n doc lap) 10 diem bang each dat chupt vao 6 chffa bien rui ro, cu the la 6 B4. Sau do chpn Define Assumption.
Bffdc 2. Bang tren cd nhidu phan phd'i xdc sud't.
Tuy nhien, thdng dung nha't la chpn phan phd'i xac sua't binh thffdng hay phan phd'i chua'n (Normal distribution) lOK.
Bffdc 3. Khai bdo bie'n dff bao (bien kel qua hay bidn phu thudc) 7,45 diem bang cdch ddt chudt vao d chffa bien dff bao, cu the la 6 B5. Sau dd chon Define forecastlOK.
Bffdc 4. Chpn Run Preferences, chpn so lan chay md phdng la 1.000 vdi khoang tin cay 1- a = 95% vdi mffc y nghia a = 5%I0K.
Bffdc 5. Nhd'n nut Start. Phan mdm OCB bat dau chay md hinb md phdng Monte Carlo. Moi lan md phdng, mdt gia tri cua bidn gia thie't se xud't hien mdt each ngau nhien ffng vdi mot kdt qua day hoc.
Chffdng trinh thffc bien 1.000 Ian nhff vay.
Ddy la md hinh phan tich kdt qua day hpc trai nghien chuan hda sau khi thffe hien 1.000 lan md phong (I.OOO trials). 0 hang cud'i ciing, khoang bien thien (-oo;-i-w) tff trff vd cung (-Infinity) den cdng vd ciing (+Infinity), cu the: Ket qua day hpc trai nghiem thd'p nhd't Id 5 didm va cao nhd't la 10 didm. Td't nhien, xac xua't dd ket qua day hpc rdi vdo khoang (5;I0) chac chan Id 100% (xem d Certainly).
4. Ke't qua nglii^n cu^
TrUdng hotp 1. Gia sff de dff bao ket qua day hpc trai nghiem nam trong khoang tff 6 diem den 8,5 diem. Chi can di chuyen tam gidc ben trai vd ben phai eho ddn khi can trdi bang 6 va di chuyen tam gidc ben phai ve ben trai cho ddn khi can phai bdng 8,5.
Bang 2. DCr lieu ket qua day hoc trai nghiem
1 3 4
A a n Diem
KQ B 5%
0 (10) 7,45
C Nghe
1 2 1,9
D Doc
2 3 2,7
E Tllay
3 4,5 3,9
F Lam
4 1,5 1,2
G L u ^ 5 2,5 2,0
H Hanh
6 1,5 1,1
K Day
7 6,5 4,6
TAP CHi CONG THIffTNG
Hinh 4: Mo hinh mo phong phan tich ket qua day hgc trai nghiem chuan hoa
Hinh 5: Mo hinh mo phong di/bdo ket qua day hgc trai nghiem thay doi idn 1
MO HiNH HO PHONG DlJ BAC KET QUA DAY HOC TRAI NGHIEM
Hinh 6: Mo hinh mo phong dUbao ket qua day hgc trai nghiem thay doi I6n 2
Nhff vay, xac sud't de ket qud day hpc trai nghiem ndm trong khoang (6; 8,5) chac chan phai la 78,11 % hay gia tri xdc sua't P value = 0,07811.
Tn0ng hdp 2. Gia sff de dff hio ke't qud day hpc trai nghiem trung binh la 8 diem. Chi can di chuyen lam giac ben trdi vd ben phai cho den khi can phai bang 8.
Vdi ket qud day hpc trdi nghigra ky vpng dat dffdc 8 diem, md hinh chay ngffdc lai cho ket qua tffdng ffng vdi md hinh kdt qua ciia dai hgc Main USA cung thay ddi. Tffc cac ky nang nghe, doc, tha'y, lam, ludn, hanh, day ddu tang 1% tffdng ffng thang diem tang cung tang 1 didm.
Nhff vay, dd dat dffde ky vong mong mud'n day hoc trai nghiem la 8 diem ngffdi day phai lffa chpn each day hpc thich hdp dd'i vdi tfeog chffdng trtnh ddo tao cu the va ddi v6i cac dd'i tffdng hoc khac nhau. Thiic ra, xet de^n ciing thi td't ca cae phffdng phap giang day deu co the ^n chffa nhffng boat ddng mang tinh tich ctfc ciia no. Dd'i vdi ngffdi day, phai thtfc sff xem ngffdi hpc la trung tdm, la chfl the cua boat ddng hpc tdp. Mudn v3y can phai cd mdt so' giai phdp, cu the ddla:
GPL Tao sff tffdng tac cao giifa ngffdi day va ngffdi hpc, giffa ngiicJi hoc vdi nhau.
GP2. Khuydn khich tff duy doc lap, sang tao, xda bd Id'i ap dat kien thffc theo kieu giao dieu.
GP3. Chu trpng phat triin ky ndng vd xay dffng thdi dp lam vide tich ctfc cho ngffdi hpc;
GP4. Hffdng den viec phat trien nhan thffc bdc cao cho ngffdi hoc do la ndng life phdn tich, tdng hdp, ddnh gia va sang tao.
S.Ketluan
Ly thuyet day hpc trai nghi$m chi ra rang, viec day hpc khdng phai la tien hanh cong vide tdi (to) ngffdi hoc thdng qua thffc thi hang loat cac ky thuat, ma nd Id cdng viec ma ngtfSt
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Hinh 8: Ty IS day hgc trai nghiem David Kolb tich hpp hieu qua hgc tap Main USA
100%
80%
I 60%
40%
10%il%
CD. IM
a MAIN P K ( S . B
iOllllliJ
Hinh 9: Diem thanh phan day hgc trai nghiem David Kolb tich hdp hiSu qua hgc tap Main
[ mm ooc TH2Y I A M LUAN
-«»MA!N - « - K a B
HANH DAY
day lam ciing (with) vdi ngffdi hpc trong bdi canh cd mot mdi quan he day y nghia va chia se kinh nghiem. Tren gdc dp ngffdi day, viec lap ke' hoach va trai nghiem eac kinh nghiem cua ngffdi hpc la mot ydu td vd cting quan trpng trong qua trinh hpc.
Thffc te cho thay, trong mdt pham vi nao do ndu ngffdi hpc khong tin tffdng vao ban thdn la cd the
hpc. Iff hpc, tff nghien cffu thi ngffdi hpc se khdng bao gid hpc dffdc Vi vay, ngffdi hpc phai nd Iffc hpc tap chu y lang nghe giang, tff hoan thien ban thdn ngffdc lai ngffdi day can phai dau tff thdi gian vao phffdng phap giang day, chi sff dung 10% thdi Iffdng diuyet giang cdn 90% thdi Iffdng cdn lai hffdng d§n ngffdi hpc va cud'i Cling phai da dang hda cdc phffdng phdp giang day kdt hdp vdi cdng nghe hien dai ciia thd ky21.
Hy vpng ffng dung md hinh mo phdng Monte Carlo vd phffdng phap day hpc trai nghiem David Kolb tich hdp vdi kdt qua hpc tap trai nghiem cua Dai hpc Main da dffdc tidn hanh phdn tich dff bao vdi sff trd giup cua phan mem phan tich dff Heu OCB cd the gop phan hd trd eac ddn vi sff nghiep gido due dao tao cd them mdt phffdng phap tich hdp khoa hpc de day hoc gdp phan tao ra ngudn nhdn Iffc cha'l Iffdng cao eho Viet Nam va thd gidi •
TAI LV^U THAM KHAO:
1. Chickering, A.,and Z Gamson(I9S7). Seven principles of good practice in under graduate education. AAHE Bulletin.
2. Hmelo-Silver C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review.
3. Kolb, D. A. (1939). The Learning Style Inventory: Technical ManuaL McBer & Co, Boston. MA.
4. Kolb, D. A (2013). Leanung styles and disciplinary differences. The modem American college
5. Kolb, D. A., & Fry, R. E. (1995). Toward an applied theoiy of experiential learning. MIT Alfred P. Sloan School of Management.
6. Lyman, F. (1987). Think-Pair-Share: An Ending Teaching Technique MAA-CIE Coop - erative News.
7 Mentkowski (2000). Organizational psychology: readings on human behavior in organizations. Prentice HalL
TAP CHi CDNG THtfONG
8. Osbom, A.F (1963) Applied imagination: Principles and procedures of creative problem solving (3rd Ed.). New York- Charles Scribner's Sons.
9. Robert, C P and Casella. G (2004). Monte Carlo Statistical Methods. New York: Springer- Verlag.
10 Reese (1998). Experiential learning- Experience as the source of learning and development. Englewood Clijfs, NJ: Prenlice-Hall.
11. Victor Weisskop (1930). Berechnung der natMiclien Limenbreite auf Grund der Diracschen Licliltheone.
Springer-Verlag.
Ngay nh^n bai: 18/4/2019
Ngay phan bidn danh gia va su'a chfla: 28/4/2019 Ngay chap nhSn dang bai: 8/5/2019
Thdng tin tdc gid:
TS. NGUYEN NAM THANG Khoa Tai chinh k§'toan
Truf&ng D^i hpc Cong nghi§p Thffc pham Thanh phd' Hd Chi Minh
THE SIMULATION MODEL WHICH ANALYSIS THE EXPERIMENTAL TEACHING METHOD
• Dr. NGUYEN NAM THANG Faculty of Finance and Accounting Ho Chi Minh City University of Food Industry ABSTRACT:
This study is to analyze David Kolb's experiential teaching method integrated with Main University's effective teaching methods. This study was carried out by using the Monte Cario simulation model with the help of Oracle Crystall Ball data analyses software to predict teaching results of the experiential teaching method in accordance with Bloom's output standards. This study's result is expected to help teachers, education and training institutions, and policy makers have an additional tool to analyze and forecast the experiential teaciiing results, thereby correspondingly adjusting teaching methods to meet expectations of building education and training strategies.
Keywords: Experiential teaching, effective teaching, predictive model of teaching experience results.
118 Sd 10-Thdng 5/2019
