190 IEEE TRANSACTIONS ON EDUCATION, VOL. 52, NO. 1, FEBRUARY 2009
Modeling Lea ne Sa is ac ion in an Elec onic
Ins umen a ion and Measu emen Cou se
Using S uc u al Equa ion Models
Se gio L. To al, Fede ico Ba e o, Ma ia R. Ma ínez-To es, Se gio Galla do, and Ma io J. Du án
Abs ac —The p e ailing endency in mode n uni e si y
e o ms is owa ds “how people lea n,” ollowing a lea ne -cen-
e ed app oach in which he lea ne is he main ac o o he
eaching-lea ning p ocess. As a consequence, one o he key in-
dica o s o he eaching-lea ning p ocess is he measu emen o
lea ne sa is ac ion wi hin he class oom. Lea ne sa is ac ion has
adi ionally been measu ed using su ey esponses o a s anda d
lea ning su ey. Howe e , mo e scien i ic analysis should be
pe o med o assess adequa ely no only lea ne sa is ac ion bu
also he main dimensions ha ha e a posi i e impac on lea ne
sa is ac ion. The pu pose o his pape is o de ine a s uc u al and
measu emen model in which causal ela ionships among hese
di e en dimensions a e adequa ely es ablished. The me hod-
ology is based on a mul i a ia e eg ession model (S uc u al
Equa ion Models) o es ablish scien i ically a s uc u al model
o lea ne sa is ac ion wi hin a class oom, measu ing i s alidi y
and eliabili y. The p oposed app oach has been applied o model
lea ne sa is ac ion in an elec onic ins umen a ion cou se a he
Uni e si y o Se ille, Spain. The esul s and implica ions o his
s udy will con ibu e o imp o e s uden sa is ac ion wi h espec
o he dimensions conside ed.
Index Te ms—Educa ional echnology, elec onic equipmen ,
labo a o ies, lea ning sys ems, planning.
I. INTRODUCTION
THE no ion o lea ne -cen e ed educa ion has been in
exis ence o a long ime [1], [2]. Ne e heless, his
concep is eeme ging in he Eu opean coun ies due o e o ms
ha a e o be implemen ed in 2010 inside he Eu opean Highe
Educa ion A ea (EHEA) [3]–[5]. Lea ne -cen e ed p ac ices
mo e he ocus om he eache o he s uden , paying mo e
a en ion o he lea ning pe o mance a he han he ins uc ion
me hodology. Ins uc ion based on a lea ne -cen e ed ame-
wo k p o ides oppo uni ies o lea ne s o d aw on hei own
expe iences and in e p e a ions o he lea ning p ocess [6]–[9].
These p ac ices ega d lea ning as a li elong p ocess a he han
as a p ocess ha akes place only in one’s you h, ollowing
he end o he majo i y o cu en highe educa ion e o ms
[10]–[12].
Manusc ip ecei ed Ma ch 9, 2007; e ised Feb ua y 27, 2008. Cu en e -
sion published Feb ua y 4, 2009.
S. L. To al, F. Ba e o, S. Galla do, and M. J. Du án a e wi h he Elec ical
Enginee ing Depa men , Uni e si y o Se ille, Se ille 41092, Spain (e-mail:
[email p o ec ed]; [email p o ec ed]).
M. R. Ma ínez-To es is wi h he Business Adminis a ion and Ma ke ing
Depa men , Uni e si y o Se ille, Se ille 41092 Spain.
Colo e sions o one o mo e o he igu es in his pape a e a ailable online
a h p://ieeexplo e.ieee.o g.
Digi al Objec Iden i ie 10.1109/TE.2008.924215
In acco dance wi h his app oach, lea ning is conside ed as
a cons uc i e p ocess. Fu he mo e, as lea ning is mo e mean-
ing ul and ele an o he s uden , eaching e iciency is also in-
c eased. This e ec is pa icula ly impo an in subjec s wi h
a high p ac ical wo k con en in which he skills and abili ies
o lea ne s need o be imp o ed, especially when s uden s ge
in ol ed in he lea ning p ocess, assuming esponsibili y o
hei own p og ess [13]. Howe e , he eaching p ocess should
be cen e ed no only on he lea ne ’s ac i i ies, bu also on he
lea ne ’s sa is ac ion, aking in o accoun wha is ele an o he
s uden . The implemen a ion o lea ne -cen e ed me hodologies
demands a p io analysis o he subjec , bo h o unde s and wha
is ele an o he s uden and o iden i y he dimensions ha ing
a highe in luence on lea ne sa is ac ion.
Sa is ac ion ela es o pe cep ions o being able o achie e
success, and eelings abou he achie ed ou comes [14], [15].
F om his pe spec i e, se e al s udies ha e explo ed s uden sa -
is ac ion o imp o e cou se planning [9], [16]. Some imes, hese
s udies a e limi ed o one-dimensional pos - aining pe cep ions
o lea ne s [17], [18]. Ope a ionally, lea ne sa is ac ion is oo
o en measu ed wi h “happy shee s” which ask lea ne s o a e
how sa is ied hey we e wi h hei o e all lea ning expe ience.
Howe e , he no ion o lea ne sa is ac ion mus be explo ed
h ough a mul idimensional analysis ha conside s a wide a-
ie y o c i ical dimensions, so as o p o ide e ec i e me ics
ha guide imp o emen s in ins uc ional design.
Lea ne sa is ac ion scales ha e been used o assess eaching
quali y wi hin In o ma ion Sys ems esea ch. Use in o ma ion
sa is ac ion (UIS) and end-use compu ing sa is ac ion (EUCS)
ins umen s a e examples o use sa is ac ion scales [19]. Bo h
o hem measu e se e al eaching quali y ac o s wi h a a ying
numbe o su ey i ems o each ac o [20], [21]. The main
d awback o hese me hods is ha hey a e p ima ily ocused
on eaching quali y an eceden s o lea ne sa is ac ion, ins ead
o conside ing he lea ne as he main an eceden . This consid-
e a ion is o pa icula impo ance in lab subjec s o in asyn-
ch onous lea ning ac i i ies, whe e he ole o he lec u e essen-
ially consis s o encou aging s uden s’ ini ia i e and mo i a ion
o ob ain a high lea ning pe o mance. In hese con ex s, now
being p omo ed in he EHEA, he con en is no so as impo an
as a e he new compe encies (combina ion o knowledge, skills
and a i ude) ha he s uden s should de elop [22].
To assess he ex en and speci ic na u e o lea ne sa is ac-
ion, di e en dimensions should be heo e ically and ope a-
ionally de ined. Ac ually, many s udies ha e been conduc ed on
his opic, employing lea ne dimensions as an eceden s. These
0018-9359/$25.00 © 2008 IEEE
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TORAL e al.: MODELING LEARNER SATISFACTION 191
s udies ocus on p io lea ne expe iences in e-lea ning cou ses
[23]–[25], on lea ne a i udes owa ds compu e s [26], [27], on
lea ne compu e anxie y [28], on lea ne In e ne sel -e icacy
[29], and on lea ne ini ial compu e skills [30].
The pu pose o his pape is no only o iden i y he an-
eceden s o lea ne sa is ac ion, bu also o de ine a s uc u al
and measu emen model in which causal ela ionships among
he di e en dimensions a e adequa ely es ablished. The
s a ing poin was he cu iculum edesign o an elec onic
ins umen a ion and measu emen cou se [25], whe e he
an eceden s o lea ne sa is ac ion we e iden i ied. These an-
eceden s will be included in a gene al model o highligh
he ela ionship among he di e en dimensions p e iously
iden i ied. The app op ia e me hodology o pe o m his ask is
S uc u al Equa ion Modeling (SEM).
SEM g ows ou o , and se es pu poses simila o mul iple
eg ession, bu in a mo e powe ul way. SEM allows modeling
o he ela ionships be ween mul iple independen and depen-
den dimensions simul aneously, while eg ession models can
analyze only one laye o linkages be ween independen and de-
penden dimensions a a ime. The e o e, SEM may be used as
a mo e powe ul al e na i e o mul iple eg ession, pa h anal-
ysis, ac o analysis, ime se ies analysis, and analysis o co-
a iance. While complex in e ela ionships canno be ully ex-
plo ed by hese echniques, SEM has he ad an age o dissec ing
hese ela ionships, assessing he o al e ec s o a iables on one
ano he . Addi ionally, SEM p o ides he associa ions be ween
a iables and es ima es he s eng hs o hese ela ionships in an
in eg a ed model [31]. The p inciple adop ed by SEM is based
on de e mining model pa ame e s so as o eplica e in he bes
possible manne he co a iance ma ix o he dimensions in he
model sys em. This app oach acili a es he es ima ion o com-
plex model sys ems, de e mining causal ela ionships among a
se o dimensions, which may include o dinal esponse a i-
ables as well as con inuous measu emen s [32]. SEM has been
success ully used in se e al disciplines including, o example,
sociology, psychology o ma ke ing [33]. In he ield o edu-
ca ional sciences, SEM has been applied o de elop accep ance
models [34] o o assess e-lea ning ools [35]. This s udy ex ends
he use o SEM o lea ning sa is ac ion modeling ollowing a
simila p ocedu e.
The pape is s uc u ed as ollows. Sec ion II desc ibes he
exis ing me hods o e alua ing lea ne sa is ac ion, pa icula ly
in a labo a o y o p ac ical lea ning en i onmen , and gi es he
esul s ob ained om a case s udy, an elec onic ins umen a ion
and measu emen cou se o e ed by he Uni e si y o Se ille.
Sec ion III illus a es he p oposed SEM-based me hodology o
alida ing he lea ne sa is ac ion model. Sec ion IV discusses
esul s and implica ions and, inally, conclusions a e d awn in
Sec ion IV.
II. LEARNER SATISFACTION MEASUREMENT IN LABORATORY
TEACHING:ACASE STUDY
T adi ionally, measu emen o lea ne sa is ac ion has been
applied o assess in o ma ion and managemen sys ems, in-
cluding class oom eaching in adi ional educa ional con ex s
[20], [32], [36]. This measu emen should no be e alua ed
using a single-i em scale, such as global sa is ac ion, and
should inco po a e di e en aspec s o lea ne sa is ac ion,
i i is o become a use ul diagnos ic ins umen . Mo eo e ,
a lea ne sa is ac ion measu emen de eloped o adi ional
educa ional con ex s is no app op ia e o a labo a o y o
p ac ical lea ning en i onmen , whe e he ole o he s uden is
comple ely di e en o ha o a s uden in a lec u e [19]. Fo
ins ance, he deg ee o he s uden pa icipa ion and ini ia i e,
he way in which he educa ional ma e ial is deli e ed, and
he possibili ies o eedback and in e ac ion wi h physical
ins umen a ion a e qui e di e en in a labo a o y en i onmen .
As lea ne sa is ac ion is an an eceden o use in en ion [37],
he e is a need o de elop a comp ehensi e ins umen o
measu ing lea ne sa is ac ion wi hin a p ac ical o labo a o y
con ex [19], [25].
In [25], a ecen ly de eloped comp ehensi e ins umen o
measu ing lea ne sa is ac ion wi hin a p ac ical o labo a o y
con ex is desc ibed. A cou se o e ed du ing he inal semes e
o he Telecommunica ion Enginee ing deg ee a he Uni e si y
o Se ille, “Elec onic Ins umen a ion and Measu emen Lab,”
is used as a case s udy. This op ional cou se consis s o 7.5
Spanish-c edi s o 75 h, one Spanish-c edi being equi alen o
10 h o lessons. The Telecommunica ion Enginee ing deg ee is
o ganized in i e academic yea s wi h e e y yea being di ided
in o wo semes e s. The cou ses usually las one semes e and
mos o hem consis o six c edi s on a e age. The lab cou se
analyzed he e is augh in he second semes e o he inal yea ,
and e e y yea abou 60 o 90 s uden s en oll in he cou se. The
main goal o he cou se is o p o ide s uden s wi h an unde -
s anding o he ope a ing p inciples and applica ions o a se-
lec ed ange o basic and ad anced ins umen s, such as logic
analyze s, oscilloscopes, spec um analyze s, LCR me e s, e c.,
while imp o ing s uden s’skills h ough labo a o y wo k ex-
pe ience. The lab wo k is based on a “hands on”ins uc ion
ocus on enginee ing opics such as modula ion echniques, mi-
c op ocesso s sys ems analysis, e lec ome y p inciples, ixed
elephone basics, ins umen a ion buses, and so on. The s uden s
lea n abou ins umen s and lab equipmen by using hem o he
analysis o in e es ing elec onic enginee ing sys ems and p in-
ciples. The cou se also enables s uden s o s eng hen o he im-
po an abili ies, in a eas such as collabo a i e wo k, inno a ion
and esea ch skills.
The cou se is o ganized in wo sepa a e lab g oups, wi h 30
o 45 s uden s pe g oup. G oup “A” akes place on Thu sdays,
om 4 PM o 9 PM. G oup “B” akes place on F idays, om 9
AM o 2 PM. The s uden s, wo king in g oups o wo o h ee,
ha e o a end o wel e o hese 5-h lab sessions. The e o e,
du ing he cou se each s uden a ends 12 sessions, o 5 h each,
and will wo k a wel e di e en wo kbenches. Each wo kbench
is composed o di e en elec onic ins umen s and p o o ype
boa ds ha s uden s ha e o use and es , espec i ely. The 12
lab sessions composing he cou se a e o ganized as ollows.
•Session 1: Design and analysis o elemen a y dc me e s and
mul ime e s as measu emen ins umen s.
•Session 2: Design and analysis o ac me e s and mul ime-
e s, including he equency esponse analysis o he me-
e s and he implemen a ion o elemen a y hal / ull b idge
ac ol me e s.
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192 IEEE TRANSACTIONS ON EDUCATION, VOL. 52, NO. 1, FEBRUARY 2009
•Session 3: Use o analog and digi al oscilloscopes, and a-
milia iza ion wi h hei basic cha ac e is ics. This lab in-
cludes he analysis o p obes, hei cha ac e is ics, pe o -
mance, applica ions, and limi a ions.
•Session 4: In oduc ion o spec um analyze s and hei
p inciples o ope a ion. This lab wo k is used o desc ibe
he ope a ion p inciples o spec um analyze s.
•Session 5: Measu emen and cha ac e iza ion o passi e
impedances and il e s using Whea s one B idges, LCR,
and Spec um Analyze s wi h acking gene a o s. The
limi a ions o p obes in high equency applica ions a e
also s udied o unde s and hei in luence on measu emen
e o s.
•Session 6: In oduc ion o he use o logic analyze s and
o he analysis o complex digi al elec onic and mic o-
p ocesso s sys ems, including sys ems and mic ocon olle
in e acing.
•Session 7: Analysis o Gene al Pu pose In e ace Buses
(GPIB) and hei applica ion o con olling elec onic
equipmen . GPIB p o ocol is used o implemen i ual
and emo e ins umen s.
•Session 8: Analysis o complex analog elec onic sys ems.
The lab wo k is based on he s udy o audio p inci-
ples, audio ampli ie s classes and hei cha ac e iza ion
me hods.
•Session 9: A de ailed desc ip ion o he elec ical ime do-
main e lec ome y (ETDR) mechanism, i s applica ions,
and a me hod o in e p e ing ETDR signal wa e o ms a e
p esen ed.
•Session 10: In oduc ion o basic elephony concep s and
undamen als. In his lab wo k, lea ne s s udy line ele-
phone communica ions p inciples and analyze a eal ele-
phone p o o ype.
•Session 11: Desc ip ion o o he digi al modula ion ech-
niques like FM and FSK, and hei applica ions. FM and
FSK modula ion echniques a e analyzed in he ime and
equency domains.
•Session 12: Analysis o ad anced elec onic digi al sys-
ems (like digi al signal p ocesso s o DSP) and p o ocols
(like in e nal Pe sonal Compu e s a chi ec u e) using logic
analyze s.
P e ious eading is equi ed o unde s and he p inciples o
ope a ion o he equipmen and ins umen s o be used in each
session. Be o e beginning he labo a o y, ins umen handbooks
and a de ailed handou o each ask a e a ailable o he s uden s.
No o mal epo s a e equi ed o s uden s o e alua e hei wo k,
bu hey ha e o answe on- he-spo ques ions du ing each lab-
o a o y session.
To assess he ex en and he speci ic na u e o lea ne sa is-
ac ion, he di e en dimensions, and a s uc u al and measu e-
men model aking in o accoun hese dimensions, mus be de-
ined. Up o 10 dimensions, shown in Table I, we e conside ed
in o de o assess lea ne sa is ac ion in he p io s udy [25]. The
selec ion o dimensions was based on he echnological accep-
ance model [38], because sa is ac ion is gene ally conside ed a
cen al media o o lea ne beha io . Mos beha io esea che s
would ag ee ha sa is ac ion in luences u u e usage in en ion
and complaining beha io . S uden s wi h high le els o sa is-
TABLE I
DIMENSIONS OF LEARNER SATISFACTION
Fig. 1. Lea ne sa is ac ion model.
ac ion a e expec ed o ha e highe le els o euse in en ion and
make ewe complain s [37].
A su ey based on hese dimensions was applied o he
cou se in o de o imp o e i s o ganiza ion in acco dance
wi h lea ne sa is ac ion measu emen s. The esul s ob ained
highligh ed hose dimensions wi h a highe in luence on lea ne
sa is ac ion, showing ha con en , use in e ace, ease o use,
and mo i a ion we e he mos app op ia e o be ein o ced.
Acco ding o his analysis, he cou se was edesigned, and
pos implemen a ion esul s we e ob ained o show he imp o e-
men s in he s uden s’de elopmen . Al hough he expe imen al
esul s ob ained clea ly showed such an imp o emen , he
esul ing model was e y simple. All he dimensions we e
co ela ed wi h lea ne sa is ac ion, and he co ela ion alue
was used in he analysis. Ne e heless, no in e ela ionships
be ween he dimensions conside ed we e analyzed. These in-
e ela ionships should be conside ed, as he co ela ion ma ix
shows ha he e a e s ong co ela ions be ween se e al o he
dimensions, and hese co ela ions can modi y he eal impac
on lea ne sa is ac ion. As a consequence, he me hodology
had o be modi ied om ha he one used in [25]. Speci ically,
SEM is applied in his case in o de o alida e a inal model
conside ing he dimensions lis ed in Table I.
III. LEARNING SATISFACTION MODEL VALIDATION
USING SEM
The model o be alida ed is shown in Fig. 1. The dimension
on he igh side is sa is ac ion, di ec ly d i en by he use in-
e ace, ease o use, en husiasm and mo i a ion, and indi ec ly
d i en by he es o dimensions. The dimensions on he le
side a e he pu e independen a iables, while he in e media e
dimensions a e dependen a iables ha may ac as an indepen-
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TORAL e al.: MODELING LEARNER SATISFACTION 193
TABLE II
VALIDATED SURVEY BASED ON THE LEARNER SATISFACTION DIMENSIONS
den a iable wi h espec o one o he . In con as wi h classical
eg ession models, SEM hypo hesizes ha dimensions such as
hose a o emen ioned a e no di ec ly obse able, and a e be e
modeled as la en a he han obse able a iables. As a conse-
quence, hey a e indi ec ly measu ed h ough a se o indica o s.
In his way, SEM makes i possible o dis inguish wo di e en
ypes o e o s: e o s in equa ions, as shown by he pa h model,
and e o s in he obse a ion o a iables [39].
The alida ed ques ionnai e p esen ed in [25] and Table II,
was also employed he e, and was dis ibu ed o and comple ed
by 284 s uden s en olled in he Elec onic Ins umen a ion and
Measu emen Lab cou se. Each dimension is measu ed using
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194 IEEE TRANSACTIONS ON EDUCATION, VOL. 52, NO. 1, FEBRUARY 2009
se e al indica o s. C onbach’s alpha index (a eliabili y index
associa ed wi h he a ia ion accoun ed o by he ue sco e o
he “unde lying dimension”) was used o p o e he eliabili y
o he ques ionnai e. This coe icien anges om 0 o 1. The
highe he sco e, he mo e eliable is he gene a ed scale. A
alue abo e 0.7 is an accep able eliabili y coe icien , al hough
lowe h esholds a e some imes used in [40]. Table II shows he
eliabili y analysis esul s, including he alue o C onbach’s
alpha gi en in pa en heses unde he dimension. The in e -i em
co ela ion associa ed wi h each i em is also shown in he
second column o Table II be ween pa en heses. Thi y- h ee
o he ques ions, he majo i y, epo sa is ac o y alues. I ems
I5 and I20 we e ejec ed due o he low in e i em co ela ion
alue.
Once he se o i ems o measu ing he unde lying dimension
has been de ined, he hypo hesis o he p oposed model will be
alida ed. I he model is analyzed om igh o le (Fig. 1),
i can be concluded ha sa is ac ion (S) is p ima ily d i en by
he Use In e ace (UI), Ease o Use (EOU), and En husiasm
and Mo i a ion (EAM). UI and EOU a e ela ed o he quali y
and e ec i eness o he ins uc o and he ins uc ion (cogni i e
dimension) while EAM loca es he ins uc o as a acili a o o
knowledge and capabili ies ansmission (a ec i e dimension).
Consequen ly, sa is ac ion is achie ed as a mix u e o cogni i e
and a ec i e dimensions. Al hough ins uc o s end o imp o e
only cogni i e dimensions, a ec i e dimensions should also be
conside ed because hey p omo e posi i e eelings owa ds he
subjec .
A clea an eceden o hese h ee dimensions is Use Con-
ol and In e ac i i y (UCI). Indeed, UCI mus be acili a ed by
he way in which con en is deli e ed (Use In e ace), which
should wo k bo h eliably and con enien ly (Ease o Use). A
he same ime, Use Con ol and In e ac i i y also encou ages
s uden s o pa icipa e ac i ely in he p ac ical wo k. As shown
in Fig. 1, his dimension is he cen al elemen in he model.
UCI is s imula ed by h ee independen dimensions and d i es
he h ee dimensions wi h a di ec in luence o e sa is ac ion.
Going om le o igh o he model, i e pu e independen di-
mensions can be men ioned. Lea ning communi y (LC), lea ne
esponsibili y (LR), and p e ious expe ience (PE) a e he h ee
dimensions ha show he s uden ’s p o ile. These h ee ep e-
sen an inpu o he cou se and, consequen ly, hey can be con-
side ed as independen a iables in he p oposed model. Use
Con ol and In e ac i i y is de e mined by he s uden p o ile.
Howe e , he ins uc o is esponsible o guiding he s uden ’s
ini ia i e and inc easing he use sa is ac ion.
The las wo pu e independen a iables a e con en (CON),
ela ed o he quali y and e ec i eness o he knowledge ans-
mission, and eedback (FED), ela ed o he s eng hening o
use s’lea ning ia e i ica ion (a ec i e dimension). Nei he o
hese a iables ha e a di ec in luence o e sa is ac ion. The i s
is modula ed by he use in e ace, he way in which his con en
is deli e ed (cogni i e dimension). The second is modula ed by
he en husiasm and mo i a ion o s uden s when pe o ming he
p ac ical wo k (a ec i e dimension).
O he dimensions could be conside ed, bu he inclusion o
oo many dimensions could cause undesi able e ec s, such as
model o e i .
Once he model has been desc ibed, he nex s ep is o ali-
da e i . The con i ma o y s uc u al and measu emen model is
ob ained and alida ed using SEM. Acco ding o [40], i SEM
is accu a ely applied i can su pass such i s -gene a ion ech-
niques as P inciple Componen s Analysis, Fac o Analysis, Dis-
c iminan Analysis, o Mul iple Reg essions. Speci ically, SEM
p o ides a g ea e lexibili y in es ima ing ela ionships among
mul iple p edic o s and c i e ion a iables, and allows modeling
wi h unobse able la en a iables. Addi ionally, SEM es ima es
he model wi hou con amina ion om measu emen e o s. The
wo app oaches o causal modeling which appea in he li e a-
u e a e as ollows.
•Pa ial Leas Squa e (PLS). The PLS me hod [40] is a
S uc u al Model Equa ion modeling echnique widely
used in social sciences and business esea ch [41]–[44],
and PLS eg ession is an ex ension o he mul iple linea
eg ession models. In i s simples o m, a linea model
speci ies he (linea ) ela ionship be ween a dependen
a iable ( he use o he ool), and a se o p edic o
a iables (ex e nal a iables p e iously ob ained). The
objec i e in PLS is o maximize he explana ion a iance.
Thus, and he signi icance o he ela ionships among
a iables o dimensions a e measu es ha indica e how
well a model is pe o ming. The concep ual co e o PLS is
an i e a i e combina ion o P incipal Componen Analysis
ela ing i ems o dimensions, and pa h analysis pe mi ing
he cons uc ion o a causal model. The hypo hesizing o
ela ionships be ween dimensions and i ems, and among
di e en dimensions is guided by he p e ious li e a u e
in his ield. The es ima ion o he pa ame e s ep esen ing
he measu emen and pa h ela ionships is accomplished
using O dina y Leas Squa es (OLS) echniques.
•Co a iance s uc u e analysis as implemen ed in he Linea
S uc u al Rela ions (LISREL) model. LISREL es ima es
model pa ame e s in an a emp o ep oduce he co a i-
ance ma ix o he measu es (o obse able a iables),
and also inco po a es o e all goodness-o - i measu es
o e alua e how well he hypo hesized model “ i s” he
da a. Co a iance s uc u e analysis is “ heo y-o ien ed,
and emphasizes he ansi ion om explo a o y o con i -
ma o y analysis”[45].
The basic dis inc ion be ween PLS and LISREL as causal
modeling me hodologies es s in hei objec i es. LISREL is
bes used o heo y es ing and de elopmen [45]; while PLS
is o ien ed owa ds p edic i e applica ions [46]. In compa ison
wi h LISREL, he objec i e o PLS is he explana ion o a i-
ance in a eg ession sense, and hus and he signi icance o
ela ionships among dimensions a e measu es mo e indica i e
o how well a model is pe o ming. “PLS is p ima ily in ended
o causal-p edic i e analysis in si ua ions o high complexi y,
bu low heo e ical in o ma ion”[45]. Fo hese easons, model
es ing was examined h ough a PLS amewo k in he p esen
case.
Following he wo-s ep analy ical p ocedu e [47], he mea-
su emen model is i s examined, and hen he s uc u al
model. The a ionale o his wo-s ep app oach is o ensu e
ha he conclusion on s uc u al ela ionship is d awn om a
se o measu emen ins umen s wi h desi able psychome ic
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TORAL e al.: MODELING LEARNER SATISFACTION 195
TABLE III
PLS RESULTS
p ope ies. The measu emen model is e alua ed in e ms o
eliabili y, in e nal consis ency, con e gen alidi y, and dis-
c iminan alidi y. Table III summa izes he ac o loadings and
a e age a iance ex ac ed om he measu es o he p oposed
esea ch model.
•Indi idual i em eliabili y. In gene al, one would like o
ha e each indica o sha ing mo e a iance wi h he com-
ponen sco e han wi h he e o a iance. This condi ion
implies ha he squa e o loadings should be g ea e han
0.70. Loadings o 0.5 and 0.6 a e accep able i he e a e
addi ional indica o s in he block o compa ison basis
[40]. This condi ion was me in his s udy, as shown in
Table III.
•Con e gen alidi y indica es he ex en o which he i ems
o a scale ha a e heo e ically ela ed should ha e a
high co ela ion. Con e gen alidi y was e alua ed o he
measu emen scales using wo c i e ia sugges ed by [42]:
(1) all indica o ac o loadings should be signi ican and
exceed 0.70 and (2) A e age Va iance Ex ac ed (AVE)
o each dimension should exceed he a iance due o
measu emen e o o ha dimension (i.e., should exceed
0.50). All he measu es mee he ecommended le els (see
Table III).
•Disc iminan alidi y is he ex en o which he measu e is
no a e lec ion o ano he a iable. Disc iminan alidi y
is indica ed by low co ela ions be ween he measu e o
in e es and he measu es o o he dimensions. E idence
o disc iminan alidi y o he measu es can be e i ied
using he squa ed oo o he A e age Va iance Ex ac ed
o each dimension highe han he co ela ions be ween i
and all o he dimensions [42]. As summa ized in Table III,
he squa e oo o A e age Va iance Ex ac ed o each
dimension (on he diagonal) is g ea e han he co ela-
ions be ween he dimensions and all o he dimensions.
The esul s sugges an adequa e disc iminan alidi y o he
measu emen s.
Nex , he s uc u al model is examined. The esea ch model
was es ed using PLS-G aph .3.0 [48]. The model was es i-
ma ed using he maximum likelihood me hod. Fig. 2 depic s i
s a is ics, o e all explana o y powe , and es ima ed pa h coe -
icien s. To assess he s a is ical signi icance o he pa h coe -
icien s, which a e s anda dized be as, a boo s ap analysis was
pe o med. Boo s apping p o ides an es ima e o he a iabili y
o he pa ame e s in a inal model. The use o boo s apping, as
opposed o adi ional - es s, allows he es ing o he signi i-
cance o pa ame e es ima es om da a which a e no assumed
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196 IEEE TRANSACTIONS ON EDUCATION, VOL. 52, NO. 1, FEBRUARY 2009
Fig. 2. PLS esul : de ailed model.
TABLE IV
PATH COEFFICIENTS TABLE (T-STATISTIC)
p
<
0
:
05
;
p
<
0
:
01
;
p
<
0
:
001
,
(0
:
05;499) = 1
:
9672
;
(0
:
01;499) = 2
:
5857
;
(0
:
001;499) = 3
:
3101
o be mul i a ia e no mal. Subsamples a e au oma ically gene -
a ed om he exis ing da a by emo ing cases om he o al da a
se gene a ed by he 284 s uden s. The numbe o andom sub-
samples o be gene a ed is se by he analys . Fo his s udy, he
numbe o boo s ap subsamples was se a 500. PLS es ima es
he pa ame e s o each sub sample and “pseudo alues”a e cal-
cula ed by applying he boo s ap o mula. Table IV shows ha
mos o he pa hs p o ed o be signi ican a he p- alue 0.001
le el. All he hypo heses abou ela ionships among dimensions
we e suppo ed.
Finally, he esul s om he analysis show he explana o y
powe o he esea ch model, e ealing ha he p oposed model
sa is ac o ily accoun ed o 70.6% o he a iance.
In summa y, he necessa y s eps o alida e a s uc u al and
measu emen model a e nex de ailed.
1) De ine a model o be alida ed. The links o he model
should be suppo ed by p e ious s udies o wo ks.
2) Design a ques ionnai e o measu e each o he dimensions
o he model. Each dimension mus be measu ed by se e al
indica o s ha can be ob ained om a su ey o s uden s.
The eliabili y o he ques ionnai e is hen checked using a
C onbach’s alpha index, emo ing hose indica o s wi h a
low in e i em co ela ion alue.
3) Analyze he s uc u al and measu emen model using
SEM. A s uc u al equa ion modeling, such as PLS, al-
lows he alida ion o he indica o s o each dimension
(measu emen model) as well as he hypo hesized links
(s uc u al model).
a) The measu emen is es ed using se e al c i e ia like
indi idual i em eliabili y, con e gen alidi y and
disc iminan alidi y.
b) The s uc u al model is es ed using a boo s ap
analysis.
IV. RESULTS AND IMPLICATIONS
Acco ding o Fig. 2, he esul s om he analysis gua an ees
ha he p oposed model accoun ed o 70.6% o he a iance in
sa is ac ion. Consequen ly, 70.6% o he a iance in s uden s’
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TORAL e al.: MODELING LEARNER SATISFACTION 197
TABLE V
NEW TEACHING ACTIVITIES AND LEARNING ENVIRONMENTS TO IMPROVE STUDENTS’SATISFACTION
Fig. 3. Imp o emen s on he cu en cou se o ganiza ion acco ding o lea ne sa is ac ion measu es.
sa is ac ion can be explained, which is an excellen esul o
his kind o analysis.
The ob ained esul co obo a es he p elimina y s udy p e-
sen ed in [25]. Topics ela ed o con ollable dimensions like
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198 IEEE TRANSACTIONS ON EDUCATION, VOL. 52, NO. 1, FEBRUARY 2009
con en , use in e ace, ease o use, and en husiasm and mo i a-
ion mus be p omo ed o imp o e s uden s’sa is ac ion. New
ac i i ies and lea ning en i onmen s can be p oposed o imp o e
he con en , use in e ace, ease o use, and en husiasm and mo-
i a ion dimensions.
In he case s udy, he Elec onic Ins umen a ion and Mea-
su emen Lab cou se, he con en dimension is imp o ed by
planning new ac i i ies in he lab, based on indus ial buses and
p o ocols, domo ic sys ems, emo e and i ual ins umen a ion
con ol and mobile echnologies. The use in e ace dimension
is imp o ed h ough he applica ion o a lea ning managemen
sys em while he ease o use dimension is enhanced using mul-
imedia echnologies and con en s o imp o e he lea ning p o-
cesses. The en husiasm and mo i a ion dimension is enhanced
using eal wo ld applica ions, wi h p ac ical esul s o he de-
eloped wo k. The inclusion o collabo a i e and coope a i e
hands-on wo k imp o es compe ences like eamwo k and col-
labo a ion skills. Finally, he use con ol and in e ac i i y di-
mension is imp o ed h ough s uden s’decision-making du ing
he class, and he eedback dimension is p omo ed by doubling
he numbe o p o esso s a ending each class. These imp o e-
men s a e de ailed in Table V and Fig. 3.
V. CONCLUSION
Lea ne sa is ac ion has been modeled in an Elec onic In-
s umen a ion and Measu emen Lab using S uc u al Equa ion
Models. The adop ed app oach is based on he lea ne ’s sa is-
ac ion, acco ding o he cu en Eu opean highe educa ion e-
o ms, whe e he ocus o a en ion is mo ing om he eache
o he lea ne . S uden s’sa is ac ion is posi i ely impac ed when
he con en is ansmi ed h ough an adequa e use in e ace,
when he in e ac ion wi h ins umen a ion equipmen and ools
is easy and adequa e o he s uden s’le el, and when hey eel
mo i a ed by he wo k hey a e equi ed o do. The model dis in-
guishes i e pu e independen a iables: con en and eedback,
ha should mainly be managed by he lec u e , and he h ee el-
emen s o he s uden s’p o ile (Lea ning Communi y, Lea ne
Responsibili y and P e ious Expe ience dimensions), which de-
pend on he a i udes and capabili ies o s uden s who a end he
cou se. The managemen o all o hese a iables is he espon-
sibili y o he lec u e , who mus also conside a i udes and ca-
pabili ies o p omo e use con ol and in e ac i i y.
In acco dance wi h he p oposed model, sa is ac ion is he
esul o wo ypes o in luences: he cogni i e and he a ec-
i e in luence. Cogni i e in luence is essen ially d i en by he
uppe -middle pa o he model, ha is, he con en o he cou se
and he way i is deli e ed o s uden s, plus he possibili ies o in-
e ac ion wi h equipmen and ools. The lowe -middle pa o he
model ep esen s he a ec i e in luence o e sa is ac ion, ha is,
s uden s’mo i a ion when wo king in he lab. Use con ol and
in e ac i i y has also an a ec i e componen in he sense ha i
can p omo e en husiasm and mo i a ion.
In acco dance wi h his analysis, se e al imp o emen s e-
ga ding he con en , use in e ace, ease o use, and mo i a ion
should be conside ed. Al hough some o he dimensions could
be conside ed, he numbe o hese should be kep low o a oid
model o e i . The p oposed me hodology can be gene alized o
any subjec in a highe educa ion cou se, and demons a es i s
use ulness in highligh ing which ele an aspec s should be im-
p o ed om a lea ne -cen e ed app oach.
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