Cogen Educa ion
ISSN: (P in ) (Online) Jou nal homepage: www. and online.com/jou nals/oaed20
So skills de elopmen in ICT s uden s: an
e alua ion o eaching me hods by uni e si y
educa o s
A iadna Llo ens, En ic T ullols, An oni Pé ez-Poch & Nikola Pe o ić
To ci e his a icle: A iadna Llo ens, En ic T ullols, An oni Pé ez-Poch & Nikola Pe o ić (2025)
So skills de elopmen in ICT s uden s: an e alua ion o eaching me hods by uni e si y
educa o s, Cogen Educa ion, 12:1, 2437906, DOI: 10.1080/2331186X.2024.2437906
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STEM EDUCATION | RESEARCH ARTICLE
So skills de elopmen in ICT s uden s: an e alua ion o eaching
me hods by uni e si y educa o s
A iadna Llo ens
a
, En ic T ullols
a
, An oni P
e ez-Poch
b
and Nikola Pe o i
c
c
a
Managemen Depa men , Uni e si a Poli
ecnica de Ca alunya –Ba celona Tech (UPC), Vilano a i la Gel
u, Spain;
b
Compu e Sciences Depa men , Uni e si a Poli
ecnica de Ca alunya –Ba celona Tech (UPC, Ba celona, Spain;
c
Facul y o
O ganiza ional Sciences, Depa men o Human Resou ce Managemen , Uni e si y o Belg ade, Belg ade, Se bia
ABSTRACT
Backg ound and con ex : The s udy explo es he ole o eaching me hods in os e ing
so skills among in o ma ion and communica ion echnology (ICT) s uden s in uni e -
si y deg ee p og ams. As he ICT sec o inc easingly alues so skills, aligning educa-
ional app oaches wi h indus y demands has become essen ial. The esea ch
su eyed expe lec u e s in elecommunica ions, compu e science, and elec onics a
a ious echnological uni e si ies. Objec i e: This s udy aims o e alua e and p opose
e ec i e eaching models ha emphasize so skills acquisi ion in ICT deg ee cou ses,
iden i ying he skills de eloped by di e en eaching me hods and de e mining he
mos impac ul app oaches. Me hod: U ilizing a su ey o expe lec u e s ac oss di e -
en echnological ields, he s udy assessed eaching con ex s in on-campus en i on-
men s. A s a is ical analysis was conduc ed o explo e he ela ionship be ween
eaching me hods and so skill de elopmen , including a co ela ion analysis o de e -
mine he e ec i eness o speci ic me hodologies. Findings: The s udy concludes ha
he combina ion o coope a i e lea ning (g oup me hod) and he lea ning con ac
(indi idual me hod) e ec i ely de elops he i e key so skills essen ial o ICT engi-
nee s. P ojec -based lea ning (PBL) and p oblem-based lea ning (PBL) eme ged as he
mos e ec i e me hodologies o os e ing hese skills, al hough implemen ing bo h
simul aneously may be edundan due o hei o e lapping bene i s. Howe e , he
skills o inno a ion, lexibili y, c ea i i y, and p oac i i y a e no adequa ely de eloped
by he examined eaching me hods, indica ing a need o speci ic ins uc ional mod-
ules. Implica ions: The indings o e aluable insigh s o he ICT Educa ion commu-
ni y, p oposing adap able eaching me hodologies o ICT deg ee p og ams ha
e ec i ely de elop so skills c i ical o he echnological and business landscapes.
These ecommenda ions ex end beyond he immedia e scope o he s udy, sugges -
ing b oade applica ions in uni e si y-le el aining.
ARTICLE HISTORY
Recei ed 11 June 2024
Re ised 1 No embe 2024
Accep ed 6 No embe 2024
KEYWORDS
ICT enginee ing educa ion;
ac i e lea ning; so skills;
employabili y; uni e si y
eaching me hods; s uden -
cen e ed lea ning; skill
acquisi ion; echnological
educa ion
SUBJECTS
S udy skills; class oom
p ac ice; cu iculum s udies;
highe educa ion
In oduc ion
The e is b oad consensus wi hin he academic communi y on he sys emic changes challenging uni e -
si y ope a ions, pa icula ly in academic planning and eaching-lea ning me hods (G aham, 2019). In
enginee ing educa ion, pos -pandemic ac o s ha e c ea ed an unce ain u u e, p omp ing some com-
men a o s o sugges ha uni e si ies should p io i ize pe sonalized lea ning and ac i e, s uden -
cen e ed app oaches (Pea s, 2022). The lessons lea ned om COVID-19 ha e d i en many lec u e s o
econside hei eaching me hods (Tondeu e al., 2023) and alida e he changes apidly implemen ed
du ing he pandemic, which had been an icipa ed since he ea ly 2000s (Na ional Academy o
Enginee ing U.S., 2004).
Eu opean policies ha e emphasized he need o uni e si y s a egies ha os e eaching inno a ion,
as ou lined by he Eu opean Commission in Janua y 2022 (Eu opean Commission, 2022). This documen
CONTACT Nikola Pe o i
c[email p o ec ed] Facul y o O ganiza ional Sciences, Depa men o Human Resou ce
Managemen , Uni e si y o Belg ade, C. Jo e Ilica 154, 11000 Belg ade, Se bia
ß2024 The Au ho (s). Published by In o ma UK Limi ed, ading as Taylo & F ancis G oup
This is an Open Access a icle dis ibu ed unde he e ms o he C ea i e Commons A ibu ion License (h p://c ea i ecommons.o g/licenses/by/4.0/), which
pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in any medium, p o ided he o iginal wo k is p ope ly ci ed. The e ms on which his a icle has been
published allow he pos ing o he Accep ed Manusc ip in a eposi o y by he au ho (s) o wi h hei consen .
COGENT EDUCATION
2025, VOL. 12, NO. 1, 2437906
h ps://doi.o g/10.1080/2331186X.2024.2437906
highligh s he apid e olu ion o skill equi emen s and unde sco es he impe a i e o uni e si y educa-
ion o adap acco dingly. As highe educa ion ins i u ions in enginee ing add ess u u e challenges and
se guidelines, i is c ucial o e-e alua e skills aining in he ICT sec o (B ynjol sson & McA ee, 2014;
Eu os a Me ada a, 2017), wi h a pa icula ocus on he c i ical ole o ac i e eaching me hodologies in
cu iculum de elopmen (Tu ull i Rubina , 2020).
The Eu opean Commission’s Digi al Educa ion Ac ion Plan has u he d i en uni e si ies ac oss
Eu ope o in eg a e mo e comp ehensi e digi al li e acy p og ams in o hei cu icula, pa icula ly o
echnological deg ees. Acco ding o Redecke and Punie (2013), uni e si ies o e ing Compu e Science
and Telecommunica ions deg ees ha e e ised hei cu icula o include no only echnical knowledge
bu also a b oade se o digi al compe encies, such as cybe secu i y, a i icial in elligence, and big da a
analy ics, aligning wi h he Eu opean Commission’s goals o os e ing digi al ans o ma ion ac oss all
sec o s.
A key epo by Gaebel e al. (2021) om he Eu opean Uni e si y Associa ion (EUA) unde sco es he
ac ion plan’s ole in os e ing cu iculum inno a ion in highe educa ion. Uni e si ies ha e inc easingly
emphasized mul idisciplina y app oaches, in eg a ing cou ses on digi al e hics, legal amewo ks, and he
socio-economic impac o echnology, which a e i al o he digi al economy. This b oadening o he
cu iculum is seen as a di ec esponse o Eu opean policies ha push o mo e comp ehensi e p epa -
a ion o s uden s o he digi al labo ma ke .
In Compu e Science and Telecommunica ions p og ams, ini ia i es unde he Digi al Educa ion
Ac ion Plan ha e led o inc eased oppo uni ies o s uden s o pa icipa e in EU- unded p ojec s, gain-
ing p ac ical expe ience h ough hands-on lea ning. Fo example, he E asmus þp og am, es uc u ed
unde he Digi al Educa ion Ac ion Plan, has o e ed speci ic digi al skill-building p og ams in pa ne ship
wi h leading ech companies ac oss Eu ope, allowing s uden s o apply hei academic lea ning o eal-
wo ld challenges (Eu opean Commission, 2022).
A manual on uni e si y eaching (Cannon e al., 2000) emphasizes ha he only ce ain y in educa-
ional sciences is ha lea ning depends on s uden ac ions. They pa icula ly s ess he impo ance o
ac i e lea ning in engaging s uden s and os e ing c i ical skills such as p oblem-sol ing and c i ical
hinking, which a e essen ial in ICT educa ion. The manual sugges s ha me hods like g oup p ojec s,
collabo a i e p oblem-sol ing, and hands-on lea ning a e e ec i e ways o help s uden s de elop key
skills, including communica ion and eamwo k. Mo eo e , Cannon, Kapelis, and Newble highligh he
impo ance o designing a cu iculum ha in eg a es hese so skills, ensu ing ha s uden s a e well-
p epa ed o he mode n job ma ke . They also unde sco e he need o bo h o ma i e and summa i e
assessmen s, which a e ins umen al in embedding so skills in o ICT educa ion.
Simila ly, Si Ken Robinson (2022) a gued ha s uden s, as he main agen s o hei lea ning, bea
esponsibili y o hei educa ional ou comes, while educa o s should s i e o c ea e op imal lea ning
condi ions. Robinson likened human de elopmen o an o ganic a he han mechanical p ocess, whe e
ou comes canno be p ecisely p edic ed, simila o a a me who nu u es condi ions o g ow h wi hou
gua an eeing esul s. This pe spec i e ames eaching me hodologies as ools ha os e en i onmen s
conduci e o e ec i e lea ning.
Since he es ablishmen o he Eu opean Highe Educa ion A ea (EHEA), Eu opean uni e si ies ha e
p io i ized s uden -cen e ed lea ning, posi ioning lec u e s as acili a o s o he eaching p ocess (Llo ens
e al., 2017). Cannon e al. (2000) iden i ied key dis inc ions be ween con en ional and s uden -cen e ed
lea ning, such as a mo e ac i e s uden ole, inc eased lexibili y in eaching me hods, and a ise in
coope a i e me hodologies.
S udies on Gene a ion Z (indi iduals bo n om 2000 onwa ds and now en e ing uni e si ies) high-
ligh hei p e e ence o eamwo k, in e ac i e lea ning, and engagemen in he lea ning p ocess, as
well as hei adap abili y o lea ning in di e se en i onmen s wi h un es ic ed access o in o ma ion
(Kozinski, 2017). These s uden s a o pe sonalized, p ojec -based, p ac ical lea ning app oaches, ha
aligning wi h con empo a y educa ional pa adigms (Fisk, 2017). O he indings emphasize he need o
de elop c i ical hinking skills among Gene a ion Z, as hey “migh no ha e g ea c i ical hinking”
(Chan & Lee, 2023), a skill ha is o g ea impo ance o ac i e lea ning.
Di e en ia ion o eaching me hodologies based on s uden ypes is no no el. Kolb’s Expe ien ial
Lea ning Model (Kolb, 1984), de eloped in he 1970s, ca ego izes s uden s based on hei lea ning
2 A. LLORENS ET AL.
p e e ences. Gi en hese insigh s, i is c ucial o iden i y sui able eaching me hods o echnological uni-
e si y p og ams, such as enginee ing.
Zabalza (1991) p oposed c i e ia o selec ing eaching me hods, including alidi y, comp ehensibili y,
a ie y, ele ance, cla i y, p ocedu al mas e y, and he inclusion o p ac ical exe cises. Neci i (1979) ca e-
go ized eaching me hods by easoning o m (deduc i e, induc i e, analogical), s uden ac i i y le el
(passi e, ac i e), and wo k ype (indi idual, collec i e, mixed). Ma io de Miguel D
ıaz (2006) s uc u ed
me hodologies based on hei o ganiza ional modali y and pa icipa ion deg ee o s uden s and each-
e s. This classi ica ion, p e iously employed by he au ho s (Llo ens e al., 2019), unde pins he me hod-
ologies conside ed in his a icle. Table 1 p esen s he eaching me hodologies commonly used in his
s udy, acco ding o Ma io de Miguel D
ıaz (2006).
This classi ica ion encompasses a wide ange o eaching s a egies, including se ice-lea ning, which
in ol es s uden s’ac i e pa icipa ion in communi y se ice and is iewed as an ex ension o p oblem-
based lea ning (Salam e al., 2019). Simila ly, challenge-based lea ning me ges p oblem-based and p o-
jec -based lea ning, ocusing on esol ing p o essional challenges wi hin eams (Cha osky e al., 2018).
Some eaching echniques and modali ies a e excluded om his s udy due o hei ex ensi e a ie y,
which p esen s a limi a ion. As no ed in he in oduc ion, alida ing uni e si y eaching me hods emains
a signi ican challenge o Eu opean uni e si ies. This s udy speci ically aims o de elop p ac ical and
applicable aining models o ICT enginee ing deg ees ( elecommunica ions, elec onics, and compu e
enginee ing) (Llo ens e al., 2017).
Mo e speci ically, his esea ch examines he op imal app oaches o cul i a ing gene al p o essional
o so skills – e e ed o as ans e sal o gene ic skills –which a e ecu en hemes in ICT sec o li e a-
u e. So skills encompass knowledge, p ac ical abili ies (skills and heu is ics), and pe sonal a ibu es
(cha ac e and alues) (G een e al., 2013). These skills a e no con ined o adi ional class oom se ings;
ongoing esea ch explo es hei acquisi ion h ough online cou ses and app op ia e lea ning ac i i ies
(Reilly & Ree es, 2023).
In a ola ile, unce ain, complex, and ambiguous (VUCA) en i onmen shaped by ad ancemen s like
a i icial in elligence and he Fou h Indus ial Re olu ion (Schwab, 2023), he e is a shi ing end away
om p o essional specializa ion. Eu opean s udies show ha ICT i ms inc easingly alue so skills when
e alua ing candida es’employabili y (Eu os a Me ada a, 2017). Gi en his labo ma ke end, i is essen-
ial o e ise uni e si y cu icula o de elop echnical and so skills o ICT p o essionals.
Acco ding o Ausubel (1963), a p oponen o cons uc i ism, meaning ul knowledge cons uc ion
equi es e lec ion and in e naliza ion, no jus explica ion. Ac i e o s uden -cen e ed me hodologies
posi ion s uden s as he ocal poin o he lea ning p ocess (P ince, 2004), con as ing wi h non-ac i e
me hods ha ocus on knowledge cha ac e iza ion and memo iza ion. The Ho
a h model (Ho
a h
e al., 2004) u he classi ies ac i e me hodologies based on hei ocus (indi idual, g oup, communi y)
and app oach (cons uc i is , explo a i e, ins uc i e).
This s udy concen a es on he ICT sec o , emphasizing ha he acquisi ion o key so skills, as
demanded by he ma ke , is c ucial o he employabili y o enginee s, alongside hei echnical expe ise.
Resea ch consis en ly shows ha so skills, pa icula ly in communica ion, eamwo k, and p oblem-sol ing,
a e i al o ICT g adua es (Pa
zu Ani
ci
c e al., 2017). The cu en s udy ocuses on hese essen ial skills, as
iden i ied in p io wo k by he au ho s (Llo ens e al., 2017), and p esen ed in Table 2, which anks key so
skills o ICT enginee s acco ding o employe eedback. Ou s udy o e s a unique con ibu ion by examin-
ing so skills wi hin he con ex o enginee ing educa ion –a pe spec i e ha emains unde explo ed. The
majo i y o esea ch on so skills eme ges om ields such as psychology, educa ion, and o ganiza ional
Table 1. Rela ionship be ween modali ies and eaching me hods (de Miguel D
ıaz, 2006).
O ganiza ional modali ies Mos common eaching me hods
Theo y classes Lec u e
Semina -wo kshops Case s udy
P ac ical classes P oblem sol ing
In e nship P oblem-based lea ning
Tu o ials P ojec -based lea ning
S udy and g oup wo k Coope a i e lea ning
Indi idual s udy and wo k Lea ning con ac
COGENT EDUCATION 3
beha io , whe e he ocus is o en on heo e ical o p ac ical applica ions ou side o echnical disciplines.
In es iga ing so skills om he an age poin o enginee ing acul y who a e ac i ely eaching in uni e -
si ies p o ides a esh pe spec i e, emphasizing he need o p ac ical applica ion wi hin echnical ields.
While esea ch in eaching p ac ices has la gely concen a ed on eache educa ion, o en highligh ing he
ole o educa o s as e ec i e designe s o ICT-in eg a ed en i onmen s and ins uc ional ole models
(Is eni
c S a
ci
c & Lebeni
cnik, 2020), o he educa ional disciplines, pa icula ly in echnical ields, ha e
ecei ed compa a i ely limi ed ocus.
Me hods
The p ima y objec i e o his esea ch is o p opose a eaching model ha le e ages a ious lea ning
me hodologies o ensu e he acquisi ion o essen ial so skills o ICT enginee s. Speci ically, he s udy
aims o e alua e which eaching me hodologies mos e ec i ely de elop he key so skills equi ed by
he ICT ma ke o deg ees in elecommunica ions, elec onics, and compu e science. To achie e his, a
quan i a i e s udy was conduc ed h ough a su ey o indus y expe s and academic p o essionals om
a ious echnological uni e si ies. The goal is o p opose a eaching model ha employs di e se me h-
odologies o acili a e he de elopmen o hese c i ical skills in he ICT sec o . This app oach builds
upon p e ious quali a i e esea ch conduc ed by he au ho s, and he new quan i a i e me hod allows
o a compa a i e analysis o he indings. No ably, he iming o his esea ch is signi ican , as he
COVID-19 pandemic has p omp ed a subs an ial shi in educa ional p ac ices, leading o a global
eassessmen o eaching me hodologies.
This s udy builds on p io esea ch by he au ho s (Llo ens e al., de Miguel D
ıaz, 2006) o analyze he
e ec i eness o a ious eaching me hods in de eloping key so skills o ICT s uden s. The eaching
me hods chosen we e iden i ied as hose mos likely o os e c i ical so skills, such as eamwo k, com-
munica ion, and p oblem-sol ing, based on p e ious s udies by he au ho s, co e ing up o 10 essen ial
skills o ICT s uden s. I is c ucial ha eaching me hods a e selec ed based on hei alignmen wi h he
in ended so skills de elopmen (Biggs, 1996), a p inciple also emphasized by Bloom’s axonomy, which
highligh s he impo ance o aligning eaching me hods wi h lea ning objec i es (Ande son & K a hwohl,
2001).
Gi en he ex ensi e ange o lea ning echniques and eaching me hods a ailable, i was no easible
o include hem all in his s udy. To a oid dilu ing he indings and o main ain a ocused analysis, a lim-
i ed se o se en commonly used eaching me hods was selec ed, as ecommended by Cohen e al.
(2017), who discuss he bene i s o na owing he esea ch scope o mo e in-dep h analysis. This selec-
ion ensu es ha he impac on so skills can be comp ehensi ely e alua ed wi hou o e ex ending he
scope o he esea ch.
The chosen me hods allow o su icien a en ion o be gi en o he execu ion and assessmen o
each, and hey a e amilia o he 33 expe academics and indus y p o essionals who pa icipa ed in
he su ey, ensu ing ha hese me hods a e al eady in use wi hin hei lessons. The main goal is o
demons a e how ac i e lea ning me hods e ec i ely os e skills commonly highligh ed in he li e a u e
(Bonwell & Eison, 1991; P ince, 2004), in cohe ence wi h he au ho s’p e ious wo k (Llo ens e al., 2017,
2019), which unde sco es he e icacy o hese eaching me hods in so skill de elopmen o ICT
Table 2. Key so skills o ICT enginee s acco ding o employe s anked in o de o
impo ance (Llo ens e al.).
Teamwo k 53%
Communica ion 43%
P oblem sol ing 42%
Planning 39%
Lea ning 34%
P oac i i y 31%
Analy ical hinking 28%
Cus ome o ien a ion 28%
In o ma ion 26%
Inno a ion 24%
4 A. LLORENS ET AL.
deg ees. The esea ch me hodology design employed in his sec ion aligns wi h he app oach p e iously
u ilized in he au ho s’ e e enced wo ks.
Gi en he a ge ed na u e o his esea ch, andom sampling was no employed. Ins ead, a non- an-
dom, judgmen al sampling app oach (also known as pu posi e o au ho i a i e sampling) was used,
selec ing pa icipan s based on he esea che s’judgmen . This echnique is app op ia e when he a ge
popula ion comp ises expe s whose de ailed insigh s a e c i ical and when andom selec ion would no
be easible. This ca e ul sampling app oach helps o minimize bias and e o s, e en wi h a small sample
size.
The expe sample consis ed o seasoned academics and also ICT p o essionals, including depa men
heads, deans o enginee ing schools, and ec o s o echnological uni e si ies. All pa icipan s ha e
ex ensi e expe ience in he ICT sec o indus y bu also in eaching inno a ion and we e ully in o med
o he s udy’s objec i es. Ensu ing sample quali y and di e si y was c ucial o minimizing bias and
enhancing he gene alizabili y o he esul s. Fo logis ical easons, he sample was limi ed o Spanish
uni e si ies, po en ially in oducing geog aphical o cul u al bias. Howe e , gi en he sha ed educa ional
and business amewo ks wi hin Eu ope, he indings a e assumed o be b oadly applicable, pa icula ly
wi hin Spain.
Da a collec ion occu ed be ween Ma ch and Ap il 2023, in ol ing 33 eache s om ou Spanish uni-
e si ies ( h ee public and one p i a e). The sample ep esen ed speci ic ICT ields: elec onics (3), com-
pu e science (19), and elecommunica ions (11). Da a we e ga he ed ia an elec onic ques ionnai e
(Google Fo ms) and analyzed using s anda d s a is ical so wa e, including R-S udio. Google Fo ms was
selec ed as he pla o m o dis ibu ing he ques ionnai e due o i s accessibili y, ease o use, and abili y
o collec and o ganize da a e icien ly. Building on p e ious s udies by he au ho s (Llo ens e al., 2017,
2019), he ques ionnai e was designed o assess he e ec i eness o each eaching me hod in os e ing
c i ical so skills among ICT s uden s, as ou lined in Table 3. Responden s we e p o ided wi h de ini ions
o each eaching me hod alongside he ques ionnai e.
Resul s
The esul s a e summa ized in Table 3, showing absolu e equencies o esponses (e.g., he numbe ‘2’
in ow 1, column 1 indica es ha only 2 ou o 33 expe s belie e ha ‘lec u e’con ibu es o de elop-
ing he ‘ eamwo k’skill). The inal columns and ows (bold and g ey) display he sample median and he
hi d qua ile o me hods and skills, espec i ely. La ge alues in he Median and Q3 columns indica e
ha ‘PBL’and ‘ABP’ ollowed by ‘Coope a i e Lea ning’and ‘Case S udy’a e he me hods ha con ibu e
he mos o o e all skill de elopmen . In con as , ‘Lec u e’is he me hod ha co e s ewe skills. No
biases ela ed o he uni e si y o ICT subsec o we e de ec ed.
Figu es 1 and 2illus a e he da a dis ibu ion om Table 3. In hese box-and-whiske plo s, he boxes
ep esen in e qua ile anges (IR), bold lines deno e medians, and whiske s ex end o da a poin s wi hin
1.5 IR. A box-and-whiske plo consis s o a box (which con ains 50% o he da a), a e ical segmen
known as a whiske ( ha indica es he sp ead o da a), and some imes indi idual poin s co esponding
o ou lie s. These plo s e ec i ely isualize cen al alues, da a dispe sion, and ou lie s, and a e widely
used in nonpa ame ic s a is ics whe e a gi en s a is ical dis ibu ion canno be assumed.
Fo example, Figu e 1 shows ha while he medians o ‘Teamwo k’and ‘Analy ical Thinking’a e simi-
la , he dispe sions di e , e lec ing non-homogeneous con ibu ions o he me hods o achie e hese
skills. The ‘Lea ning’skill demons a es s ong homogenei y (me hods con ibu e mo e o less equally o
he achie emen o his skill), indica ed by he small box size, while he ‘Inno a ion’skill shows lowe
cen al alue, sugges ing he me hods a e no sui able o achie ing his skill. Figu e 2 highligh s
‘P ojec -Based Lea ning’and ‘P oblem-Based Lea ning’as he mos e ec i e me hods, wi h high medians
and low dispe sion, whe eas ‘Lec u e’is a ed less a o ably.
The analysis conside ed only alues abo e he hi d qua ile (Q3) o explo e he ela ionship be ween
eaching me hods and skills, as displayed in Tables 4 and 5. Missing alues ep esen ins ances whe e
skill achie emen o me hod e ec i eness alls below Q3. The hi d qua ile c i e ion o e s obus ness
agains ou lie s, making i mo e eliable han he ‘mean þs anda d de ia ion’app oach commonly used
in o he s udies.
COGENT EDUCATION 5
Table 3. Responses in absolu e equencies.
Skills
Me hods Teamwo k Communica ion
P oblem
Sol ing Planning Lea ning P oac i i y
Analy ical
hinking
Cus ome
o ien a ion In o ma ion Inno a ion Flexibili y C ea i i y Median Q3
Lec u e 2 9 7 5 17 2 16 5 8 1 5 2 5 8.25
Case S udy 31 16 16 13 12 20 26 11 22 7 14 11 15 20.5
P oblem sol ing 5 4 21 7 19 12 28 2 13 6 5 8 7.5 14.5
P oblem based lea ning (ABP) 31 17 21 15 20 20 29 13 23 14 14 16 18.5 21.5
P ojec based lea ning (PBL) 26 22 20 28 20 22 26 14 26 16 23 21 22 26
Coope a i e lea ning 33 27 9 19 22 20 16 5 13 10 18 13 17 20.5
Lea ning con ac 3 11 13 23 23 6 8 12 8 3 6 2 8 12.25
Median 26 16 16 15 20 20 26 11 13 7 14 11 14
Q3 31 19.5 20.5 21 21 20 27 12.5 22.5 12 16 14.5 21
6 A. LLORENS ET AL.
Table 4 should be ead ow-wise, indica ing which so skills a e de eloped by each eaching me hod.
Fo example, he i s ow shows ha ‘lec u e’is associa ed wi h ‘commi men o lea ning’(52%),
‘analy ical hinking’(48%), and ‘communica ion’(27%). P ojec -based lea ning eme ges as he me hod
os e ing he wides ange o so skills, whe eas p oac i i y, inno a ion, lexibili y, and c ea i i y a e no
p edominan ly de eloped by any me hod, acco ding o he expe s. Only alues abo e Q3 a e shown o
emphasize he mos impo an con ibu ions.
Table 5 should be ead column-wise, showing which eaching me hodologies mos e ec i ely
de elop speci ic so skills. Fo ins ance, ‘ eamwo k’is nu u ed by ‘case s udy’(94%), ‘p oblem-based
lea ning’(94%), and ‘coope a i e lea ning’(100%). The da a e eal ha p oblem-based and p ojec -based
lea ning a e he me hods ha cul i a e he mos skills, up o eigh each. Only alues abo e Q3 a e dis-
played in o de o emphasize de mo e impo an con ibu ions.
Figu es 3 and 4g aphically show he ela ionship be ween skills and me hods in de ail. The e ical
dashed lines ep esen he hi d qua ile. The x-axis is he pe cen age.
Figu es 5 o 8 u he de ail how di e en me hods con ibu e o skill acquisi ion. To enhance isual
cla i y, each igu e p esen s only wo me hods, wi h absolu e equencies indica ed wi hin he ci cles.
A deepe analysis was conduc ed using he co ela ion ma ix (Table 6). The highes co ela ion was
obse ed be ween ‘Case S udy’and ‘P oblem-Based Lea ning’(0.92), indica ing ha bo h me hods
de elop simila skills wi h a ying in ensi ies (see Figu e 7). ‘Lec u e’and ‘P oblem Sol ing’also showed
a mode a e co ela ion (0.70, see Figu e 6). The co ela ion be ween ‘P ojec -Based Lea ning’and
‘P oblem-Based Lea ning’was 0.55, p ima ily di e ing in he de elopmen o ‘Planning’and ‘Flexibili y’
skills (see Figu e 5).
Figu e 1. Box-and-Whiske plo o so skills om Table 3.
Figu e 2. Box-and-Whiske Plo o eaching me hods om Table 3.
COGENT EDUCATION 7
Table 4. Teaching me hods abo e Q3 in pe cen ages.
Skills
Teaching me hods Teamwo k Communica ion
P oblem
Sol ing Planning Lea ning P oac i i y
Analy ical
hinking
Cus ome
o ien a ion In o ma ion Inno a ion Flexibili y C ea i i y
Lec u e 27 52 48
Case s udy 94 79 67
P oblem sol ing 64 58 85
P oblem-based lea ning 94 88 70
P ojec -based lea ning 79 85 79 79
Coope a i e lea ning 100 82 67
Lea ning con ac 39 70 70 36
8 A. LLORENS ET AL.
Educa ion a his Uni e si y. He has been Di ec o o he Teache s’T aining Pos g adua e Deg ee a UPC (2016-
2024) and Depu y Di ec o o he Ins i u e o Educa ion Sciences (ICE-UPC). He is a membe o he unded esea ch
g oup EduSTEAM Uni e si y Lea ning Resea ch G oup a UPC. His esea ch is ocused on Quali y and in Ac i e
Me hodologies in Highe Educa ion. He has published mo e han 25 pape s in academic jou nals, and.pa icipa ed in
12 unded esea ch p ojec s ela ed o STEAM Educa ion.
Nikola Pe o i
cis doc o al candida e specializing in Quan i a i e Managemen , wi h a pa icula ocus on Human
Resou ce Managemen . He is a Teaching Assis an a he Facul y o O ganiza ional Sciences, Uni e si y o Belg ade,
Human Resou ces Managemen depa men , whe e deli e s ins uc ion ac oss se e al cou ses, including Human
Resou ces Managemen , T aining and De elopmen , and e Lea ning. He also published se e al scien i ic pape s on
he opic o HRM, Lea ning Analy ics and People Analy ics.
ORCID
Nikola Pe o i
ch p://o cid.o g/0000-0001-5977-7509
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