Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
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HOW CAN EXPERT CONSENSUS METHODS ENHANCE
THE DESIGN OF IMMERSIVE LEARNING PRACTICAL
MODELS FOR DEAF OR HARD-OF-HEARING STUDENTS
IN TVET?
RINI HAFZAH BINTI ABDUL RAHIM1, DINNA NINA BINTI MOHD NIZAM1, NUR FARAHA
BINTI MOHD NAIM1 and ASLINA BAHARUM2
1Use Expe ience G oup, Facul y o Compu ing and In o ma ics, Uni e si y Malaysia Sabah, 88400 Ko a
Kinabalu, Sabah, MALAYSIA
2 Depa men o Da a Science and A i icial In elligence, School o Enginee ing and Technology, Sunway
Uni e si y, Banda Sunway, 47500 Selango , MALAYSIA
E-mail: 1RINI_HAFZAH_DI21@ilu .ums.edu.my, 2dinn[email p o ec ed].my, 3 a aha.na[email p o ec ed]u.my,
[email protected]
ABSTRACT
T adi ional audi o y-based eaching app oaches limi he e ec i eness o p ac ical skills acquisi ion o
Dea o Ha d-o -Hea ing (DHH) s uden s in Technical and Voca ional Educa ion and T aining (TVET).
Despi e inc eased in e es in imme si e echnologies like augmen ed eali y (AR), he ield lacks alida ed,
inclusi e ins uc ional models ailo ed o DHH lea ne s. This s udy add esses his gap by in eg a ing he
Nominal G oup Technique (NGT) and Fuzzy Delphi Me hod (FDM) o design and alida e an Imme si e
Lea ning P ac ical Skills (ILPS) model. The no el y lies in he combined use o NGT and FDM o
consensus-building among expe s in AR, gami ica ion, and DHH educa ion—an app oach no commonly
applied in inclusi e model de elopmen . Resul s e ealed a s ong expe consensus (>97%) on 15 i ems
ac oss h ee co e cons uc s: Lea ning Inpu Medium, P ac ical Skills Module, and AR Gami ica ion
Fea u es. This esea ch o e s a eplicable and pa icipa o y model de elopmen p ocess and in oduces a
alida ed amewo k o inclusi e imme si e lea ning in TVET. The s udy con ibu es new knowledge by
demons a ing how expe -d i en me hods can ope a ionalize inclusi e pedagogy h ough imme si e
echnologies. This s udy demons a es how combining FDM and NGT may success ully e alua e inclusi e
design elemen s o imme si e lea ning. The esul s suppo he de elopmen o a p ac ical skills model
wi h a DHH ocus and p o ide a epea able amewo k o inclusi e cu iculum co-c ea ion. This
combina ion s eng hen consensus among 11 panel o expe s and acco ding o he s udy's indings, he
NGT and FDM app oach has made i simple and quick o esea che s o con i m c ucial de ails ha should
be highligh ed. To help DHH s uden s lea n mo e e ec i ely, i is ad ised ha mo e esea ch be done in
collabo a ion wi h cou se designe s. To p o ide a scalable app oach o de eloping imme si e, accessible
lea ning en i onmen s in specialized educa ional con ex s, his s udy hopes o demons a e how e ec i ely
NGT and FDM collabo a e o inclusi e ins uc ional design.
Keywo ds: Educa ional Technology, Teaching And Lea ning, Hea ing Impai ed, Highe Educa ion,
Model De elopmen .
1. INTRODUCTION
DHH s uden s ace unique challenges in
acqui ing p ac ical skills due o limi a ions in
con en ional audi o y-based eaching and lea ning
me hods [1]. DHH s uden s encoun e signi ican
challenges in mas e ing p ac ical skills wi hin
TVET se ings due o he limi a ions o
con en ional audi o y-dependen eaching me hods.
Despi e ad ancemen s in imme si e echnologies
such as AR and gami ica ion, he e emains a
conspicuous lack o alida ed, inclusi e
ins uc ional models ha add ess he unique
communica ion and lea ning needs o DHH
lea ne s. Fu he mo e, ew s udies ha e
sys ema ically applied s uc u ed expe consensus
me hods like he NGT and FDM o de elop and
alida e such models. This ep esen s a c ucial
Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
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me hodological and pedagogical gap. Wi hou
accessible, expe -in o med amewo ks, DHH
s uden s isk u he ma ginaliza ion in skill-based
lea ning en i onmen s. This s udy is needed o
b idge his gap by o e ing a no el, alida ed, and
eplicable imme si e lea ning model, one ha
le e ages expe insigh s o ensu e ele ance,
inclusi i y, and p ac ical e ec i eness in eaching
DHH lea ne s essen ial echnical skills.
In TVET se ings, whe e p ac ical skills
acquisi ion is c ucial, hese challenges a e
especially p onounced [2–4]. The absence o
adequa e and inclusi e ins uc ional models o en
hinde s DHH s uden s om ully engaging in
lea ning ac i i ies, impac ing hei skill p o iciency
and long- e m ca ee p ospec s. The e is a g owing
need o imme si e, accessible lea ning models ha
le e age cu ing-edge echnologies o b idge his
gap, os e ing an en i onmen whe e DHH s uden s
can acqui e p ac ical skills mo e e ec i ely [5–7].
Imme si e lea ning echniques,
pa icula ly when combined wi h echnology-d i en
app oaches, hold conside able po en ial o
imp o ing accessibili y and lea ning ou comes o
DHH s uden s. Techniques such as AR and
gami ica ion ha e been shown o enhance
engagemen and comp ehension by c ea ing an
in e ac i e and isually ich lea ning expe ience
[8,9]. Al hough AR and gami ica ion a e
inc easingly used in educa ional esea ch [10], ew
s udies ha e e ec i ely add essed he needs o
DHH s uden s, pa icula ly in p ac ical skill-based
lea ning con ex s. P io wo ks [11] and [12] ha e
explo ed assis i e echnologies and sign language
sys ems ye o en lack a comp ehensi e model o
imme si e lea ning ailo ed o DHH lea ne s in
TVET en i onmen s. Mo e c i ically, hese s udies
gene ally omi s uc u ed consensus me hods o
model alida ion. The absence o pa icipa o y
echniques such as he NGT and FDM unde mines
he igo and inclusi eness o p io model
de elopmen e o s. Add essing hese gaps, he
cu en s udy p oposes and alida es an ILPS model
h ough a dual-me hod consensus app oach,
con ibu ing a no el, empi ically g ounded
amewo k o inclusi e imme si e educa ion.
Howe e , o design an e ec i e imme si e
lea ning model o DHH s uden s, i is essen ial o
in eg a e inpu om subjec ma e expe s and
s akeholde s. The NGT and FDM a e pa icula ly
aluable o his pu pose, enabling a sys ema ic
consensus-building p ocess ha inco po a es expe
opinions and p io i izes model elemen s acco ding
o eal-wo ld ele ance and e icacy [13,14].
NGT sessions usually consis o i e o en
pa icipan s and las be ween one and hal o wo
hou s [12,13,14]. Acco ding o Lloyd-Jones,
Fowell, and Bligh (1999), he esea che 's job in
NGT is o acili a e and adminis e , which
minimizes e ec on he da a [18]. In many esea ch
me hodologies whe e he esea che 's
p econcep ions a e en o ced h ough ques ion
aming and answe coding, Lomax and McLeman
(1984) e e o he "omniscience o he esea che "
[19]. In NGT, his is a oided since g oup membe s
o ganize, classi y, and p io i ize he eplies.
Howe e , he e ec i eness o he app oach depends
on how well he s imulus ques ion is o mula ed,
and i is impe a i e ha he esea che is clea abou
he in o ma ion hey hope o ob ain om he
p ocedu e. In hei 1975 s udy, Delbecq, Van de
Ven, and Gus a son con as ed NGT wi h Fuzzy
FDM [20].
The e ised measu emen scales a e
e i ied using a FDM. The use o uzzy Delphi is
based on he p ac ical esul s o he many IT/IS
esea ch a ia ions o e ed by. This ool is a e y
help ul me hod when a g oup o expe s mus
accep a gi en le el o esea ch. Addi ionally, he
Fuzzy Delphi p ocedu e is an in e es ing me hod o
g oup decision-making conce ning he ague
no ions o expe opinion alignmen [21]. In o de
o ensu e accu acy and consis ency o opinion,
su ey me hodologies a e employed in conjunc ion
wi h lowe expenses, which allows expe s o ully
exp ess hei hough s wi hou ea o
misunde s anding and allows hei esul s o be
implemen ed apidly.
The pu pose o his p ojec is o c ea e an
ILPS ha is especially sui ed o DHH s uden s in
TVET se ings. By using NGT and FDM, we wan
o de e mine he essen ial elemen s o an inclusi e
model, e alua e i s iabili y, and make su e i
sa is ies he unique equi emen s o DHH s uden s.
In o de o p o ide mo e equi able educa ional
oppo uni ies o he de elopmen o p ac ical skills
o ma ginalized lea ne s, his pape examines he
me hodology used, he esul s ob ained om expe
inpu s, and he implica ions o his imme si e
lea ning model on he skill acquisi ion o DHH
s uden s.
Gi en ha DHH s uden s equi e p ac ical
skills o e ec i e imme si e lea ning, ou goals
we e o: (1) desc ibe he cha ac e is ics o ILPS o
help hem lea n mo e e ec i ely; and (2) p io i ies
hose ILPS o in e en ion, keeping in mind ha
DHH s uden s equi e p ac ical skills o e ec i e
imme si e lea ning. By combining NGT and FDM
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analysis, we we e able o each a consensus o
expe opinion in o de o accomplish hese goals.
2. LITERATURE REVIEW
P e ious s udies in he ield o educa ional
echnology ha e widely documen ed he po en ial
o AR and gami ica ion o enhance lea ning
engagemen , pa icula ly in STEM and highe
educa ion con ex s [10]. Addi ionally, esea ch
a ge ing DHH s uden s has la gely cen e ed a ound
assis i e ools such as sign language ecogni ion
sys ems o oice- o- isual ansla ion pla o ms
[11] and [12]. Howe e , hese e o s o en emain
isola ed echnological in e en ions wi hou
in eg a ion in o a pedagogically sound o alida ed
ins uc ional model. Mo e impo an ly, ew s udies
ha e sough o de elop inclusi e lea ning
amewo ks speci ically ailo ed o DHH s uden s
in TVET, whe e hands-on skill acquisi ion is
pa amoun . Fu he mo e, he use o consensus-
based me hods such as he NGT and FDM is la gely
absen in p io li e a u e, esul ing in models ha
may lack bo h p ac ical ele ance and inclusi i y.
Mo i a ed by hese gaps, he p esen s udy
in oduces a alida ed ILPS model o DHH
lea ne s, co-de eloped h ough s uc u ed expe
consensus. Unlike p e ious wo k, his s udy does
no simply e alua e echnological a o dances, bu
con ibu es a scien i ically g ounded, scalable
amewo k o inclusi e p ac ical skills aining an
inno a ion in bo h me hodology and educa ional
applica ion.
3. METHODOLOGY
The design and de elopmen o an
imme si e lea ning model o p ac ical skills
a ge ed o DHH s uden s equi e a igo ous and
inclusi e app oach o ensu e ha he model is bo h
e ec i e and mee s he speci ic lea ning needs o
hese s uden s. This s udy employs a mixed-me hod
app oach, u ilizing he NGT and he FDM o
ga he , analyze, and p io i ize expe inpu . These
me hods enable a s uc u ed, consensus-d i en
p ocess ha inco po a es di e se pe spec i es,
acili a ing he design o a lea ning model ha is
ailo ed o he unique equi emen s o DHH
s uden s in TVET con ex s.
3.1 Phase 1: Iden i ying Key Model
Componen s using he Nominal G oup
Technique (NGT)
NGT is a me hodical echnique ha inds a
g oup's common iewpoin s on a gi en subjec
[22]. Delbecq, Van de Ven, and Gus a son
desc ibed social planning scena ios as ollowing:
explo a o y esea ch; ci izen engagemen ; use o
in e disciplina y specialis s; and p oposal
assessmen [15]. O iginally, i was hough o as a
"pa icipa ion echnique o social planning
si ua ions". Since hen, he me hod has been used in
many di e en g oup con ex s, including social
science empi ical esea ch. Al hough i has been
u ilized in educa ion esea ch o some deg ee [23–
25], i seems o be mo e equen ly employed in he
ield o heal h s udies when i comes o social
science esea ch. The NGT p ocess is qui e
egimen ed and consis s o ou main s ages:
1. Coming up wi h ideas on i s own in
esponse o a p omp .
2. Round- obin sha ing (and lis ing) o hese
concep s wi hou deba e.
3. Making each concep clea on i s own and
assembling ela ed concep s in o g oups.
4. Indi iduals o e o choose which ideas
come i s .
The e is deba e on he op imal sample size
o employ when using NGT echniques in esea ch.
Acco ding o ce ain schola s, NGT may be
conduc ed on a la ge g oup o a single coho
[19,26,27], bu i can also be b oken up in o smalle
g oups o acili a e e ec i e communica ion,
depending on he needs o he s udy. Because o
his, he sample sizes shown in Table 1 ha ha e
been used in p e ious esea ch p ojec s a e as
ollows:
Table 1: NGT Sample Size [28].
Au ho
Sample
Van de Ven dan
Delbecq (1971)
5 – 9 expe s/pa icipan s
Ho on (1980)
7
–
10 expe s/pa icipan s
Ha ey dan Holmes
(2012)
6 – 12 expe s/pa icipan s
Abdullah & Islam
(2011)
7 – 10 expe s/pa icipan s
Ca ney e al (1996) Min. 6
expe s/pa icipan s
Because o he a o emen ioned e e ence,
he esea che chose 11 expe s o ake pa in he
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s udy's NGT and FDM me hod. This sum is deemed
sui able o his in es iga ion gi en he exis ing
ci cums ances ha es ic in e ac ions.
3.2 Phase 2: Valida ion and Expe Consensus
Using Fuzzy Delphi Me hod (FDM)
The p ocedu e is b oken down in o a
numbe o p ima y phases o inish he me hod.
P io o using he Delphi Fuzzy echnique, he
ini ial s age in ol es de eloping he i ems ha
equi e expe app o al. Selec ing quali ied expe s
wi h backg ounds in academia and indus y is he
second s age. In o ma ion om chosen expe s
mus be ga he ed wi hin a speci ied ime ange o
he hi d s ep. In o de o gene a e signi ican
co ela ions be ween he wo de ined ou comes,
cus ome engagemen , and he la en a iable o
assessmen , he inal s age is an analy ic echnique.
Mo e in o ma ion abou FDM s eps is p o ided in
Table 2.
Table 2: FDM S eps.
S ep
Fo mula ion
1.Expe selec ion Fo his esea ch, a o al o
ele en expe s we e in ol ed.
Se e al expe s we e in i ed
in o de o de e mine he
impac o he e alua ion
c i e ia on he a iables ha
would be in es iga ed
u ilising linguis ic a iables.
Among o he hings, hese
p o ide desc ip ions o
possible issues wi h he i em.
2.De e mining linguis ic scale This me hod yields uzzy
iangle numbe s, o iangula
uzzy numbe s, om all
linguis ic a iables. The
linguis ic a iables a e
con e ed a his s age by
adding uzzy numbe s o hem
[26] (m1, m2, m3) is he
iangula uzzy numbe ,
which ep esen s he alues
m1, m2, and m3. m1 and m2
ep esen he lowes and mos
easonable alues,
espec i ely, whils m3
ep esen he highes alues.
To ansla e linguis ic
a iables in o uzzy numbe s,
on he o he hand, uzzy
scales a e made using
iangula uzzy numbe s.
Odd digi s ep esen he
numbe o le els on he uzzy
scale. Figu e 1 show
T iangula uzzy numbe .
Figu e 1: T iangula uzzy numbe
3.The De e mina ion o
Linguis ic Va iables and
A e age Responses
Once he selec ed specialis
esponds, he esea che needs
o con e all Like scales o
uzzy scales. This p ocess is
also known as de e mining he
a e age esponse o each
uzzy numbe
[30]
.
4.The de e mina ion o
h eshold alue "d"
When de e mining he deg ee
o ag eemen among
specialis s, he h eshold alue
is c ucial [28]. The ollowing
o mula is used o ge he
dis ances o each uzzy
numbe , m = (m1, m2, m3)
and n = (m1, m2, m3):
Figu e 2: T
h eshold alue "d"
5.Iden i y he alpha cu
agg ega e le el o uzzy
assessmen
Each objec is assigned a
uzzy numbe a e expe
consensus [32]. Fuzzy alues
a e calcula ed and de e mined
using he ollowing o mula:
Amax=(1) Ú4 (m1 + 2m2 +
m3).
6.Di uzzica ion p ocess This p ocess uses he o mula
Amax = (1) Ú4 (a1 + 2am +
a3). When he esea che uses
a e age uzzy numbe s o
a e age answe s, a sco e
numbe be ween 0 and 1 is
gene a ed [32]. A = 1/3 * (m1
+ m2 + m3), A = 1/4 * (m1 +
2m2 + m3), and A = 1/6 *
(m1 + 4m2 + m3) a e he
h ee o mulas ha a e used in
his ope a ion. The median
alue o "0" and "1" is α-cu
= (0 + 1) / 2 = 0.5; his is he
A-cu alue. I he esul ing A
alue is less han he α-cu
alue = 0.5, he i em will be
ejec ed because his does no
imply expe ag eemen . The
alpha cu alue mus be mo e
han 0.5
[33]
.
7.Ranking p ocess The placemen app oach
selec s i ems based on
de uzzi ica ion alues and
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expe ag eemen ; he elemen
wi h he highes alue is
de e mined by he mos
impo an posi ion
[34]
.
In o de o ge a high deg ee o expe
consensus on he ea u es ha bes p omo e he
de elopmen o p ac ical skills in DHH s uden s,
he FDM p ocess e ec i ely assis ed in alida ing
and e ining he sugges ed model elemen s. These
esul s show he e ec i eness o FDM in inclusi e
design, gua an eeing ha he inished model is
p ac ical and ele an especially o he a ge
s uden s who a e DHH s uden s.
4. RESULT
The esul s o his s udy illus a e he
p ocess and ou comes o designing an imme si e
lea ning p ac ical skill model o DHH s uden s in
TVET. Th ough he NGT and FDM, a se o key
elemen s was iden i ied, alida ed, and e ined o
c ea e a model ha emphasizes accessibili y,
engagemen , and p ac ical skills acquisi ion. This
sec ion p esen s indings om each phase o he
s udy, including expe inpu analysis, consensus
me ics, and p elimina y pilo es ing ou comes
wi h DHH s uden s.
4.1 NGT Findings:
Table 3: Main Cons uc s.
I ems/ Elemen s
Cons uc 1
Cons uc 2
Cons uc 3
Vo e 1
6
7
7
Vo e 2
7
7
7
Vo e 3
6
7
7
Vo e 4
7
7
6
Vo e 5
7
7
7
Vo e 6
7
7
6
Vo e 7
7
7
7
Vo e 8
6
6
6
Vo e 9
7
7
6
Vo e 10
6
7
7
Vo e 11
7
7
7
To al Coun
73
76
73
Pe cen age
94.81
98.7
94.81
Rank P io i y
2
1
2
To al
Consensus
Sui able Sui able Sui able
Table 4: Cons uc 1: DHH Lea ning Inpu Medium.
I ems/
Elemen s
Tex
Pic u e
Video
Sign
Language
Vo e 1
6
5
7
7
Vo e 2
6
6
5
7
Vo e 3
6
5
7
7
Vo e 4
7
6
7
7
Vo e 5
7
7
7
7
Vo e 6
7
6
7
6
Vo e 7
7
7
7
7
Vo e 8
6
6
6
6
Vo e 9
7
7
7
7
Vo e 10
6
5
7
7
Vo e 11
6
6
5
7
To al Coun
71
66
62
65
Pe cen age
92.21
85.71
93.51
97.4
Rank
P io i y
3 4 2 1
To al
Consensus
Sui able Sui able Sui able Sui able
Table 5: Cons uc s 2: P ac ical Skills Lea ning Module.
I ems/
Elemen s
Sa e y
S ep by S ep
Demons a ion
P ac ical Skill
Vo e 1
7
4
7
7
Vo e 2
6
6
5
7
Vo e 3
7
5
7
7
Vo e 4
6
7
7
7
Vo e 5
7
7
7
7
Vo e 6
5
6
6
7
Vo e 7
7
7
7
7
Vo e 8
6
6
6
6
Vo e 9
7
7
7
7
Vo e 10
7
4
7
7
Vo e 11
6
6
5
7
To al Coun
71
65
71
76
Pe cen age
92.21
84.42
92.21
98.7
Rank
P io i y
2 3 2 1
To al
Consensus
Sui able Sui able Sui able Sui able
Table 6: Cons uc 3: AR Gami ica ion Fea u es.
I ems/
Elemen s
Use In e ace
3D Model
Gameplay
Le el
Di icul y
Challenge
Rewa d
Sco e
Vo e
1
5 7 6 6 6 7 5
Vo e
2
6 6 7 5 6 6 7
Vo e
3
6 7 7 5 6 6 6

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Vo e
4
7 7 6 5 7 7 7
Vo e
5
6 5 6 5 4 5 7
Vo e
6
6 4 6 6 7 6 6
Vo e
7
7 7 7 6 6 7 7
Vo e
8
6 5 5 5 5 4 5
Vo e
9
7 7 7 6 5 7 7
Vo e
10
6 7 6 6 6 7 5
Vo e
11
6 6 7 6 6 6 7
To al
Coun
68 68 70 61 64 68 69
Pe ce
n age
88.3
1
88.3
1
90.9
1
79.2
2
83.1
2
88.3
1
89.6
1
Rank
P io i
y
3 3 1 5 4 3 2
To al
Conse
nsus
Sui
able
Sui
able
Sui
able
Sui
able
Sui
able
Sui
able
Sui
able
Table 3 o Table 6 displays he model's
o e all ag eemen and e alua ion sco es. This
esea ch shows ha all model build concen a ions
a e wi hin he ideal ange. I is now necessa y o
he p opo ion o exceed 70% in ligh o he esul s
o hese in es iga ions. E e y i em abo e 70%
expe consensus, acco ding o he s udy o expe
app o al da a. A ew s udies ha bols e his idea
include Mus apha e al. (2022) and Deslandes,
Mendes, Pi es (2010). This enables he esea che s
o d aw he conclusion ha he model's essen ial
componen s a e p ac ical and well-liked by he
in ended audience. The leng hy ounds o expe
judgemen needed by he Delphi me hod migh be
eplaced wi h a as e al e na i e, he modi ied
NGT me hodology [25,30,32].
4.2 FDM Findings: Valida ion and Consensus on
Model Componen s
Table 7: P ac ical Skills Lea ning Module.
De uzzi ica ion
Repo Resul
Sa e y
S ep by S ep
Demons a ion
P ac ical Skill
Expe 1
0.05249
0.1837
0.05249
0.02099
Expe 2
0.00525
0.04724
0.12072
0.02099
Expe 3
0.05249
0.06823
0.05249
0.02099
Expe 4
0.00525
0.10497
0.05249
0.02099
Expe 5
0.05249
0.10497
0.05249
0.02099
Expe 6
0.12072
0.04724
0.05249
0.02099
Expe 7
0.05249
0.10497
0.05249
0.02099
Expe 8
0.00525
0.04724
0.00525
0.03674
Expe 9
0.12072
0.06823
0.12072
0.15221
Expe 10
0.05249
0.1837
0.05249
0.02099
Expe 11
0.00525
0.04724
0.12072
0.02099
S a is ics
I em1
I em2
I em3
I em4
Value o he
i em
0.04772
0.09161
0.0668
0.03435
Value o he
cons uc
0.06012
I em < 0.2
11
11
11
11
% o i em < 0.2
100%
100%
100%
100%
A e age o %
consensus
100%
De uzzi ica ion
0.90909
0.81818
0.90909
0.96364
Ranking
2
3
2
1
S a us
Accep
Accep
Accep
Accep
Table 8: DHH S uden Lea ning Inpu Medium
De uzzi ica ion
Repo Resul
Tex
Pic u e
Video
Sign Language
Expe 1
0.02624
0.09972
0.03674
0.0105
Expe 2
0.02624
0.01575
0.13646
0.0105
Expe 3
0.02624
0.09972
0.03674
0.0105
Expe 4
0.03149
0.01575
0.03674
0.0105
Expe 5
0.03149
0.07348
0.03674
0.0105
Expe 6
0.03149
0.01575
0.03674
0.04724
Expe 7
0.03149
0.07348
0.03674
0.0105
Expe 8
0.02624
0.01575
0.02099
0.04724
Expe 9
0.03149
0.07348
0.03674
0.0105
Expe 10
0.02624
0.09972
0.03674
0.0105
Expe 11
0.02624
0.01575
0.13646
0.0105
S a is ics
I em1
I em2
I em3
I em4
Value o he
i em
0.02863
0.0544
0.05344
0.01718
Value o he
cons uc
0.03841
I em < 0.2
11
11
11
11
% o i em < 0.2
100%
100%
100%
100%
A e age o %
consensus
100%
De uzzi ica ion
0.94545
0.87273
0.93636
0.98182
Ranking
2
4
3
1
S a us
Accep
Accep
Accep
Accep
Table 9: Imme si e Lea ning Augmen ed Reali y
Gami ica ion Fea u es
De uzzi ica ion
Repo Resul
Use In e ace
3D Model
Gameplay
Le el Di icul y
Challenge
Rewa d
Sco e
Expe 1 0.12
072
0.06
823
0.01
575
0.05
249
0.03
149
0.06
298
0.11
547
Expe 2 0.00
525
0.01
05
0.04
199
0.06
298
0.03
149
0.00
525
0.05
774
Expe 3 0.00
525
0.06
823
0.04
199
0.06
298
0.03
149
0.00
525
0
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31s May 2025. Vol.103. No.10
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4251
Expe 4 0.05
249
0.06
823
0.01
575
0.06
298
0.08
923
0.06
298
0.05
774
Expe 5 0.00
525
0.10
497
0.01
575
0.06
298
0.19
945
0.11
022
0.05
774
Expe 6 0.00
525
0.22
044
0.01
575
0.05
249
0.08
923
0.00
525
0
Expe 7 0.05
249
0.06
823
0.04
199
0.05
249
0.03
149
0.06
298
0.05
774
Expe 8 0.00
525
0.10
497
0.13
122
0.06
298
0.08
398
0.22
569
0.11
547
Expe 9 0.05
249
0.06
823
0.04
199
0.05
249
0.08
398
0.06
298
0.05
774
Expe
10
0.00
525
0.06
823
0.01
575
0.05
249
0.03
149
0.06
298
0.11
547
Expe
11
0.00
525
0.01
05
0.04
199
0.05
249
0.03
149
0.00
525
0.05
774
S a is ic
s
I em
1
I em
2
I em
3
I em
4
I em
5
I em
6
I em
7
Value o
he i em
0.02
863
0.07
825
0.03
817
0.05
726
0.06
68
0.06
107
0.06
299
Value o
he
cons uc
0.05617
I em <
0.2
11
10
11
11
11
10
11
% o
i em <
0.2
100
%
90%
100
%
100
%
100
%
90%
100
%
A e age
o %
consens
us
97%
De uzzi
ica ion
0.90
909
0.88
182
0.92
727
0.80
909
0.84
545
0.89
091
0.9
Ranking
2
5
1
7
6
4
3
S a us
Acc
ep
Acc
ep
Acc
ep
Acc
ep
Acc
ep
Acc
ep
Acc
ep
Table 7 o Table 9 show esul s o FDM,
ollowing da a p ocessing, he da kened h eshold
alue is highe han he 0.2 h eshold alue (> 0.2)
(see able 9). In o he wo ds, he e a e expe whose
iews do no acco d o e en coincide on some
issues. The a e age alue o all Ne Collabo a i e
Lea ning cons uc s and componen s, on he o he
hand, displays he a e age h eshold alue (d) <
0.2, o 0.08625. The i em has a high deg ee o
expe ag eemen i he h eshold (d) a e age alue
is less han 0.2 [35,36]. In he meanwhile, he o al
expe ag eemen pe cen age is 100%; 100%; 97%,
which is mo e han >75% and sa is ies he
equi emen s o expe ag eemen on his issue.
Fu he mo e, he a e age uzzy answe , o Alpha-
Cu de uzzi ica ion alues, all su pass α-cu => 0.5.
The alpha cu alue should be mo e han 0.5 and
should be disca ded i i is less han 0.5 [29,30,32].
5. STUDY CONTRIBUTION
Mos p io esea ch on imme si e lea ning
o DHH s uden s has explo ed he use o AR o
gami ica ion indi idually, o en wi hou a
s uc u ed app oach o model de elopmen o
alida ion. These s udies a ely in eg a e inclusi e
design me hodologies ha sys ema ically
inco po a e expe opinion ac oss disciplines.
Mo eo e , alida ion p ocesses in ea lie s udies
ypically o e look he speci ic needs o DHH
lea ne s in p ac ical skill se ings, pa icula ly
wi hin he TVET domain.
This s udy di e s by in eg a ing he NGT
and FDM, a me hodological combina ion no
p e iously applied in his con ex o de elop and
alida e an ILPS model. I es ablishes expe
consensus on h ee co e cons uc s (Lea ning Inpu
Medium, P ac ical Skills Module, and AR
Gami ica ion Fea u es), iden i ying 15 key
elemen s alida ed wi h o e 97% ag eemen . This
app oach no only ensu es con en alidi y bu also
e lec s inclusi e and accessible pedagogical
design.
The e o e, he key con ibu ion o his
s udy lies in i s pa icipa o y, consensus-d i en
de elopmen p ocess ha esul s in a scalable and
eplicable model o inclusi e skill-based educa ion
o DHH s uden s.
6. CHALLENGE AND OPEN RESEACH
ISSUES
E en hough his s udy used NGT and
FDM o e ec i ely cons uc and es an ILPS
model o DHH s uden s in TVET, he e a e s ill a
numbe o obs acles and un esol ed esea ch
ques ions.
6.1 Implemen a ion and Usabili y Tes ing
Despi e being app o ed by consensus o
expe s, he concep has no ye been pu o he es
in ac ual class oom se ings. Ac ual lea ning
esul s, lea ne engagemen , and usabili y a e s ill
no quan i ied. Pilo s udies wi h DHH s uden s
should be used in u u e s udies o u he hone and
con ex ualize he model.
6.2 Lea ne -Cen ic Model Re inemen
The cu en model is d i en by expe s.
Al hough his imp o es con en alidi y, DHH
s uden s hemsel es did no di ec ly con ibu e o i .
I s e icacy and inclusi eness would be imp o ed
by inco po a ing lea ne inpu ia use expe ience
esea ch o pa icipa o y design.
6.3 Scalabili y Ac oss Disciplines
Al hough he model is designed o TVET,
i has no been es ed o i s applicabili y o o he
ields (such as science, hospi ali y, o au omo i e
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4252
enginee ing). In o de o e alua e he ILPS model's
adap abili y, u u e esea ch migh in es iga e
domain-speci ic modi ica ions.
6.4 In eg a ion wi h B oade Pedagogical
F amewo ks
Aligning he model wi h well-known
educa ional heo ies like Cogni i e Load Theo y
and Uni e sal Design o Lea ning (UDL) migh be
ad an ageous. To assess how he app oach wo ks
wi h o complemen s hese inclusi e educa ion
amewo ks, mo e esea ch is equi ed.
6.5 Technological In as uc u e and Teache
Readiness
Adop ing imme si e echnology like AR
needs ins i u ional suppo , aining, and
app op ia e in as uc u e. Fu u e implemen a ion
s udies mus add ess he un esol ed ques ion o
whe he widesp ead adop ion in si ua ions wi h
limi ed esou ces is easible.
In addi ion o imp o ing he sugges ed
ILPS pa adigm, esol ing hese un esol ed
p oblems will u he he con e sa ion a ound
inclusi e ins uc ional design and imme si e
lea ning ools o special educa ion needs.
7. CONCLUSION
In conclusion, his s udy success ully
de eloped and alida ed an ILPS model speci ically
designed o DHH s uden s in TVET. The scien i ic
con ibu ion o his esea ch lies in i s no el
in eg a ion o he NGT and FDM o sys ema ically
elici , e ine, and alida e expe consensus on
c i ical componen s o imme si e, inclusi e
ins uc ional design. This me hodological
inno a ion add esses a majo gap in he li e a u e
by p o iding a alida ed model ha p io i izes
accessibili y, engagemen , and pedagogical
ele ance. The ou come is a scien i ically g ounded
amewo k comp ising 15 key elemen s ac oss
h ee alida ed cons uc s: Lea ning Inpu Medium,
P ac ical Skills Module, and AR Gami ica ion
Fea u es. This esea ch no only ad ances he
me hodological igo in inclusi e educa ion model
de elopmen bu also o e s p ac ical guidance o
u u e implemen a ions in imme si e lea ning
en i onmen s aimed a ma ginalized lea ne
popula ions.
This s udy adds subs an ially o he
exis ing body o knowledge by add essing a c i ical
and unde explo ed a ea, de eloping alida ed
imme si e lea ning models o DHH s uden s in
echnical and oca ional educa ion. While p e ious
esea ch has examined AR and gami ica ion in
gene al educa ional se ings, ew ha e a ge ed
accessibili y in model de elopmen , and none ha e
applied he combined use o he NGT and FDM o
his con ex . This dual-me hodological app oach
no only enhances he igo o model alida ion bu
also ensu es inclusi i y by inco po a ing expe
consensus in o e e y s age o design. Consequen ly,
his s udy con ibu es a no el, empi ically
suppo ed amewo k ha can be adap ed o use
ac oss inclusi e educa ional echnologies. I ills
bo h a me hodological and pedagogical gap and
o e s ac ionable insigh o esea che s and
p ac i ione s commi ed o equi able and imme si e
echnical educa ion.
Conside ing ha DHH s uden s need
p ac ical skills o success ul imme sion lea ning,
his s udy app op ia ely ou lines he ai s o ILPS
o aid in hei lea ning and anks hose ILPS o
assis ance. The esul s show ha he o e all expe
ag eemen pe cen age is g ea e han 75% and
mee s he c i e ia o expe ag eemen on his
ma e . The combined use o NGT and FDM o e s
a obus , e icien pa hway o inclusi e model
alida ion. This app oach educes bias and ensu es
ha educa ional models o DHH s uden s a e bo h
pedagogically sound and p ac ically alida ed.
Fu u e cu iculum de elope s can adap his me hod
o di e se lea ne needs in echnical educa ion.
Using FDM ins ead o he adi ional
Delphi echnique allowed his s udy o be inished
quickly and wi h expe consensus, which was one
o i s main s eng hs. Fu he mo e, ce ain
specialis s who could ha e s onge opinions han
o he s we e no able o domina e he discussion due
o he o ganized echnique o NGT. This migh
occu , o ins ance, when a senio expe o ocus
g oup membe has a epu a ion o being asse i e
o dominee ing, which in luences he opinions o
o he expe s.
This app oach will undoub edly lowe he
possibili y o bias by gua an eeing anonymi y,
encou aging he expe s' opinions o
uncon en ional iewpoin s, and allowing esponses
o be en i ely independen wi hou he ea o
c i icism om o he pa icipan s, which is ypically
p esen in any egula g oup discussions o
mee ings. One o he me hod's d awbacks, hough,
is ha he expe s mus be eminded epea edly o
p o ide hei answe s.
Because i imp o es anspa ency and
expedi es he p ocess, using NGT and FDM
oge he o es ablish expe consensus is s ongly
ad ised. The anking o he socioecological isk
a iables was con i med by he FDM analysis's
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31s May 2025. Vol.103. No.10
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ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
4253
obus ness. The e o e, i is s ongly ad ised o
include FDM in decision-making esea ch and
p ocedu es.
While his s udy success ully de eloped
and alida ed an imme si e lea ning p ac ical skills
model o DHH s uden s using expe consensus,
se e al limi a ions mus be acknowledged. Fi s , he
eliance on expe opinion, hough igo ous ia
NGT and FDM, excludes di ec eedback om
DHH lea ne s, limi ing insigh s in o use -cen e ed
design p e e ences. Second, he ela i ely small and
con ex -speci ic expe panel may no cap u e
b oade educa ional di e si y, a ec ing
gene alizabili y. Thi d, he model’s e ec i eness
has no ye been empi ically es ed in class oom
se ings, meaning i s pedagogical impac emains
heo e ical. Finally, logis ical aspec s such as
esou ce a ailabili y, eache eadiness, and
ins i u ional adop ion ba ie s we e beyond he
scope o his s udy. Recognizing hese limi a ions
no only enhances he anspa ency o his esea ch
bu also p o ides a oadmap o u u e
in es iga ions ocused on model implemen a ion
and lea ne ou comes.
Fu u e esea ch migh employ o he
echniques by including DHH s uden s and a mo e
comp ehensi e s udy se ing ha sugges s his ILPS
ac oss di e en ields and cu icula. Based on he
esul s o his s udy, u u e esea ch can also c ea e
a pa icula module. Pe haps a specialized e e ence
ma e ial o lec u e s o use while c ea ing and
o ganizing hei classes will be c ea ed in he u u e
wi h he de elopmen o a unique imme si e
lea ning echnology usage module.
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