Enhancing Manu ac u ing T aining Th ough VR Simula ions
Vladisla Li∗, Ilias Siniosoglou†‡, Panagio is Sa igiannidis†‡ and Vasileios A gy iou∗
Abs ac —In con empo a y aining o indus ial manu ac u -
ing, econciling heo e ical knowledge wi h p ac ical expe ience
con inues o be a signi ican di icul y. As companies ansi ion
o mo e in ica e and echnology-o ien ed se ings, con en ional
aining me hods equen ly inadequa ely equip wo ke s wi h
essen ial p ac ical skills while main aining sa e y and e iciency.
Vi ual Reali y has eme ged as a ans o ma ional ins umen o
ackle his issue by p o iding imme si e, in e ac i e, and isk-
ee eaching expe iences. Th ough he simula ion o au hen ic
indus ial en i onmen s, i ual eali y acili a es he acquisi ion
o i al skills o ainees wi hin a egula ed and s imula ing
con ex , he e o e mi iga ing he haza ds linked o expe ien ial
lea ning in he wo kplace. This pape p esen s a sophis ica ed
VR-based indus ial aining a chi ec u e aimed a imp o ing
lea ning e icacy ia high- ideli y simula ions, dynamic and
con ex -sensi i e scena ios, and adap i e eedback sys ems. The
sugges ed sys em inco po a es in ui i e ges u e-based con ols,
educing he lea ning cu e o use s ac oss all skill le els. A new
sco ing me ic, namely, VR T aining Scena io Sco e (VRTSS),
is used o assess ainee pe o mance dynamically, gua an eeing
ongoing engagemen and incen i e. The expe imen al assessmen
o he sys em e eals p omising ou comes, wi h signi ican en-
hancemen s in in o ma ion e en ion, ask execu ion p ecision,
and o e all aining e icacy. The esul s highligh he capabili y
o VR as a c ucial ins umen in indus ial aining, p o iding
a scalable, in e ac i e, and e icien subs i u e o con en ional
lea ning me hods.
Index Te ms—Vi ual Reali y T aining, Use Engagemen ,
Imme si e Lea ning, In e ac i e Simula ions, Heal h and Sa e y
Educa ion, Task Pe o mance, T aining E ec i eness, Indus ial
Manu ac u ing
I. INTRODUCTION
In he mode n indus ial e a, wi h he ansi ion o Indus y
5.0 and i s emphasis on human-cen ic, sus ainable ope a ions,
he o ganiza ion o manu ac u ing en i onmen s is unde going
signi ican e olu ion. This ans o ma ion spans om he phys-
ical laye o equipmen and senso deploymen s o he digi al
laye o da a p ocessing and decision-making sys ems [1], [2].
One majo d i e o his shi is he in eg a ion o Vi ual
Reali y (VR) echnologies, which s eamline aining, design,
and ope a ional wo k lows. The high h oughpu o da a gen-
e a ed in eal- ime manu ac u ing p ocesses unde sco es he
need o imme si e solu ions [3]. VR’s abili y o p o ide
in ui i e, con ex -awa e simula ions ensu es a seamless link
be ween i ual p o o ypes and eal-wo ld implemen a ion,
∗V. Li and V. A gy iou a e wi h he Depa men o Ne wo ks and Digi al
Media, Kings on Uni e si y, Kings on upon Thames, Uni ed Kingdom -
E-Mail: { .li, asileios.a gy iou}@kings on.ac.uk
†I. Siniosoglou and P.Sa igiannidis a e wi h he Depa men o Elec ical
and Compu e Enginee ing, Uni e si y o Wes e n Macedonia, Kozani, G eece
-E-Mail: {isiosoglou,psa igiannidis}@uowm.g
‡I. Siniosoglou and P. Sa igiannidis a e wi h Me a-
Mind Inno a ions P.C., Kozani, G eece - E-Mail:
{isiosoglou,psa igiannidis}@me amind.g
o e ing indus ies ools o b idge gaps in wo ke aining,
p ocess op imiza ion, and ope a ional sa e y [4].
The in eg a ion o VR echnology in o manu ac u ing ain-
ing ep esen s a ans o ma i e leap o wa d in b idging heo-
e ical knowledge wi h p ac ical applica ion [5]. This s udy
explo es how VR simula ions can enhance aining e ec-
i eness, accessibili y, and use engagemen in manu ac u ing
en i onmen s. By o e ing imme si e, con ex -awa e scena ios
ailo ed o indus y-speci ic needs, VR aining modules em-
powe employees o gain hands-on expe ience in a sa e and
con olled se ing [6], [7].
Despi e he p omise o VR in manu ac u ing aining, se -
e al challenges [4] ha e hinde ed i s widesp ead adop ion.
One majo di icul y lies in ensu ing he scalabili y o VR
solu ions ac oss di e se manu ac u ing en i onmen s. Manu-
ac u ing ope a ions o en a y widely in complexi y, equi ing
adap able aining modules ha e lec speci ic wo k lows,
equipmen con igu a ions, and sa e y p o ocols. De eloping
a sys em capable o accommoda ing such a iabili y, while
main aining he ealism and e ec i eness o he aining
expe ience, demands signi ican echnical and design expe -
ise [8]. Fu he mo e, achie ing high-quali y, in e ac i e VR
simula ions in ol es o e coming ha dwa e limi a ions, such
as ensu ing compa ibili y wi h a wide ange o VR de ices
and op imizing sys em pe o mance o p e en la ency o
discom o du ing use.
Ano he signi ican challenge is ensu ing ha VR sys ems
emain use - iendly o a wo k o ce ha may ha e lim-
i ed amilia i y wi h ad anced digi al echnologies [9]. Many
ainees, pa icula ly hose wi h minimal exposu e o VR
sys ems, equi e o ien a ion sessions o na iga e he imme si e
en i onmen e ec i ely. The s eep lea ning cu e o some
use s can de ac om he aining expe ience, necessi a ing
he design o in e aces ha a e in ui i e and accessible ac oss
a ying skill le els. Addi ionally, he eliance on eal- ime
da a p ocessing o pe sonalized eedback in oduces chal-
lenges ela ed o da a in eg i y and secu i y. Manu ac u ing
en i onmen s o en deal wi h sensi i e ope a ional da a, e-
qui ing obus enc yp ion and access con ols o sa egua d
p op ie a y in o ma ion. Add essing hese challenges is c i -
ical o unlocking he ull po en ial o VR as a ool o
imp o ing manu ac u ing aining ou comes. To his end, his
pape ocuses on o e coming he a o emen ioned challenges,
e ec i ely aiming o c ea e an ad anced and high ideli y VR
aining ecosys em o he high isk - high pace indus ial
en i onmen . Speci ically, he con ibu ions o his pape can
be summa ized as ollows:
•An ad anced scheme and me hodology o indus ial
manu ac u ing VR aining wi h con ex -awa e scena ios
ha mimic eal-wo ld en i onmen s and enable ainees
o be e comp ehend lea ning ma e ials and si ua ions.
•A high ideli y and ealis ic simula ion en i onmen o
enhancing manu ac u ing aining si ua ional awa eness
wi h adap i e eedback mechanisms.
•A me hodology o dynamic scena io adap a ion based on
use eedback and ac ions.
•Na u al ges u e-based con ols and minimal lea ning
cu e in e aces ensu e seamless na iga ion o use s o
all skill le els, complemen ing he imme si e expe ience.
•A No el sco ing mechanism in eg a ed in o in e ac i e
scena ios o e alua e and main ain use mo i a ion.
The es o his pape is o ganized as ollows: he ela ed
wo k is discussed in Sec ion II, ollowed by an o e iew
o he me hodology in Sec ion III. Sec ion IV p o ides a
comp ehensi e analysis o he a ailable da a, as well as a se ies
o quan i a i e esul s. Sec ion Vo e s concluding ema ks.
II. LITERATURE REVIEW
Due o he powe ul na u e o VR in aining employees, he
echnology has seen a big ise in implemen a ions, especially
in he indus ial domain. This is powe ed by he ac ha sa e y
is essen ial in such domains bu so is he need o pu pose ully
ain and e alua e employees be o e ac ually gi ing aces o
dange ous and o cou se delica e indus ial equipmen . To his
end, a lo o ela ed wo k has been seen in his ield.
Fo example, in [10] he au ho s p opose a VR aining
sys em o each open-ended psychomo o skills o d illing
wi h a 3-axis milling machine. The simula ion includes mod-
ules o sa e y, u o ials, open-ended p ac ice, and e alua ion
agains enginee ing s anda ds, enabling mul iple pa hways
o ask comple ion. A use s udy wi h enginee ing s uden s
showed highe ask success a es, ewe mis akes, and as e
comple ion imes compa ed o adi ional me hods. The s udy
also highligh s he cogni i e, psychomo o , and a ec i e ben-
e i s o open-ended VR aining, o e ing a no el app oach
o enhancing indus ial skill acquisi ion h ough lexible, ex-
plo a o y lea ning.Despi e i s e ec i eness, he me hodology
may ace challenges in indus ial se ings, whe e he di e si y
o equipmen and en i onmen al a iabili y could hinde i s
gene alizabili y.
Likewise, in [11], he au ho s p opose a VR pla o m o
enhance he assembly and inspec ion o indus ial componen s
using imme si e i ual en i onmen s (IVEs). The sys em in e-
g a es BIM models and 3D lase -scanned da a, enabling use s
o manipula e and e alua e pa s o disc epancies. Le e aging
a mo ion- acked VR se up, he pla o m highligh s de ec i e
componen s and ensu es quali y con ol by snapping compo-
nen s in o place i wi hin de ined ole ances. This app oach
acili a es aining, educes on-si e e o s, and s eamlines
quali y assu ance p ocesses o complex assemblies, o e ing
signi ican ime and cos sa ings. While he p oposed VR
pla o m is e ec i e o aining and quali y con ol, i may
ace challenges in scaling o la ge , mo e in ica e indus ial
se ings wi h highly complex assemblies. The eliance on 3D
scanning accu acy and po en ial noise in poin cloud da a could
limi i s p ecision in de ec ing disc epancies. Simila ly, in [12]
he au ho s p esen a VR-based Ope a o T aining Simula o
(VR OTS) designed o simula e no mal, s a up, and shu down
condi ions in manu ac u ing en i onmen s. Use s can ain
as ield o con ol panel ope a o s, engaging in imme si e
scena ios ha eplica e eal-wo ld asks. U ilizing he Me a
Oculus Ques 2, he sys em in eg a es ac ion indica o s and
in e ac i e en i onmen s o enhance ope a ional e iciency and
sa e y.
Ano he in e es ing implemen a ion can be seen in [5],
whe e he au ho s p esen a VR pla o m o addi i e man-
u ac u ing (AM) educa ion, simula ing a 3D p in ing lab
wi h hands-on asks like pa ame e selec ion, sa e y compli-
ance, and cos es ima ion. The s udy shows imp o ed s uden
a en ion, accu acy, and sa is ac ion, hough challenges in-
clude imme si e s imuli and limi ed eedback du ing decision-
making. Simila ly, [13] in oduces a Vi ual Reali y T aining
App en iceship (VRTA) o he cold sp ay p ocess, ea u ing
six modules, including powde eede assembly. The VRTA
emphasizes ealism and in e ac i e ins uc ions, e ec i ely
ans e ing skills o eal-wo ld asks. Howe e , limi a ions like
lack o physical ool ealism, non-in ui i e in e ac ions, and a
small sample size hinde wide applicabili y.
III. METHODOLOGY
The p oposed me hodology in eg a es VR echnologies o
enhance aining in indus ial en i onmen s, ocusing on im-
me si e, con ex -awa e lea ning expe iences. The amewo k
is designed o simula e eal-wo ld scena ios, enabling use s o
p ac ice c i ical p o ocols, such as eme gency esponses, in a
sa e, con olled en i onmen . Ges u e-based con ols and use -
iendly in e aces a e employed o ensu e accessibili y o
use s o a ying echnical p o iciencies. This sec ion de ails
he de elopmen o VR modules, including scena io design,
in e ac ion mechanisms, and e alua ion s a egies. Emphasis is
placed on aligning he simula ions wi h indus y-speci ic needs
o maximize ele ance, while also add essing me ics such
as aining e ec i eness, use engagemen , and knowledge
e en ion. An ou line o he p oposed me hodology is depic ed
in Figu e 1, also showing he s eps ollowed in sequence.
A. 3D Asse s o Ad anced Realism
The ini ial de elopmen o he VR aining en i onmen
elied on de eloping he asse s o ep esen he manu ac u ing
se ing om sc a ch wi h he use o 3D p imi i es such as
cubes and sphe es wi hin Uni y. This decision was made wi h
he goal o speed up he ini ial p o o yping phase and s a
expe imen ing wi h he in e ac i e ea u es in oduced wi h he
VR echnology. Fu he mo e, i was la gely d i en by esou ce
cons ain s as i was di icul o ind highly speci ic 3D asse s
o he indus ial domain in o de o c ea e de ailed, cus om-
designed, and ealis ic indus ial equipmen o p o ide he
necessa y ealism o each scena io. To his end, Uni y’s buil -
in p imi i es we e adap ed o simula e indus ial equipmen
and ools in a manne ha allowed o e ec i e in e ac ion
wi h he use .
Fig. 1: Li e Scena io Scene Asse s
To enhance he simula ion’s ealism and usabili y, hi d-
pa y asse s we e in eg a ed in o he en i onmen whe e e
possible, o igina ing om a ailable designs o indus ial
equipmen and speci ica ions based on he de eloped scena -
ios. Howe e , he a ailabili y o ee, high-quali y indus ial
asse s online p o ed o be signi ican ly limi ed. The sca ci y
o asse s ailo ed speci ically o manu ac u ing en i onmen s
necessi a ed he use o gene ic o simpli ied al e na i es. Fo
ins ance, a he han inco po a ing ully de ailed indus ial
machine y, simpli ied cus om-de eloped asse s we e used.
These simpli ied asse s p o ided a unc ional ep esen a ion
o he aining en i onmen wi hou de ac ing om he co e
objec i e o enabling use in e ac ion and imme si e lea ning.
A a la e s age, hype - ealis ic 3D asse s we e in oduced
o closely, i no en i ely, esemble eal-wo ld indus ial equip-
men . These asse s signi ican ly enhanced he au hen ici y o
he aining en i onmen , allowing use s o engage wi h highly
de ailed and accu a e ep esen a ions o machine y and ools.
The imp o ed le el o de ail con ibu ed o a mo e imme si e
and e ec i e aining expe ience, b idging he gap be ween
i ual simula ion and eal-wo ld applica ion.
(a) POV 1 (b) POV 2
Fig. 2: Li e Scena io Scene a di e en poin s o iew (POVs).
Despi e hese limi a ions, he chosen app oach success ully
con eyed he concep o in e ac ing wi h objec s and na iga -
ing indus ial wo k lows in a VR se ing. As can be seen in
Figu e 2, simpli ied objec s, such as basic le e s and indus ial
boxes, we e a anged o c ea e a plausible aining scena io.
By ocusing on unc ional ep esen a ion a he han high-
ideli y isuals, i was ensu ed ha use s could engage wi h
he aining scena ios meaning ully.
B. In eg a ion o In e ac i i y
To enhance use expe ience and con ey he needed knowl-
edge o ainees, in e ac i e elemen s we e also inco po a ed
in o he VR aining modules enhancing engagemen and
p o iding an imme si e lea ning expe ience. The ollowing
componen s o med he co e o his in e ac i e amewo k:
1) Ques ionnai es: In e ac i e ques ionnai es we e embed-
ded wi hin he VR modules o e alua e ainee knowledge
and unde s anding o indus ial p o ocols. These quizzes we e
s a egically placed a key poin s du ing he aining o ein-
o ce lea ning objec i es and assess e en ion. The ques ions
we e designed o add ess heo e ical knowledge, p ac ical ap-
plica ion, and p oblem-sol ing skills, ensu ing comp ehensi e
e alua ion. The VR en i onmen enabled a unique app oach o
ques ionnai es by p esen ing hem in a dynamic and imme si e
manne . Fo ins ance, use s could in e ac wi h i ual panels
o sc eens o selec answe s, making he assessmen p ocess
engaging and in ui i e. This immedia e eedback mechanism
also allowed ainees o co ec misconcep ions in eal ime,
os e ing a deepe unde s anding o he ma e ial. The in-
eg a ion o ques ionnai es no only measu ed p og ess bu
also mo i a ed ainees o ac i ely engage wi h he con en
h oughou he aining session.
2) In e ac able Objec s: Vi ual objec s, modelled a e
ools and equipmen ound in indus ial se ings, we e in e-
g a ed in o he VR modules o p o ide a hands-on lea ning
expe ience. These objec s allowed ainees o p ac ice asks
such as ope a ing machine y, handling ools, o assembling
componen s. The design p io i ized ealism and unc ionali y,
enabling ainees o in e ac wi h he i ual objec s as hey
would in a eal-wo ld se ing. Fo example, ainees could
g asp, o a e, and posi ion objec s using na u al hand ges u es,
acili a ed by VR con olle s and acking sys ems. This im-
me si e in e ac ion helped b idge he gap be ween heo e ical
knowledge and p ac ical applica ion. Fu he mo e, hese in-
e ac able objec s (Figu e 4) we e inco po a ed in o aining
scena ios o simula e wo k lows and p ocedu es, ensu ing
ha ainees de eloped bo h echnical skills and si ua ional
awa eness. The inclusion o such ealis ic and unc ional
i ual ools signi ican ly enhanced he o e all e ec i eness
o he aining p og am by p o iding a angible and engaging
way o p ac ice c i ical asks. These de eloped modules acil-
i a ed in ui i e use -objec in e ac ion, eplica ing eal-wo ld
handling o indus ial ools and equipmen in a i ual se ing.
By in eg a ing hese in e ac i e elemen s, he VR en i onmen
o e ed an engaging and imme si e pla o m o indus ial
aining.
3) Li e Scena ios: Real- ime dynamic scena ios we e de-
signed o simula e complex, decision-making si ua ions in
he indus ial con ex . These scena ios challenged ainees o
espond e ec i ely o unexpec ed e en s, such as equipmen
mal unc ions o sa e y haza ds, while dynamically adap ing he
scena io o he ainees esponses. By eplica ing eal-wo ld
condi ions, li e scena ios encou aged c i ical hinking and
p oblem-sol ing. The VR en i onmen p o ided a sa e space
o ainees o expe imen wi h di e en s a egies wi hou he
isk o eal-wo ld consequences. Fo example, ainees migh
need o iden i y a simula ed equipmen ailu e and execu e
he app op ia e epai p ocedu e unde ime cons ain s, while
e alua ing he accu acy and imeliness o ainee eedback
h ough o de o ac ions (see Figu e 3) . This in e ac i e
and imme si e expe ience enables ainees o build con idence
and eadiness o ac ual wo kplace si ua ions. Addi ionally,
he adap i e na u e o he scena ios allowed o di icul y
le els o be adjus ed based on ainee pe o mance, ensu ing
pe sonalized and p og essi e lea ning ou comes.
C. High Fideli y In e ac ion Me hodology
To enable a seamless and imme si e VR aining expe ience,
a sophis ica ed in e ac ion me hodology was de eloped. This
me hodology cen e ed a ound he design o h ee in e ac-
i e modules: Mul iple-Choice Ques ions (MCQs), In e ac i e
Ques ions (IQs), and Li e Scena ios. The sys em aimed o in e-
g a e dynamic ques ion-and-answe unc ionali ies, in e ac i e
objec handling, and adap i e eal- ime scena ios, enhancing
engagemen and lea ning ou comes o ainees.
1) Mul iple-Choice Ques ions (MCQs): The MCQ module
was designed o acili a e knowledge assessmen in an engag-
ing and in ui i e manne . The sys em p esen ed ainees wi h
a se ies o MCQs ha included isual asse s and con ex ual
elemen s. To achie e his, an in e ac i e use in e ace (UI)
was implemen ed, as can be seen in Figu e 4, dynamically
synch onized wi h ques ion and answe da abases. The module
u ilized da a s uc u es o s o e ques ion me ada a, such as ex ,
asse de ails, and co ec ness lags. Real- ime upda es ensu ed
seamless e ie al o ques ions and answe s, while sco ing and
p og ess me ics we e logged h ough a me ics manage . This
design no only e alua ed use unde s anding bu also p o ided
ins an eedback o ein o ce lea ning.
2) In e ac i e Ques ions (IQs): The IQ module expanded
he aining amewo k by in eg a ing isual and spa ial in e -
ac ion elemen s, as seen in Figu e 5. These ques ions equi ed
use s o engage wi h he en i onmen , such as selec ing o
manipula ing i ual objec s. A dynamic asse managemen
sys em allowed he loading o ques ion- ela ed isuals and
in e ac ion poin s. This enhanced ealism and ensu ed ha
ainees could physically in e ac wi h aining elemen s, im-
p o ing skill acquisi ion. Addi ionally, he sys em suppo ed
li e scena ios, dynamically adap ing IQ con en o ma ch eal-
ime si ua ions, u he enhancing engagemen .
3) Li e Scena ios: The li e scena io module in oduced
eal- ime decision-making asks o simula e complex indus-
ial si ua ions. Scena ios we e s uc u ed o challenge use s
by equi ing hem o ollow speci ic ac ion sequences o
esol e ope a ional challenges. The sys em acked use ac-
ions, eco ded me ics, and e alua ed pe o mance agains
p ede ined benchma ks. By in eg a ing isual ins uc ions,
in e ac i e componen s, and dynamic eedback, his module
eplica ed eal-wo ld scena ios while main aining a sa e and
con olled en i onmen .
4) Sys em A chi ec u e and Implemen a ion: The de elop-
men o hese modules was suppo ed by C# sc ip ing in Uni y,
le e aging modula and eusable code s uc u es. Two compo-
nen s, he MCQ and IQ Manage modules we e de eloped o
enabled managing use in e ac ions and isual asse synch o-
niza ion. API-based asynch onous calls enabled e icien da a
e ie al and asse in eg a ion, ensu ing a esponsi e and lag-
ee use expe ience. Visual elemen s, such as asse bundles
and answe oggles, we e seamlessly inco po a ed in o he
VR in e ace, o e ing an imme si e and engaging aining
en i onmen .
D. Ac i e Me ic Acquisi ion & Dynamic Scena io Adap a ion
A p oposed me hodology o ac i e me ic acquisi ion and
dynamic scena io adap a ion was de eloped o enable eal- ime
acking, e alua ion, and adap a ion wi hin he VR aining
en i onmen . This sys em le e ages ad anced me ic collec ion
echniques o moni o use pe o mance and dynamically mod-
i y scena ios o enhance engagemen and lea ning ou comes.
1) Ac i e Me ic Acquisi ion: The co e o he sys em’s da a
collec ion is managed by a me ics manage componen , which
acks and eco ds use in e ac ions, ask pe o mance, and
engagemen me ics. A unique session iden i ie , gene a ed
du ing ini ializa ion, ensu es da a consis ency o each use
session. Me ics a e ca ego ized in o wo p ima y ypes: pe -
o mance me ics and use expe ience (UX) me ics.
Pe o mance me ics include imes amps, ask comple ion
da a, success a es, and sco es, which a e logged in eal
ime. These me ics p o ide insigh s in o ainee p og ess
and p o iciency. Fo ins ance, imes amps ack he du a ion
o speci ic asks, while success lags and sco es quan i y
use pe o mance in li e scena ios. Da a is se ialized in o a
s anda dized o ma o local s o age and can also be uploaded
o an online se e o cen alized analysis.
UX me ics ocus on use engagemen and in e ace us-
abili y. These include session-speci ic me ada a such as ask
names and use in e ac ion da a, which a e used o e alua e
and e ine he aining expe ience. Bo h ypes o me ics a e
uploaded o online da abases o u he p ocessing, allowing
o comp ehensi e epo ing and he iden i ica ion o pa e ns
ha in o m u u e de elopmen .
2) Dynamic Scena io Adap a ion: Dynamic scena io adap-
a ion is a key ea u e enabled by eal- ime me ic acquisi ion.
Du ing li e scena ios, he sys em moni o s use beha iou and
compa es i agains p ede ined benchma ks, such as he co ec
sequence o ac ions. A me ics manage uses his da a o
dynamically adjus he complexi y o s uc u e o scena ios.
Fo example, i a use demons a es di icul y comple ing
asks, he sys em may educe scena io complexi y o p o ide
addi ional guidance. Con e sely, i a use pe o ms excep ion-
ally well, mo e challenging asks can be in oduced o main ain
engagemen and encou age g ow h.
Fig. 3: An example sequence o ac ions o li e scena ios in he VR aining applica ion.
(a) POV 1 (b) POV 2
Fig. 4: MCQ Scene a di e en poin s o iew (POVs)
(a) POV 1 (b) POV 2
Fig. 5: IQ Scene a di e en poin s o iew (POVs)
This adap abili y is suppo ed by he seamless in eg a ion
o da a e ie al and e alua ion sys ems wi hin he VR en-
i onmen . The me ics manage p ocesses me ics such as
ask comple ion o de , imes amps, and success a es o assess
pe o mance in eal ime. The sys em hen uses his in o -
ma ion o modi y scena io pa ame e s dynamically, ensu ing
an op imized and pe sonalized aining expe ience o each
use . The collec ed me ics can be b oadly ca ego ized in o
wo ypes: pe o mance me ics and use expe ience (UX)
me ics. Pe o mance me ics p o ide quan i a i e da a on use
p o iciency, while UX me ics ocus on e alua ing he quali y
o he use in e ace and in e ac ion design. The adop ed
me ics a e analysed below:
3) Pe o mance Me ics:
1) Task Comple ion Da a: This me ic eco ds whe he
speci ic asks we e comple ed success ully du ing he
aining session, p o iding a bina y ou come o ask
p og ess [14]:
C=(1i ask comple ed success ully
0i ask ailed o incomple e (1)
The sum o all comple ed asks ac oss sessions gi es an
o e all comple ion a e o he aining.
2) Times amps: Measu es he du a ion Ti aken by a use
o comple e a speci ic ask o scena io. The a e age ask
comple ion ime o n asks is calcula ed as [15]:
A e age Task Time =Pn
i=1 Ti
n(2)
3) Success Ra es: T acks he p opo ion o success ully
comple ed asks ela i e o he o al numbe o asks
a emp ed [16]:
Success Ra e =Numbe o Success ul Tasks
To al Tasks A emp ed ×100%
(3)
4) Use Pe o mance Sco e: Quan i ies use pe o mance
by assigning weigh ed sco es Si o comple ed asks.
Fo li e scena ios, sco es may be no malized using
complexi y ac o s Wi[17]:
Sco e =
n
X
i=1
Si·Wi(4)
5) O de o Ac ions: Logs he sequence o use ac ions
and calcula es he simila i y be ween use ac ions and
he co ec p ocedu al o de using sequence ma ching
echniques, such as [18]:
O de Accu acy =Co ec Ac ions in O de
To al Expec ed Ac ions ×100%
(5)
4) Use Expe ience (UX) Me ics:
1) Engagemen Le els: Assesses use ac i i y and a en-
ion by analysing in e ac ion equency F, calcula ed as
he numbe o in e ac ions pe uni ime [19]:
Engagemen F equency =To al In e ac ions
Session Du a ion (6)
2) In e ac ion Da a: Cap u es de ails o use ac ions, such
as selec ing objec s o answe ing ques ions, o analyse
in e ace usabili y and esponsi eness. In e ac ion me -
ics a e agg ega ed o e ime o iden i y usage ends
[20].
3) Session Me ada a: Includes ask names and use ac i i-
ies wi hin a session, p o iding con ex o pe o mance
and engagemen da a. While no speci ic o mula applies,
his da a suppo s quali a i e assessmen s o use expe-
ience [21].
These me ics we e chosen o hei abili y o quan i a i ely
and quali a i ely assess bo h he e ec i eness o he aining
sys em and he use expe ience. By le e aging hese me ics
and hei de i ed o mulas, he VR amewo k ensu es an
adap i e and pe sonalized aining en i onmen ha mee s he
di e se needs o i s use s. In o de o quan i y be e he
pe o mance o each ainee, selec ed pe o mance me ics
we e u ilised o c ea e a uni ied e alua ion me ic which is
ou lined below.
E. VR T aining Scena io Sco e (VRTSS)
The li e scena io module in eg a es a s uc u ed sco ing
me hodology o assess ainee pe o mance in a dynamic and
objec i e manne . This app oach ensu es ha bo h si ua ional
co ec ness and sequence o de a e e alua ed o p o ide a
comp ehensi e assessmen o ainee p o iciency.
To be e assess use pe o mance wi hin VR aining sce-
na ios, his pape in oduces a no el me ic known as he
VRTSS. This me ic is designed o e alua e bo h p ocedu al
accu acy and he co ec sequencing o ac ions, o e ing a
obus measu e o lea ning e ec i eness.
The VRTSS o mula is de ined as:
V RT SS = 0.3X+ 0.2Y+√0.25XY (7)
He e, Xdeno es he co ec ness o he o de o si ua ions,
measu ed as he p opo ion o co ec ly sequenced s eps.
Ydeno es he co ec ness o indi idual ac ions, cap u ing
he aw accu acy o ainee decisions. The squa e oo e m
in oduces a geome ic mean componen o balance he impac
o bo h co ec ness measu es. To e alua e he co ec ness o
he o de o si ua ions (X), a sequen ial ma ching algo i hm
compa es he use ’s execu ion o de agains he p ede ined
co ec sequence. Ma hema ically, his is exp essed as:
X=1
N
N
X
i=1
δ(Si, Pi)(8)
He e, Nis he o al numbe o si ua ions in he scena io Si
ep esen s he co ec o de o he i h si ua ion and Pideno es
he o de in which he ainee execu ed he i h si ua ion.
δ(Si, Pi)is an indica o unc ion:
δ(Si, Pi) = (1,i Si=Pi
0,o he wise (9)
This unc ion ensu es ha only co ec ly placed si ua ions
con ibu e o he co ec ness sco e. The o al coun o co ec ly
placed si ua ions is hen no malized o e N o p oduce a
p opo ion-based me ic.
To calcula e X, he inpu om he ainee is compa ed wi h
he g ound- u h, as demons a ed in he Figu e 3. Fo ins ance,
i he inpu om he ainee di e s om he g ound- u h
hen he ainee ailed o pe o m an ac ion when i is needed.
Likewise, i he ainee’s inpu ma ches he g ound- u h hen
he ac ion was pe o med igh on ime, i.e., in he igh
sequence. The VR aining applica ion keeps ack o ainee’s
ac ions du ing he li e scena io and does such compa ison
o all s eps in he sequence o ac ions o g ound- u h. The
esul is ep esen ed as ei he 0 o misma ch, and 1 o ma ch
leading o a lis o 0’s and 1’s. The e o e, he sum o his lis
is di ided by he o al numbe o s eps and cons i u es X, i.e.
he co ec ness o he o de o ac ions. To calcula e Y, he VR
aining applica ion simply checks whe he indi idual ac ions
we e pe o med co ec ly. Fo example, i he ac ion is o s op
he machine, he ainee needs o selec he co ec bu on ou
o ou possible op ions p esen du ing he scena io. Simila ly,
o he p e ious pa ag aph, he o al numbe o co ec ac ions
is di ided by he o al numbe o ac ions in he g ound- u h
leading o he mean a e age o he co ec ness o he ac ions.
The 0.3, and 0.2cons an s we e chosen o highligh he bias
owa ds he co ec ness o he o de o ac ions due o he ac
ha he li e scena io aims o assess mo e he o de o ac ions
pe o med by he ainee wi h he ega d o he co ec ion o
ac ions. The √0.25XY componen is he e o highligh he
in e wined na u e o he me ics as well as o s abilise he
ou pu wi hin he ange o [0,1].
The VR aining sys em implemen s his sco ing model
h ough eal- ime acking o use in e ac ions. Each ainee’s
pe o mance is logged and compa ed agains he p ede ined
co ec sequence, p o iding immedia e eedback on bo h ask
execu ion accu acy and p ope o de ing. This ensu es ha he
aining en i onmen accu a ely simula es eal-wo ld p oce-
du al compliance and enables use s o e ine hei decision-
making p ocesses h ough i e a i e a emp s.
IV. USER TESTING & EVALUATION
A. Use Feedback and Su ey Resul s
The VR aining applica ion ecei ed p edominan ly posi i e
eedback om pa icipan s, many o whom a ed hei expe-
ience wi h sco es o 4 o 5 on a scale o 1 o 5. This high
le el o sa is ac ion highligh s he o e all success o he sys em
in deli e ing an engaging and e ec i e aining expe ience.
A s andou ea u e o use s was he in ui i e na iga ion,
which allowed hem o easily explo e he applica ion wi hou
con usion o us a ion. This ease o use was pa icula ly
app ecia ed by hose who we e new o i ual eali y en i-
onmen s, as i minimized he lea ning cu e and made he
expe ience mo e accessible. Ano he key highligh was he li e
scena io elemen o he applica ion, which was desc ibed as
bo h engaging and highly ele an o eal-wo ld applica ions.
Pa icipan s el ha his componen p o ided a ealis ic and
imme si e way o p ac ice heal h and sa e y p ocedu es, ein-
o cing hei unde s anding in a p ac ical con ex . The aining
me hod was also p aised o i s abili y o help use s e ain
c i ical in o ma ion, wi h many no ing ha i enhanced hei
con idence in applying heal h and sa e y measu es in ac ual
haza dous si ua ions. O e all, use eedback unde sco es he
sys em’s abili y o e ec i ely combine usabili y wi h p ac ical
aining ou comes, posi ioning i as a aluable ool o heal h
and sa e y educa ion. Some examples o use sa is ac ion and
eedback a e gi en in Figu e 6. In he p o ided samples i
can be seen ha use s can lea n mo e easily and con iden ly
using he de eloped VR sys em han he echnical manual o
he ideo u o ials.
(a) VR (b) Manual (c) Video
(d) VR (e) Manual ( ) Video
(g) VR (h) Manual (i) Video
Fig. 6: Use Sa is ac ion Compa ison Examples: Q1-How well do you unde -
s and he heal h and sa e y p ocedu es a e comple ing he aining?—Q2-
How con iden a e you in applying he heal h and sa e y p ocedu es in a
eal-wo ld haza dous en i onmen ?—Q3-Do you belie e he aining me hod
you used made i easie o s ay ocused h oughou he lea ning p ocess?
Mo eo e , Table Ishows he summa y s a is ics o use
e alua ions o he simula ion, including con idence in appli-
ca ion, e en ion e ec i eness, engagemen le el, ealism, and
eme gency p epa edness. The esul s indica e ha while use s
gene ally ind he simula ion e ec i e o e en ion (Mean =
3.53) and mode a ely engaging (Mean = 3.13), he e is no able
a iabili y in esponses. Con idence in applica ion is ela i ely
s ong (Mean = 3.40), bu eme gency p epa edness ecei es he
lowes a ing (Mean = 2.80), sugges ing a po en ial a ea o
imp o emen .
The me ics indica e a gene ally posi i e ecep ion o he
aining sys em ac oss mul iple dimensions. Con idence in
applica ion and e en ion e ec i eness ecei ed some o he
highes a ings, sugges ing ha use s ound he aining bo h
p ac ical and memo able. Engagemen le el and ealism o he
simula ion we e also a ed posi i ely, hough he e is oom
o imp o emen in hese a eas o enhance imme sion and
TABLE I: UX Feedback E alua ion
Con idence
in
Applica ion
Re en ion
E ec i e-
ness
Engagemen
Le el
Realism
o Simula-
ion
Eme gency
P epa edness
coun 15.00 15.00 15.00 15.00 15.00
mean 3.40 3.53 3.13 3.13 2.80
s d 1.24 0.99 1.51 1.25 1.37
min 1.00 2.00 1.00 1.00 1.00
25% 2.50 3.00 2.00 2.00 2.00
50% 4.00 3.00 3.00 3.00 3.00
75% 4.00 4.00 4.50 4.00 3.00
max 5.00 5.00 5.00 5.00 5.00
use in e es . Eme gency p epa edness ecei ed he lowes
a e age a ing, poin ing o a po en ial need o mo e de ailed
scena ios o addi ional guidance o be e equip use s o high-
s ess si ua ions. These insigh s p o ide aluable di ec ion
o e ining he sys em o add ess speci ic use needs and
expec a ions.
B. Sub ask Analysis
The MCQs module p o ed o be an e ec i e componen o
he aining sys em, wi h >90% success a e and an a e age
comple ion ime o jus 20 seconds. This e iciency demon-
s a es ha he ques ions we e well-designed and s aigh o -
wa d, allowing pa icipan s o quickly assess hei knowledge
wi hou con usion. Many use s commen ed on he cla i y o he
ques ions and hei ele ance o he aining objec i es, which
con ibu ed o he high success a e. Fu he mo e, as illus a ed
in Table III, he majo i y o pa icipan s did no encoun e
signi ican di icul ies wi h he mul iple-choice ques ion com-
ponen o he aining. This ou come was expec ed, as his
sec ion closely esembles con en ional heo y-cen e ed ain-
ing me hods, such as s anda dized mul iple-choice es ing. The
amilia i y o his o ma enabled pa icipan s wi hou p io
exposu e o he VR aining applica ion o quickly comp ehend
he aining objec i es and comple e he ask success ully.
Ne e heless, he e we e ins ances in which pa icipan s we e
unable o comple e he ask due o a lack o amilia i y wi h
VR echnology and he ope a ional aspec s o VR ha dwa e,
such as ges u e ecogni ion came a acking. This inding
sugges s ha an ini ial amilia isa ion phase wi h he VR
ha dwa e may be bene icial be o e commencing he aining.
Simila ly, he IQ module also achie ed a >90% success
a e bu equi ed a longe a e age comple ion ime o 3 min-
u es and 7 seconds. This module in ol ed mo e complex asks,
such as in e ac ing wi h i ual objec s, which use s gene ally
ound engaging despi e occasional challenges. Some pa ici-
pan s no ed di icul ies wi h objec manipula ion, sugges ing
ha e ining he in e ac ion mechanics could enhance he use
expe ience. In he in e ac i e ques ion sec ion, pa icipan s
we e gene ally able o g asp he concep wi h ease and p oceed
h ough he aining. In some cases, such as Pa icipan 4
(as shown in Table III), pe o mance in he IQ sec ion was
supe io o ha in he MCQ sec ion, as i was pe cei ed o be
mo e in ui i e and closely aligned wi h eal-wo ld scena ios.
O e all, he pe o mance amongs he pa icipan s emained
he same as o MCQ.
Finally, he li e scena io module, while highly engaging,
had a lowe success a e o >75%. Pa icipan s spen an
TABLE II: Task Pe o mance Summa y
Sub ask A g. Comple ion Time Range Success Ra e Commen s
MCQ Ques ions 20 sec 15-30 sec >90% Use s comple ed he MCQ Ques ions quickly and
e icien ly, indica ing a well-designed and s aigh -
o wa d ask.
In e ac i e Ques ions 3 min 7 sec 33 sec - 3 min 24 sec >90% Use s equi ed mo e ime due o he in e ac i e
na u e o ques ions bu achie ed ull success. Some
in e ac ions could be e ined.
Li e Scena io 2 min 24 sec 55 sec - 3 min 35 sec >75% This sub ask had he wides ange in comple ion
imes, sugges ing some use s s uggled wi h aspec s
o he scena io. Mo e guidance may be needed.
TABLE III: Sample o T ainee Lea ning Pe o mance Me ics
SubTask MCQ In e ac i e Li eScena io
VRTSS mean 0.700000 0.533333 0.432320
VRTSS s d 0.333333 0.358323 0.109813
VRTSS P-Value 0.045134 0.044080 0.000073
Success Ra e (%) 80.00% 60.00% 40.00%
a e age o 2 minu es and 24 seconds comple ing his module,
wi h signi ican a iabili y in comple ion imes. This a iabili y
highligh s he need o clea e guidance o mo e in ui i e de-
sign elemen s o help use s na iga e he scena ios e ec i ely.
Table II summa ises he s a is ics o he ainees o each
sub ask. This inal module o he aining p o ed o be he
mos challenging o pa icipan s; howe e , i was also he
mos engaging. This module has been ocused on he p ac ical
side o he aining, i.e. lea n-by-doing. Fo ce ain indi iduals,
such as Pa icipan 1, his sec ion was compa a i ely easie ,
indica ing ha his aining app oach may be pa icula ly
bene icial o indi iduals who ind adi ional me hods o
aining less e ec i e.
V. CONCLUSIONS
T aining employees in indus ial ope a ions can be a chal-
lenging ask, especially o he high-pace and high- isk en i-
onmen o indus ial manu ac u ing. The VR echnilogy poses
as a e y p omising solu ion o b idging he gap o on-hands
expe ience while in a sa e, con led and moni o en i onmen
ha can gauge he in e es o po en ial ainees. I also p o ides
a pla o m o in e ac i e and adap ice lea ning wi h be e
esu ls. To his end, his s udy p esen s he de elopmen o
a VR aining sys em and me hodology o indus ial manu-
ac u ing. The me hodology includes a no el me ic, VRTSS
Sco e, o measu e he lea ning e iciency and capabili ies o he
ainies in a a ie y o asks in h ee dis inc sub asks, namely,
i)Mul iple-choice Ques ions, ii) In e ac i e Ques ions and iii)
Li e Scena ios, o gauge usabili y, knowledge e en ion, and
use engagemen . The expe imen al esul s highligh he sys-
em’s e ec i eness, pa icula ly in he MCQ and in e ac i e
ques ion modules, which exhibi ed a >90% success a e
and demons a ed clea use comp ehension and e iciency.
A comp ehensi e analysis o use eedback unde sco es he
s eng hs o he VR aining sys em, including i s ease o
na iga ion, imme si e li e scena io expe iences, and high
engagemen le els. Despi e hese successes, we did ind some
limi s, mos no ably in he Li e Scena io sub ask, whe e we
had lowe success a es and mo e comple ion ime luc ua ion,
bo h o which poin ed o possible usabili y issues. Based on
hese esul s, ce ain weaks a e in o de , such making he
in e ac ion dynamics s onge , making he sys em guidance
be e , and simpli ying he expe ience o e all so i can ca e
o a wide ange o use skills.
Fu u e esea ch will aim o imp o e ask-speci ic in e ac-
ions, op imize engagemen -d i en design componen s, and
in eg a e addi ional use aid mechanisms o make his ain-
ing pla o m mo e eliable and esilien . These upda es will
con i m he VR sys em’s wo h o mo e ex ensi e uses in
imme si e aining se ings and u he cemen i s place in
heal h and sa e y educa ion. The sugges ed i ual eali y
aining sys em can de elop in o a powe ul esou ce o
aining and educa ion p ac ices by sys ema ically add essing
hese ac o s.
ACKNOWLEDGEMENT
This wo k was unded by UK Resea ch and Inno a ion
(UKRI) unde he UK go e nmen ’s Ho izon Eu ope und-
ing gua an ee [g an numbe 10047653] and unded by he
Eu opean Union [unde EC Ho izon Eu ope g an ag eemen
numbe 101070181 (TALON)].
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