Gonne mann-Mülle , Jana; Wo schack, Philip; K zywdzinski, Ma in; G onau,
No be
A icle — Published Ve sion
Applied Knowledge and Augmen ed Reali y – B idging he
gap be ween lea ning and applica ion
Indus y 4.0 Science
P o ided in Coope a ion wi h:
WZB Be lin Social Science Cen e
Sugges ed Ci a ion: Gonne mann-Mülle , Jana; Wo schack, Philip; K zywdzinski, Ma in; G onau,
No be (2025) : Applied Knowledge and Augmen ed Reali y – B idging he gap be ween lea ning
and applica ion, Indus y 4.0 Science, ISSN 1434-1700, GITO Ve lag, Be lin, Vol. 41, Iss. 5, pp. 22-29,
h ps://doi.o g/10.30844/I4SE.25.5.22
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/331853
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Applied Knowledge and Augmen ed Reali y
T adi ional aining me hods a e eaching hei limi s in a wo ld whe e indus ial
p ocesses a e inc easingly complex and digi al. Digi al ans o ma ion equi es
new lea ning o ma s o eplace adi ional pape -based ins uc ions. The use
o augmen ed eali y is conside ed p omising in his ega d. Bu a e he
ad an ages in use - iendliness and knowledge ans e in complex wo king
en i onmen s eally ha signi ican ?
Con ac
[email p o ec ed]
lswi.de/?lang=en
Keywo ds
augmen ed eali y, lea ning and aining,
knowledge ans e , lea ning ac o ies,
digi al ans o ma ion
AdobeS ock/Sho media
Jana Gonne mann-Mülle is a Resea ch Assis an and doc o al candida e a he Chai o Business In o ma ics, specializing in p ocesses
and sys ems, and in he esea ch g oup “Educa ion o he Digi al Wo ld” a he Weizenbaum Ins i u e o he Ne wo ked Socie y.
D . Philip Wo schack is Head o he esea ch g oup “Wo king wi h A i icial In elligence” a he Weizenbaum Ins i u e o he Ne wo ked
Socie y. As Scien i ic Edi o -in-Chie a he Be lin Social Science Cen e (WZB), he o e sees he “Social Repo : A Da a Repo o Ge many,”
which is published by he Fede al S a is ical O ice, he WZB, and he Fede al Ins i u e o Popula ion Resea ch (BiB).
P o . Ma in K zywdzinski is Di ec o a he Weizenbaum Ins i u e o he Ne wo ked Socie y, Head o he Resea ch G oup “Globaliza ion,
Wo k and P oduc ion” a he WZB, and P o esso o In e na ional Labo Rela ions a Helmu Schmid Uni e si y in Hambu g.
P o . No be G onau holds he Chai o Business In o ma ics, P ocesses and Sys ems a he Uni e si y o Po sdam and is P incipal In es-
iga o o he esea ch g oup “Educa ion o he Digi al Wo ld” a he Weizenbaum Ins i u e o he Ne wo ked Socie y.
DOI: 10.30844/I4SE.25.5.22
The illus a ion he e has been emo ed o copy igh easons.
24 Indus y 4.0 Science 2025, 5
The ORCID iden i ica ion numbe s o he
au ho s o his a icle can be iewed a :
h ps://doi.o g/10.30844/I4SE.25.5.22
This is an open access a icle in compliance wi h he condi ions
o he C ea i e Commons A ibu ion License, which allows
o he dissemina ion and ep oduc ion in any medium, wi h
he p o ision ha he o iginal wo k is ci ed co ec ly.
Applied Knowledge and Augmen ed Reali y
B idging he gap be ween lea ning and applica ion
Jana Gonne mann-Mülle , Philip Wo schack, Ma in K zywdzinsk and No be G onau,
Uni e si y o Po sdam
The inc easing complexi y o indus ial en i onmen s demands new compe encies
om wo ke s, pa icula ly he abili y o in e ac wi h ad anced digi al sys ems.
T adi ional aining me hods o en all sho in suppo ing he e ec i e ans e
o applied knowledge o such con ex s, and he e ec i eness o his ans e ,
as measu ed by pe o mance-based ou comes, emains o be in es iga ed. To
add ess his gap, he p esen s udy employed a be ween-subjec s expe imen al
design compa ing augmen ed eali y- and pape -based ins uc ions wi hin a
ealis ic p oduc ion aining scena io. The esul s show ha pa icipan s who
lea ned wi h augmen ed eali y comple ed he p oduc ion p ocess signi ican ly
as e and wi h ewe e o s han hose using pape ins uc ions. In addi ion,
lea ne s using augmen ed eali y epo ed highe usabili y and expe ienced
lowe cogni i e load du ing aining. These indings sugges ha augmen ed
eali y can enhance he ans e o p ac ical skills in indus ial se ings, suppo ing
mo e e icien and accu a e ask execu ion. Fu u e esea ch should alida e
hese esul s wi h la ge and mo e balanced samples.
Digi al ans o ma ion and Indus y 4.0 signi ican ly
eshape indus ial wo kplaces by in oducing ad anced
echnologies and inc easing au oma ion, undamen ally
al e ing he na u e o wo k and he compe encies equi ed
o employees [1, 2, 3]. As p oduc ion en i onmen s
con inue o e ol e h ough in e connec i i y and
au oma ion, wo ke s a e equi ed o de elop new skills,
such as he abili y o in e ac wi h sophis ica ed
echnologies, including a i icial in elligence (AI) and digi al
sys ems, and engage in complex p oblem-sol ing. To
keep up wi h he in eg a ion o new echnologies and
esul ing p ocess modi ica ions, li elong lea ning and
aining a e c ucial [4], and wo ke s mus s ay lexible o
apply new echnologies e ec i ely ac oss dynamic and
apidly changing con ex s [5].
T adi ional educa ional me hods, such as lec u es and
ex books, o en p o e inadequa e in de eloping hese
new compe encies because hey lack eal-wo ld applicabili y
and he hands-on expe ience needed o e ec i e skill
acquisi ion [6, 7, 8]. To add ess hese limi a ions, lea ning
ac o ies ep esen an
al e na i e by o e ing
applied, in e ac i e aining
o ma s ha mo e
e ec i ely suppo he
de elopmen o p ac ical
skills and compe encies in
ealis ic, indus y-aligned
en i onmen s. Lea ning
ac o ies enable skill
de elopmen h ough
ealis ic simula ions o
manu ac u ing en i on-
men s, allowing o p ac -
ical compe ence-building
simila o ealis ic
en i onmen s [9, 10, 11].
Inco po a ing digi al echnologies in o hese simula ion
en i onmen s u he enhances hei e ec i eness by
enabling lea ne s o di ec ly engage wi h complex
echnological in e aces and decision-suppo sys ems.
Augmen ed eali y p o ides an oppo uni y o ans e ing
applied knowledge [12]. I o e s lea ning in eg a ed in
he wo k en i onmen , wi h i ual cues and 3D elemen s
[13] ha enable s ep-by-s ep lea ning in a ealis ic
en i onmen [14, 15]. Building on his ounda ion, i
p o ides a p omising ex ension o lea ning ac o y
en i onmen s by seamlessly in eg a ing ins uc ional
con en in o eal-wo ld con ex s, b idging he gap be ween
abs ac ins uc ion and hands-on applica ion.
So a , lea ning wi h augmen ed eali y has been shown
o be aluable in e ms o imp o ed lea ning ou comes,
including inc eased pe o mance [16], mo e posi i e lea ne
a i udes, highe sa is ac ion wi h he aining p ocess [17,
18, 19], and educed lea ning ime [13]. While exis ing
s udies highligh he mo i a ional and cogni i e bene i s
o augmen ed eali y-based lea ning, empi ical e idence
on i s e ec i eness in he ans e o applied knowledge
o he wo kplace emains sca ce.
Lea ning ou comes a e o en assessed h ough knowledge
es s o ques ionnai es on mo i a ion and pe cei ed
compe ence, which o en ail o cap u e whe he lea ne s
can e ec i ely apply he acqui ed skills in p ac ice. The
p esen s udy add esses his gap wi h he ollowing
esea ch ques ion (RQ):
Indus y 4.0 Science 2025, 5 © 2025 The Au ho s. Published by GITO
DOI: 10.30844/I4SE.25.5.22
4 o 7
RQ: To wha ex en does aining wi h augmen ed eali y
acili a e he ans e o applied knowledge in o he
p oduc ion se ing?
To answe he RQ, his pape applied an expe imen using
a ealis ic aining and applica ion scena io in which
pa icipan s use augmen ed eali y o lea n a p oduc ion
p ocess. The ans e o applied knowledge is subsequen ly
e alua ed based on hei abili y o apply he ask wi hou
u he ins uc ion in he manu ac u ing en i onmen .
The me hodology, esul s, and implica ions a e p esen ed
below.
Me hodology
The aining scena ios a e e alua ed wi h he aim o
alida ing he impac o applied augmen ed eali y-
suppo ed aining me hods. Add essing he RQ, his s udy
uses a one- ac o be ween-subjec design o compa e
people lea ning wi h augmen ed eali y ins uc ion wi h
people lea ning wi h pape ins uc ions. The s udy design
complied wi h he app o al o he e hics commi ee o he
au ho s’ esea ch ins i u e.
The s udy ook place a he au ho s’ uni e si y be ween
No embe 2023 and May 2024. Be o e pa icipa ion,
indi iduals we e in o med o hei igh s, including
olun a y pa icipa ion and he abili y o wi hd aw om
he s udy a any ime. Pa icipan s we e hen p o ided
wi h a gene al explana ion o he ac o y en i onmen ,
which included he p oduc ion se up and wo ks a ion.
They we e hen andomly assigned o one o he
ins uc ional condi ions: adi ional pape -based
ins uc ions (con ol) o augmen ed eali y-based
ins uc ions deli e ed ia head-moun ed display (HMD).
All pa icipan s unde wen a calib a ion p ocedu e o he
augmen ed eali y HoloLens, which was used as an
augmen ed eali y HMD.
In he lea ning phase, pa icipan s comple ed h ee
p oduc ion cycles ( e e ed o as lea ning ounds), each
in ol ing six p oduc ion s eps. Each lea ning ound was
igge ed by he a i al o a wo kpiece a he s a ion.
Pa icipan s o bo h g oups ecei ed iden ical ins uc ional
con en . Howe e , he deli e y o ma di e ed: he con ol
g oup used p in ed ma e ials p esen ed o e h ee pages
in he sequence o he p oduc ion p ocess, while he
augmen ed eali y g oups accessed he same in o ma ion
ia he augmen ed eali y HMD (HoloLens), o e ing eal- ime
digi al guidance di ec ly in hei isual ield ( ollowing [20]).
Upon comple ing he lea ning phase, pa icipan s
comple ed a ollow-up ques ionnai e, which included
scales measu ing usabili y and cogni i e load. Following
he lea ning phase, he s udy p og essed o he applica ion
scena io, which se ed as a es o knowledge ans e
and pe o mance. Pa icipan s comple ed 15 p oduc ion
ounds wi hou any ins uc ional aid. The pu pose was o
e alua e how well pa icipan s could independen ly apply
wha hey had lea ned.
Pa icipan s assumed he ole o ac o y wo ke s engaged
in manu ac u ing op ical lenses. The p oduc ion asks
Figu e1: P oduc ion se ing used du ing lea ning and applica ion (le ) and isualiza ion o he augmen ed eali y ins uc ion
( igh ).
26 Indus y 4.0 Science 2025, 5
equi ed pa icipan s o e i y he accu acy o incoming
o de s, conFigu especi ic machine pa ame e s, moni o
ongoing p oduc ion p ocesses, and conduc ho ough
quali y con ol checks. Pa icipan s unde wen h ee
lea ning ounds.
Assessmen me hods
The ini ial ques ionnai e cap u ed demog aphic a iables
( o example age, gende , employmen s a us), p e ious
expe ience wi h augmen ed eali y, and amilia i y wi h
p oduc ion- ela ed asks.
A e comple ing all h ee lea ning ounds, pa icipan s
esponded o he cogni i e load scale de eloped by Klepsch
e al. (2017). This ins umen consis s o eigh i ems a ed
on a 7-poin Like scale om 1 (“comple ely un ue”) o
7 (“comple ely ue”). Example s a emen s include: “Du ing
his ask, i was exhaus ing o ind he impo an
in o ma ion,” “The design o his ask was e y incon enien
o lea ning,” and “Du ing his ask, i was di icul o
ecognize and link he c ucial in o ma ion.”
Addi ionally, pa icipan s comple ed he Sys em Usabili y
Scale (SUS; B ooke, 1986). The scale includes en i ems
a ed on a 5-poin Like scale anging om 1 (“s ongly
disag ee”) o 5 (“s ongly ag ee”). Sample i ems include
s a emen s such as “I ound he sys em unnecessa ily
complex” and “I hough he sys em was easy o use.” In
addi ion o he subjec i e e alua ion o lea ning, objec i e
da a on lea ning du a ion we e also collec ed. Fo each
ound, lea ning du a ion was de ined as he ime in e al
be ween he a i al o he wo kpiece and he comple ion
o he inal sub ask.
Lea ning ou comes we e assessed by ask comple ion
ime and numbe o e o s. This me ic cap u ed he o al
ime equi ed o pa icipan s o comple e a p oduc ion
ound wi hou guidance. The ask comple ion ime was
measu ed om he a i al o he wo kpiece o he
pa icipan ’s inal in e ac ion wi h he p oduc ion line.
E o s we e de ined as any inco ec in e ac ions wi hin
he wo kspace. These included, o ins ance, p essing he
w ong bu on on he machine e minal, selec ing inco ec
pa ame e s o machine calib a ion, o speci ying he
w ong numbe o lenses in he quali y check.
Sample
Da a om 87 pa icipan s (39 emales and 46 males, wo
p e e no o say), on a e age 25 yea s old (SD=5.91), we e
in eg a ed in o he da a analysis. The pa icipan s we e
ec ui ed ia mailing lis s and announcemen s a se e al
uni e si ies and andomly assigned o one g oup, ei he
augmen ed eali y (N= 69) o pape ins uc ions (N=17).
Be o e he expe imen , people we e asked abou hei
expe ience wi h augmen ed eali y HMD. 64.29% said hey
had ne e used augmen ed eali y be o e, 34.29% said
hey had a ely used augmen ed eali y, and 1.43% said
hey had used augmen ed eali y occasionally. Addi ionally,
he pa icipan s a ed hei expe ience wi h p oduc ion
en i onmen s: 13.79% had expe ience and 86.2% did no .
Resul s: Imp o ed lea ning wi h
augmen ed eali y
This sec ion ou lines he examina ion o he ga he ed da a,
which was compa ed be ween he augmen ed eali y and
pape ins uc ion g oups. The en i e lea ning las ed on
a e age 7.24 minu es. Pa icipan s who lea ned wi h
augmen ed eali y needed 7 minu es, while hose who
lea ned wi h pape ins uc ions needed 8.24 minu es.
They equi ed app oxima ely 17.7% mo e ime o comple e
he lea ning compa ed o hose who lea ned wi h
augmen ed eali y ins uc ion.
Complemen ing he objec i e da a, hey e alua ed he
usabili y o he lea ning ins uc ion and he cogni i e load
hey pe cei ed du ing lea ning. The esul s show ha
lea ne s p e e augmen ed eali y (M=72.54, SD=19.57)
o e pape ins uc ions (M=67.08, SD=17.75) in e ms o
usabili y and epo less cogni i e load a ising om he
ins uc ion (augmen ed eali y M=8.43, SD=4.28, pape
ins uc ion M=9.94, SD=4.15).
Add essing he RQ, how well he pa icipan s could ans e
and apply he lea ned skills in o he p oduc ion p ocess
wi hou ecei ing addi ional ins uc ions, ime and numbe
o e o s we e conside ed. The ime me ics e eal ha
pa icipan s who lea ned wi h he augmen ed eali y
ins uc ion we e able o apply he s eps o he p oduc ion
p ocess 1 minu e as e (M=21.59, SD=4.5) han hose who
lea ned wi h pape ins uc ions (M=22.58, SD=3.92). In
addi ion, people who lea ned wi h he augmen ed eali y
ins uc ion made ewe e o s (M=4.48, SD=4.10) when
hey applied he p oduc ion s eps wi hou ins uc ion han
people who had lea ned wi h he pape ins uc ion (M=5.17,
SD=4.31). Figu e2 p esen s all esul s.
Answe ing he esea ch ques ion, he indings indica e
ha augmen ed eali y acili a es he ans e o applied
knowledge in o eal-wo ld p oduc ion se ings. Pa icipan s
who ecei ed augmen ed eali y-based ins uc ion applied
he p oduc ion ask as e and wi h ewe e o s in a
ealis ic p oduc ion se ing han hose who used
con en ional pape -based ins uc ions. No only did hey
need less ime o apply he p oduc ion wi hou ins uc ion,
hey also equi ed less ime du ing lea ning. The objec i e
da a was suppo ed by a posi i e e alua ion o he usabili y
and cogni i e load using augmen ed eali y. Taken oge he ,
hese indings p o ide empi ical suppo o he
e ec i eness o augmen ed eali y-based aining in
Indus y 4.0 Science 2025, 5 © 2025 The Au ho s. Published by GITO
DOI: 10.30844/I4SE.25.5.22
6 o 7
p omo ing mo e e icien lea ning p ocesses and acili a ing
he ans e o applied knowledge o complex eal-wo ld
p oduc ion asks.
Implica ions and u u e esea ch
Based on he s udy’s indings, we sugges wo implica ions:
Fi s , ou indings indica e ha augmen ed eali y o e s
an oppo uni y o p o ide aining di ec ly wi hin he wo k
en i onmen . Unlike adi ional class oom o manual-based
aining, augmen ed eali y can guide employees s ep-by-
s ep wi hin he p oduc ion se ing, making lea ning mo e
con ex - ele an and educing he gap be ween aining
and applica ion.
In ou s udy, lea ne s applied he p oduc ion p ocess
as e and wi h ewe e o s in he p oduc ion p ocess
wi hou ins uc ion. Fo companies, his sugges s educing
ini ial e o s, which can lead o signi ican cos sa ings.
Secondly, ou indings indica e ha using AR o lea ning
enables employees o each ope a ional eadiness mo e
quickly. Companies in oducing new p ocesses o
echnologies could bene i om augmen ed eali y’s abili y
o lowe he lea ning cu e, especially o inexpe ienced
wo ke s who migh o he wise s uggle wi h abs ac
ins uc ions, which means educed onboa ding ime.
Ne e heless, he s udy’s limi a ions should be acknowledged
o an accu a e unde s anding o he indings. Fi s , he
ela i ely small sample size and unbalanced g oup
assignmen may limi he gene alizabili y o he esul s and
es ic he s a is ical powe needed o mo e obus
in e en ial analyses. Fu he mo e, po en ial dis o ions due
o sampling a iabili y o uncon olled con ounding ac o s
may ha e in luenced he obse ed e ec s. As a consequence,
he decision was made o ocus on desc ip i e s a is ics
only, as he a ailable da a lacked su icien powe o suppo
meaning ul in e en ial calcula ions.
Second, al hough he s udy was conduc ed in a ealis ic
p oduc ion se ing o enhance ecological alidi y, he
sample was ela i ely homogeneous, which may limi he
gene alizabili y o he indings. Fu u e esea ch should
seek o eplica e hese esul s using la ge and mo e
di e se samples o s eng hen he empi ical e idence and
allow o mo e p ecise s a is ical analyses, including obus
in e en ial es ing.
This s udy con ibu es o he esea ch on augmen ed
eali y in lea ning by o e ing a pe o mance-based
e alua ion o applied knowledge ans e wi hin a ealis ic
indus ial se ing. The indings demons a e ha
augmen ed eali y can e ec i ely suppo he ans e o
p ac ical skills, as pa icipan s who lea ned wi h augmen ed
eali y applied he p oduc ion p ocess mo e quickly and
wi h ewe e o s han hose who ecei ed pape -based
ins uc ion.
The wo k was suppo ed by he Ge man Fede al Minis y o
Educa ion and Resea ch (BMBF), g an numbe s 16DII137
(Weizenbaum-Ins i u e) and 16DII131 (Weizenbaum-Ins i u e).
Bibliog aphy
[1] G onau, N.; Webe , E.: Reihen olgeplanung im Zei al e on
Indus ie 4.0 Op imie ung in de We ks a e igung. In: p od
23 (2018) 1, pp. 1–26. DOI: 10.30844/3_2018-1_23-26.
[2] B igh , A. G.; Ponis, S. T.: In oducing gami ica ion in he AR-
enhanced o de picking p ocess: A p oposed app oach. In:
Logis ics 5 (2021) 1, p. 14. DOI: 10.3390/logis ics5010014.
[3] G onau, N.: Modeling he Handling o Knowledge o Indus y
4.0. In: Shishko , B. (ed.): Lec u e No es in Business In o ma ion
P ocessing. Cham: Sp inge In e na ional Publishing, 2021, pp.
207–223. DOI: 10.1007/978-3-030-79976-2_12.
Figu e2: O e iew o ou comes assessed du ing he lea ning p ocess and he subsequen applica ion o acqui ed knowledge
in a p oduc ion en i onmen .
Va iable En i e Sample AR Ins uc ion Pape Ins uc ion
Lea ning Phase
Usabili y 71.48 (19.13) 72.54 (19.57) 67.08 (17.75)
Cogni i e Load 8.72 (4.26) 8.43 (4.28) 9.94 (4.15)
Lea ning Time (min) 7.24 (2.33) 7.01 (2.35) 8.24 (2.02)
T ans e and Applica ion Phase
Task comple ion ime (min) 21.75 (4.78) 21.59 (4.5) 22.58 (3.92)
Numbe o e o s 4.68 (4.15) 4.48 (4.10) 5.17 (4.31)
No e: Values a e p esen ed as means (s anda d de ia ion)
28 Indus y 4.0 Science 2025, 5
[4] Sau e , B.; Daling, L.: Mixed Reali y Suppo ed Lea ning o
Indus ial on- he-job T aining. In: P oceedings o he Con e ence
on Lea ning Fac o ies (CLF) 2021, G az, Aus ia: SSRN, 2021.
DOI: 10.2139/ss n.3864189.
[5] K zywdzinski, M.; E e s, M.; Ge be , C.: Con ol and Flexibili y:
The Use o Wea able De ices in Capi al- and Labo -In ensi e
Wo k P ocesses. In: ILR Re iew 77 (2024) 4, pp. 506-534. DOI:
10.1177/00197939241258206.
[6] G onau, N.; Ull ich, A.; Teichmann, M.: De elopmen o he
indus ial IoT compe ences in he a eas o o ganiza ion, p ocess,
and in e ac ion based on he lea ning ac o y concep . In:
P ocedia Manu ac u ing 9 (2017), pp. 254-261. DOI: 10.1016/j.
p om g.2017.04.029.
[7] Teichmann, M.; Ull ich, A.; Wenz, J.; G onau, N.:
He aus o de ungen und Handlungsemp ehlungen be iebliche
Wei e bildungsp axis in Zei en de Digi alisie ung. In: HMD
P axis de Wi scha sin o ma ik 57 (2020), pp. 512-527. DOI:
10.1365/s40702-020-00614-x.
[8] Vlado a, G.; Heu s, A.; Teichmann, M.: Dem Mi a bei e zu
Diens en. Wei e bildung und Quali izie ung als Pe sonennahe
Diens leis ung. In: HMD 57 (2020) 4, pp. 710-721. DOI: 10.1365/
s40702-020-00626-7.
[9] G onau, N.; Kluge, A.; Haase, J.; Thim, C.: Expe ien ial lea ning
ac o ies: B idging he gap be ween lab and ield expe imen s.
In: P oceedings o he 13 h Con e ence on Lea ning Fac o ies
(CLF 2023), Roches e , NY: Social Science Resea ch Ne wo k,
2023. DOI: 10.2139/ss n.4469819.
[10] Teichmann, M.; Ul ich, A.; G onau, N.: Subjec -o ien ed lea ning
- A new pe spec i e o oca ional aining in lea ning ac o ies.
In: P ocedia Manu ac u ing, B aunschweig, Ge many: Else ie ,
2019, pp. 72-78. DOI: 10.1016/j.p om g.2019.03.012.
[11] Teichmann, M.; Le kemann, V.; G onau, N.: Digi aliza ion,
demog aphic change and deca boniza ion: Eigh pi o al
compe encies o lea ning ac o ies. In: Thiede, S.; Lu e s, E.
(eds.): Lea ning Fac o ies o he Fu u e, Cham, Swi ze land:
Sp inge , Cham, 2024, pp. 313-320. DOI: 10.1007/978-3-031-
65411-4_37.
[12] P a , A.; Spann, M.: Man and machine: AR-based oca ional
aining o aci knowledge asks. In: ECIS 2024 P oceedings,
Cyp us, G eece, 2024.
[13] Gonne mann-Mülle , J.; Leins, N.; G onau, N.; Kosch, T.: Value
by design: Reducing cogni i e load by using isual guidance in
augmen ed eali y—An eye- acking s udy. In: ICIS 2024
P oceedings, 2024.
[14] B unzini, A.; Cicca elli, M.; Sa ini, M.; Pape i, A.; Ge mani, M.:
A compa a i e s udy o he assessmen o ma ke -less mixed
eali y applica ions o ope a o aining. In: In e na ional Jou nal
o Compu e In eg a ed Manu ac u ing (2024), pp. 1-23. DOI:
10.1080/0951192X.2024.2314793.
[15] Funk, M.; Bächle , A.; Bächle , L.; Kosch, T.; Heiden eich, T.; e
al.: Wo king wi h augmen ed eali y? A long- e m analysis o
in-si u ins uc ions a he assembly wo kplace. In: P oceedings
o he 10 h In e na ional Con e ence on Pe asi e Technologies
Rela ed o Assis i e En i onmen s, Island o Rhodes G eece:
ACM, 2017, pp. 222-229. DOI: 10.1145/3056540.3056548.
[16] Leins, N.; Gonne mann-Mülle , J.; Teichmann, M.: Compa ing
head-moun ed and handheld augmen ed eali y o guided
assembly. In: J Mul imodal Use In e aces 18 (2024) 4, pp.
313-328. DOI: 10.1007/s12193-024-00440-1.
[17] D ouo , M.; Le Bigo , N.; B ica d, E.; Boug ene , J.-L. D.; Nou i ,
V.: Augmen ed eali y on indus ial assembly line: Impac on
e ec i eness and men al wo kload. In: Applied E gonomics 103
(2022), p. 103793. DOI: 10.1016/j.ape go.2022.103793.
[18] Buchne , J.; Bun ins, K.; Ke es, M.: The impac o augmen ed
eali y on cogni i e load and pe o mance: A sys ema ic e iew.
In: Compu e Assis ed Lea ning 38 (2022) 1, pp. 285-303. DOI:
10.1111/jcal.12617.
[19] Yu, Z.: Me a-analyses o e ec s o augmen ed eali y on
educa ional ou comes o e a decade. In: In e ac i e Lea ning
En i onmen s 32 (2023) 8, pp. 1-15. DOI:
10.1080/10494820.2023.2205899.
[20] Gonne mann-Mülle , J.; K üge , J. M.: Unlocking augmen ed
eali y lea ning design based on e idence om empi ical
cogni i e load s udies—A sys ema ic li e a u e e iew. In:
Compu e Assis ed Lea ning 41 (2024), p. e13095. DOI: 10.1111/
jcal.13095.
4541667839559
ISBN 9783955454166
Knowledge Modeling and Desc ip ion Language (KMDL) 3.0 No be G onau
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