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A Research Agenda to Deploy Technology Enhanced Learning with Augmented Reality in Industry


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To apply Technology Enhanced Learning (TEL) with Augmented Reality (AR) in industry, a suitable methodology is necessary. This work focuses on how to deploy and evaluate AR learning scenarios in industrial environments. The methodology evolved within the two EU projects FACTS4WORKERS and iDEV40 and has been improved iteratively. The first step is to investigate the use case at the industry partner. Then the appropriate concept is defined. The next step is to develop a first prototype. This prototype is then improved during several iterations according to the feedback of the industry partner. When the prototype reaches an appropriate Technology Readiness Level (TRL), a final evaluation is carried out to verify the software artifact against the gathered requirements.
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A Research Agenda to Deploy Technology Enhanced
Learning with Augmented Reality in Industry
Michael Spitzer
Virtual Vehicle Research Center
Graz, Styria, Austria
Inge Gsellmann
Virtual Vehicle Research Center
Graz, Styria, Austria
Matthias Hebenstreit
Virtual Vehicle Research Center
Graz, Styria, Austria
Stelios Damalas
Virtual Vehicle Research Center
Graz, Styria, Austria
Martin Ebner
Graz University of Technology
Graz, Styria, Austria
To apply Technology Enhanced Learning (TEL) with Aug-
mented Reality (AR) in industry, a suitable methodology is
necessary. This work focuses on how to deploy and evalu-
ate AR learning scenarios in industrial environments. The
methodology evolved within the two EU projects
FACTS4WORKERS and iDEV40 and has been improved it-
eratively. The rst step is to investigate the use case at the
industry partner. Then the appropriate concept is dened.
The next step is to develop a rst prototype. This proto-
type is then improved during several iterations according to
the feedback of the industry partner. When the prototype
reaches an appropriate Technology Readiness Level (TRL), a
nal evaluation is carried out to verify the software artifact
against the gathered requirements.
Augmented Reality, AR, Technology Enhanced Learning,
TEL, Problem-based Learning, On-the-job Training
Industry is changing rapidly at the moment. IT-driven, often
highly disruptive changes are aecting many businesses, es-
pecially, but not exclusively, in the manufacturing industry.
This process is often referred to as Industry 4.0 [
]. New de-
vices and technologies addressed at both private customers
Main Author
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the owner/author(s).
MuC’19 Workshops, Hamburg, Germany
Proceedings of the Mensch und Computer 2019 Workshop on Smart
Collaboration - Mitarbeiter-zentrierte Informationssysteme in der Produk-
tentstehung. Copyright held by the owner/author(s). 300-05
and industry are emerging on the market. Digitizing compa-
nies and keeping pace with new devices, technologies and
software is a great challenge. In order for companies to adapt
quickly, it is very important to train and teach employees
how to use these new technologies. Bridging the gap between
new technologies and their application in industrial and ed-
ucational settings, however, can be a very demanding task.
Therefore, a methodology is needed to integrate new tech-
nologies in educational and training-on-the-job scenarios.
Especially after smart glasses, such as Vuzix, Google Glass,
Microsoft HoloLens and Magic Leap, were rolled out, there
were several attempts to apply these technologies in indus-
try. Stocker et al. [
] deployed a software demonstrator for
smart glasses, which presents a checklist in the user’s eld
of vision to help workers doing maintenance and assembly
tasks in the automotive domain. The assistance tool was eval-
uated by experts in automotive production. Although they
were skeptical about interaction techniques such as touch
and speech recognition, the evaluation clearly showed the
potential of these devices and technologies. The context of
use was rated as promising if the quality of the devices were
to improve. The current generation of smart glasses oers
distinct improvements in display quality, speech recognition
and input options. Therefore, it is necessary not to focus
on the specic device but more on the context of use and
whether the smart glasses scenario makes sense. The hard-
ware is rapidly improving and smart glasses manufacturers
are launching new devices at short intervals.
Technology Enhanced Learning
Browne, Hewitt, and Walker [
] dene TEL as any online
facility or system which supports learning and teaching. A
great challenge for TEL is that simply digitizing analog learn-
ing material is not sucient. Teaching methodologies also
need to change [
]. This does not only apply to education in
general but also to educational scenarios in industry settings.
MuC’19 Workshops, Hamburg, Germany Spitzer et al.
Augmented Reality
An early AR survey was carried out by Azuma [
], who
describes AR as a variation of Virtual Reality (VR): While the
user is fully immersed in VR and cannot see the real world
around him or her, AR allows the user to see the real world
with virtual objects superimposed upon or overlaid with the
real world.
We developed a research agenda to plan, conduct and eval-
uate TEL AR projects in industry. This research agenda
evolved during two EU research projects, namely
developed a prototype to help maintenance workers to clean
the lens of a laser cutter [
]. In iDEV40, we are develop-
ing an AR-based assembly support system prototype in a
special purpose engineering domain. In FACTS4WORKERS,
we gathered the requirements for a TEL AR system from
the industry partner as a rst step. During an on-site visit,
personas and problem scenarios were identied. The AS-IS
situation was analyzed and a TO-BE situation was developed
]. After identifying the requirements, we dened a suitable
didactic concept. The main use case focus for this research
agenda is on maintenance and assembly tasks. A signicant
challenge when learning how to do maintenance tasks is that
workers usually do their training directly on the machine.
This means that during this training phase, the machine can-
not be used to produce anything.
The challenge is not only to create a didactic concept which
reduces the downtime of the machine, but also to ensure
learning success. The next step after creating the didactic
concept was developing the rst prototype. This step is very
important in order to verify the gathered requirements. At
the beginning, the actual user requirements are often unclear
and only become more dened once users from the target
group have started testing the rst prototype to get an initial
idea of the features [
]. The next step is then to gather feed-
back from the industry partner, especially from users from
the target group. After that, the qualitative feedback from the
users is considered for the next iteration of the prototype. To
improve the prototype iteratively, common agile software de-
velopment practices are followed [
]. After several iterations,
when the prototype reaches the desired Technology Readi-
ness Level (TRL), a nal evaluation is carried out to validate
the software artifact against the predened requirements.
The validation framework that was used was developed to t
various kinds of use cases and the dierent industry partners
[8]. Figure 1 summarizes the research agenda.
Figure 1: Research agenda for TEL AR in industry
Use case investigation
In the rst phase, an on-site visit at the industry partner is
necessary to gather the real-world context of the use case.
Current processes and challenges are identied using inter-
views and by observing users from the target group. Another
important aspect of the on-site visit is to get into direct con-
tact with the workers to build trust and to involve them in
the development of the TEL AR prototype.
Didactic concept
With the advent of Industry 4.0, the role of humans in
production-related elds is changing. Workers are confronted
with new challenges, meaning that they need new skills. One
possible solution how to train and teach employees to master
these new challenges is to apply and further develop Tech-
nology Enhanced Learning in industry. One particular issue
is how to support the digitization of domain-specic knowl-
edge without losing any of it. [
]. We use a problem-based
learning setting as a starting point [
]. Additionally, we con-
nect a virtual classroom scenario with an in-situ learning
scenario at the workplace. The didactic concept is separated
into two TEL learning scenarios. First, participants study
learning material such as manuals or other documentation.
The rst TEL phase is a virtual learning scenario in a virtual
learning environment where the training object is displayed
as a hologram in the eld of view of the learner. The mainte-
nance or assembly artifact can be investigated in 3D from
dierent angles and viewing directions. An animation shows
the steps that are necessary to complete the learning scenario.
The hologram is enriched with symbols and text annotations.
Working with teaching material in 3D has the advantage
A Research Agenda to Deploy Technology Enhanced Learning with Augmented Reality in Industry MuC’19 Workshops, Hamburg, Germany
that the 3D animation can be observed from dierent angles
which could improve the learning experience. The success
of such learning scenario depends on the spatial abilities of
the learner. Learners with low spatial capabilities tend to
be cognitively overloaded by the learning situation [
]. In
this case other learning materials and scenarios should also
be considered. The classroom scenario works in any envi-
ronment. The real production machine, or any other tools
or equipment are not needed. After the classroom training,
a feedback and reection phase follows to ensure that the
virtual learning situation was successful. The same situation
is then played out again for training at the machine or as-
sembly station. This training-on-the-job scenario is the nal
step before the participants are able to perform the task by
themselves. Figure 2 summarizes the didactic concept. The
TEL software used is the same in both scenarios. In the rst
scenario, the TEL software is a virtual setting. For the sec-
ond scenario, AR is used to overlay additional information
and training material above real-world objects. To enable an
easy transition between the rst and the second scenario,
the same user interface (UI) is used within the TEL software.
Figure 2: Didactic concept
AR Prototype
In both projects, the Microsoft HoloLens is used to implement
the prototypes. The generic methodology can be applied to
any kind of AR device. An advantage of using smart glasses
is that the users have their hands free to conduct the learning
situation. The key feature of the TEL AR software artifact
is that the UI is consistent for both TEL scenarios. Figure 3
shows the UI of the application. The UI controls the mainte-
nance or assembly instruction animations. The animations
can be paused, forwarded, rewound and repeated. The UI is
based on the UI of commodity video players so that it can
be easily recognized by the users. The menu can be freely
positioned in space to prevent it from blocking the line of
sight of the user. The 3D cube can represent any kind of
3D object, such as a machine that needs maintenance or an
actual product that is being assembled. The opacity of the
computer-generated object can be changed using the cube
symbols at the bottom.
Industry partner feedback
The AR prototype is tested at the industry partner and is
then improved according to the feedback provided by users
from the target group from industry. Several iterations may
be necessary before an appropriate TRL level is reached and
before the AR prototype is ready for the nal evaluation.
Figure 3: AR Prototype - UI controls
Final Evaluation
Within the FACTS4WORKERS project, an evaluation frame-
work was developed to validate the software prototypes
deployed at the industry partners. The framework separates
the evaluation into two dierent strategies. The rst strat-
egy is an impact analysis. The second strategy is a qual-
ity validation. Both strategies are divided in human-driven
approaches and data-driven approaches. Human-driven ap-
proaches are surveys, interviews and observations. Data-
driven approaches include log analysis and application data.
Data-driven approaches are often available at a higher level
of maturity of the software artifact. Human-driven approaches
also work during the very early stages of the project [
]. Fig-
ure 4 summarizes the evaluation methodology. We gathered
feedback from several industry partners according to which
the FACTS4WORKERS prototypes are a good t for the de-
ned use cases. As a result, a start-up that is now using the
prototype to build products has emerged from the research
To implement TEL with AR in industry, it is necessary to
have an adequate research agenda. The research agenda
presented here was used in the FACTS4WORKERS project to
deploy several TEL AR prototypes at several industry partner
sites and is now also to be used in the iDEV40 project. The
prototypes were evaluated by users from the target group
and were improved iteratively. During the nal evaluation
in the FACTS4WORKERS project, the software prototype
MuC’19 Workshops, Hamburg, Germany Spitzer et al.
Figure 4: Evaluation framework [11]
could be validated against the requirements dened in the
rst phase of the project.
The project FACTS4WORKERS has received funding from
the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 636778. The project
iDEV40 has received funding from the Electronic Component
Systems for European Leadership Joint Undertaking under
grant agreement No 783163. This Joint Undertaking receives
support from the European Union’s Horizon 2020 research
and innovation programme and Austria, Spain, Finland, Ire-
land, Sweden, Germany, Poland, Portugal, Netherlands, Bel-
gium, Norway. The publication was written at VIRTUAL
VEHICLE Research Center in Graz and partially funded by
the COMET K2 - Competence Centers for Excellent Tech-
nologies Programme of the Federal Ministry for Transport,
Innovation and Technology (bmvit), the Federal Ministry
for Digital, Business and Enterprise (bmdw), the Austrian
Research Promotion Agency (FFG), the Province of Styria
and the Styrian Business Promotion Agency (SFG).
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This Report records the results from a national Survey, undertaken by UCISA, with financial support from the JISC, into matters pertaining to Technology Enhanced Learning (TEL). It builds upon similar Surveys which were conducted in 2001, 2003 and 2005 and for which at each stage a longitudinal analysis was undertaken. In the Survey TEL was defined as any online facility or system that directly supports learning and teaching. This may include a formal VLE, an institutional intranet that has a learning and teaching component, a system that has been developed in-house or a particular suite of specific individual tools. The main thrust of the Report analyses the returns from the 2008 Survey but where appropriate, the longitudinal analysis is continued. The 2005 Survey took place in the same year as the publication of two highly influential strategy documents on e-learning, by the Higher Education Funding Council for England (HEFCE) and the Department for Education and Skills (DfES ). By this time, e-learning was clearly on the Government’s national agenda. Since then, other major developments have included the HEA e-learning benchmarking programme and the rise and rise of Web 2.0. E-learning was also ranked 2nd in the 2006/07 UCISA list of Top 10 Concerns. A summary of the findings of the Report is as follows. Learning and teaching activities are consolidated longitudinally as the primary drivers for considering using TEL, although meeting student expectations is increasingly close as the next most important driver. The presence of a committed local champion continues to be the strongest influence on the rate at which TEL is developed and processes promoted within an institution. Strategies are becoming much more embedded, with the biggest change internally since 2005 being the rise to prominence of e-learning strategies and externally, those from HEFCE and JISC have received increased prominence. Central funding for service support and project funding has assumed an even greater significance as a means of enabling development, with support, ranging from pedagogic to technical being provided from a variety of different types of units. Blackboard continues as the most used enterprise or institutional VLE. However, when also including VLEs that are used more locally, e.g. within departments, then Moodle is most used with a rapid rise since 2005. Overall, there is a vastly reduced range of VLEs in use since 2005. The tools that have increased significantly in prominence are those for podcasting, e-portfolios, e-assessment, blogs and wikis. Career enhancement opportunities in TEL development still appear quite limited and lack of time was identified as the main barrier to further developments to promote TEL. Outsourcing is being increasingly evaluated for the hosting of systems, though there is no evidence that it is, as yet, widely employed for major, strategic institutional delivery. Regarding new demands that would impact upon the provision of support, streaming media, mobile computing, podcasting and Web 2.0 were discernibly the greatest. Staff skills were overwhelmingly noted as the greatest challenge that these new demands would create, with staff development and strategies being seen as the primary remedies. Throughout much of the data, subtle but clearly identifiable differences continue to be discernible between pre-92 and post-92 universities.
Empirical studies that focus on the impact of three-dimensional (3D) visualizations on learning are to date rare and inconsistent. According to the ability-as-enhancer hypothesis, high spatial ability learners should benefit particularly as they have enough cognitive capacity left for mental model construction. In contrast, the ability-as-compensator hypothesis proposes that low spatial ability learners should gain particular benefit from explicit graphical representations as they have difficulty mentally constructing their own visualizations. This study examines the impact that interactive 3D models implemented within a hypermedia-learning environment have on understanding of cell biology. Test scores in a subsequent knowledge acquisition test demonstrated a significant interaction term between students' spatial ability and presence/absence of 3D models. Only students with high spatial ability benefited from the presence of 3D models, while low spatial ability students got fewer points when learning this way. When using 3D models, high spatial ability students perceived their cognitive load to be low whereas the opposite was true for low spatial ability students. The data suggest that students with low spatial ability became cognitively overloaded by the presence of 3D models, while high spatial ability students benefited from them as their total cognitive load remained within working memory limits.
A two-phased research project comparing the prototyping approach with the more traditional life cycle approach finds that prototyping facilitates communication between users and designers during the design process. However, the findings also indicate that designers who used prototyping experienced difficulties in managing and controlling the design process.
This paper surveys the current state-of-the-art in Augmented Reality. It describes work performed at many different sites and explains the issues and problems encountered when building Augmented Reality systems. It summarizes the tradeoffs and approaches taken so far to overcome these problems and speculates on future directions that deserve exploration. This paper does not present new research results. The contribution comes from consolidating existing information from many sources and publishing an extensive bibliography of papers in this field. While several other introductory papers have been written on this subject [Barfield95] [Bowskill95] [Caudell94] [Drascic93b] [Feiner94a] [Feiner94b] [Milgram94b] [Rolland94], this survey is more comprehensive and up-to-date. For anyone interested in starting research in this area, this survey should provide a good starting point. Section 1 describes what Augmented Reality is and the motivations for developing this technology. Four classes of potential applications that have been explored are described in Section 2. Then Section 3 discusses the issues involved in building an Augmented Reality system. Currently, two of the biggest problems are in registration and sensing, so those are the subjects of Sections 4 and 5. Finally, Section 6 describes some areas that require further work and research. 1.2 Definition
Manifesto for agile software development
  • Kent Beck
  • Mike Beedle
  • Arie Van Bennekum
  • Alistair Cockburn
  • Ward Cunningham
  • Martin Fowler
  • James Grenning
  • Jim Highsmith
  • Andrew Hunt
  • Ron Jeffries
Kent Beck, Mike Beedle, Arie Van Bennekum, Alistair Cockburn, Ward Cunningham, Martin Fowler, James Grenning, Jim Highsmith, Andrew Hunt, Ron Jeffries, et al. 2001. Manifesto for agile software development. (2001).