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|id=Vol-1669/WS6_3_092_Paper
|storemode=property
|title=Towards a Classification of Learning Support Systems at the Digitized Workplace
|pdfUrl=https://ceur-ws.org/Vol-1669/WS6_3_092_Paper.pdf
|volume=Vol-1669
|authors=Wael Alkhatib,Christoph Rensing
|dblpUrl=https://dblp.org/rec/conf/delfi/AlkhatibR16
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==Towards a Classification of Learning Support Systems at the Digitized Workplace==
https://ceur-ws.org/Vol-1669/WS6_3_092_Paper.pdf
Raphael Zender (Hrsg.): Proceedings of DeLFI Workshops 2016
co-located with 14th e-Learning Conference of the German Computer Society (DeLFI 2016)
Potsdam, Germany, September 11, 2016 188
Towards a Classification of Learning Support Systems at
the Digitized Workplace
Wael Alkhatib1 and Christoph Rensing2
Abstract: In light of the transformation to cyber-physical production systems in Industry 4.0, the in-
creasing trend of digitalization and product customization together with the demographic changes in
Germany reveal a clear need for supporting the employees’ sustainable competences development at
the workplace. In this context, technology-enhanced learning environments provide new approaches
for developing the vocational training system at the workplace. This paper discusses the pedagogical,
didactic and technical aspects that characterize learning systems at the manufacturing and digitized
workplace. Furthermore, a set of current learning solutions for supporting the employees’ informal
learning under industrial settings will be classified based on the introduced scheme as a foundation
for a more comprehensive overview in future.
Keywords: Workplace Learning; Assistance System; Learning Platform; Industry 4.0.
1 Motivation
The increasing digitization of manufacturing enabled by cyber-physical systems (CPS)
and of other processes in the supply chain will enforce changes in terms of qualification
requirements, the quality of work, the organization forms of work and cooperation between
humans and technology [Bo15]. The assembly industry will need innovative learning so-
lutions to provide the workers with the required competences for assembling, picking and
setting-up the products through short and fast learning activities. Furthermore, the move
towards digitalization in the context of industry 4.0 will cause steady increase in the com-
plexity of production process control as well as the operation and maintenance of the
equipments [HK14]. Accordingly, the employees’ tasks will primary involve activities of
monitoring highly automated processes and regular intervention to keep the process under
normal operating conditions.
Recently, many research projects have addressed the major challenges for updating the
vocational training system in order to meet the growing need for sustainable competences
development. However, to the best of our knowledge, there is no comprehensive scheme
to classify the various workplace learning solutions, which can be essential to identify
areas that are currently disregarded or deemed challenging for research. In this work, we
propose a first classification scheme which explains and relates the different concepts and
terms that characterize learning solutions for the digitized workplace.
1 Technische Universität Darmstadt, Fachgebiet Multimedia Kommunikation, Rundeturmstr. 10, 64283 Darm-
stadt, Germany, wael.alkhatib@kom.tu-darmstadt.de
2 Technische Universität Darmstadt, Fachgebiet Multimedia Kommunikation, Rundeturmstr. 10, 64283 Darm-
stadt, Germany, christoph.rensing@kom.tu-darmstadt.de
Towards a Classification of Learning Support Systems at the Digitized Workplace 189
2 Classification Criteria
The proposed criteria for classifying the workplace learning systems, in accordance with
the technological, didactic and pedagogical perspectives, were selected based on an inten-
sive study of related work and analysis of twenty two concrete workplace learning systems.
2.1 Job Type
Due to the variety of activities at the workplace, it is basically useful to align the analysis to
the nature of each activity. One potential approach is to classify activities according to their
task diversity (the number of different tasks) and the presence or absence of analyzability
(the possibility of dismantling the task into standardized steps) [LM07].
• Routine Work: Routine work follows a mechanical structure where a low level of
task diversity and a high level of standardized task steps can be recognized.
• Engineering (technical skilled workers): In engineering the task diversity is higher
than in routine work but the analyzability is still moderate or high.
• Crafted Work (craft industry): It is characterized by low task diversity and moderate
standardization of the task steps.
• Non-Routine Work: Non-routine work represents the other extreme side of activity
nature with high task diversity and low standardization of task steps.
2.2 Workplace Learning
Eraut et al. [Er10] proposed a scheme for classifying different modes of work-related learn-
ing. We complemented Eraut’s definition of modes with new examples.
• Work Processes with Learning as a By-Product: Learning occurs spontaneously and
unintentional through knowledge acquisition during the work process, e.g. trying
things out or working alongside others and observing them.
• Learning Processes at or near the Workplace: Learning schemes in or near the work-
place include processes whose primary object is learning. Supervision, coaching
and mentoring are at or near the learner’s normal workplace. Self-directed learn-
ing towards a new qualification based on learning resources like online trainings
or microlearning contents and learning in collaborative learning scenarios with col-
leagues, like synchronous online classrooms, are other examples.
• Learning Activities Integrated within the Processes: Learning activities can be found
in short opportunistic episodes during the work, also they are embedded within most
of the working and learning processes. These activities include asking questions and
getting information as a proactive activities. Additionally, listening and observing
activities can help in learning tacit knowledge. Other activities include learning from
mistakes, reflection and giving and receiving feedback.
190 Wael Alkhatib and Christoph Rensing
2.3 Instructional Strategies
Cognitive apprenticeship is a social learning theory in which a master of a skill tries to
help apprentice to become an expert through legitimate peripheral participation. It has
been shown to be valuable in vocational training where a learner should be accompanied
by an expert [JT94]. Collins et al. proposed six instructional strategies [CBH91].
• Modeling: In modeling an expert demonstrates a task explicitly by explaining how
and why to perform different activities for task completion.
• Coaching: Coaching involves observing the learner task performance and providing
feedback and hints to improve his performance.
• Scaffolding: Scaffolding is the act of putting into place strategies and methods to
adjust the task complexity to match the learner performance level and to guide the
learner through the task.
• Articulation: Articulation is the process by which the learner tries to articulate to
other learners his knowledge, reasoning, or problem-solving process.
• Reflection: In reflection the learner looks back and analyzes his process and compare
it with those of the expert to highlight the differences and similarities.
• Exploration: Exploration involves pushing the learner to frame his own interesting
problems and questions and then take the initiative to solve these problems.
2.4 Learning Methods
Different forms of technology-enhanced learning, relevant in vocational training, can be
distinguished.
• Distance and Web-based Training and Blended Learning: Distance and web-based
training combine the provision of learning resources in different formats i.e. au-
dio/video, text and animation with communication functionality i.e. in forums, blogs
or online conferences. Blended learning is a hybrid methodology where portion of
the traditional classroom learning activities are replaced by distance and web-based
training forms.
• Social and Collaborative Learning: Social learning is characterized by participation
and collaboration. The knowledge in social learning is collaboratively developed and
processed. In this form of learning, social media and community platforms or spe-
cialized computer-supported collaborative learning (CSCL) tools are used in order
to develop new knowledge in exchange with other members [Di99].
• Mobile and Ubiquitous Learning: Mobile learning mainly focuses on the mobil-
ity of the learner and describes the support of learning processes by using mobile
devices. It allows the learners ubiquitous ”just-in-time” delivery of knowledge and
information in the actual context [SE13] independent from the location.
Towards a Classification of Learning Support Systems at the Digitized Workplace 191
• Microlearning: Microlearning encourages just-in-time learning in small steps with
the aid of small learning units. It aims to quickly provide information to close
knowledge gaps without interrupting the employees current activities for a long time
[dWR13].
• Game-based Learning: In game-based learning, learning is motivated by the satis-
faction of the need for competition, socialization or recognition. The required skills
and knowledge are gradually built within the repeated gamecycle.
• Learning in Simulation, Virtual 3D World and Immersive Learning Environment: In
these scenarios, the learning process takes place through an interactive learning plat-
form with direct feedback. Virtual 3D can serve as a simulation of the environments,
events or processes of reality [Hö13]. While in immersive learning environments,
the user is located in the real environment which is extended with virtual elements
or digital information.
• Self-directed Learning: Knowles defines Self-directed learning as ” the process by
which individuals take the initiative, with our without the assistance of others, in
diagnosing their learning needs, formulating learning goals, identifying human and
material resources for learning, choosing and implementing appropriate learning
strategies, and evaluating learning outcomes” [Kn75].
2.5 Automatic Customization and Adaptation
The use of digital technologies provides high potential for customization and adaptation
of the learning process or content to the learner skills and needs.
• Context-Aware/ Pervasive Learning: Context-aware learning systems detect the user
context by collecting information about the physical environment through sensors
data and adapt the system based on this information [LR14].
• Adaptive Learning: In adaptive learning the learning system adjusts the learning
content and steps based on the learner current and historical performance. Next
learning steps are proposed based on measuring different parameters that charac-
terize the learner performance and comparing it with other learners.
2.6 Technical Competences
This work primary focuses on workplace learning in manufacturing and digitized industry.
Competences needed in this field can be distinguished as follows:
• Operating: Workers responsible for operating a machine should have deep under-
standing of the process and be capable of keeping the machine running under normal
production conditions.
192 Wael Alkhatib and Christoph Rensing
Tab. 1: Clustering of the examples presented in this survey
Towards a Classification of Learning Support Systems at the Digitized Workplace 193
• Manual and Assembly Work: Assembling is a manufacturing process in which work-
ers have to fabricate and join parts to construct the final product.
• Controlling: Controlling includes proactive actions and operating the machine dur-
ing normal and exceptional production conditions. Thus detailed knowledge of the
dependencies between the machine’s individual components is needed.
• Maintenance: Maintenance describes the process where a worker undertakes the
required actions to repair and conserve the machine operations under near normal
conditions. Thus detailed knowledge of the machine individual components depen-
dencies on each other is needed.
3 Exemplary Classification
With respect to the pedagogical, technical and didactic concepts described in the previ-
ous section, six assistance systems and learning platforms will be introduced and catego-
rized based on the proposed scheme in Table 1 . APPsist project [Ul15] proposes a new
mobile, context-aware and intelligent-adaptive assistance system for knowledge and ac-
tion support in the Shopfloor. The joint project KOLA [He15] is aiming to orientate the
professional training of the various learning locations to provide the necessary skills for
the work process. KOLA focuses on offering the trainees a demand-oriented companion
and reducing the learning gap or separation between different locations. PLANT@HAND
[AB15] introduces a self-directed assistance system based on the model of cognitive ap-
prenticeship for the industrial assembly workplace. PLuTO project [BR15] is concerning
the demographic changes by ensuring the experience of older employees over their en-
tire life cycle through recording and exchanging episodic knowledge between older and
younger employees. The joint project LAYERS develops mobile and social technologies
that support informal learning in the workplace for Small and Medium sized Enterprises
(SMEs) within regional innovation clusters. Finally, MoLeApp project [Ja14] focuses on
supporting mobile learning processes in technical vocational education using competence
snippets instead of comprehensive materials.
4 Discussion and Future Work
This work proposed a first classification scheme which highlights the different terms and
concepts characterizing learning systems at the digitized workplace. The classified ap-
proaches show that mobile, social and microlearning are currently the trends for gaining
new knowledge, skills and experience at the workplace. Additionally, the selected solu-
tions focus on work activities which are characterized by moderate to high analyzability
while a shortage in research regarding non-routine and crafted work is recognized. In fu-
ture work, using the addressed criteria, a comprehensive overview will be provided of the
existing assistance systems and learning platforms as well as current research projects in
light of the recent advancement in Multimedia-based learning environments. Furthermore,
challenging or disregarded fields will be highlighted to encourage future research in the
field of vocational training in the context of the new manufacturing revolution.
194 Wael Alkhatib and Christoph Rensing
References
[AB15] Aehnelt, Mario; Bader, Sebastian: Information assistance for smart assembly stations.
In: Proceedings of the 7th International Conference on Agents and Artificial Intelligence
(ICAART 2015). volume 2, pp. 143–150, 2015.
[Bo15] Botthof, Alfons: Zukunft der Arbeit im Kontext von Autonomik und Industrie 4.0. In:
Zukunft der Arbeit in Industrie 4.0, pp. 3–8. Springer, 2015.
[BR15] Blümling, Sabrina; Reithinger, Norbert: PLuTO-Portable Lern-und Wissensplattform
zum Trans-fer episodischen Wissens in Organisationen. In: Proceedings der Pre-
Conference Workshops der 13. E-Learning Fachtagung Informatik. p. 3, 2015.
[CBH91] Collins, Allan; Brown, John Seely; Holum, Ann: Cognitive apprenticeship: making think-
ing visible. American Educator, 6:38–46, 1991.
[Di99] Dillenbourg, Pierre: What do you mean by collaborative learning. Collaborative-learning:
Cognitive and computational approaches, 1:1–15, 1999.
[dWR13] de Witt, Claudia; Reiners, Almut: Mobile Learning: Potenziale, Einsatzszenarien und Per-
spektiven des Lernens mit mobilen Endgeräten. Springer-Verlag, 2013.
[Er10] Eraut, Michael; Hirsh, Wendy et al.: The significance of workplace learning for individu-
als, groups and organisations. Skope Oxford, 2010.
[He15] Hellriegel, Jan; Osranek, Regina; Prescher, Thomas; Rensing, Christoph; Weber, Harald:
Herausforderungen und Konsequenzen für die Konzeption eines digitalen Lernraumes in
der beruflichen Erstausbil-dung zur Förderung der Lernortkooperation. In: Proceedings
der Pre-Conference Workshops der 13. E-Learning Fachtagung Informatik. 2015.
[HK14] Hirsch-Kreinsen, Hartmut: , Welche Auswirkungen hat” Industrie 4.0” auf die Ar-
beitswelt?, 2014.
[Hö13] Höntzsch, Susan; Katzky, Uwe; Bredl, Klaus; Kappe, Frank; Krause, Dirk et al.: Simula-
tionen und simulierte Welten. Lernen in immersiven Lernumgebungen. In: L3T. Lehrbuch
für Lernen und Lehren mit Technologien. 2013.
[Ja14] Jaschke, Steffen: Mobile learning applications for technical vocational and engineering
education: The use of competence snippets in laboratory courses and industry 4.0. In:
Interactive Collaborative Learning (ICL), 2014 International Conference on. IEEE, pp.
605–608, 2014.
[JT94] Johnson, Scott D; Thomas, Ruth G: Implications of Cognitive Science for Instructional
Design in Technology Education. Journal of Technology Studies, 20(1):33–45, 1994.
[Kn75] Knowles, Malcolm Shepherd: Self-directed learning. 1975.
[LM07] Liker, J.K.; Meier, D.: Toyota Talent: Erfolgsfaktor Mitarbeiter - wie man das Potenzial
seiner Angestellten entdeckt und fördert. New York: McGraw-Hill, 2007.
[LR14] Lucke, Ulrike; Rensing, Christoph: A survey on pervasive education. Pervasive and Mo-
bile Computing, 14:3–16, 2014.
[SE13] Schön, Sandra; Ebner, Martin: Lehrbuch für Lernen und Lehren mit Technologien: 2.
Auflage (2013). epubli, 2013.
[Ul15] Ullrich, Carsten; Aust, Matthias; Blach, Roland; Dietrich, Michael; Igel, Christoph;
Kreggenfeld, Niklas; Kahl, Denise; Prinz, Christopher; Schwantzer, Simon: Assistenz-
und Wissensdienste für den Shopfloor. In: Proceedings der Pre-Conference Workshops
der 13. E-Learning Fachtagung Informatik. 2015.