Wearable Experience: New Educational Media for Knowledge Intensive Training

Mikhail Fominykh
Europlan UK ltd, United Kingdom 
Molde University College, Norway
Volga State University of Technology, Russia
mihail.fominyh@gmail.com

Abstract: Wearable computing and augmented reality are disruptive technologies. They fundamentally change the way we educate and train people to a master level of performance. With advanced sensors we can capture experience as it emerges. For example, a trainee can receive live guidance in the form of semi-transparent 3D hands that appear at the right place spatially and are operated by a remote expert using sensor data. Captured guidance provides reference to scale, allowing repeated access to the information asynchronously at the right time and in the right place where it is most urgently needed. Expert guidance can be captured with wearable sensors and later re-enacted by trainees with augmented reality creating a believable illusion of a master-apprentice knowledge sharing. The captured experience therefore represents a new type of educational media that has properties of carrying both explicit and tacit knowledge. This new media helps to convert experience to knowledge and enable learning by bringing closer the theoretical knowledge and immediate experience, which are traditionally separated. Tailored content of captured experience can be presented with augmented reality using intuitive and immersive user interfaces. This can have a positive impact on mental processing and memorization, not only adding scaffolds for high performance, but also acting as a safety net preventing potential problems sensed in the environment. Learning how to master a complex task usually involves reflecting on your own performance, looking back at your behavior and comparing it to that of others. The goal of this new training methodology is to enable the full cycle of immersive experience observing an expert, training with and without guidance, and observing own performance.

Introduction
Practical experience activities are central for many types of education. Often these include iterative training and reflection to prepare for both procedural tasks and unexpected situations. Such practice-oriented and hands-on learning activities are the focus of this paper. New advanced technologies (such as virtual and augmented reality) are becoming available and accessible both at the workplace and in the universities to improve the effectiveness of training, increase safety, and reduce the cost.
In developing many skills and competences, the most valuable and useful types of knowledge (that speed up uptake and impact) reside not in written instructions and databases, but in activities, practices, shared experiences, and relations between participants. The former type of knowledge is known as explicit and the latter as tacit (Polanyi 1966). These two types of knowledge are mutually dependent and mutually constituting in both professional development and education (Nonaka and Takeuchi 1995).
Obtaining tacit knowledge is essential for practice-oriented and knowledge-intensive fields in formal education and even more important in vocational training. It involves learning to handle difficult situations and complex procedures such as operating machinery that requires the deep knowledge of a domain and a high level of skills. Examples of professions where such training is required include airplane pilots, ship navigators, security and emergency workers, engineers, surgeons, and astronauts. At the same time, the whole spectrum of manual workers needs iterative procedural training. Such training fits both highly skilled and low skilled manual workers, as both need to drill into their pre-existing knowledge and sharpen their skills on multiple standard tasks.
Development and application of new technologies can not only allow for performing such training in a safe life-like environment, but also to capture the knowledge contained in the training activity. If knowledge is stored as activities, it can be re-experienced as many times as needed, analyzed and reflected upon individually or together with an expert (Fominykh et al. 2014; Fominykh et al. 2015).
Learning from Experience and Knowledge Conversion
Taking a perspective that competence is a potential for action, human activity is the process leading to its development. In order to convert experience into knowledge and enable learning, the information relevant for a master level of performance (aka ‘theory’), traditionally, is separated from the immediate experience of competent action (aka ‘practice’). Traditionally, key roles in this conversion have been held by skilled teachers using direct instruction as well as by authors producing media such as textbooks or instructional films (Fig. 1). 

immediate experience 
(aka ‘practice’)

information for a master level
(aka ‘theory’)

Figure 1. Traditional conversion – creating knowledge from practical experience
With technical advances and with growing access to advanced ICT, however, new opportunities arise for capturing, sharing, and re-enactment that do not rely on strict separation of knowledge from its application (Fominykh et al. 2015), also giving more room to new roles in knowledge production beyond the specialist curator (Fig. 2).

immediate experience 
(aka ‘practice’)

information for a master level
(aka ‘theory’)

VR, AR
ICT

Figure 2. Use of technology - creating more knowledge from experience and feeding back
Tacit knowledge is usually conveyed to explicit knowledge through narratives and shared experience. Together with iterative training, this creates embodied, believable and rich learning experience through activity (Fominykh et al. 2014). Capturing tacit knowledge from activity facilitates sharing and transfer, giving rise to new applications such as accurate preservation of activity (also registering the methods and procedures involved), retrospective analysis and reflection on action, and scripted re-enactment in related sessions.
Examples of rich experiences include safety training simulators, forensic recreation of crime scenes, task sharing from expert to novice at the workplace, or training of operation or maintenance of advanced machinery. It is evident that capturing ‘rich’ experiences from such ‘fluid’ containers poses a significant challenge and not only to traditional repositories. Still, the knowledge they carry is essential for many high-skill professions (Haldin–Herrgard 2000). Drawing upon the work in activity theory (Engeström 1999), activity can be seen as a primary source for the development of knowledge from experience. As knowledge containers, activities can be characterized by their narratives, the collaboration, actors and objects involved, their governing rules, etc.
Both students and workers need iterative procedural training to drill into their knowledge and sharpen their skills on a large number of standard tasks and situations. Such training can also help to prepare students for unexpected situations. Working on a large number of different tasks, a student builds knowledge about cases and patterns to be prepared for the unexpected. This gives the student such abilities and high-level skills as handling emerging issues effectively, recognizing patterns, and making decisions in non-standard situations. Understanding of learning trajectories that allow for dealing with uncertainty is provided by the threshold concept framework (Cousin 2006; Flanagan 2014; Meyer and Land 2006). It addresses how dealing with uncertainty and liminality is identified as crucial barriers to overcome in the learning process.
Such abilities are required in highly complex workplace situations where a duality of competences and skills is necessary. On the one hand, there is the need for a unified response pattern, such that each skill and competence can be drilled iteratively, thereby ensuring a degree of predictability in behavior from the students and providing grounding for creative solutions. This is researched in trainings towards change of behavior (Kalz et al. 2015). On the other hand, emergency situations are characterized by a high degree of unpredictability, a lack of control of the parameters involved and often a minimum of time to assess the situation and the measures to be taken, which additionally require effective communication (Klemke et al. 2014). The ability to deal with and act within the framework of uncertainty becomes crucial. Therefore, a combination of these two sets of competences requires highly sophisticated scenarios, iterative and regular training to achieve the sufficient ‘time on task’, and comprehensive debriefing.
Wearable Experience Training Methodology
The Wearable Experience (WE) training methodology aims to provide an innovative learning method that is based on the idea of capturing the experience of an expert and enabling trainees to ‘wear’ it while re-enacting, thus giving the trainee access to the tacit knowledge of the expert. This methodology is based on learning theories of situated learning (Young 1993), active learning (Bonwell and Eison 1991; Prince 2004), problem-based learning (Kilroy 2004), and experiential learning (Kolb and Fry 1975) which make the learner responsible for the learning process and embed this process into a meaningful context.
The idea of the WE methodology is to capture key aspects of the experience of an expert at work on a specific task and make this available live (and recorded to re-live) for trainees working on the same task. Several challenges are to be addressed in reaching this vision: how to capture the experience by means of Wearable Technology (WT), how to experience more of it by re-enactment using Augmented Reality (AR), what knowledge can be captured, and what is the possibility for a trainee to experience the phenomenology of the expert through applying WE.
The WE methodology is currently being developed by a European consortium of experts in technology and training (http://wekit.eu/) to be validated in vocational training, capturing workplace experience and combining it with technical documentation. It will be an emerging pedagogy, offering a unique multi-sensory ‘one to one teaching’ experience and supporting different learning styles. It will also allow trainers to have a more accurate and up-to-date follow up of each learner based on the analysis of captured trainee’s experience. Combined with an ‘entertaining’ learning approach and regular feedback that are key for motivation, learning results of trainees will improve.
The focus on workplace learning is motivated by one of the key challenges facing industrial training, which is related to a so-called low skills trap. On the one hand, employees with lower skills (including skilled manual workers in knowledge-intensive areas) do not sufficiently participate in training, being unable to escape their often precarious labor market position (Eurostat, lifelong learning database). In addition, the same categories of workers are often mentioned among those who will be affected by robotization, computerization and general automation of the industry, especially in the developed countries (Bowles 2014; Frey and Osborne 2013). These workers will need to be re-trained for more knowledge-intensive skills. On the other hand, the cost of training represents one of the major barriers to a broader engagement of workers in training as reported by employers (Eurostat). Therefore, the WE methodology focuses on (i) continuously up-skilling already skilled workers and (ii) developing new skill sets for low skilled workers who usually get the least training but need it the most.
In this context, the training method is likely to decrease the time needed to create and update experiential content and lower the average cost of training per participant (e.g. less time spent by trainers on coaching). This additional economic incentive is expected to widen the group of learners engaged in workplace training.
The learning method primarily focuses on increasing training efficiency (more tries during the same period of time and less trainer involvement) and effectiveness (active learning under a direct guidance). Key challenges here include (a) user workflow appropriate to the task at hand, (b) intuitive presentation of complex information, (c) conformance to regulation and quality standards and (d) streamline content authoring that leverages existing repositories.
The direct training experience, knowledge, data, tools, and services necessary to overcome these challenges are often fragmented across the silos of technical communication, training, services and support, and engineering. Training with WE will allow to bring these separated communities together, bridging the gap between abstract knowledge and its practical application in order to support an Industry 4.0 (Hermann et al. 2015) of smart machines and open services with the approaches and technology required for optimal human learning and development. Workplace learning tasks that can directly benefit include, for example, taking responsibility for anticipating risks, mitigating errors, training associated with commissioning newly-purchased equipment, and eventually adjusting its operational parameters to address new needs.
Technologically, the approach applies performance augmentation as a scaffold and procedural tasks. The physical experience will be augmented with guiding instruction, bringing up machine sensor data and additional information (such as social content in form of hints and tips) where required, using AR as a knowledge medium, while at the same time offering traditional textual and rich media learning resources. Real-time feedback and user behavior validation will be provided so as to allow for formative assessment and progress control.
Expert experience can be captured using wearable sensors, recording the head position in the surrounding environment, the vision area, gestures, and other data in order to preserve and later deliver the experience to trainees. The captured experience will be provided to trainees in the form of wearable AR at the site of recording (workplace) or simulated remotely.
The WE methodology consists of three major phases described in the scenario below. The methodology consists of three major phases: capturing expert experience (Fig. 3a), trainees wearing expert experience (Fig. 3b), and analysis and post processing (Fig. 3c). In phase A, the WE methodology relies on the use of ambient wearable sensors to record the vision, voice, position, gestures, and surrounding environment of a user (expert when capturing an activity, Fig. 3a). All captured data will be processed and analyzed by a Learning Experience Model (LEM, Fig. 3), applying AR-LEM (Wild et al. 2014). And in phase B, it will be delivered for other users (trainees) through re-enactment by “wearing” the recorded experience (Fig. 3b). The delivered experience will be augmented with the prior captured data (for example, superimposing the hands of the expert). In addition, biofeedback data (such as brain activity, skin conductivity, and heart rate) can, if necessary, be collected from both the expert and the trainees to determine the emotional state of the trainee, give hints during performance (e.g., to calm down) and use in post analysis (phase C, Fig. 3c).
The colored modules of the diagram indicate active components on each phase. The blue color is used for live/synchronous human activities, green – for post/asynchronous human activities, and red – for actions performed by software and hardware (Fig. 3).

Figure 3. Phases of the Wearable Experience methodology
The three phases of the WE methodology are conducted sequentially, and a single captured expert experience (phase A) can be used in multiple training (phase B) and evaluation (phase C) sessions.
Phase A. Expert experience capturing is conducted first. A specific task/activity should be selected. The expert comes to the regular workplace and wears the WE capturing kit. The recording button is pressed on the kit, and the expert starts performing the work task as usual or as the expert would normally explain it to a trainee. The sensors of the WE capturing kit record the shape of the workspace using 3D scanning and the position of the expert within it. The gaze direction, 360 video, and gestures are captured too. The expert adds an element of explanation to his/her performance by narrating what is happening (for example, “now I am turning this part of the machine” or “I have to be careful and check this indicator before proceeding”). This is captured too. When the task is completed, the recording stops.
Phase B. Trainee wears expert experience. A trainee is about to be trained to perform the same task. The trainee comes to the same workplace where the expert experience was captured. The trainee wears the WE capturing kit and the WE re-enactment kit. The start re-enactment button is pressed on the kits. The sensors capture the workplace around the trainee and match it with the one previously captured for the expert. When the match is achieved, the system will know the relative positions of both the expert and the trainee at each point in time. The position of the expert will be displayed for the trainee via the see-through AR glasses. The trainee comes closer to the position where the expert was standing in the beginning of the recorded activity. The trainee listens for the voice of the expert for instructions on how to start the procedure. When the trainee is standing close to the expert, he/she also sees the direction of the expert’s gaze and the expert’s gestures are overlaid via the AR glasses, as for example, with Ghost hands (Scavo et al. 2015). The trainee starts to perform the task by following the narration of the expert and copying the way he/she operates the objects with hands. The AR elements are limited to minimally required and are not distracting the trainee. It is easy for the trainee to follow the instructions of the expert, as they are contextualized and synchronized with the expert actions. For example, when the expert is saying “these two bolts must be loosened”, the trainee will see that the expert was pointing to those bolts with his/her finger. The trainee can pause or restart the re-enactment at any time if they are lagging behind the expert. The WE capturing kit is recording the trainee experience in the same way it did for the expert in phase 1. The voice of the trainee is recorded too. Optionally, phase 2 can support (a) assessment of the trainee through capturing his/her activity without expert guidance and (b) re-enactment of experience in any other environment (not in the place where it was captured) with capabilities limited to observing expert performance.
Phase C. Experience analysis. The expert and several trainees gather in a classroom for a debrief session. The captured experiences are visualized on a large screen one by one or two at the same time. The experience of the expert is compared to the experiences of the trainees. Critical errors are spotted and explained. The captured trainee experience is saved into a personal profile.
Augmented Reality and Wearable Technologies: State of the Art
AR provides an enriched view onto the physical world, adding layers with contextually useful information, delivered visually or by stimulating other senses using wearable and hand-held devices (Azuma et al. 2001). AR allows the real-time fusion of computer-generated content with features of the real world. The augmented elements provide access to information that the user cannot directly detect with their own senses, enhancing user perception which helps them to perform real-world tasks more efficiently.
Both AR and WT are rapidly growing markets amongst the information and communication technologies. In 2014, the Gartner's Hype Cycle for emerging technologies placed AR halfway to the plateau of productivity, reaching it in 5-10 years (Rivera and van der Meulen 2014). More than 20 manufacturers are striving for leadership with their own AR glasses, including Google, Epson, Sony, and Microsoft. According to the report “Smart Glasses Market 2014” by AugmentedReality.Org, the number of AR glasses sold globally is now measured in tens of millions, and the yearly growth is estimated between 50% and 300% in the next few years. The AR market is growing 80-100% per year and estimated by Digi Capital to reach USD 120 billion by 2020.
The “Tech Trends 2014” report by Deloitte stresses that now is the time for wearable computing, highlighting its potential for “improving supply chains, workflows, and processes to drive down costs and increase competitiveness”. It points out the complexity and multidisciplinarity of the field. According to the report “Putting Wearable Displays to Work in the Enterprise” by Accenture, the adoption wave of AR and WT will be determined by enterprises, as they can “improve employee efficiency, enhance training and ongoing communication, reduce nonproductive time and rework, shrink decision time frames”. Many industrial use cases of AR and WT in, for example, manufacturing, construction, health, the service sector, or in trade & sales can be found and technology is currently being introduced in front-runner organizations at large.
The characteristics of AR are strongly related with the senses of presence, immediacy, and immersion.  The nature of the technologies used by AR provides an embodiment dimension (Fishkin 2004) that conditions the way users interact with their surrounding physical context. Observation (perception) relies on the recognition and interpretation of sensory stimuli, whereas reflection builds on augmented information that can be interacted with while exploring the physical environment. In educational context, these features enable the activities to engage students cognitively and affectively, and enhance the impact of learning based on real-world experiences (Chang et al. 2013).
AR increasingly accommodates innovative devices and technologies, while at the same time leveraging our understanding of human-computer symbiosis by enhancing sensory feedback and enabling interaction with augmented information based on actions in the physical world (Furht 2011). 
The first technologies used to capture or ‘sense’ a physical contextualized experience enabled AR tracking using the sensors available in handheld devices: visual and audio sensors (e.g., camera, microphone), location and orientation sensors (e.g., GPS, digital compass), inertial sensors (gyroscopes, accelerometers), etc. Modern technologies are capable of capturing users’ mental and physiologic states using biological signals (Mercier-Ganady et al. 2014), including psychometrics (e.g., electroencephalography), facial expression analysis (face detection, feature tracking, expression and eye-tracking), and biosensors (e.g., electrodermal activity, temperature, blood volume pressure, heart rate, galvanic skin response and foot pressure).
The ability within one field of view, to be both in the world and to see yourself in it, the power of looking through, and occupying, your own field of vision (Gibson 1994) is becoming one of the most important tools for capturing live experience with AR. Wearable computers allow a much closer association with the user and a higher degree of freedom. The WT sees, hears and perceives the user’s physical state. A more complete perception of the scene implies not only being able to sense persons in the environment, but also to perceive the environment itself and the objects within the scene. The goal is to be able to establish a perceptually correct connection between real-world objects and digital content. This is currently achieved using sensor-based, vision-based and hybrid tracking techniques (Feng et al. 2008).
New Educational Media for Knowledge Intensive Training
The frontiers of research in educational technology are pushing the boundaries of possible, posing new challenges and asking encouraging questions. The work on WE is a search for an answer for the following questions. Can we experience more of the expert knowledge using emerging technologies? To what extent can we experience the phenomenology of the expert? To what extent is digital technology changing the way we experience being in the world? The mechanism of capturing and re-enactment of learning activities using AR and WT is a step towards creating a new educational media that has great potential.
Capturing activity at the right level of abstraction, while still retaining all relevant detail, is a complex task, not only from the technological point of view, as a significant part of the valuable experience has to be subducted in the process. Conventional approaches such as video recording or screen capturing provide only limited points of view, significantly reducing the wealth of information available from direct experience as well as its immediacy and authenticity. These technologies map activities in the real world to flattened representations with limited affordances (Morozov et al. 2013). Learning to be effective needs more than that. Its feedback loop character requires an iterative cycle of invention, observation, reflection and action/revision (Sterman 1994). The first two stages are concerned with how contextualized information is measured, captured, stored and relayed to the individual, whereas the last two create a pathway to judgment and decision making (Goetz 2011). AR and WT can help to overcome present reductionist limitations in capturing activities by implementing more open loops (Fominykh et al. 2015).
Experts predict that we are on the verge of ubiquitously adopting AR to enhance our perception and help us see, hear, and feel our environments in new and enriched ways (van Krevelen and Poelman 2010). We are now in an era where context adaption and augmentation is central. Mobile technology is moving us from an era of ‘information communication’ to ‘experience communication’ (Cheok 2011). Well-designed AR tools should enable the creation of situations and concepts that could not have been realized before by uniting the affordances, features and possibilities of both the analogue (physical) and the digital (virtual) worlds at the same time.
Conclusions: Beyond State of the Art
The WE methodology constitutes a conceptually new, paradigm-shifting training method that has a potential to be superior to those currently used both pedagogically and economically: from ‘tell me’ and ‘show me’ training to fast-track mastery (‘see what expert sees’, ‘do what expert does’, ‘make it your own’). 
The focus of the WE is on reflection in-action by workplace learners, rather than a more usual in formal education approach to perform critical reflection on past activities. Another difference is that WE is focusing on self-directed learning in industry (Carneiro et al. 2011), prioritizing procedural and practice-based learning. The major anticipated advance is with the help of AR and WT to direct attention during initial learning, in ways that support stimulated recall of experiential learning, and other improvements on mainstream reflective approaches to learning.
Pedagogically, the WE goes beyond the usually-cited literature on far transfer (Barnett and Ceci 2002; Haskell 2001) and beyond the state of the art in reflective learning that is still dominated by pre-technological education research on reflection-in-action vs reflection-on-action (Schön 1983; Kolb and Fry 1975). The educational theory and approaches have not yet adjusted to the affordances of advanced sensors, AR and many other existing and emerging ways to augment activities and direct attention. For example, the error analysis of activities captured using WE can be very informative, using data mining to identify what patterns of errors or successes have what causes or correlates. This leads to (a) effective training, as the method allows to perform as many trials as necessary to sharpen the skill, (b) increased confidence, using direct comparison of trainee and expert performance, and (c) improvement of process workflow analysis.
The closest existing technological solutions are usually scanning the environment and providing augmented instructions, including haptics, audio and vision (Huang et al. 2007). However, the learner does not experience being expert and there is no sharing experience or elaborated learning approach design together with such systems. Examples of using AR and WT are mostly limited to remote assistance and immersive learning games based on scripted (Ternier et al. 2012; Schneider et al. 2015). The technology behind WE will allow to complement existing or new technical documentation at the right level of abstraction and enabling comparison of performances using the recorded data. Experience re-enactment is believable and realistic enough for the trainee to obtain its essence without being too distracted. This can be indicated by the ability to orient/navigate in the mixed reality environment, to identify the position and the spatial orientation of the recorded expert, to identify the point of view of the expert in dynamics, to follow the expert’s position, orientation and point of view without notable latency, and to understand (or copy) the actions of the expert.
The work on the WE methodology involves experimentation with knowledge sharing and conversion methods, expert guidance, mastering high-level skills, performance augmentation, and reflection. An experiential learning platform is being built based on the recent advances of AR and WT to enable practical use of this methodology. Captured experience that can be re-enacted will constitute a new type of media. In the educational context, the more aspects of experience we can capture and make available for re-enactment, the more expert knowledge this new type of media will be able to pass to trainees.
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Acknowledgements

The work presented in this paper is partly supported by WEKIT, a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687669. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.
The author would like to thank consortium partners and collaborators who contributed to the ongoing work presented in this paper, namely Fridolin Wild, Carl Smith, Victor Alvarez, Paul Lefrere, Roland Klemke, and Veronika Kupriyanova.