Application of AR

Modeling the Instructional Design of a Language Training for Professional Purposes, Using Augmented Reality

Terry Inglese and Safak Korkut

Abstract Thischapterpresentstheinstructionaldesignofalanguage-trainingmodel for professional and vocational purposes on behalf of the Swiss railway industry, specifically designed for German-speaking train drivers and train operators, who work for the Schweizerische Südostbahn (SOB). In fact, around 50 train drivers and train operators need to learn Italian and be able to communicate clearly and confidently in this language by 2021. Thanks to the opening of the Gotthard Base Tunnel in 2016, some Swiss railway companies are expanding their business port- folios also in the Italian speaking region of Switzerland. Augmented Reality (AR), specifically the Blippar app, is used here as an additional and motivating guide to learning technical terms and nouns, verbs, and dialogue structures, in short: essential railway communication features between train drivers and train operators. The final goal of the chapter is to describe how railway professional trainees, learning a new language, are actively designing their own language learning contents, using AR.

Keywords Augmented reality (AR) · Situation awareness (SA) · Multimedia learning theory · DART co-creation model · Bloom’s learning objectives taxonomy · Language training for professional purposes

1 Introduction

For some Swiss railway companies, the opening of the Gotthard Base Tunnel in 2016 was an opportunity to expand their own business portfolios, also reaching the Swiss Italian-speaking destinations (from Airolo to Bellinzona). A great business oppor- tunity indeed, but also a new challenge, because German-speaking train drivers and train operators (also defined as dispatchers) must be able to communicate confidently

T. Inglese (B) · S. Korkut Institute for Information Systems, School of Business, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Peter Merian-Strasse 86, 4002 Basel, Switzerland e-mail: [email protected]

S. Korkut e-mail: [email protected]

© Springer Nature Switzerland AG 2021 R. Dornberger (ed.), New Trends in Business Information Systems and Technology, Studies in Systems, Decision and Control 294, https://doi.org/10.1007/978-3-030-48332-6_14

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also in the Italian language. For these railways companies, this means investing in their employees’ linguistic training and offering a specifically designed language training curriculum for professional purposes for numerous German-speaking train drivers and train operators, who in addition to their already dense training programs, are obliged to learn a new language accordingly.

The authors of this chapter developed a didactical model, involving the use of AR (Augmented Reality), which actively includes the trainees, so that they become motivated participants also in the design of the instructional contents to be learned. The training is designed specifically for German-speaking railway professionals who work for the Schweizerische Südostbahn (SOB). In fact, around 50 SOB train drivers and train operators are required to learn Italian and be able to communicate clearly and confidently in this language by 2021.

First, the chapter briefly describes the SOB training case study, together with the learning needs of the train drivers and train operators. Second, the instructional design model, consisting of the DART (an acronym for Dialogue, Access, Risk, Transparency) co-creation model, the Mayer’s Multimedia Learning Theory, the Situation Awareness (SA) construct and the Bloom’s Learning Objectives Taxonomy will be explained. Third, as currently being in a test-phase, a concrete AR training sequence, designed by the train drivers themselves, will be described and specified. The limitations of the case study will also be highlighted.

2 The SOB Case Study

Driving a train is a complex task, especially in Switzerland, because on a normal working day, a train driver travels through the different Swiss language regions. In addition, train drivers and train operators are confronted with several special trainings and examinations during their career, such as regular assessments and tests to update their knowledge and the skills of their workflow and train management, as important professional goals in a continually changing environment. The railway drivers and operators must also update their emergency procedures, including firefighting, evac- uation methods, and communication for safety emergencies. These cognitive tasks needtobecommunicatedclearlyandcompetently, andinmultiplelanguages. Inaddi- tion, with the introduction of rapidly changing communication and control systems with increasingly complex procedures, train drivers and train operators are equally forced to cope with high skill requirements with little autonomy [1, 2]. Moreover, recent studies [3–8] show that the lack of personal contact between train operators and train drivers is perceived as a negative condition. Furthermore, such circumstances might become the potential cause of miscommunications and/or misunderstandings, due to a lack of mutual understanding and the lack of a (mandatory) read-back of protocol-requiring commands, guaranteeing safety procedures, not only in the context of aviation [9], but also in rail transport. As a matter of fact, according to recent studies in professional language training in aviation [9], key features of

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effective communication depend correspondingly on assumed expertise and profes- sional behaviors. When it falls short of expectations, communication is expanded and prolonged to fill these gaps. Nevertheless, these specific communication aspects are not adequately taken into account in language training for professional purposesin the Swiss railway sector [3–5, 8, 10].

In addition, recent work in applied linguistics for professional purposes [9] under- lines the importance of redesigning the constructs of language for specific purposes (LSP). As a matter of fact, ensuring that the correct communication unfolds in railway safety contexts is a compelling didactic and instructional topic, which needs to be designed, taught and learnt in professional language-training curricula. Therefore, language instructors are invited to work together with professional railway experts [9] to plan meaningful and work-based training curricula, actively involving in the design of instructional learning sequences and materials also—and this aspect repre- sents the added value of the present case study—the learners themselves, such as in the SOB case study.

An exploratory analysis conducted by the authors of the chapter reported some gaps in the current didactic offer of these specific language training curricula, and the following research question was formulated: How should a new didactic model be developed and implemented to meet the specific communication needs of train drivers and train operators, also taking into account the potential lack of motivation to learn a new language for these adult learners who might see the training as an additional hardship? Railway employees could be less committed to the language training, because they might prioritize their professional assessments and technical examinations over learning a new language. Therefore, the motivation to complete a new language training could be lower than expected.

2.1 Many Languages, Sufficient Language Skills, but at Which Language Level?

The Swiss Federal Office of Transport (FOT) determines which binding language regions correspond to the responsible operation centers [10]. In fact, the so-defined Fahrdienstvorschriften (FDV—the Swiss Federal Office of Transport’s Driving Regulations) state that: “There are procedures in place to ensure that (especially with regard to the management of dangerous goods) the infrastructure manager can easily and promptly notify the competent person who is proficiently skilled and has sufficient language skills.” [10]. This means that train drivers, travelling between Lausanne’s and Pollegio’s operation centers, must have the “necessary language skills” in French, German, and Italian to cross these diverse Swiss linguistic regions. An essential question that arises could be: Which level corresponds to the necessary language skill? In fact, between 1989 and 1996, the Common European Framework of Reference for Languages (CEF or CEFR) standardized the new language skills to six levels (A1, A2, B1, B2, C1, and C2). Level A1 refers to the basic ability

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to communicate and exchange information in a simple way, and A2 is the ability to deal with simple, straightforward information and begin to express oneself in familiar contexts. B1 is the ability to express oneself in a limited way in familiar situations and to deal in a general way with non-routine information and B2 is the capacity to achieve most goals and express oneself on a number of topics. Finally, C1 and C2 denote the fluent conversation level. More specifically, the required language skill for train drivers and train operators, specified in the EU (European Union) regulations, is level B2.

Within the Swiss railway operations, the responsible infrastructure managers (ISB) are the ones who regulate the use of languages for operational communi- cation and process purposes. It is the responsibility of the railway companies them- selves to determine the language skill levels of train drivers who cross the multi- lingual geographical areas. While the Swiss infrastructure managers (ISB) regu- late the linguistic interfaces in their operating regulations, the railway companies are responsible for providing the so-called sufficient language training to their own professional personnel. Nevertheless, it is not yet clear in the Swiss railway network which language level could correspond to the “sufficient language skills”, so that in the event of possible accidents there could be uncertainty due to miscommunication [3–5, 8].

2.2 The SOB Language Learning Framework and Andragogy

The SOB (the Schweizerische Südostbahn) currently offers intensive Italian language training (level A1-A2) for train drivers and train operators who are required to be able to communicate in Italian in early 2021. As part of this training, the language instructor, in collaboration with the trainees themselves, will design part of the instructional content actively using AR technologies to provide trainees with various modalities for stimulating the learning experience and improving the mastery of rele- vant safety-based dialogic structures in the Italian language. The implications for the design of a solid language-training program are at the heart of SOB: learning should be based on a real diagnosis of the adult learners’ needs, based on competencies and skills embedded in realistic work-based scenarios.

The final goal for the SOB train drivers and train operators will be reaching the ability to speak Italian clearly and confidently, especially when confronted with unexpected situations, where reaction time and fast decision-making are requested. Consequently, and based on preparation and research carried out by the authors of this chapter, it is essential to develop a language training curriculum for professional purposes, based on the following three pillars: (a) defining the specific phraseologies for the most relevant routine situations, safety issues, and emergency communica- tion with special attention to the avoidance of misunderstandings; (b) focusing on a quasi-realistic training environment that mirrors genuine work-related scenarios

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and events; (c) designing specific training modules, according to the requirements defined by the authorities and the railway companies to allow effortless and proficient communication in the language training. The authors of this chapter base the three communication pillars in adult learning andragogy principles.

Houle [11] was one of the first scholar who started for the first time a stream of studies putting adult learners in the focus of educational and psychological research, understanding their motivation to continue learning. He found out that “continuing learners” were: (a) goal-oriented, using education to accomplish their objectives; (b) activity-oriented, finding meaning in learning something new and were finally (c) learning-oriented, seeking knowledge for its own sake. Furthermore, Knowles [12] proposed a profile of an adult learner and defined andragogy as “the art and science of helping adults learn, in contrast to pedagogy as the art and science of teaching children” [12] (p. 4344; [13]). He summarized four assumptions in andragogy: first, the concept of the adult learner, who moves from dependency toward increasing self- directedness, but at different rates and in different moments in their lives; therefore, instructors have the responsibility to encourage the self-directed drive, although adult learners may be dependent in particular situations. The second assumption is the role of the learners’ experiences. As adult learners grow, they develop a precious reservoir of work and life experiences, which can become part of the resources for designing the learning contents, both for themselves and for other adult learners. In addition, these learners can attach more meaning to learning gained from past and present everyday work-based experiences. The third assumption is the readiness to learn: adult learners are willing to learn something new, if they can project and implement new learning in real tasks and problems that need to be solved. Finally, the forth assumption is the orientation to learning: adult learners consider education and learning opportunities to develop improved skills, and they want to apply their new skills immediately in their professional life. The focus of the training should be on competence-oriented development and on performance-based development.

Basedonthepersonalconversationandinformaldataofthesixcurrentparticipants in this case study (the average age is 55), for the SOB train drivers and train operators, learning a new language could be a stressful experience due to the additional pressure they might feel. To meet this challenge, a new way to motivate these trainees has been instructionally designed, with the goal to understand how Augmented Reality (AR) could promote learning and motivation. A specific instructional model was designed and will be described in the next section.

3 The Instructional Model Explained

For the SOB railway professionals, starting from an A1 level, the following four- layered instructional model was conceptualized, taking into account: (a) the Situation Awareness (SA) [14], (b) the Mayer’s Multimedia Learning Theory [15–19], (c) the DART—Dialogue, Access, Risk, Transparency—Co-Creation model [20–23] and finally (d) the Bloom’s taxonomy’s six learning objectives [24–27].

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Fig. 1 Instructional model on designing the language training for professional purposes (own illustration of the authors)

The Situation Awareness (SA) model [14] is relevant, because it is designed for complex and dynamic systems, such as the railway sector, where decisions often have to be made under time pressure and based on environmental events that could occur unexpectedly. Therefore, the cognitive comprehension of the situation and the way in which train drivers need to react to future driving situations are essential preconditions for the development of the instructional model. In addition, the Mayer’s Multimedia Learning Theory [15–19] was selected on the basis of strong scientific evidence-based principles in the design of instruction sequences using multimedia. Furthermore, taking into account the six learning objectives of Bloom’s taxonomy [24–27] was a guarantee that all the layers of the learning processes and objectives would be included. Finally, the DART co-creation model [20–23] is an approach to actively involve professional learners in the design of the instructional content through a bottom-up teaching and learning method.

In Fig. 1, the authors of the chapter graphically summarize the four-layered model in an illustration. In the next section, each layer of the instructional model is first described and later explained by a concrete example.

3.1 The Situation Awareness Model

Situation Awareness (SA) is defined as the awareness of what is happening in and around the environment, along with the understanding of how users use this infor- mation. The concept of SA is commonly applied in complex and dynamic operative situations, such as air traffic control, military training or surgery training. In such

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cases, the ability to process the information plays a key role in raising awareness, perceiving and comprehending the situation, and making the right decision. Endsley [14] (p. 14) refers to SA in three levels where each level is built on the previous level:

1. Perception of elements in the current situation 2. Comprehension of the current situation 3. Projection of future status.

The first level is perception, related to operators perceiving the incoming informa- tion.Thismeansthatthenecessaryinformationisavailableandattentionisfocusedon the important information. Without the right information, operators have no chance to understand and control a system. The second level is comprehension, i.e. the ability to understand the perceived information, interpret the information on one’s goals and to remember the relevant parts of the information. The third level is projec- tion, consequently when the important information is perceived and understood, it is possible to understand the dynamics of a system, project and anticipate future decision-making in order to influence and control the state of a system. After having described the three levels of SA, Endsley’s model [28] connects SA to decisions, which will lead to the performance of action. These three steps are also influenced by task and environmental factors, such as workload, stressors, system design, and complexity, as well as individual factors such as goals, preconceptions, knowledge, experience, training, and abilities (Fig. 2).

The SA concept started to develop fully at the end of the 1980s, especially in the field of aviation, safe control, safety training and investigations ofaircraft acci- dents, and not surprisingly with the increase of automation in flight control [29]

Fig. 2 Endsley’s model of situation awareness in dynamic decision-making systems [28] (p. 35)

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(p. 190). Endsley [28] defined it for dynamic systems because SA plays a critical role in decision making, especially in complex and dynamic systems, followed by the execution of actions. “A person’s SA is restricted to limited attention and working memory capacity… long-term memory stores, most likely in the form of schemata and mental models, can largely circumvent these limits by providing for the integra- tionandcomprehensionofinformationandtheprojectionoffutureevents(thehighest levels of SA), even on the basis of incomplete information and under uncertainty. SA is largely affected by a person’s goals and expectations, which will influence how attention is directed, how information is perceived, and how it is interpreted. This top-down processing will operate in tandem with bottom-up processing in which salient cues will activate appropriate goals and models” [28] (p. 49).

Endsley [30] studied the phenomenon of human error and SA. According to her findings, 88% of human errors were found to be due to problems with Situa- tion Awareness, because the situation the persons were in was misinterpreted. For Endsley, the best way to support human performance is to develop high levels of Situation Awareness. Moreover, Kaber and Endsley [31] defined that SA can be applied to contexts where there are multiple goals that must be pursued by oper- ators simultaneously, such as multiple tasks that have different relevance to goals competing for the operators attention; operator performance under time stress, and negative consequences associated with poor performance (p. 190).

Problems occur when there are failures to detect critical cues about the state of the system, failures to interpret the meaning of perceived information, failures to understand individual task responsibilities and the responsibilities of the others, and errors in communication with other operators in the team and with other teams. In fact, this is defined as the loss of Situation Awareness. Moreover, Endsley [14] summarized that first SA “is goal-oriented…the elements of the environment that people need to be aware of are based on the goals associated with that job. A goal- directed task analysis […] provide[s] the basis for understanding how SA maps to goals and for creating goal-driven designs.” (p. 49) Second, SA directly supports the cognitive processes of the operator, processes which will be represented by the system interfaces and are based on a high level of SA. For the third and final aspect, the user must have control over the expected and unexpected environments’ SA through user-centered design. Consequently, user-centred learning scenarios for the SOB trainees need to promote the development of language skills, which should be applied consciously and continously, as SA Endsley’s approach suggests [14].

3.2 The Mayer’s Multimedia Learning Theory

The Mayer’s multimedia learning theory and his principles on how to design multi- media instruction [15–19] is based on cognitive theories about how people learn. The theory is based on the dual coding theory [32, 33], the working memory model [34], and the cognitive load theory [35, 36]. Mayer defined the twelve principles in the design of multimedia instructional content. His theory represents a benefit also

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in designing Augmented Reality applications for learning purposes [17]. The twelve principles are summarized in Table 1.

It is crucial to apply Mayer’s science of instruction when designing instruc- tional content, which needs to be in line with cognitive psychology and the learning processes. These principles guide the language instructor, as well as the trainees, in the planning and the production of the multimedia AR training contents, defined by the authors as AR vignettes, which will be explained later.

Table 1 Table summarizing the Mayer’s 12 multimedia principles [15–19]

Principle How it works

1. The Coherence Principle when extraneous words, pictures, and sounds are excluded rather than included subjects learn better

2. The Signaling Principle when cues that highlight the organization of the material to be learned are added subjects learn better

3. The Redundancy Principle subjects learn better from graphics and narration, rather than from graphics, narration and on-screen text

4. The Spatial Contiguity Principle when corresponding words and pictures are presented close to each other rather than far away on the page or screen, subjects learn better

5. The Temporal Contiguity Principle subjects learn better when corresponding words and pictures are presented simultaneously and not sequentially

6. The Segmenting Principle subjects learn better when a multimedia lesson is presented in user-paced segments rather than as a continuous unit

7. The Pre-Training Principle subjects learn better from a multimedia lesson when they know the names and characteristics of the main concepts

8. The Modality Principle subjects learn better from graphics and narrations than from animation and on-screen text

9. The Multimedia Principle subjects learn better from words and pictures than from only words

10. The Personalization Principle subjects learn better when in multimedia presentations words are presented in conversational style rather than formal style

11. The Voice Principle subjects learn better when the narrative in multimedia lessons is spoken in a friendly human voice instead of a machine voice

12. The Image Principle subjects do not necessarily learn better from a multimedia lesson when the speaker’s image is added to the screen

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3.3 The DART Model

According to Prahalad and Ramaswamy [20], the customer should be actively involved in the creation processes in order to create real added value in services and/or products. Additionally, the consumer-to-business-to-consumer co-creation model (C2B2C) will help companies unlock competitive advantages, and employees can also establish this competitive advantage. The employees’ engagement is as important as the customers’ and both are considered to be co-creators of the final product or service by offering a major improvement to companies seeking advan- tages in overly saturated markets. The term co-creation signifies [37] a “joint creation of value by the learner and the instructional designers team; it allows the learner to co-construct the learning experience to suit his/her context and learning needs; joint problem definition and problem-solving; creating a learning experience and environ- ment in which learners can have an active dialogue and co-construct personalized and different learning experiences, through the continuous dialogue and co-constructing personalized learning experiences and innovating the learning environments for new co-creation experiences.” (p. 8) (see also [20–23]).

Prahalad and Ramaswamy suggest the DART model (an acronym for Dialogue, Access, Risk, Transparency) with its four co-creation building blocks. Dialogue implies interactivity, deep engagement, ability and willingness to act between learners and instructors; Access and Transparency relate to information in teaching and learning processes, and the Risk assessment is a clear assessment by the learners and instructors of the benefits of the risks when learners are the decision-makers of their own learning [37] (p. 9). The DART model is the framework, which aims to bring together learners and instructors as designers of training models, involving the learners’ bottom-up design and the use of new technologies such as AR. Recently, Dollinger, Lodge, and Coates [21] adapted the co-creation model for higher education purposes. They summarized co-creation as a combination of co-production (knowl- edge sharing, equity, and interaction) and value-in-use (experience, personalization, and relationship). In this way, co-creation might promote results in innovation, knowl- edge and relation [21] (p. 224), a framework worth adapting when designing and co-designing an instructional model for teaching and learning a new language for professional purposes with adult learners such as train drivers and train operators.

3.4 The Bloom’s Learning Objective Taxonomy

The Bloom’s six learning objectives taxonomy [24–27] is a classic construct in designing instructional activities. It implies six cognitive processes: 1. remem- bering; 2. understanding; 3. applying; 4. analyzing; 5. evaluating/synthesizing and 6. creating. The first two are considered the lower cognitive processes and the last four are considered the higher cognitive processes. In terms of learning a new language, remembering and understanding technical terms and verbs are two basic cognitive

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processes, which set the foundations for applying the linguistic elements for e.g., creating small dialogues. With the higher cognitive processes of analyzing and eval- uating/synthesizing the learners must make use of what they have learned and be able to adapt it to new communication scenarios. The last higher cognitive process, creating, reflects the ability to generate something new byapplying the previous five cognitive processes, and designing a more complex dialogue.

The aim of the SOB case study is to create learning conditions under which train drivers and train operators are able to competently and safely create their own learningcontent,suchasdialoguesonwork-basedsituationsbyusingspecificrailway terminologies and verbs through dialogues. To achieve this goal, the SOB case study also entails the use of Augmented Reality (AR) by designing a series of so-called AR vignettes.

4 Augmented Reality Technology

AR technology uses a device to bring digital holographic information to the users’ real environment, i.e. a head-mount display, such as Microsoft Hololens or a modern smartphone, such as iPhone X. The projection of virtual elements to the real environ- ment enhances user perception and opens up new possibilities to interact with these elements between virtuality and reality, and in real-time [38]. The most broadly accepted definition of AR comes from Azuma et al. [39] and specifies AR as a system that “supplements the real world with virtual (computer-generated) objects that appear to coexist in the same space as the real world”.

Today’s AR technology is resourceful to overlay text, image, audio, video, and three-dimensionalelementsintherealenvironment.Itisabletoanchortheseelements torealobjectssothatwhentheusermovesintherealspacethevirtualelementsremain in the anchored position. This provides flexibility to observe digital artifacts from multipleangles. Thevirtual informationis integratedintotheusers’ environment with few or no glitches. AR provides the ability to consolidate and visualize information for users so that they can be more productive, more effective, or make better decisions [40]. The ability to overlay digital information in the immediate environment makes AR an ideal tool to provide information in unknown or unexpected situations. The technology has a positive impact on how users work with digital information on an additional layer and how they access data [40]. This provides a door to immersive learning experiences with the use of AR technology. Moreover, modern education scenariosconsidermultimediamaterialindispensableforeffectivelearningoutcomes [41]. The advantage of physical objects, however, is that they can be touched and allow natural interaction [42], which is not possible with digital objects. As the printed book is still very important for language learning, AR offers the potential to overlay additional useful information [43].

In the field of education, where most research evidence for book augmentation is found, the combination of a printed book and AR shows great learning potential. The instructive added value of using digital data on books can be further developed to

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overcome the analogue limitation of space, and to ensure the topicality of information in printed books. There are several examples in education that use AR solutions in combination with printed books. Studies have shown that AR has the potential to digitally enhance books in the field of education, to achieve better interaction or learning effects [42, 44, 45].

However, one of the major drawbacks of AR technology is the novelty effect [46]. Researchers have addressed this tendency when most of the users try it for the first time [47]. The novelty effect leads to improved performance with the interest in using new technology. As the interest in the new technology decreases, the user engagement might not be as high as it was at the beginning because users have become familiar with the technology [48–50]. Therefore, the AR implementation strategy should take this tendency into account and balance the workload and the commitment to designing the own AR learning content, through a bottom-up approach which is explained in the next section with a concrete instructional example.

5 An Augmented Reality Vignette Example

The SOB railway professionals will be actively engaged in designing their own AR learning vignettes. The authors of the chapter call a short instructional AR sequence a vignette, using an app called Blippar. In fact, Blippar (www.blippar. com) is a Software as a Service (SaaS) company founded in 2011 that specializes in mobile AR and provides a platform for enhancing images with AR without prior programming knowledge with the aim to gradually standardize AR formats. The SOB learners will use the Blippar app together with the multimedia learning theory to help shape their own instructional content, by selecting and designing their own learning materials based on their rich and diversified work experience. In collabora- tion with the language instructor who will apply the DART model, the SOB learners select work-based Situation Awareness scenarios (SA) and design their own set of learning materials.

Train drivers and train operators need to be ready to react to numerous unexpected situations (in reference to SA) and be able to communicate in a foreign language with confidence and clarity. Sixteen common SA-related situations while driving a train were identified. These will be visually designed and represented using the AR learning vignettes. The sixteen situations include: 1. calling and starting a conver- sation; 2. avoiding misunderstandings; 3. communicating shunting movement; 4. communicating being “ready to drive”; 5. standing and waiting in front of a signal; 6. communicating a signal fault or defect; 7. communicating a brake failure or defect; 8. being ready to write the technical forms; 9. reporting irregularities; 10. commu- nicating a locomotive defect; 11. being able to communicate an alarm ZKE (train control device); 12. communicating in case of accidents; 13. what to do in case of release of dangerous substances; 14. communicating clear announcements; 15. spot- ting events on the platform; and finally 16. communicating emergency calls. Each of these situations is outlined by the perception of the Situation Awareness (SA), by

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Fig. 3 Photo taken by a train driver

the comprehension of it, by the projection in making the right decision and finally in communicating in a clearly and securly in Italian.

Based on the Bloom’s taxonomy [24–27], the instructional design of the AR vignettes is applied in a class of six SOB train drivers. All six trainees have an A1 level (beginners). The training is designed also to reach an A2 level, specially designed for mastering technical terms, verbs, and being able to communicate through short but clear dialogues, where safety expressions are a requirement.

The training involves class participation during 12 lessons of two hours each, together with an additional three hours of self-study. At the end of the 12 lessons, the trainees will perform a one-hour language exam within a simulator. Their language performance will be voice-recorded and analyzed, according to the following criteria of pronunciation, structure, vocabulary, fluency, comprehension, and interactions [9]. Additionally, reaction time and decision-making will also be examined.

When creating the AR vignettes, each trainee selects a relevant situation and the visual representation of thier own work environment which corresponds to an SA- related task, and provides self-made photos. For example, Fig. 3 represents a derailed train. This is a picture taken by a train driver. Using the DART model, the instructor adapts the instructional contents dialoguing with the trainees. The instructor is given Access to new instructional content, according to the professional background of the learners, opening up to the Risk of using new instructional content and finding meaningful ways (Transparency) to create the instructional contents and the learning sequences together with the learners.

Each learner then annotates technical terms and specific verbs in Italian on a transparent sheet over the photos, which they previously produced (Fig. 4).

Figure 4 shows how technical terms, such as il segnale di rallentamento, la loco- motiva, la linea di contatto, il pilone, la gru (the deceleration signal, the locomotive, the contact line, the pylon, the crane) are annotated in the picture by the train driver trainee.

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Fig. 4 Photo taken by a train driver, enhanced by specific terms chosen by the train driver, implemented in the AR vignette, using the Blippar app

In a second step, the instructor allows the trainees to read these words out-loud to an audio-recording the correct pronunciation. These audio files will be part of the AR vignette. Using the Blippar mobile AR, the instructor will combine words and verbs with audio files to create the multimodal AR vignettes ready to be shared in class and at work. In a further step, the trainees will use the same photos to create complete sentences and embed them in short and structured dialogues, first in a written format and in a second step as audio files. Through this co-created and co-learned approach, the number of words, verbs, and dialogues is multiplied by the number of trainees and by the number of AR-learning vignettes produced thatreach a proportional size that cannot otherwise be achieved in traditional language-learning scenarios.

Through these instructional design processes, all six Bloom’s learning objectives will be experienced using AR: trainees remember and understand new words and verbs; they apply these through reading, writing, pronouncing, and designing their own collection of AR vignettes. They analyze new work-based situations in which they evaluate the use of learned words, verbs, and sentence structure, and finally create new dialogues.

The instructor ensures that the Mayer’s multimedia learning principles [17] are applied: the Multimedia Principle: subjects learn better from words and pictures than from words, followed by the Coherence Principle: subjects will learn better when extraneous words, pictures, and sounds are excluded rather than included. In this case, learners use only their specific technical words and verbs and create specific dialogues based on selected Situation Awareness events. Additionally, the Signaling Principle will guide the instructional design: when they add cues (visual and audio) to the essential material to be learned, subjects learn better. In fact, this is related to the Redundancy Principle and the Modality Principle, because subjects learn better

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from graphics and narration. The Spatial and Temporal Contiguity Principles will also promote learning, because when corresponding words and pictures are presented near to each other and simultaneously, rather than far away from each other on the screen, the subjects learn better. The Segmenting Principle: the subjects learn better when a multimedia lesson is presented in user-paced segments, rather than as a continuous unit. The Pre-Training Principle states that subjects learn better from a multimedia lesson if they know the names and characteristics of the main concepts. The Personalization Principle will also be taken into account because words are presented in conversational style rather than in formal style. In this process, the VoicePrinciplewillsustainlearning,becauseifthenarrativeisspokenwithafriendly human voice and not with a machine voice, the subjects learn better.

6 Conclusion

In this chapter, the SOB case study was first described, together with the training needs of the train drivers and train operators. Secondly, the four-layered instructional design model, composed of the Situation Awareness (SA) construct, the Mayer’s Multimedia Learning Theory, the DART co-creation model, and finally the Bloom’s Learning Objectives Taxonomy, was explained. Thirdly, as they are currently in a test-phase, the authors described a concrete AR training sequence, which they defined as AR-learning vignette, designed by an SOB train driver. In fact, with today’s AR technology, overlaying information on photos that enhances the information to be learned, (in this case technical terms and nouns, verbs and dialogues created by the learner) is effortlessly created on individual mobile devices. Moreover, AR technology has the capability to add motivation to the learning experience, because adult learners become active instructional designers of their own learning content. A follow-up project could consider adding a Virtual Reality (VR) component to the training, which is currently in the brainstorming phase.

One relevant limitation is that no official data sets have yet been gathered, as the instructional model and the teaching is currently in a test-phase. The AR-learning vignettes are still in the process of being produced and tested. Nevertheless, one lesson learned which could be shared is that the six train drivers are actively engaged and very motivated in the co-creation of their own learning AR vignettes. In informal exchanges, all trainees strongly advocated a curriculum tailored to their learning needs and based on real work-based scenarios and the use of responsive technologies.

Afollow-uppublicationisforeseencontainingthequantitativeandqualitativedata and the results of the official language performance of an oral exam conducted in a simulator. The current instructional model is not limited to learning a new language for professional purposes. Consequently, the authors suggest testing the AR-learning vignettes embedded in the four-layer instructional model in similar teaching-learning professional scenarios.

220 T. Inglese and S. Korkut

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