Sensors can monitor physical attributes and record multimodal data in order to provide feedback. The application calligraphy trainer, exploits these affordances in the context of handwriting learning. It records the expert’s handwriting performance to compute an expert model. The application then uses the expert model to provide guidance and feedback to the learners. However, new learners can be overwhelmed by the feedback as handwriting learning is a tedious task. This paper presents the pilot study done with the calligraphy trainer to evaluate the mental effort induced by various types of feedback provided by the application. Ten participants, five in the control group and five in the treatment group, who were Ph.D. students in the technology-enhanced learning domain, took part in the study. The participants used the application to learn three characters from the Devanagari script. The results show higher mental effort in the treatment group when all types of feedback are provided simultaneously. The mental efforts for individual feedback were similar to the control group. In conclusion, the feedback provided by the calligraphy trainer does not impose high mental effort and, therefore, the design considerations of the calligraphy trainer can be insightful for multimodal feedback designers.

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Body-worn sensors can be used to capture, analyze, and replay human performance for training purposes. The key challenge to any such approach is to establish validity that the captured expert experience is actually suitable for training. In this paper, to evaluate this, we apply a questionnaire-based expert assessment and a complementary trainee knowledge assessment to study the approach adopted and the models generated with the WEKIT solution, a hardware and software application that complements Augmented Reality glasses with wearable sensor-actuator experience. This solution was developed using the ID4AR framework which as also developed within the WEKIT project. ID4AR framework is a domain agnostic framework which can be used to design augmented reality and sensor based applications for training. The study presented triangulates validity across three independent test-beds in the professional domains of aircraft maintenance, medical imaging, and astronaut training, with 61 experts completing the expert survey and 337 students completing the trainee knowledge test. Results show that the captured expert models were positively received in all three domains and the identified level of acceptance suggests that the solution is capable of capturing models for training purposes at large.

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Augmented reality and sensor technologies have been analysed extensively in several domains including education and training. Although, varieties of use cases and applications exist, these studies were conducted in controlled laboratory environments. This paper reports on the first user study of augmented reality prototype developed to support students to learn from trainers in professional domains using augmented reality and sensors. The prototype records the performance of trainers in the first phase to support students by making it available during practice in the second phase. The performance data is made available to both the students and trainers in the third phase for reflection. A total of 142 participants which included trainers and students from three professional domains, namely 1) aircraft maintenance 2) medical imaging and 3) astronaut training, evaluated the prototype. The trainers used the prototype to record their performance while the students used the prototype to learn from the recorded performance. Participants from the three professional domains evaluated the usability of the prototype by means of a questionnaire. Randomly selected participants were also interviewed to collect their opinions and suggestion for further usability improvement. Furthermore, they also evaluated the implementation of the instructional design methods, which were identified prior in a literature review, with a brief questionnaire. The questionnaire was designed to measure the acceptance of the implementation of instructional design methods and to evaluate its adherence to the authors definition. The results of this study show that the usability of the prototype is below expected standard acceptable level. The results of the questionnaire on the implementation of the instructional design methods varied show above average acceptance levels by both the trainers and the students in the three professional domains. To conclude, the prototype shows potential to be used in different domains to support expertise development.@en

Experts are imperative for training apprentices, but learning from experts is difficult. Experts often struggle to explicate and/or verbalize their knowledge or simply overlook important details due to internalization of their skills, which may make it more difficult for apprentices to learn from experts. In addition, the shortage of experts to support apprentices in one-to-one settings during trainings limits the development of apprentices. In this review, we investigate how augmented reality and sensor technology can be used to capture expert performance in such a way that the captured performance can be used to train apprentices without increasing the workload on experts. To this end, we have analysed 78 studies that have implemented augmented reality and sensor technology for training purposes. We explored how sensors have been used to capture expert performance with the intention of supporting apprentice training. Furthermore, we classified the instructional methods used by the studies according to the 4C/ID framework to understand how augmented reality and sensor technology have been used to support training. The results of this review show that augmented reality and sensor technology have the potential to capture expert performance for training purposes. The results also outline a methodological approach to how sensors and augmented reality learning environments can be designed for training using recorded expert performance.

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