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Beginner table-tennis players require constant real-time feedback while learning the fundamental techniques. However, due to various constraints such as the mentor’s inability to be around all the time, expensive sensors and equipment for sports training, beginners are unable to get the immediate real-time feedback they need during training. Sensors have been widely used to train beginners and novices for various skills development, including psychomotor skills. Sensors enable the collection of multimodal data which can be utilised with machine learning to classify training mistakes, give feedback, and further improve the learning outcomes. In this paper, we introduce the Table Tennis Tutor (T3), a multi-sensor system consisting of a smartphone device with its built-in sensors for collecting motion data and a Microsoft Kinect for tracking body position. We focused on the forehand stroke mistake detection. We collected a dataset recording an experienced table tennis player performing 260 short forehand strokes (correct) and mimicking 250 long forehand strokes (mistake). We analysed and annotated the multimodal data for training a recurrent neural network that classifies correct and incorrect strokes. To investigate the accuracy level of the aforementioned sensors, three combinations were validated in this study: smartphone sensors only, the Kinect only, and both devices combined. The results of the study show that smartphone sensors alone perform sub-par than the Kinect, but similar with better precision together with the Kinect. To further strengthen T3’s potential for training, an expert interview session was held virtually with a table tennis coach to investigate the coach’s perception of having a real-time feedback system to assist beginners during training sessions. The outcome of the interview shows positive expectations and provided more inputs that can be beneficial for the future implementations of the T3.

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.

Abstract. In this chapter, we present a conceptual reference framework for designing augmented reality applications for supporting training. The framework leverages the capabilities of modern augmented reality and wearable technology for capturing the expert's performance in order to train expertise. It has been designed in the context of WEKIT project which intends to deliver a novel technological platform for industrial training. The framework identifies the state-of-art augmented reality training methods which we term as "transfer mechanism" from an extensive literature review. Transfer mechanisms exploit the educational affordances of augmented reality and wearable technology to capture the expert performance and train the novice. The framework itself is based upon Merrienboer's 4CID model which is suitable for training complex skills. The 4CID model encapsulates major elements of apprenticeship models which is a primary method of training in industries. The framework itself complements the 4CID model with expert performance data captured with help of wearable technology which is then, exploited in the model to provide a novel training approach for efficiently and effectively master the skills required. In this chapter, we will give a brief overview of our current progress in developing this framework. Bibeg limbu:Bibeg limbu is currently a PhD student at Open University of Netherlands. He received his Master in Educational Technology from University of Saarland, Germany. He's a games, multimedia and instructional designer by education and is interested in researching expertise, Augmented Reality and Wearable Technology.Mikhail Fominykh: Mikhail Fominykh is an enthusiast, researcher, developer, and project manager in the field of Technology-Enhanced Learning. He currently is an associate professor at Molde University College (Norway), an adjunct professor at the Volga State University of Technology (Russia), and a project manager at Europlan-UK ltd (United Kingdom). In 2015, he started and coordinated a successful Horizon 2020 grant proposal which is now the WEKIT research project, developing training with augmented reality. Mikhail obtained his PhD at the Department of Computer and Information Science at the Norwegian University of Science and Technology, and later did a postdoc at the Program for Learning with ICT at the same university. He has experience in the area of technology-enhanced learning from working on several national and international R&D projects, developing successful grant proposals, developing educational simulators, courses, training programs, and their technological support. He published in several journals and books, and presented research findings at over 50 academic conferences. Roland Klemke: Prof. Dr. Roland Klemke is researcher at the Welten Institute of the Open University of the Netherlands. He leads national and international research projects in the TEL field. Research topics include serious gaming, mobile learning, augmented reality, open architectures, emerging standards, we...