Beginner table-tennis players require constant real-time feedback while learning the funda-mental 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 afore-mentioned 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.@en

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback.@en

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback.@en

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback.@en

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback.@en

Studies in Learning Analytics provide concrete examples of how the analysis of direct interactions with learning management systems can be used to optimize and understand the learning process. Learning, however, does not necessarily only occur when the learner is directly interacting with such systems. With the use of sensors, it is possible to collect data from learners and their environment ubiquitously, therefore expanding the use cases of Learning Analytics. For this reason, we developed the Multimodal Learning Hub (MLH), a system designed to enhance learning in ubiquitous learning scenarios, by collecting and integrating multimodal data from customizable configurations of ubiquitous data providers. In this paper, we describe the MLH and report on the results of tests where we explored its reliability to integrate multimodal data.@en

Studies in Learning Analytics provide concrete examples of how the analysis of direct interactions with learning management systems can be used to optimize and understand the learning process. Learning, however, does not necessarily only occur when the learner is directly interacting with such systems. With the use of sensors, it is possible to collect data from learners and their environment ubiquitously, therefore expanding the use cases of Learning Analytics. For this reason, we developed the Multimodal Learning Hub (MLH), a system designed to enhance learning in ubiquitous learning scenarios, by collecting and integrating multimodal data from customizable configurations of ubiquitous data providers. In this paper, we describe the MLH and report on the results of tests where we explored its reliability to integrate multimodal data.@en

Studies in Learning Analytics provide concrete examples of how the analysis of direct interactions with learning management systems can be used to optimize and understand the learning process. Learning, however, does not necessarily only occur when the learner is directly interacting with such systems. With the use of sensors, it is possible to collect data from learners and their environment ubiquitously, therefore expanding the use cases of Learning Analytics. For this reason, we developed the Multimodal Learning Hub (MLH), a system designed to enhance learning in ubiquitous learning scenarios, by collecting and integrating multimodal data from customizable configurations of ubiquitous data providers. In this paper, we describe the MLH and report on the results of tests where we explored its reliability to integrate multimodal data.@en

This paper introduces the Visual Inspection Tool (VIT) which supports researchers in the annotation of multimodal data as well as the processing and exploitation for learning purposes. While most of the existing Multimodal Learning Analytics (MMLA) solutions are tailor-made for specific learning tasks and sensors, the VIT addresses the data annotation for different types of learning tasks that can be captured with a customisable set of sensors in a flexible way. The VIT supports MMLA researchers in 1) triangulating multimodal data with video recordings; 2) segmenting the multimodal data into time-intervals and adding annotations to the time-intervals; 3) downloading the annotated dataset and using it for multimodal data analysis. The VIT is a crucial component that was so far missing in the available tools for MMLA research. By filling this gap we also identified an integrated workflow that characterises current MMLA research. We call this workflow the Multimodal Learning Analytics Pipeline, a toolkit for orchestration, the use and application of various MMLA tools.@en

This paper introduces the Visual Inspection Tool (VIT) which supports researchers in the annotation of multimodal data as well as the processing and exploitation for learning purposes. While most of the existing Multimodal Learning Analytics (MMLA) solutions are tailor-made for specific learning tasks and sensors, the VIT addresses the data annotation for different types of learning tasks that can be captured with a customisable set of sensors in a flexible way. The VIT supports MMLA researchers in 1) triangulating multimodal data with video recordings; 2) segmenting the multimodal data into time-intervals and adding annotations to the time-intervals; 3) downloading the annotated dataset and using it for multimodal data analysis. The VIT is a crucial component that was so far missing in the available tools for MMLA research. By filling this gap we also identified an integrated workflow that characterises current MMLA research. We call this workflow the Multimodal Learning Analytics Pipeline, a toolkit for orchestration, the use and application of various MMLA tools.@en

The WEKIT.one prototype is a platform for immersive procedural training with wearable sensors and Augmented Reality. Focusing on capture and re-enactment of human expertise, this work looks at the unique affordances of suitable hard- and software technologies. The practical challenges of interpreting expertise, using suitable sensors for its capture and specifying the means to describe and display to the novice are of central significance here. We link affordances with hardware devices, discussing their alternatives, including Microsoft Hololens, Thalmic Labs MYO, Alex Posture sensor, MyndPlay EEG headband, and a heart rate sensor. Following the selection of sensors, we describe integration and communication requirements for the prototype. We close with thoughts on the wider possibilities for implementation and next steps.@en

While digital education technologies have improved to make educational resources more available, the modes of interaction they implement remain largely unnatural for the learner. Modern sensor-enabled computer systems allow extending human-computer interfaces for multimodal communication. Advances in Artificial Intelligence allow interpreting the data collected from multimodal and multi-sensor devices. These insights can be used to support deliberate practice with personalised feedback and adaptation through Multimodal Learning Experiences (MLX). This chapter elaborates on the approaches, architectures, and methodologies in five different use cases that use multimodal learning analytics applications for deliberate practice.@en

We developed the CPR Tutor, a real-time multimodal feedback system for cardiopulmonary resuscitation (CPR) training. The CPR Tutor detects mistakes using recurrent neural networks for real-time time-series classification. From a multimodal data stream consisting of kinematic and electromyographic data, the CPR Tutor system automatically detects the chest compressions, which are then classified and assessed according to five performance indicators. Based on this assessment, the CPR Tutor provides audio feedback to correct the most critical mistakes and improve the CPR performance. To test the validity of the CPR Tutor, we first collected the data corpus from 10 experts used for model training. Hence, to test the impact of the feedback functionality, we ran a user study involving 10 participants. The CPR Tutor pushes forward the current state of the art of real-time multimodal tutors by providing: 1) an architecture design, 2) a methodological approach to design multimodal feedback and 3) a field study on real-time feedback for CPR training.@en

This paper presents results from our design and evaluation studies of the Learning Analytics Cockpit (LA Cockpit) for a quiz app, which aims to provide lecturers with important information about students’ knowledge levels. We define a LA Cockpit as a tool for instructors that enables them to steer students’ learning process by providing a LA Dashboard which visualizes students’ learning indicators and an intervention feature enabling instructors to give feedback based on students’ knowledge levels. To address the needs of lecturers we applied the Double Diamond (DD) design process model which consists of four stages: discover, define, develop & refine. Following the DD process, we first conducted a qualitative study by interviewing four lecturers and student teachers to discover their needs. Results from the interviews allowed us to define requirements of the lecturers. We used these results to develop the first version of the tool where we refined it through informal feedback by the interviewed teachers. In preparation for a larger effectiveness-study, we evaluated the LA Cockpit in terms of usefulness and usability in a preliminary study with 16 university lecturers. Results from this qualitative study indicate that the LA Cockpit can measure the students’ knowledge level and supports self-reflection for lecturers. Moreover, results show that the LA Cockpit enables lecturers to address knowledge gaps and provide interventions to students before the exams.@en

This paper presents results from our design and evaluation studies of the Learning Analytics Cockpit (LA Cockpit) for a quiz app, which aims to provide lecturers with important information about students’ knowledge levels. We define a LA Cockpit as a tool for instructors that enables them to steer students’ learning process by providing a LA Dashboard which visualizes students’ learning indicators and an intervention feature enabling instructors to give feedback based on students’ knowledge levels. To address the needs of lecturers we applied the Double Diamond (DD) design process model which consists of four stages: discover, define, develop & refine. Following the DD process, we first conducted a qualitative study by interviewing four lecturers and student teachers to discover their needs. Results from the interviews allowed us to define requirements of the lecturers. We used these results to develop the first version of the tool where we refined it through informal feedback by the interviewed teachers. In preparation for a larger effectiveness-study, we evaluated the LA Cockpit in terms of usefulness and usability in a preliminary study with 16 university lecturers. Results from this qualitative study indicate that the LA Cockpit can measure the students’ knowledge level and supports self-reflection for lecturers. Moreover, results show that the LA Cockpit enables lecturers to address knowledge gaps and provide interventions to students before the exams.@en

Im Forschungsfeld Learning Analytics haben Studien konkrete Beispiele dazu geliefert, wie die direkte Interaktion mit sog. Learning-Management-Systemen zur Optimierung und zum besseren Verständnis von Lernprozessen dienen kann. Allerdings findet Lernen nicht nur in der unmittelbaren Interaktion mit einem solchen System statt. Mittels Sensoren können Daten zu Lernenden und ihrer Umgebung überall erfasst werden, womit die Nutzungsbeispiele für Learning Analytics erweitert werden. Daher haben wir einen multimodalen Lern-Hub (MLH) entwickelt, d. h.ein System zur Verbesserung des Lernens in übergreifenden Situationen. Hierzu werden multimodale Daten aus individualisierbaren Konfigurationen erfasst und integriert. Im vorliegenden Beitrag wird der MLH beschrieben. Ergebnisse einer Studie zur Zuverlässigkeit des Systems in Hinblick auf die Integration multimodaler Daten werden dargelegt.@en