The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

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

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data

Music Learning with Massive Open Online Courses

Steels, Luc et al.-- Editors: Luc SteelsMassive Open Online Courses, known as MOOCs, have arisen as the logical consequence of marrying long-distance education with the web and social media. MOOCs were confidently predicted by advanced thinkers decades ago. They are undoubtedly here to stay, and provide a valuable resource for learners and teachers alike. This book focuses on music as a domain of knowledge, and has three objectives: to introduce the phenomenon of MOOCs; to present ongoing research into making MOOCs more effective and better adapted to the needs of teachers and learners; and finally to present the first steps towards 'social MOOCs’, which support the creation of learning communities in which interactions between learners go beyond correcting each other's assignments. Social MOOCs try to mimic settings for humanistic learning, such as workshops, small choirs, or groups participating in a Hackathon, in which students aided by somebody acting as a tutor learn by solving problems and helping each other. The papers in this book all discuss steps towards social MOOCs; their foundational pedagogy, platforms to create learning communities, methods for assessment and social feedback and concrete experiments. These papers are organized into five sections: background; the role of feedback; platforms for learning communities; experiences with social MOOCs; and looking backwards and looking forward. Technology is not a panacea for the enormous challenges facing today's educators and learners, but this book will be of interest to all those striving to find more effective and humane learning opportunities for a larger group of students.Funded by the European Commission's OpenAIRE2020 project.Peer reviewe

Sharing and reusing data and analytic methods with LearnSphere

This tutorial will explore LearnSphere, an NSF-funded, community-based repository that facilitates sharing of educational data and analytic methods. The tutorial organizers will discuss the unique research benefits that LearnSphere affords. We will focus on Tigris, a workflow tool within LearnSphere that helps researchers share analytic methods and computational models. Attendees will integrate their analytic methods or models into LearnSphere\u27s Tigris in advance of the tutorial, and these methods will be made accessible to all tutorial attendees. We will learn about these different analytic methods during the tutorial and spend hands-on time applying them to a variety of educational datasets available in LearnSphere\u27s DataShop. Finally, we will discuss the bottlenecks that remain, and brainstorm potential solutions, in openly sharing analytic methods through a central infrastructure like LearnSphere. Our goal is to create the building blocks to allow groups of researchers to integrate their data with other researchers to advance the learning sciences as harnessing and sharing big data has done for other fields