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Toward the integration of monitoring in the orchestration of across-spaces learning situations ....................................................................................................................... 15 Towards a distributed framework to analyze multimodal data ....................................... 52
Web-based Interactive and Visual Data Analysis for Ubiquitous Learning Analytics......... 65 Seeing Learning Analytics Tools as Orchestration Technologies: Towards Supporting Learning Activities across Physical and Digital Spaces ..................................................... 70 Towards integrated learning design with across-spaces learning analytics: a flipped classroom example......................................................................................................... 74
Visualizing workplace learning data with the SSS Dashboard .......................................... 79 Learning Analytics and Open Learning Modelling for Professional Competence Development of Firefighters and Future Healthcare Leaders ........................................... 87 Workplace Learning Analytics for Facilitation in European Public Employment Services... 91 Data Analysis of Workplace Learning with BOOST .......................................................... 98 1Download individual papers from the workshop website: https://sites.google.com/site/crosslak2016/program 2Download individual papers from http://learning-layers.eu/laforwork
II Students’ learning commonly occurs in spaces and at moments that go beyond formal education, and this learning is not constrained to a single physical or digital environment. Educational providers deploy a variety of educational resources in both online and face-to-face settings. These technologies allow learners to get remote access to educational resources from different physical spaces (ubiquitous learning support) or to enrich their learning experiences in the classroom in ways that were not previously possible (face-to-face learning support). It is of high relevance to the LAK community to explore blended learning scenarios where students can interact at diverse digital and physical learning spaces. The challenge is to find the best approaches that can be applied to automatically capture traces of students’ activity, and understand how learning analytics techniques can be used in heterogeneous contexts.
The aim of this workshop was to gather the sub-community of LAK researchers, learning scientists and researchers from other communities, interested in ubiquitous, mobile and/or face-to-face learning analytics. An overarching concern was how to integrate and coordinate learning analytics to provide continued support to learning across digital and physical spaces. The goals of the workshop were to share approaches and identify a set of guidelines to design and connect Learning Analytics solutions according to the pedagogical needs and contextual constraints of practitioners.
The papers presented in this workshop proceedings book present a wide variety of cases of learning analytics solutions aligned in different ways to the following four themes:
A total of 14 papers were submitted and accepted. A blind review process was followed to assure the quality of the papers and their relevance to the workshop. An international program committee was conformed to perform the revision process. Program committee:
Bertrand Schneider (Stanford University, USA)
Cynthia D'Angelo (SRI International, USA) Juan Alberto Muñoz (Universidad de Valladolid, Spain)
Joris Klerkx (KU Leuven, Belgium)
Kate Thompson (Griffith University, Australia) Mandy Lupton (Queensland University of Technology, Australia)
María Jesús Rodríguez Triana (EPFL, Switzerland) Mar Pérez-Sanagustín (Pontificia Universidad Católica de Chile)
Patricia Santos (University of the West of England, UK)
Simon Buckingham Shum (University of Technology Sydney, Australia) Víctor H. Menéndez Domínguez (Universidad Autónoma de Yucatán, México)
Erik Duval (KU Leuven, Belgium) Maarten de Laat (Open Universiteit Nederland, The Netherlands
Special section about learning analytics in the workplace By Tobias Ley An area of research that especially focuses on learning analytics across contexts and spaces is learning analytics for the workplace. Whereas Learning Analytics in educational settings very often follow a particular pedagogical design, workplace learning is much more driven by demands of work tasks or intrinsic interests of the learner, by self-directed exploration and social exchange that is tightly connected to processes and the places of work. Hence, learning interactions at the workplace are to a large extent informal, not embedded into a pedagogical scenario and cover a multitude of different physical and virtual contexts.
The papers in this special section therefore focus especially on workplace and professional contexts. Experiences with learning analytics come from Health Care (Ruiz-Calleja et al. and Hansen et al.), from emergency services (Hansen et al.), public employment services (Attwell et al.), construction (Ruiz-Calleja et al.) and micro enterprises (Kravcik et al.). One common challenge that all papers outlined for learning analytics in workplace settings was the flexibility of work arrangements, places and processes. This needs a flexible technological architecture and a good understanding of the interaction of formal and informal learning processes. Two papers (Kravcik et al. and Hansen et al.) followed what is usually considered a top-down strategy, for instance, by deriving competences or learning indicators from business or training goals. The other two (RuizCalleja et al. and Attwell et al.) followed a bottom-up strategy mainly building on social learning and informal knowledge sharing. All papers also brought up the special challenge to integrate learning systems and analytics in work practices.
The four papers listed in this section were part of the Learning Analytics for Workplace and Professional Learning Workshop, which was organised by: Tobias Ley, Ralf Klamma, Stefanie Lindstaedt and Fridolin Wild, also for Lak’ 16. http://learning-layers.eu/laforwork/