Introduction to cross LAK 2016: Learning analytics across spaces

For the LAK (Learning Analytics and Knowledge) community, it is highly important to pay attention to the development and deployment of learning analytics solutions for blended learning scenarios where students work at diverse digital and physical learning spaces and interact in different modalities. This workshop has been a first attempt in gathering the sub-community of LAK researchers, learning scientists and researchers from other communities, interested in ubiquitous, mobile and/or face-to-face learning analytics. It was clear for all the attendees that a key concern that has not been deeply explored yet is associated with the mechanisms to integrate and coordinate learning analytics to provide continued support to learning across digital and physical spaces. The two main goals of the workshop were to share perspectives and identify a set of guidelines that could be offered to teachers, researchers or designers to create and connect Learning Analytics solutions according to the pedagogical needs and contextual constraints to provide support across digital and physical learning spaces

CROSSMMLA Futures: Collecting and analysing multimodal data across the physical and the virtual

Workshop proposal for CrossMMLA focused on collecting and analysing multimodal data across the physical and the virtual. Under the current global pandemic, cross physical and virtual spaces play a substantial factor and challenge for MMLA, which is focused on collaborative learning in physical spaces. The workshop proposes an asynchronous format that includes pre-recorded video demonstrations and position papers for discussion, followed by a half-day virtual meeting at LAK'2021

Context-aware multimodal learning analytics taxonomy

Analysis of learning interactions can happen for different purposes. As educational practices increasingly take place in hybrid settings, data from both spaces are needed. At the same time, to analyse and make sense of machine aggregated data afforded by Technology-Enhanced Learning (TEL) environments, contextual information is needed. We posit that human labelled (classroom observations) and automated observations (multimodal learning data) can enrich each other. Researchers have suggested learning design (LD) for contextualisation, the availability of which is often limited in authentic settings. This paper proposes a Context-aware MMLA Taxonomy, where we categorize systematic documentation and data collection within different research designs and scenarios, paying special attention to authentic classroom contexts. Finally, we discuss further research directions and challenges.Analysis of learning interactions can happen for different purposes. As educational practices increasingly take place in hybrid settings, data from both spaces are needed. At the same time, to analyse and make sense of machine aggregated data afforded by Technology-Enhanced Learning (TEL) environments, contextual information is needed. We posit that human labelled (classroom observations) and automated observations (multimodal learning data) can enrich each other. Researchers have suggested learning design (LD) for contextualisation, the availability of which is often limited in authentic settings. This paper proposes a Context-aware MMLA Taxonomy, where we categorize systematic documentation and data collection within different research designs and scenarios, paying special attention to authentic classroom contexts. Finally, we discuss further research directions and challenges

MOOC adaptation and translation to improve equity in participation

There is an urgent need to improve elementary and secondary school classroom practices across India and the scale of this challenge is argued to demand new approaches to teacher professional learning.  Massive Open Online Courses (MOOCs) represent one such approach and which, in the context of this study, is considered to provide a means by which to transcend traditional training processes and disrupt conventional pedagogic practices. This paper offers a critical review of a large-scale MOOC deployed in English, and then in Hindi, to support targeted sustainable capacity building within an education development initiative (TESS-India) across seven states in India.  The study draws on multiple sources of participant data to identify and examine features which stimulated a buzz around the MOOCs, leading to over 40,000 registrations and a completion rate of approximately 50% for each of the two MOOCs

Seeing learning analytics tools as orchestration technologies: Towards supporting learning activities across physical and digital spaces

© Copyright 2016 for this paper by its authors. This panel paper proposes to consider the process that learners or educators commonly follow while interacting with learning analytics tools as part of an orchestration loop. This may be particularly valuable to facilitate understanding of the key role that learning analytics may have to provide sustained support to learners and educators. The complexity of learning situations where learning occurs across varied physical spaces and multiple educational tools are involved requires a holistic and practical approach. The proposal is to build on principles of orchestration that can help link technical and theoretical aspects of learning analytics with the practitioner. The panel paper provides: 1) a brief description of the relevance of the notions of orchestration and orchestrable technologies for learning analytics; and 2) the illustration of the orchestration loop as a process followed by learners or educators when they use learning analytics tools

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks