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CIC academic presents at international learning analytics conferences in Europe

In April 2017, CIC academics Roberto Martinez-Maldonado, Andrew Gibson and Simon Buckingham-Shum had multiple learning analytics research papers accepted for two international conferences held in Romania and Slovakia this month (July 2017).

ICALT 2017 (International Conference on Advanced Learning Technologies) was held in the city of Timisoara, Romania from July 3-7; and UMAP 2017 (User Modelling, Adaptation and Personalisation) took place in Bratislava, Slovakia from July 9-12.


ICALT2017

 

Roberto and colleagues presented the following two papers at ICALT 2017:

 

Bringing Physicality to Learning Analytics: Towards Proximity, Motion, and Location Tracking and Sensemaking

Citation:

Martinez-Maldonado, R., Yacef, K., Santos, A., Buckingham-Shum, S., Echeverria, V., Santos, O. C., and Pechenizkiy, M. (2017) Bringing Physicality to Learning Analytics: Towards Proximity, Motion, and Location Tracking and Sensemaking. IEEE International Conference on Advanced Learning Technologies, ICALT 2017, (to appear).

Abstract:

A large number of learning tools offering some sort of adaptation or personalisation features rely mainly on the analysis of logged interactions between students and particular user interfaces. Much less attention has been given to the capture and analysis of physical/kinaesthetic learning tasks or of the physical aspects so often present in ‘traditional’ intellectual tasks, although these are both important in the full development of a life-long learner. However, the increasing progress in ubiquitous technology makes it easier and cheaper to track students’ physical actions unobtrusively, making it possible to consider such data for research, educator interventions, and even feedback to students. This paper (1) discusses existing literature focused on supporting physical learning and the physicality of learning using proximity, motion and location analytics and sensors; and, based on this, (2) illustrates the feasibility and potential of these analytics for teaching and learning through four case studies from three different contexts: i) proximity (a health simulation lab); ii) motion (a dance studio and martial arts training); and iii) location (a small-group collaboration classroom) analytics.

 

 That Dashboard Looks Nice, But What Does It All Mean? Towards Making Meaning Explicit in the Learning Analytics Design Process

Citation:

Gibson, A. and Martinez-Maldonado, R. (2017) That Dashboard Looks Nice, But What Does It All Mean? Towards Making Meaning Explicit in the Learning Analytics Design Process. IEEE International Conference on Advanced Learning Technologies, ICALT 2017, (to appear).

Abstract:

As learning analytics (LA) and big data innovations become more common, teachers and students are often required to not only make sense of the User Interface (UI) elements of an analytics system, but also to associate the underneath data structures (and possible insights gained) with pedagogical and learning intentions. Thus, the design of these systems is becoming critical due to their potential impact on teaching and learning. We suggest that the dominant way of thinking about the relationship between representation and meaning results in an overemphasis on the UI, and that re-thinking this relationship is necessary. We propose a conceptual view which can trigger discussion among the LA and Technology-Enhanced Learning communities to consider a different way of thinking by making meaning explicit in the design process. This may result in a deeper consideration of system level elements such as algorithms, data structures and information flow. We illustrate the importance of our view through two cases of LA design in the areas of Writing Analytics and Multi-modal Dashboards.


UMAP2017

At UMAP 2017 in Bratislava, two additional papers were presented along with a tutorial session on cross-space learning analytics design:

 

Modelling Embodied Mobility Teamwork Strategies in a Simulation-Based Healthcare Classroom

 

Citation:

Martinez-Maldonado, R., Pechenizkiy, M., Power, T., Buckingham-Shum, S., Hayes, C. and Axisa, C. (2017) Modelling Embodied Mobility Teamwork Strategies in a Simulation-Based Healthcare Classroom. International Conference on User Modelling, Adaptation and Personalization, UMAP 2017, (to appear).

Abstract:

In many situations, it remains critical for team members to develop strategies to effectively use the physical space and tools available to complete demanding tasks. However, despite the availability of affordable sensors and analytics for instrumenting physical space, relatively little progress has been made in modelling the embodied dimensions of co-located teamwork to explain and support space-use strategies. The contribution of this paper is an in-the-wild pilot study through which we explore a methodology to model embodied mobility teamwork strategies in the context of healthcare education. We developed the means for tracking, clustering and processing student-nurses’ mobility data around a patient manikin. We illustrate the feasibility of our approach by discussing ways to make sense of mobility data to uncover meaningful trends, and the inherent challenges of applying physical space analytics in authentic settings.

Let’s dance: how to build a user model for dance students using wearable technology.

Citation:

Santos, A., Martinez-Maldonado, R., and Yacef, K. (2017) Let’s dance: how to build a user model for dance students using wearable technology. International Conference on User Modelling, Adaptation and Personalization, UMAP 2017, (to appear).

Abstract:

Motor skill learning is an area where wearable technology and user modelling can be synergistically combined for supporting it. In this paper, we explore how a simple accelerometer sensor can be used to capture motion data associated with critical aspects of learning in the context of social dancing. For this, we developed a prototype mobile app that tracks students’ motion data whilst they practice dance exercises. This paper describes a set of features, such as rhythm, body awareness and consistency, which can be automatically gathered and included into a dance student model. These modelled dancing features can be presented back to the students as feedback, in the form of i) summaries, ii) visualisations, or iii) narratives. We illustrate the feasibility and potential of modelling these features through a study with beginner students taking dance classes during three weeks.

Tutorial: Designing Cross-Space Learning Analytics and Personalised Support

To be delivered in conjunction with UMAP 2017 (co-organised with Davinia Hernandez-Leo and Abelardo Pardo), Bratislava, Slovakia.

Student’s learning happens where the learner is, rather than being constrained to a single physical or digital environment. In fact, students commonly interact at diverse physical spaces and with a variety of educational tools. In this tutorial, participants will explore the challenges of designing data-intensive solutions to support learning and provide feedback to students in blended learning scenarios through collaborative prototyping.  Specifically, this tutorial explores a number of design and prototyping issues such as defining the short-term future vision of ubiquitous and pervasive learning support, dealing with heterogeneous data, collecting multimodal sources of student’s data beyond clickstreams and acknowledging potential ethical issues that may arise. By bringing together researchers, practitioners, designers and makers in an intense but reflective day of prototyping cross-space learning analytics and adaptation experiences, we believe this tutorial will advance the development of a vision of the kind of work that needs to be done to make real progress in this interesting area of learning support.


 

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