Dandelion Diagram: Aggregating Positioning and Orientation Data in the Visualization of Classroom Proxemics

In the past two years, an emerging body of HCI work has been focused on classroom proxemics - how teachers divide time and attention over students in the different regions of the classroom. Tracking and visualizing this implicit yet relevant dimension of teaching can benefit both research and teacher professionalization. Prior work has proved the value of depicting teachers' whereabouts. Yet a major opportunity remains in the design of new, synthesized visualizations that help researchers and practitioners to gain more insights in the vast tracking data. We present Dandelion Diagram, a synthesized heatmap technique that combines both teachers' positioning and orientation (heading) data, and affords richer representations in addition to whereabouts - For example, teachers' attention pattern (which directions they were attending to), and their mobility pattern (i.e., trajectories in the classroom). Utilizing various classroom data from a field study, this paper illustrates the design and utility of Dandelion Diagram.Comment: To be published in CHI'20 Extended Abstracts (April 25-30, 2020), 8 pages, 4 figure

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

Designing OLMs for reflection about group brainstorming at interactive tabletops

Brainstorming is a valuable and widely-used group technique to enhance creativity. Interactive tabletops have the potential to support brainstorming and, by exploiting learners' trace data, they can provide Open Learner Models (OLMs) to support reflection on a brainstorming session. We describe our design of such OLMs to enable an individual to answer core questions: C1) how much did I contribute? C2) at what times was the group or an individual stuck? and C3) where did group members seem to 'spark' off each other? We conducted 24 brainstorming sessions and analysed them to create core brainstorming models underlying the OLMs. We evaluated the OLMs in a think-aloud study designed to see whether learners could interpret the OLMs to answer the core questions. Results indicate the OLMs were effective and that it is valuable, that learners benefit from guidance in their reflection and from drawing on an example of an excellent group's OLM. Our contributions are: i) the first OLMs supporting reflection on brainstorming; ii) models of brainstorming that underlie the OLMs; and iii) a user study demonstrating that learners can use the OLMs to answer the core reflection questions

A student-facing dashboard for supporting sensemaking about the brainstorm process at a multi-surface space

© 2017 Association for Computing Machinery. All rights reserved. We developed a student-facing dashboard tuned to support posthoc sensemaking in terms of participation and group effects in the context of collocated brainstorming. Grounding on foundations of small-group collaboration, open learner modelling and brainstorming at large interactive displays, we designed a set of models from behavioural data that can be visually presented to students. We validated the effectiveness of our dashboard in provoking group reflection by addressing two questions: (1) What do group members gain from studying measures of egalitarian contribution? and (2) What do group members gain from modelling how they sparked ideas off each other? We report on outcomes from a study with higher education students performing brainstorming. We present evidence from i) descriptive quantitative usage patterns; and ii) qualitative experiential descriptions reported by the students. We conclude the paper with a discussion that can be useful for the community in the design of collective reflection systems

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

Mapping learner-data journeys: Evolution of a visual co-design tool

© 2018 Copyright held by the owner/author(s). In this paper we present a three-phase process for crafting Learner-Data Journey maps and using them as communication tools to involve other stakeholders in the co-design of a data-intensive educational tool. The three phases in this process are i) scaffolding groups of learners to collaboratively co-create a Learner-Data Journey based on their own experience, ii) distilling key insights from these journey maps, and iii) providing the means for multiple stakeholders to integrate and synthesise key insights from these journey maps to suggest design requirements. We illustrate the process and the kind of tools that can support the co-creation of Learner-Data Journeys in two educational scenarios where learners have become partners of their own 'surveillance'

DBCollab: Automated feedback for face-to-face group database design

© 2017 Asia-Pacific Society for Computers in Education. All rights reserved. Developing effective teamwork and collaboration skills is regarded as a key graduate attribute for employability. As a result, higher education institutions are striving to help students foster these skills through authentic learning scenarios. Although face-to-face (f2f) group tasks are common in most classrooms, it is challenging to collect evidence about the group processes. As a result, to date, it is difficult to assess group tasks in ways other than through teachers' direct observations and students' self-reports, or by measuring the quality of their final product. However, there are other critical aspects of group-work that students need to receive feedback on, for example, interaction dynamics or the collaboration processes. This paper explores the potential of using interactive surfaces and sensors to track key indicators of group-work, to provide automated feedback about epistemic and social aspects. We conducted a pilot study in an authentic classroom, in the context of database design. The contributions of this paper are: 1) the operationalisation of the DBCollab tool as a means for supporting group database design and collecting multimodal traces of the activity using interactive surfaces and sensors; and 2) empirical evidence that points at the potential of presenting these traces to group members in order to provoke immediate and post-hoc productive reflection about their activity

Large scale predictive process mining and analytics of university degree course data

© 2017 ACM. For students, in particular freshmen, the degree pathway from semester to semester is not that transparent, although students have a reasonable idea what courses are expected to be taken each semester. An often-pondered question by students is: "what can I expect in the next semester?" More precisely, given the commitment and engagement I presented in this particular course and the respective performance I achieved, can I expect a similar outcome in the next semester in the particular course I selected? Are the demands and expectations in this course much higher so that I need to adjust my commitment and engagement and overall workload if I expect a similar outcome? Is it better to drop a course to manage expectations rather than to (predictably) fail, and perhaps have to leave the degree altogether? Degree and course advisors and student support units find it challenging to provide evidence based advise to students. This paper presents research into educational process mining and student data analytics in a whole university scale approach with the aim of providing insight into the degree pathway questions raised above. The beta-version of our course level degree pathway tool has been used to shed light for university staff and students alike into our university's 1,300 degrees and associated 6 million course enrolments over the past 20 years