LATUX: an Iterative Workflow for Designing, Validating and Deploying Learning Analytics Visualisations

Abstract

Designing, validating, and deploying learning analytics tools for instructors or students is a challenge that requires techniques and methods from different disciplines, such as software engineering, human-computer interaction, computer graphics, educational design, and psychology. Whilst each has established its own design methodologies, we now need frameworks that meet the specific demands of the cross-disciplinary space defined by learning analytics are needed. In particular, LAK needs a systematic workflow for creating tools that truly underpin the learning experience. In this paper, we present a set of guiding principles and recommendations derived from the LATUX workflow. This is a five-stage workflow to design, validate, and deploy awareness interfaces in technology-enabled learning environment. LATUX is based on well-established design processes for creating, testing, and re-designing user interfaces. We extend existing approaches by integrating the pedagogical requirements needed to guide the design of learning analytics visualizations that can inform pedagogical decisions or intervention strategies. We illustrate LATUX in a case study of a classroom with collaborative activities. Finally, the paper proposes a research agenda to support designers and implementers of learning analytics interfaces. 1 Keywords: Design, HCI, groupware, visualizations, design, dashboard, awareness, software engineering 1 An earlier, shorter version of this paper (Martinez-Maldonado et al., 2015) is the foundation for this article, which has been extended in light of feedback and insights from LAK ʼ15.

How to provide automated feedback on the writing process? A participatory approach to design writing analytics tools

“…In response, there has been a growing interest in collaborating with educational stakeholders early on in the design of writing analytics tools, and learning analytics tools in general (e.g. Buckingham Shum, Ferguson, & Martinez-Maldonado, 2019;Dollinger, Liu, Arthars, & Lodge, 2019;Martinez-Maldonado et al, 2016;Wise & Jung, 2019) By including information from writing specialists to identify why and how particular affordances are needed, rather than simply including all features that are technically feasible, the design could be improved (Cotos, 2015). This way, the design can also be better tuned to the educational context (Conde & Hern andez-Garc ıa, 2015).…”

Creating Effective Learning Analytics Dashboards: Lessons Learnt

“…Each dashboard builds upon findings of the previous, taking into account the stakeholders and the specific learning context in which it will be deployed. They are built as low-fidelity prototypes at first, with four high-fidelity dashboard prototypes deployed in authentic settings during pilot studies [25]. The dashboards were developed using web technologies such as D3.js, Processing.js, and Node.js.…”

Teacher-Centered Dashboards Design Process

“…Although the framework is an excellent thinking tool, to evaluate the impact of a dashboard (e.g., see (Molenaar and van Campen, 2017), it mainly captures the evaluaEon part, and it does not provide a full model of how to design dashboards guiding the whole process from domain characterizaEon to evaluaEon. Another welcome excepEon is the four stages workflow (problem idenEficaEon, lowfidelity prototyping, high-fidelity prototyping, pilot studies) by MarEnez-Maldonaldo et al (2015) to guide the design and deployment of awareness tools for instructors and students. However, the workflow does not capture the principles of visualizaEons nor the challenges to tackle while designing dashboards.…”