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Teledermatology generally involves doctors taking images of patients; however, patients increasingly want to own or have easy access to their health data. MySkin-Selfie ( http://myskinselfie.com) is a mobile phone application (app) designed to improve the quality, consistency and accessibility of patient-held photos, and was developed to give patients the ability to generate and hold their own skin images to help guide their skin care. This study assessed the usability of this app in a cohort of patients attending a National Health Service Dermatology clinic. Patients were asked to use the app but were not given specific tasks to achieve. Of the 102 patients recruited, 32 downloaded the app and registered an account, 21 took at least one photo (median 5, range 1-103) and 19 completed the usability questionnaire. The majority of questionnaire respondents found the app easy to use but were more neutral on whether it really helped them to manage their skin problem. MySkinSelfie has been shown to be easy to use. Self-monitoring of skin problems may be useful for a subset of patients, and this is likely to depend on diagnosis, age and other patient factors.

One of the ultimate goals of several learning analytics (LA) initiatives is to close the loop and support students' and teachers' reflective practices. Although there has been a proliferation of end-user interfaces (often in the form of dashboards), various limitations have already been identified in the literature such as key stakeholders not being involved in their design, little or no account for sense-making needs, and unclear effects on teaching and learning. There has been a recent call for human-centred design practices to create LA interfaces in close collaboration with educational stakeholders to consider the learning design, and their authentic needs and pedagogical intentions. This paper addresses the call by proposing a question-driven LA design approach to ensure that end-user LA interfaces explicitly address teachers' questions. We illustrate the approach in the context of synchronous online activities, orchestrated by pairs of teachers using audio-visual and text-based tools (namely Zoom and Google Docs). This study led to the design and deployment of an open-source monitoring tool to be used in real-time by teachers when students work collaboratively in breakout rooms, and across learning spaces.

Remote meetings have become the norm for most students learning synchronously at a distance during the ongoing coronavirus pandemic. This has motivated the use of artificial intelligence in education (AIED) solutions to support the teaching and learning practice in these settings. However, the use of such solutions requires new research particularly with regards to the human factors that ultimately shape the future design and implementations. In this paper, we build on the emerging literature on human-centred AIED and explore students' experiences after interacting with a tool that monitors their collaboration in remote meetings (i.e., using Zoom) during 10 weeks. Using the social translucence framework, we probed into the feedback provided by twenty students regarding the design and implementation requirements of the system after their exposure to the tool in their course. The results revealed valuable insights in terms of visibility (what should be made visible to students via the system), awareness (how can this information increase students' understanding of collaboration performance), and accountability (to what extent students take responsibility of changing their behaviours based on the system's feedback); as well as the ethical and privacy aspects related to the use of collaboration analytics tools in remote meetings. This study provides key suggestions for the future design and implementations of AIED systems for remote meetings in educational settings.

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