This blog post makes available publicly simple use cases and scenarios we are using in various internal projects and discussions at the Open University on the application of Learning Analytics to support disabled students and to identify accessibility deficits in online teaching and learning. Scenarios based on potential use cases are useful as design tools and in communicating ideas around complex systems with multiple actors. Three scenarios are presented here that were adapted from those created for a concept paper  for the Society for Learning Analytics Research (SoLAR – www.solaresearch.org). Credit goes to my colleagues Rebecca Ferguson and Annika Wolff for writing them. Discussion of these use case scenarios is deferred to subsequent blog posts but comments and questions on them would be most welcome.
Kris is a student who has access to a dashboard of analytics that provide him with feedback when he is at his computer or using a mobile device. He has set the dashboard to send him a weekly summary of his activity on university sites and on a set of external sites where he has chosen to share his data with the analytics system. He receives basic statistics on attendance, participation and marks on his formal assignments and exams. He receives personalised recommendations suggesting resources and contacts available at his location and relevant to his range of learning interests. However, what he finds most useful for reflection are the visual ‘mirrors’ that the system presents to him, plus suggestions of ways in which he might become a more effective, strategic learner.
Jenny teaches one of Kris’ courses. Her dashboard is designed for educators, and can be configured to illuminate problems and progress on the course. Visualisations provide an overview of the course’s social network and indicate students ‘at risk’, as defined by a range of algorithms that match online behaviour to predictive models based on past cohorts. There are different categories of risk, so Jenny can easily filter to see more details behind each student’s classification. One risk category flags students who have a disability and who are nearing a point in the course that previous analysis has identified as being a potential problem area for such students. Kris has a visual impairment and Jenny sees that this is a good time to enquire whether he needs any additional support. She logs all contact (including the form it takes), generating data that can be used to evaluate the effectiveness of the predictive methods. Jenny has also chosen to view aggregates of all the personal Learning Analytics on her students’ dashboards, providing a deeper level of insight into how they are self-reporting and evidencing their progress.
Natalie is a module manager at the OU. She is exploring statistics from a previous module presentation in order to discuss changes for the following presentation. Natalie can see from the visualisations of VLE data combined with assessment data that students who performed poorly on the second assessment had engaged significantly less with quizzes than those who did well. The system flags up that a significant proportion of these students have declared a disability. Natalie compares datasets and finds a similar pattern on other modules, indicating a potential accessibility issue with the quizzes presented alongside module materials. She undertakes some follow-up activities, contacting a percentage of students from several modules with a questionnaire to try to identify the cause of the problem before passing her findings to the university’s accessibility experts for detailed evaluation and remedial action.
 George Siemens, Dragan Gasevic, Caroline Haythornthwaite, Shane Dawson, Simon Buckingham Shum, Rebecca Ferguson, Erik Duval, Katrien Verbert, Ryan S. J. d. Baker, (2011), Open Learning Analytics: an integrated & modularized platform – Proposal to design, implement and evaluate an open platform to integrate heterogeneous learning analytics techniques. Available at: http://www.solaresearch.org/ (accessed 12 October 2012)