StREAM creates a model for the student’s engagement by counting their use of the various proxies and resources that represent participation in their course, and then assigning an engagement score to each student that can be used at an institutional level to identify risk and mount new lines of enquiry, and also by the individual student for self-reflection and calibration purposes, much like a fitness app for their education.
Empirical evidence at our customers shows that, as might be expected, student engagement is the strongest predictor of progression and attainment, studies have shown that whilst factors such as demographics and entry tariffs are useful, by far the greatest predictor of success is in what the student does, not who they are, and so we measure the “do not the who”.
NO, whilst we recognise that demographics in Higher Education are important. They can be, and are used in a variety of ways by universities from a planning and reporting perspective. When taken in the context of learning analytics however they can become a toxic component that creates bias in application, and risks overlooking students in crisis who may well not fit the profile. One of the major factors behind the design decision however was to ensure that universities could share the data with the primary protagonist – the student themselves – so we wanted to let them measure themselves against a metric they could influence, rather than using factors that they cannot change such their demographic markers.
We democratise the data because we believe the data belongs to the student, by excluding toxic factors such as demographic profiles we enable the institution to empower the student themselves, providing a tool for self-reflection/calibration and significantly increasing the responder community in an environment where, university resource is often stretched.
StREAM does not create any new data, the system creates the model for engagement passively (which ensures accuracy, consistency and helps to drive ubiquity) using the high frequency digital interactions that the student makes on a regular basis. By democratising the data to the students, including them in the steering group and ensuring that the information is never used punitively, you can ensure that these very legitimate concerns can be addressed.
This again is an important factor to consider when providing the students with their engagement data, and much thought and discussion (with the NTU student’s union and ethics committee) went into the design principles of the system for this reason. As well as the accumulative view we also include a daily score, this then allows the student (in much the same way as a fitness app works) to witness the value of the various actions against their daily score, thus encouraging them to do more of the right things that can impact their outcome. In a recent survey the NTU asked their student’s the question, “if we knew you were likely to drop out of university would you want us to tell you” to which 94% of the students replied that they did and so we believe that used appropriately and delivered in a way that is easily consumable will make StREAM an important support tool for every student.
Having now delivered multiple times into the UK HE sector we are able to connect to a wide range of systems out of the box, these include but are not limited to, Tribal SITS, Elucian’s Banner, Unit4, Blackboard, Canvas, D2L, Moodle, Turnitin, Talis, Panopto, Ex Libris/Campus M and many more. The StREAM application also exposes an API that customers can build against, allowing for a rich seem of data that can be either extracted into other campus systems such as case management and CRMS systems, or for other 3rd parties to create integrations for new systems. We are certified with the IMS and are also working towards the Caliper standard.
We don’t have a ‘minimum’ requirement, however, diversity in data is a key requirement. ‘most’ Universities have similar systems, such as online learning environments (VLE/LMS) or ePortfolio products, these offer rich data points. Also, library systems that may offer book loan history, reading lists or access to online journals. The student information system is a prerequisite as this provides the key data that all data is built from.
We have an array of different evidence that demonstrates the impact of the StREAM application.
The application design has been considered to accommodate a range of digital literacy capability, simple, intuitive by design. The premise of the solution was to collate information to allow staff to have a richer view of a student’s learning journey – this, therefore, is to SAVE time, not add to it.
LA is not a technology project, it’s a business transformation project. This understanding all the upstream business processes and the impacts that they have when materialised into application like StREAM requires a whole University approach.
Nottingham Trent University were our first customer and have supported the ongoing development through what has become and iterative process. NTU’s maturity in their use of learner analytics and the extent to which they have been able to embed the tools into standard business practice, coupled with the research they have been involved in through projects such as the ABLE (http://www.ableproject.eu/) provide a rich seam of insight for anyone wanting to explore the use of Learner Analytics technologies in their institution.
We have a proven programmatic delivery process gated to support governance steps and controls which accommodates risk management steps. If the University can provide the appropriate resources (whether technical or business) at the appropriate times we expect to deliver in less than 60 days, however, we would recommend an appropriate level of flexibility and any deployment should be followed by thorough user acceptance steps, warranty and some sort of pilot before full University wide production roll out.
Of course, whenever you use personally identifiable data you should consider GDPR. All of the data that is used in StREAM would be data that is already available to the university and used as part of your student educational contract. Many universities have used the ‘legitimate interests’ clause in the GDPR to avoid the need to seek consent. However, we would urge Universities to seek out their own guidance, especially if using other tools that use protected data fields, especially in the process of automated decision.
If you use any personal data in any automated decision making you must declare this when making an intervention