It’s important that any data collected and processed for developing Learning Analytics (LA) is used to drive action. Data must reflect the students’ current learning path and be actionable. Aggregating data and applying analysis retrospectively will not enable pre-emptive interventions. It’s not just about which data you use, but how you use it.
Using live behavioural data from a range of the institution’s operational systems is key to deriving insight into how a student is engaging with their studies. In order to support students when necessary it is key to understand when their behaviour changes at a point in time when something can be done.
Solutionpath’s StREAM algorithm aggregates institutional data from a range of sources; provides a daily engagement score and alerts to behavioural changes. This enables university staff to pre-empt any issues affecting student welfare or learning journey and act accordingly.
We are now working with 15 Higher Education Institutions (and counting) and for each implementation we created ‘out of the box’ proven connectivity for most systems. Each institution we work with has variations in the data sources that are used to drive insight and action. There are many sources that can be fed into an algorithm to support LA activity, its not a one size fits all approach. If you’d like to know more about the different data sources and inputs we work with, contact us and we’ll be happy to discuss.
To deliver the objective of supporting the student in their attainment objectives, its key that any Learning Analytics technology supports the Learning Journey of the student and motivates them to progress.
Below is a quick summary of the functionality that would be considered key to any successful Learning Analytics technology.
When data is transformed in the right way, Learning Analytics is an invaluable tool for identifying students who are at risk of leaving their course. The use of near real time behavioural data ultimately means that flags can be raised at the very point a student’s behaviour changes. This enables university staff to act immediately based on data, rather than traditional methods of observable behaviour. It also enables universities to focus effort where and when necessary; generating efficiencies in pastoral activities.
Solutionpath StREAM technology has a predictive component – it is unique in being able to categorise students by their propensity for persistence based on their engagement with university .
Our experience highlights that a strong LA solution can identify students at risk 8 weeks earlier, on average, than traditional methods, and supported by a strong support framework to manage the interventions, this can lead to decreases in student attrition.
Many institutions who embark on the Learning Analytics journey encounter obstacles with implementing the technology due to problems associated with existing legacy systems and their ability to extract and transform the data in order to make it useful.
No University is codified for LA, these systems reside downstream from a range of business systems and processes that are not maintained for the purposes of supporting an LA solution, this aspect requires you to work with a partner with extensive experience of delivering against a commercial level Service Level Agreement (SLA).
Ultimately to gain the most actionable insight for LA implementation, there is a requirement to aggregate data across a number of organisational systems, this can take time and resource. Following this, data needs to be analysed and algorithms generated to enable action from the data. The time involved to achieve this, with no prior experience could be extensive.
At Solutionpath we have now implemented this process with a large number of institutions and developed solutions with wide range of pre-integrated out of the box connectors enabling us to easily digest data and transform it. This reduces the technical burden on IT departments removing any requirement for internal development resource to be deployed to any LA project involving Solutionpath StREAM technology.
Many institutions are held up in their implementation of Learning Analytics due to concerns over data quality and security. Often projects encounter stalls in the process from concerns that data quality will impede the results and a view is taken that value won’t be driven from existing data assets.
Due to the unique way in which Solutionpath’s StREAM technology works, issues in data quality are overcome through data source calibration with an algorithm that adapts to the data input quality to provide actionable insight.
If the process of implementing Learning Analytics is approached in the right way, the data and technology can be used immediately to support institutions in their pastoral support processes.
The StREAM algorithm is designed to aggregate data and give an immediate reading of a student’s engagement. This is then calibrated against their cohort to determine how well they are engaging against their peer group. The technology then uses the data to trigger alerts where students are disengaging. Ultimately this means that immediate interventions can be actioned to engage with student’s and understand their current situation.
Implementing Learning Analytics can appear a daunting task for some institutions, we have created a six part guide that covers some of the key areas to consider when thinking about implementing technology to support Learning Analytics in your institution. Part 1 – What data gives results It’s important that any data collected and processed for […]
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