In the last decade or so the Higher Education (HE) sector has become a very competitive environment. Year-on-year, applications come in from across the UK, with institutions fighting to fill their courses with students who they believe have the best chance of succeeding for themselves and for the university. In this increasingly competitive market, attracting and retaining students is a top priority for all HE institutions. The general shift toward a more student (or customer) focused business model by many institutions shows evidence of this and suggests that universities are strategizing how they can best acquire and retain students.
Institutions are beginning to recognise the importance of data science practices to enhance their understanding of their students and staff alike. In many cases, the application of data science techniques and platforms to analyse large data sets has proved extremely beneficial. Better quality, more detailed, and wider-spread data allows for better decision making and accurate strategic management of business operations. These capabilities can provide a university with a competitive edge over rivals in acquisition and retainment of their students.
A New Challenge
Elastacloud's work in the Higher Education (HE) sector aims to bring intelligence to institutions' data, helping to inform decisions and operations. Uniquely, we combine expert data science and machine learning capabilities with a high level of industry knowledge, helping us to provide the best solutions to our customers. Within the last year, a large UK university with a recognised diverse student base consulted Elastacloud, wanting to understand how students from different demographic groupings at their university engaged with the digital practice enhancement applications they had created. They also wanted to measure the students' digital capabilities and find out the extent to which interventions enhanced their skills.
The university wanted to understand these key points so they could make informed decisions about how to help improve their students' digital capabilities; for this they needed Learning Analytics. In the context of HE, Learning Analytics (LA) is the measurement, collection, analysis and reporting of data about students for the purposes of understanding and optimising learning and the environments in which it occurs. Universities implement LA for a number of reasons, chiefly to retain students. The project sought to understand what effects using LA to make educational interventions has on distinct demographic groupings.
The Project
We began to explore and aggregate the university's demographic, biographic, and Virtual Learning Environment (VLE) usage data. Our Data Scientists were there to guide and inform the data exploration process. Through applying LA, machine learning technology, and data science techniques, we were able to identify cohorts of students and observe which demographic groups had low engagement, low attainment, or had high drop-out rates. The importance of this part of the project was that we were able to use engagement with VLE as marker for students at risk of dropping out of university.
By using LA programmes, Elastacloud provided the university's project team with key information and insight into VLE usage and student digital behaviour trends. In addition, a baseline of engagement with the VLE was determined so that when interventions were made, they could be analysed for their effectiveness. The objective was to increase student and staff involvement with VLE and deliver student behaviour measurement capabilities for the university.
The Outcomes
The project found that there is a relationship between a student’s engagement with the VLE and their likelihood of dropping out. As a result, the university now has actionable insights into their previously untapped data, allowing them to work towards reducing drop-out rates amongst all demographics of students.
Additionally, on our journey to answering the key questions posed by this project's aims, we were able to create new opportunities with machine learning analytic tools. The university aims to deploy and monitor these tools for student and employer use, ensuring engagement goals are met and feedback capabilities are encouraged. These include self-auditing staff programmes and student skills reflection areas which enable them to find help and access personalised feedback to improve their digital capabilities. The adoption of these machine learning tools has given the university the means and understanding to identify students requiring extra support and make interventions accordingly thus deterring them from dropping out.
The university was impressed by what we were able to achieve together and how quickly we were able to achieve it. As such they continue to consult Elastacloud on large scale data projects as we help to answer some of the sector's most pressing questions.