High quality learning programmes deliver outstanding results for an institution, its teachers and pupils. They also fulfil a need, ensuring that what’s being taught is preparing students to fill vacant roles. Education equips them with useful knowledge and understanding. Finally, it’s also important to determine if access to a programme and achieving the best results is available to diverse student. But, does everyone benefit equally?

Education analytics are essential to deliver on these defining characteristics of high quality learning programmes. Here, we explore how and where education analytics can play an important role in the delivery of effective learning programmes. 

Analytics in the Classroom 

For teachers, education analytics provide a key to unlocking student success. Gathering data on the way students respond to different approaches, such as different styles of delivering information or different ways to structure classes, provides meaningful insights into how students learn best and ultimately reveals the most effective methods to support their achievement. 

With a clear picture of how their students learn, teachers are able to implement informed personalisation, adapting the context and style of classes to suit their pupils. Ongoing assessment of student achievements provides a clear picture of whether and where their interventions are working, and where they are not. Furthermore, analysis of student outcomes determines which students are thriving and which need additional support. Using education analytics, teachers can better divide their time and dedicate resources to helping those who need it.

Using descriptive analytics, which explore past data to identify trends, teachers can ascertain which of their methods have worked best over time. The same data can support teachers to predict outcomes, using prescriptive analytics. In the classroom, teachers can use this approach to provide students with predicted grades and motivate them, as well as to determine the best approach in a given set of circumstances.

Classroom analytics have historically been labour intensive, based on teachers aggregating and analysing data gathered across multiple paper sources for each of their students. However, with the rise in digital learning and in hybrid approaches, teachers are now much better equipped to use data analytics to support effectiveness and boost student outcomes. Having easy access to data that demonstrates whether there are particular subjects students are finding difficult, or if any students in particular are struggling, enables teachers to focus their energy and time more efficiently. 

For students, the intelligent use of data by teachers supports their path to better learning outcomes. For teachers, data analytics supports them to achieve the best results for their students and their school, resulting in greater job satisfaction. Access to digital technologies to analyse data frees teachers up from using their time on labour intensive administrative processes, so they can focus on teaching. 

Analytics for Ongoing Success

Descriptive analytics support the incremental success of learning programmes. Teachers can use data gathered over time to build detailed student profiles and boost their overall effectiveness. Using these profiles, which can be continually refined as new data is collected, enables teachers to build learning models that direct effective interventions towards the students who will benefit from that approach. Profiles are created using a combination of static and circumstantial data, including information about their background as well as on their performance in the classroom. 

Diagnostic analytics offer a means by which teachers can ascertain what’s gone wrong. If a teacher notices a sudden dip in student or class achievement, they know that something needs to change. By iterating and testing different approaches to resolving the challenge, teachers can get their class back on track. Using diagnostic analytics, teachers can identify trends and correlations over time to ascertain the best solutions, or to avoid facing particular challenges.

Organisational Use of Analytics

Data analysis is useful beyond the classroom. For organisations delivering high quality learning programmes, data analytics provide the evidence they need to attract students and secure funding. 

Reputation is incredibly important for many learning institutions. And reputation in education is built upon good results. Accurately predicting and reporting student outcomes therefore is key to institutional success. Demonstrating effectiveness is also essential to securing funding and to maintaining the support of board members, for example, or other key leadership figures. Not only that, but knowledge that they are part of a successful institution is also a motivating factor for teachers and students alike, whose pride in past results will continue to drive future success. 

Analytics in the Sector

The broadest use of analytics is to support the learning and growth of the sector as a whole. Data gathered across learning programmes being delivered by different institutions to different student profiles, provides educational researchers with the raw materials they need to build increasingly accurate student profiles, to model learning journeys across student types, and to identify effective solutions to different classroom challenges.

Best Practices to Implementing Analytics in Learning Programmes

As with any application of big data, it is imperative that the ethics of how and why student data is being used is thoroughly thought through. There are some best practices which can guide learning programme providers to ensure that they are using data in the most effective, and equitable, way. Key is making sure that any algorithms, such as predictive algorithms, do not contain any bias. If student demographics are changing, it’s important to recognise how using existing data sets to inform algorithms may affect the predictions made for students from different ethnic backgrounds, for example. Using algorithms can also result in implicit bias, since algorithms are programmed by humans. How questions that inform algorithms are framed can drastically alter results. To get this right, academic institutions may prefer to collaborate with a technology provider who offers advice and services to navigate this particularly tricky challenge. 

Training is also fundamentally important. For teachers to be able to make the best use of the insights that analytics can provide, they need to be equipped with the tools to understand the data. Collaborating with a technology provider to create dashboards where teachers can access relevant insights can boost the success of this approach. 

Finally, students must have ownership of their data. Of course, the richer the dataset the more accurate and effective student profiles will be. However, there are some elements of data, such as mental health information, that students may not wish organisations to use so it is essential student permission is sought to disclose these details.