Author: Juan Batley, Learner Analytics Specialist, University of Portsmouth 

What is Learner Analytics?

Students provide their universities with a lot of data. However, how many Universities can say that all of the data collected adds value to and improves the student experience?

The first question often asked is what is Learner or Learning Analytics (LA)? A fair question to start with, I would suggest that it is the use of intelligent data and analysis to discover information that will enable universities to make better, more informed decisions. Learner Analytics can be used to address attainment gaps, to improve progression and retention, and to enhance the teaching and learning, as just some examples, by examining all of the available student data through complex, multifaceted, intersectional lenses.

The benefits of Learner Analytics include providing students and staff (via online and mobile platforms) with insightful data to make better decisions, understanding students’ performances (across their time at university), offering details of learning behaviours and student engagement, highlighting students who may be at risk and in need of greater support, indicating where to undertake interventions to improve outcomes, adding value to the student experience, improving universities effectiveness in all of the work they do for and with students and staff, and improving educational research across the sector.

Improving Student Experiences and Outcomes

As headlines like “Students expect personal data to improve university experience” and “Students call on higher ed to better utilize data to maximize student experience” make clear, students want the data collected from them by universities to be used to improve their student experience. Learner Analytics should increase the interest in the big data collection within universities. The competition for students across the higher education sector, including new online competitors, will increase the need for smarter analysis of student data. Existing student information systems and digital data need to evolve with the times, allowing for improvements to and greater flexibility in the learning environment. 

There are there three key areas for an organisation to establish Learner Analytics.

1.  Student facing systems: Learning analytics should help each student understand their own development throughout their time at university, including, for example, comparing their progress in one year compared to the previous year or comparing their attendance record or their outcomes in their coursework to the averages of their peers. An online Learner Analytics system for students would establish targets or metrics as key indicators to progress.

2.  Staff facing systems: Learner Analytics should look at engagement and attainment of students to assist in the development of targeted support for students and the development of targeted improvement strategies to address inequalities, such as attainment gaps and withdrawal gaps, allowing for improved retention and equal opportunities for attainment.

3.  Decision-making analysis: Business analysis through Learner Analytics should improve the university’s effectiveness and define opportunities to change with regard to student experiences and outcomes, competition across the sector, recruitment and funding changes and challenges, and openness of reporting.

Data-Driven Decision-Making

Are university decisions about how best to improve student experiences and outcomes always based on the available data or are assumptions and untested hypotheses used to inform approaches to issues that arise?

For example, consider this excerpt from the Changing Mindsets Mid-Project Report (2018:18):

Recent news reports indicate that universities are admitting twice as many students with BTEC qualifications as they did a decade ago (Turner, 2018). However, the type of qualification on entry may not explain the persistent attainment gaps. In three out of the four schools in which we are running the Changing Mindsets intervention at the lead institution, the University of Portsmouth, White students who entered with BTECs were more likely to have attained a good degree compared to their White peers with A-levels. As the illustrative example in the table below shows, in one of the participating intervention school, the five year attainment data average showed that White students who entered with BTECs were more likely to receive a good degree (79.1%) than White students with A level qualifications (77.3%). However, BME students with BTECs were significantly less likely to receive a good degree (39%) compared with BME students with A level qualifications (51.9%) and with White students with BTECs and White students with A levels.

Learner Analytics can be used to examine existing data to answer complex questions about how best to address inequalities and to support students and improve their engagement, progression, development opportunities, and overall experience.

There is a view that educational analytics could be in two camps, firstly reporting to government, funding agencies, administrators, executive board and secondly to academics. I am of the view that Learner Analytics should bridge the gap to combine academic analytics and Learner Analytics into an institutional information reporting system using business intelligence as a platform. Thereby providing an overview of the key concepts, issues, and questions from all of the stakeholders.

The willingness of universities to publish the progression, attainment and retention of students as a metric that is benchmarked against other universities represents both an opportunity and a threat because it will lead to important questions about what universities are doing to address persistent inequalities, such as admission gaps, withdrawal gaps, and attainment gaps between students based on their demographics. Using Learner Analytics to better understand existing data provides universities with an opportunity to be proactive about inequalities within their students’ experiences, to deliver targeted, informed solutions, and, ultimately, could lead to growth and change for the better.

Further Reading

Advance HE’s Higher Education Academy defines Learner Analytics with the aim of improving teaching and learning practice: https://www.heacademy.ac.uk/knowledge-hub/learning-analytics

The Higher Education Statistics Agency (HESA) provides higher education data and analysis:  https://www.hesa.ac.uk/data-and-analysis

The impact of Universities on the UK economy report: https://www.universitiesuk.ac.uk/policy-and-analysis/reports/Documents/2014/the-impact-of-universities-on-the-uk-economy.pdf

Universities generate £95 billion for the UK economy article: https://www.timeshighereducation.com/news/universities-generate-ps95-billion-uk-economy

Learning analytics in higher education: an analysis of case studies
https://www.emeraldinsight.com/doi/full/10.1108/AAOUJ-01-2017-0009

JISC report: Learning analytics and student success – assessing the evidence
http://repository.jisc.ac.uk/6560/1/learning-analytics_and_student_success.pdf

How Can Learning Analytics Improve the Student Experience?
https://www.edsurge.com/news/2016-08-31-how-can-learning-analytics-improve-the-student-experience

Smarter Strategies for change: Using Learner Analytics to address inequalities in student experiences and outcomes
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