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Championing success at The Open University (OU): How the Faculty of Business and Law (FBL) Module Champions are harnessing Artificial Intelligence (AI) to support tutors and students

Student retention is one of the most complex challenges in higher education (HE) and particularly within online distance learning. The sector is seeing a decrease in student demand and an increase in competitors in the online market.

We know that retaining our students and supporting them to complete and pass their studies is vital to not only our economic health but importantly to delivering our mission at the OU. Our tutors are key to student success and the more informed a tutor is, the better placed they are to prevent drop out through proactive outreach and support. Often, by the time we notice a student struggling, it’s too late to offer meaningful support. Harnessing AI through Predictive Learning Analytics (PLA) enables HE Institutions to address issues such as retention (Clarke and Nelson, 2013). The OU has been one of the forerunners of adoption of PLA technology in online education (Shacklock, 2016).

In the Faculty of Business & Law (FBL) we have supported our tutors to be ‘early adopters’ and they have benefited from using our PLA developed in house by the OU, to have sight of student behaviour at their fingertips!

How can Learning Analytics (LA) and Predictive Learning Analytics (PLA) help tutor student engagement?

Our PLA is the Early Alerts Indicator Dashboard (EAID) and provides weekly predictions on student assignment submission using a traffic light system to pinpoint students at various levels of risk of failure (Kuzilek et al., 2015). It also offers longer term predictions on specific student milestones such as completing and passing a module (Calvert, 2014). In addition, the dashboard offers tutors a clear, accessible overview of key student data—such as engagement, participation, and assessment progress—enabling them to spot and address potential challenges early. It enables tutors to act proactively, rather than responding after issues arise thus having the potential to impact positively on student learning outcomes (Herodotou et al., 2019b, 2019a, 2020).

In FBL, by implementing EAID Modules Champions, we saw a significant jump of 14% user engagement by providing this structured organisational support (Herodotou et al., 2020) and a little ‘nudge’ (Thaler and Sunstein, 2008). Below we share what we did, how we engaged our teams, and how this initiative has started making a positive impact on both tutors and students.

Introducing Dashboard Module Champions

We recognised that tutors’ intentions to use technology and behaviour can be influenced by their peers (Ajzen, 1991) and peer to peer recommendation is the best marketing tool. Therefore we moved our focus from line management promotion of the EAID to peer (tutor) promotion by appointing tutors as Dashboard Module Champions for every FBL module. Building on scholarship from Schofield and Elder, (2022) champions received access to template resources and training to make their roles more manageable and effective. We created a community of practice (Lave and Wenger, 1991) for the champions, brought them together for a briefing prior to the start of the academic year, created a group email for open communication and held a lesson learnt session towards the end of the year. These champions commenced work in the 2023/2024 academic year.

What do our Dashboard Module Champions do?

  1. Collaborate: During module briefings at the start of modules, Dashboard Champions share EAID benefits and offer support encouraging tutors to use the tool and continue to work with the tutors during the presentation.
  2. Tailor resources: Tailor standard template student emails for their module and share these with the tutors.
  3. Monitor key dates: Identify crucial points and nudge tutors when to check the dashboard and send the emails.
  4. Train and support: Promote how to use EAID to better engage students, being available for queries and discussion in tutor forums.

The impact so far

What have we learnt?

Figure 1: EAID active users as a percentage of those given access in each university faculty over the last three years. OU faculties shown are Faculty of Arts and Social Sciences (FASS), Faculty of Business and Law (FBL), Faculty of Science, Technology, Engineering and Mathematics (STEM) and Faculty of Wellbeing, Education and Language Studies (WELS)

  1. Increase in EAID use: Our Module Champions have raised the profile of using the dashboard to proactively support students, evidenced by an increase of 14% in 23/ 24. FBL have the highest proportion of unique active users at 62% - leading the way across the university.
  2. Proactive interventions: Our Champions nudge tutors when to check the dashboard enabling tutors to address issues early, ensuring timely student communication.
  3. Time savings: By having all the student indicators at their fingertips, it saves tutors developing their own records and early intervention can prevent issues spiralling.
  4. Feedback from tutors is positive:

‘Champions provide consistent communication to keep me on track with student engagement at critical times especially in the lead up to the first assignment.’

‘Champions are helpful with questions on how best to use the tool.’

‘They provide insights into how to tailor support to students that need help.’

‘The EAID thread set up by the champion in the tutors’ forum helped develop a sense of community spirit with like-minded tutors throughout the module, from start to finish.’

Next steps

Looking ahead we plan to:

  1. Expand usage: Increase adoption and training across all modules.
  2. Refine processes: Use feedback from tutors to make the dashboard more user-friendly.
  3. Measure outcomes: Track student retention and engagement rates to assess the long-term impact.

Conclusion

PLA does transform how we support students, making retention efforts proactive rather than reactive. With the dedication of our Dashboard Module Champions and the support of our tutors, we are confident that this initiative will have a lasting impact on student success.

If you are considering implementing this in your faculty, our advice is simple:

  1. Collaborate with your most active EAID tutors
  2. Provide a toolkit for them to work with
  3. Ensure regular communication from Champions to nudge colleagues

Have questions or insights? Reach out to us and share your experience.

Project team:

Nicola McDowell is The Open University Business School EAID Lead.

Glen Marshall is The Open University Law School EAID Lead.          

Claire Maguire is The Open University Faculty of Business and Law EAID Lead.

This blog was written by Nicola McDowell and Claire Maguire.

References

Ajzen, I. (1991) ‘The theory of planned behavior’, Organizational Behavior and Human Decision Processes, 50(2), pp. 179–211. Available at: https://doi.org/10.1016/0749-5978(91)90020-T.

Calvert, C.E. (2014) ‘Developing a model and applications for probabilities of student success: a case study of predictive analytics’, Open Learning, 29(2), pp. 160–173. Available at: https://doi.org/10.1080/02680513.2014.931805.

Clarke, J. and Nelson, K. (2013) ‘Perspectives on Learning Analytics: Issues and challenges. Observations from Shane Dawson and Phil Long’, The International Journal of the First Year in Higher Education, 4(1). Available at: https://doi.org/10.5204/intjfyhe.v4i1.166.

Herodotou, C., Hlosta, M., Boroowa, A., Rienties, B., Zdrahal, Z. and Mangafa, C. (2019a) ‘Empowering online teachers through predictive learning analytics’, British Journal of Educational Technology, 50(6), pp. 3064–3079. Available at: https://doi.org/10.1111/bjet.12853.

Herodotou, C., Naydenova, G., Boroowa, A., Gilmour, A. and Rienties, B. (2020) ‘How can predictive learning analytics and motivational interventions increase student retention and enhance administrative support in distance education?’, Journal of Learning Analytics, 7(2), pp. 72–83. Available at: https://doi.org/10.18608/JLA.2020.72.4.

Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z. and Hlosta, M. (2019b) ‘A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective’, Educational Technology Research and Development, 67(5), pp. 1273–1306. Available at: https://doi.org/10.1007/s11423-019-09685-0.

Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., Vaclavek, J. and Wolff, AnnikaItem, J. (2015) ‘OU Analyse : Analysing at - risk students at The Open University’, Learning Analytics Review, LAK15, 1, pp. 1–16.

Lave, J. and Wenger, E. (1991) Situated learning: legitimate peripheral participation. Cambridge: Cambridge University Press.

Schofield, C. and Elder, T. (2022) Integrating OUAnalyse into DE100 to aid retention and progression. Milton Keynes.

Shacklock, X. (2016) From Bricks to clicks. The Potential of Data and Analytics in Higher Education. Available at: https://www.policyconnect.org.uk/research/report-bricks-clicks-potential-data-and-analytics-higher-education

Thaler, R.. and Sunstein, C.. (2008) Nudge improving decisions about Health, wealth and happiness. Yale University Press.

Bios

Nicola McDowell

Nicola McDowell is a Lecturer and Student Experience Manager (SEM) in the Open University Business School. She is the Business School Lead for the use of Predictive Learning Analytics by tutors to support students at risk of drop out. Nicola is a Chartered Member of the Chartered Institute of Personnel and Development (MCIPD), a Certified Management and Business Educator (CMBE), recognised as a Senior Fellow of the Higher Education Academy (SFHEA) and holds a BSc(Hons) in Management Science with Marketing and an MSc in HRM.

 

Claire Maguire

Claire Maguire is a Senior Lecturer and the Head of Student Experience at the Open University Business School. She is the Faculty of Business and Law Lead for the use of Predictive Learning Analytics by tutors to support students' progression and completion. Prior to joining the OU she was with Cranfield University and worked with a range of employers covering both private and public sectors. Claire is a Certified Management and Business Educator (CMBE), recognised Senior Fellow of the Higher Education Academy (SFHEA), holds an MSc in Personnel and Employee Relations and a BA(Hons) in History & Economics.