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Supporting students

Improving success and satisfaction of credit transfer students entering L3 modules in Science

Project leader(s): 
Eleanor Crabb and Jane Loughlin

Credit transfer students are often regarded as having the potential to succeed in distance learning. However for a number of years analysis of the success of new students entering directly at L3 on Q64 (BSc Natural Sciences) has shown lower pass-rates for students compared to continuing students, in a large part due to high withdrawal rate of these students. Further analysis for the 17J cohort by D. Appleton (Senior Advisor Science SST) showed that this is a particular issue for students registered on the Open degree where students are awarded the maximum level of CT, irrespective of area of prior study. In addition, it has been reported that across all qualifications a higher proportion of 240 credit transfer students get 3rd class honours than 120 credit transfer or no credit transfer students, and a lower proportion get 1st class honours (RPL workshop data).

Two issues are likely to contribute to the high drop-out rate/ lower success of these students:

  1. ensuring that students are registered on the correct module (particularly an issue for the QD and R28 students);
  2. the support provided to students as they transfer to a different style of study.

Our research questions are therefore:

  • What can be done to ensure that students are on the correct module?
  • What do 240-credit transfer students do to acclimatize themselves to distance learning at the OU and how effective is it in terms of success and satisfaction?
  • What ‘quick fixes’ can we put in place to help credit transfer students transition successfully into the OU?
  • What does evaluation of these fixes suggest we put in place for the longer term?

The impacts of this work are potentially

  • Enabling proactive contact with CT students from staff/ student buddies
  • Provision of resources to support CT students
  • Increasing awareness of how best to support CT students amongst ALs

This would lead to the following outcomes

  • Increased retention/ pass rates/ attainment levels amongst CT students – in line with student success priority
  • Appropriate resources to support credit transfer students, such as pre-module briefing materials for use in Adobe Connect

Recommendations on improving support to credit transfer students for use by module teams, ALs, colleagues in the SST and student buddies.

Crabb, E and Loughlin, J. (2019) Project poster (PDF)

Disproved predictions of at-risk students: Some students fail despite doing well, others succeed despite predicted as at-risk

Project leader(s): 
Martin Hlosta

Most of the research around the identification of at-risk students and the prediction of their performance using Machine Learning focuses on developing the most accurate model. Despite recognising the importance of transparency and understanding of the models, little effort has been made to investigate the errors made by these models. In this project, we address this gap by analysing large errors of predicting students at-risk of not submitting their assignments, when even the sophisticated machine learning model was confident about a student outcome, yet the result was different. 

The underlying predictions are part of OUAnalyse and are available in most of the undergraduate OU modules to all tutors via the Early Alert Indicators (EAI) Dashboard. The models are updated every week to capture the dynamic changes in student learning behaviour. 

We analysed both groups of errors: students predicted to submit their assignment, yet they did not (False Negative) and students predicted not to submit yet they did (False Positive). We conducted mixed-method analysis, combining quantitative analysis of predictions of more than 25,000 students with follow-up online interviews with 27 of them and thematic analysis. We focused on undergraduate level 1 modules on STEM faculty and analysed the predictions for the 1st Tutor Marked Assignment (TMA).

The quantitative analysis revealed that the most prevalent factor in False Positives was immediate growth of student activity after the predictions were generated. Interviews revealed that amongst those students the most prevalent themes were students that were working last minute and were able to overcome last-minute problems, students that had high study workload and dropped some of their other modules, or students who had either the knowledge required for the TMA or studied outside the VLE. In False Negatives, non-submission of assignments was explained mostly by financial reasons, family responsibilities or deferring the module because of high study workload. 

Overall, the factors explaining the different outcomes were not related to any of the student data currently captured by the model. As a result of this study, data related to student finance will be part of the OUAnalyse model. We proposed that the absence of missing data can be handled by either giving students an initial questionnaire or letting tutors know so they are able to capture this before the module starts.  Intervention strategies based on student recommendations are suggested as well as considerations that we will make available to tutors in the OUA training materials, which might lead to better understanding of the capabilities of Predictive Learning Analytics and subsequently its better usage.

Online peer mentoring at scale: Benefits and impacts from a student buddy perspective

Project leader(s): 
Julie Robson and Chris Hutton

The project aims to evaluate student buddy experience on S112, S(XF)206, S209 and S390 in EEES to understand the drivers behind volunteering, impacts on employability and whether sustainable communities of buddies can be fostered. Much has been written about the value of such peer mentoring schemes for students; results suggest that these schemes are beneficial (Motzo 2016, Robson et al., 2018a Robson et al., 2018b). Less is known about the effects of being a student buddy and impacts on their own development and employability (Robson and Forbes, 2016).

As volunteers, buddies available time is variable and cannot be constrained by the peer-mentoring project, creating uncertainty regarding the number of buddies needed to provide a sustainable level of service and for longitudinal continuity. The project will evaluate the sustainability of the voluntary EEES student buddy scheme.

The impact of mentoring on student buddies is vital to evaluating sustainability. The project would compare buddies’ expectations of the role and its benefits, especially related to employability skills at the beginning of their term with reflection on their experiences at the end of their first and second years of office. The research questions to be explored in this project are:

  • Are peer mentoring schemes involving volunteer students sustainable and can they help build a student buddy community across modules?
  • What are the benefits of being a student buddy in terms of transferable skills gained?
  • How can being a student buddy contribute to their employability skills?

Results could be used to tailor both the buddy role itself, improve advertising and recruitment of buddies and determine the requirements of a sustainable student asynchronous, peer mentoring scheme. Outcomes will inform the case for mainstreaming student online peer support across the university and inform other HEIs wanting to engage their distance learners.


Motzo, A. (2016). Evaluating the effects of a ‘student buddy’ initiative on student engagement and motivation. In C. Goria, O. Speicher, & S. Stollhans (Eds), Innovative language teaching and learning at university: enhancing participation and collaboration (pp. 19-28). Dublin:

Robson, J.M., and Forbes, T., (2016) Vertical Peer to Peer mentoring in the Faculty of Social Sciences: a pilot study. Open University internal report unpubl. (Online) Available from:

Robson, J.M.,  and Forbes, T., (2016) Vertical Peer to Peer mentoring in the Faculty of Social Sciences: a pilot study. Open University internal report unpubl. (Online) Available from:

Robson, J., Crabb, E. and Lotze, N., (2018) Evaluating different operating models of study buddy schemes – what works best for students? Horizons in STEM: Making Connections, innovating and sharing pedagogy 2018, 28-29th June University of Hull

Robson, J., Wheeler, P. and Church, K. (2018) Peer mentoring schemes for Distance Learners; a successful working example from Environmental Science. Horizons in STEM: Making Connections, innovating and sharing pedagogy 2018, 28-29th June University of Hull

Wakeham, W. (2016) Wakeham review of STEM degree provision and graduate employability [Online],HM UK Government. Available at: [Accessed 10/02/19]

Wakeham, W. (2017) Keynote presented at HEA STEM Conference 2017: Achieving Excellence in Teaching and Learning. Manchester, UK, 1-2 Feb [Online]. Available at:

Robson, J. and Hutton, C. (2019) Project poster (PDF)