How Student Data and Analytics can be used to Target Intervention and Improve Student Outcomes
•Download as PPTX, PDF•
1 like•536 views
Presentation delivered by John Gledhill at EUNIS 2015, showing how student analytics can be used to help universities better target intervention strategies at those students most in need and how to improve outcomes for students.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (19)
Similar to How Student Data and Analytics can be used to Target Intervention and Improve Student Outcomes
Similar to How Student Data and Analytics can be used to Target Intervention and Improve Student Outcomes (20)
Recently uploaded
Recently uploaded (20)
How Student Data and Analytics can be used to Target Intervention and Improve Student Outcomes
- 1. John Gledhill Product Director - HE Student Insight: Student and learning analytics
- 2. Agenda Institutional challenges - knowing the students Institutional self-study – trend analysis of student attainment related to student use of the VLE and library Tailoring to meet student requirements Focusing on student engagement – 80% from a 25 mile radius – commuter students Development of tutoring – inclusivity rather than deficit Diverse 23,000 strong student population 42% students from BaME background Average age: 27 years Mixed entry tariff profile Provision of support that has a bespoke and personalised feel
- 3. Agenda Development partnership Objectives • Predict student academic performance to optimise success • Predict students at risk of non- continuation • Build on research into link between VLE activity and academic success • Scale data processing • Understand risk factors and compare to cohorts 3 years of matched student and activity data used to build predictive models Staff can use student, engagement and academic data to understand how they affect student outcomes. Information accessible in one place on easy to understand dashboards. Integrated with Tribal SITS:Vision and staff e:vision portal Consultation with academic staff on presentation and design Accuracy of module academic performance predictions 79% Based on module academic history and demographic factors
- 4. Student Insight Data collection and Data mining Create models Predict and understand Patterns Relationships Trends Behaviours
- 5. Student Insight Collect SIS integration Student activity data 1 Identify Outcome analysis Risk prediction Student tagging 2 Awareness Monitor cohorts and tags Student engagement Understand risk influences 3 Act Proactively manage progress Record decisions and actions Manage student interventions 4 Improve Assess intervention effectiveness Student feedback 5
- 7. Agenda Identify Tag students at risk Monitor risk by student cohort See what factors affect outcomes
- 8. Agenda Understand risk influences Prediction trend View what influenced the prediction Compare to student cohorts
- 9. Agenda Record decisions and actions Log decisions and actions Maintain history of decisions made
- 10. Agenda Manage student interventions Inform student support team Cases allocated to student support staff Track cases through to completion
- 11. Agenda Assess intervention effectiveness View impact of actions made Take into account student feedback
- 12. Agenda Solution focussed Monitor Record Share Predict Prevent Support Intervene Personalise Use of Student Insight
- 13. Agenda Fit for the future Attainment plus Student partnership Statutory obligations Student academic experience Resource management Data and analytics
- 14. Thank you
Editor's Notes
- Main message is to focus on knowing the student body from the meta point of view and then developing ways to acknowledge the features of the student body and engage with a systemmatic approach that includes bespoke elements. The diversity of the student body from entry has resulted in the desire to tailor student support and guidance – not to ‘plug gaps’ rather to embrace the diversity. Commuter student populations have specific requirements focssed on spending limited time on campus, juggling work and family commitments Personal tutoring as we have known it is subject to update and development In the sector we are all working to provide strong student support that has meaning and value for each student.
- Student Insight has been developed through a close working partnership between the University of Wolverhampton and Tribal. The original objectives of this partnership were to identify how we could build on initial research carried out by the university and build a solution that could enable Wolverhampton to benefit from improved use of student data. The outcome of this partnership is the Student Insight product that has been developed as a configurable solution that can now be adopted by other institutions. Consultation with academic staff at the university has enabled us to design the system in such a way so that it is flexible and can be tailored to meet the unique needs of each institution. Accuracy figure of 79% is based on Year 1 performance and is a predictor of Year 2 performance which at Wolverhampton is the key academic period as performance begins to affect final degree outcome.
- Institutions today collect a large amount of data about their students. Most of the structured data is held on a student information system such as our own SITS:Vision, but is also created as a by product of student’s interactions with teaching and learning resources, such as the VLE. This data contains valuable information about how students are engaging with their course. However, it can be challenging to use due to the difficulty of collecting and analyzing these large datasets. This data contains patterns, relationships and trends that are difficult to spot with traditional business intelligence tools and retrospective reporting. Student Insight is Tribal’s learning analytics platform that allows you to use data collected from a range of data sources and utilize these patterns to help you to understand and optimize student progression, performance and outcomes. The system uses data mining and machine learning techniques to create predictive models that represent the patterns, relationships and trends in this data. Every institution is different, and so rather than provide you with a fixed model that can’t be changed, the system can be configured by each institution. The models that are generated can be used in two ways. Firstly, they can be used to help academic and support staff across an institution identify earlier which students are at risk, by using the model to make a prediction of the likely outcomes for a student. Secondly, they can be used in a “descriptive” way, to help staff understand what factors affect student outcomes. Typically, the system is being used to help predict and understand student non-continuation and academic performance. However, one of the unique characteristics of Student Insight is its ability to be focused on other problems too.
- Student Insight is best described in a 5 stage process: Student Insight allows you to collect data from any data source that contains information about students and their interactions. It is integrated with Tribal’s SITS:Vision SIS and ESD student information desk products. However, you may also upload data manually from any data source using a flexible data mapping tool. An open API is also provided that allows you to directly load data into Student Insight from other data sources. Once data has been collected and loaded into the system, Student Insight provides a range of tools that allow you to use that data to identify students who may be at risk of poor outcomes. Student Insight gives you awareness by allowing you to monitor the progress of groups of students through information displayed about their current progress and predictions about where we think they may end up. Once you have identified an individual or group of students at risk, it is important that you act by potentially meeting with the student and agreeing the steps that should be taken to try to mitigate that risk. This allows you to more strategically target intervention on the right group of students earlier than would otherwise be possible. Once we have put in place an intervention we should continue to monitor the students and see whether it is possible for further improvements to be made. For example, did the interventions we made actually make a difference? Would we want to use those interventions again.
- Student Insight will use the models it has trained to generate risk predictions against each individual student. The risk predictions allow staff to see the likelihood of a particular outcome. For example, on this screen we can see that the system has indicated the risk of failing each module that the student is currently taking. Global Business Environment has a high risk of failure and this may indicate a module that the student is struggling with. If student retention is a particular issue in this course, we may also choose to view a course withdrawal risk prediction, that indicates the likelihood that the student may withdraw from this course early.
- In addition to individual student predictions, Student Insight provides simple dashboards that can enable any member of staff to monitor the current and predicted performance of students in different groups. The system represents the curriculum model of the institution and this is used to allow staff to view current and predicted performance for students in any part of the organizational structure. It is also used to control security, allowing a course director direct access to the students on those courses they are responsible for. The member of staff may monitor the course to view the predicted risk for the whole group of students on that course, or by module that students may take on that course. I can also view a breakdown of the historic outcomes for any group of students and see what combination of factors resulted in the highest likelihood of poor outcomes. For example, I may see how academic results are affected by student characteristics, prior attainment results and engagement. Finally, I can tag individual or groups of students of interest and then monitor the current and predicted performance for students with that tag. This lets me group together a set of students who may have a certain combination of risk factors, and monitor that group to check that they are performing as expected.
- Unlike other learning analytics solutions, Student Insight does not provide a “black box”, but lets you understand why it generated a certain prediction. The “Influence Chart” lets you see what factors led to the system making a certain prediction and allows you to compare an individual student’s prediction to the rest of the cohort. In this way, Student Insight helps to inform discussions with the student about what potential problem areas might be. This helps me to target the right areas and resolve the right problems.
- It is commonly referred to as “actionable insights” – i.e. once we have identified an issue, then it is important that an analytics tool allows us to act on it and record the decisions that were made and the actions that were or will be taken. Student Insight allows staff to record interventions directly in the system. This generates a complete history of the decisions which were made.
- The system integrates directly with the Tribal ESD student support desk system. This allows interventions to be passed to student support teams for action, monitoring and progression. For example, if a discussion with the student has highlighted that they have a financial issue which is impeding their studies, then I may wish to put them in contact with a support team who can give the student further support and guidance.
- Does an intervention lead to an improvement and, ultimately, help the student to be successful? The system allows staff to see a history of risk predictions which enables them to view whether the predicted outcomes for the student are getting better or not. By overlaying this history with key intervention points, we may be able to spot whether an intervention has resulted in an improvement or whether further intervention is required.
- Solving problems associated with lack of engagement, having a common record for all student support mechanisms and staff to use. Sharing the analytics with students and relevant staff – to have a single record of tutoring and associated activity. Predicting progress and achievement at a stage when intervention can affect the attainment trajectory – the vagaries of degree classification algorithm Providing matched interventions Making it bespoke as far as possible, but also enabling students to see where their progress is in relation to others. – NB the Uni of Manchester approach from the 1980s!
- Attainment in an inclusive environment – not ‘plugging deficits but utilising a system to provide early notification of issues, helping address these and also tracking interventions to loop back to resourcing the most effective interventions. Keeping the student informed and in partnership working on the most appropriate support UKVI and PSRB requirement to demonstrate engagement Academic experience – helping to track progress and working to show predictions at a point when students can take control – resource management – tactical provision of most effective resources