Long term stability in the Higher Education distance learning sector, and how this can be achieved through the use of analytics and management information.
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The document discusses using analytics and management information to achieve long-term stability in the higher education distance learning sector. It notes increased tuition fees, student choice, and expectations as external factors impacting institutions. Analytics can help institutions improve student retention and success by identifying at-risk students, factors impacting outcomes, and personalizing support. The process involves collecting student data, identifying outcomes and risks, monitoring engagement, managing interventions, and assessing effectiveness to leverage existing data and drive student progression.
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Long term stability in the Higher Education distance learning sector, and how this can be achieved through the use of analytics and management information.
- 1. Long term stability inthe HigherEducationdistance learning sector, and how thiscan be achieved through the use of analyticsand management information. JonBaldwin ManagingDirector, Market Development Tribal 1
- 2. Tuition fees Increased student choice Increased student expectations External factors Student Competitive environment Removal of student number cap Institution Improve student retention Increase student success Attract successful students Student employability Institution goals Impacts A changing landscape…
- 3. • Which students are more likely to drop out? • Which students could be making better progress? • What factors are impacting student outcomes? • Identifying what students need help with • Making support more personal The burning questions…
- 4. Create models Data collection and Data mining Predict and understand Patterns Relationships Trends Behaviours What to do with the data…
- 5. 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 The process…
- 6. Why do it? • Leverage investment in SIS and data • Greater and more meaningful engagement with students • Greater understanding of student needs • Identify students who may be at risk earlier • Target strategic interventions with the right students • Drive student progression
- 7. Thank you… JonBaldwin ManagingDirector, Market Development Tribal jon.baldwin@tribalgroup.com www.tribalgroup.com 7
Editor's Notes
- Why do students drop out? Answering this question has kept academics and administrators awake for years. To answer this question, we first need to find solutions to a whole raft of other issues, such as which students are more likely to drop out? Which students could be making better progress? What factors are impacting student outcomes? The intelligent use of ‘big data’ can hold the key to identifying the students that need help and making the support offered to them more personal. Making use of this information is now being used to not only increase retention rates but to nurture talent, push up attainment and improve student experience.
- Background (to be used if necessary) Institutions today collect a large amount of data about their students. Most of the structured data is held on a student information system, but is also created as a by-product of student’s interactions with teaching and learning resources, such as the Virtual Learning Environment (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 analysing large datasets. This data contains patterns, relationships and trends that are difficult to spot with traditional methods and retrospective reporting. The challenge to the modern institution is how to spot these patterns and how these patterns and trends affect student progression, performance and outcomes. Firstly, data patterns can be used to help academic and support staff across an institution, identify earlier which students are at risk and make a prediction of the likely outcomes. Secondly, they can be used in a ‘descriptive’ way to help the institution understand the factors affecting student outcomes.
- The context is big data
- 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.