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Learning Analytics - What Do Stakeholders Really Think?

Presented at ALT-C 2016

In our increasingly digital world our students leave an ever-growing electronic footprint behind them as they pass through the physical and virtual campus. This data is still a greatly underused asset (Higher Education Commission, 2016) although a number of UK higher education institutions have already implemented descriptive, inferential and/or predictive Learning Analytics (LAs) solutions using a wide variety of approaches, see for example Sclater (2014).
This paper discusses recent research at an English post-92 university aimed at investigating the benefits and challenges of using LAs. Prompted by a perception that some voices had yet to be given a loud enough voice in relation to a systematic use of big data in the higher education sector. It was particularly concerned with gaining a better understanding of the hopes, fears and needs of those on whom it would be most likely to impact.

This presentation will focus on our findings from of a series of focus groups and interviews with students, university governors and academic professional and support staff that took place during the 2015/16 academic year. Questions were framed around understanding views about the purpose of LAs, concerns about the type of data liable to be used, perceptions of how likely being more informed would result in changes in behaviour and outcomes, and finally how should data be presented.

The results indicated that each group had different areas of interest when it came to the type of data of interest. These spanned the range from what may be regarded as strict LAs to Academic Analytics (for an explanation of the differences see Long and Siemens (2014)). A common theme however, was that most felt that being better informed would lead to better decision making. However, having knowledge about one’s own performance, particularly in relation to peers, was unwelcome in some quarters.

When examining student concerns about data, overall there was a low degree of anxiety and a high degree of trust that the institution in general and tutors in particular would behave responsibly. Concerns about legal and ethical problems were most likely to be voiced by academic, professional and support staff. Transparency, and finding creative approaches to promoting it, was identified as vital by most groups.

During this session we will share our findings in more detail and reflect on our understanding of variations in perceptions between and within different stakeholder groups. We will demonstrate and share a checklist of institutional risks and responsibilities that was developed as an aid to identifying, understanding and managing each of these areas.

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Learning Analytics - What Do Stakeholders Really Think?

  1. 1. @PlymUniASTI /PlymUniASTIasti@plymouth.ac.ukwww.plymouth.ac.uk/asti Learning Analytics: What Do Stakeholders Really Think? Prof Neil Witt, Dr Anne McDermott, Prof Pauline Kneale & Prof David Coslett
  2. 2. Academic Support, Technology & Innovation  ASTI and Teaching and Learning Support investigating the potential of using Learning Analytics as a means of enhancing the student experience  Grant from the HEA’s Strategic Excellence Initiative for Vice-Chancellors Background Teaching and Learning Support
  3. 3. Academic Support, Technology & Innovation  The electronic footprint our students leave behind when they interact with our digital systems e.g. digital learning environment, electronic library, ePortfolio  This and other data sources used to track student engagement and to identify those who may be in danger of failing  Web pages and apps used to present various data visualisations for personal tutors, students and others Teaching and Learning Support Definition of Learning Analytics
  4. 4. Academic Support, Technology & Innovation Teaching and Learning Support Definition of Learning Analytics Institution Faculty School Programme Module Individual Academic Analytics Learning Analytics Data Granularity of Data
  5. 5. Academic Support, Technology & Innovation Teaching and Learning Support A student perspective?
  6. 6. Academic Support, Technology & Innovation Teaching and Learning Support Understanding our Stakeholders’ Perspectives
  7. 7. Academic Support, Technology & Innovation  Mostly information already collected in a range of ways, from a range of systems for a range of purposes  Array of challenges             Systematic identification of ‘at risk’ students may place an unsustainable obligation to act on the University Challenges:  Ethical  Legal  Data  Technical  Policy  Process Teaching and Learning Support Key issues, challenges and concerns
  8. 8. Academic Support, Technology & Innovation  Transparency vital to maintain trust  High degree of confidence that staff would deal with their data in a ‘professional way’  Varied in degree of comfort with easy access to data about their own performance - closely monitored students seemed least worried e.g. health areas  Most, but not all, keen to compare their profile with anonymised cohort average or an ‘ideal’ student, so consider ‘opt out/in’  Could motivate some but discourage others  University’s response to a ‘red flag’ should not be an automatic process but the start of a conversation Teaching and Learning Support Stakeholder Perspectives: Students
  9. 9. Academic Support, Technology & Innovation  More concerns than students over legal / ethical data use  Support and training to understand responsibilities  Need for openness and transparency  Focus should be on benefitting student not institution  Questioned effect on retention students leave for many reasons  Policy changes needed, e.g. attendance monitoring  Much of this data is already collected in disparate ways  Need to ensure compliance with legislation for current, retrospective and future use of data Teaching and Learning Support Stakeholder Perspectives: Academic, Technical & Support Staff
  10. 10. Academic Support, Technology & Innovation  Need shared vision of what is meant by Learning Analytics  Culture of respect for information and anonymity required  Concerns about the scope and quality of current data  Analytics data should be triangulated with other information  Opposing views about students having own and cohort data ‘why would they want to know?’ ‘what are they afraid of?’  Some concerns it could be demotivational, a distraction or encourage a strategic approach to study  Could give insight into characteristics of a successful programme, trajectory of a successful student, value added over the course of a programme (Learning Gain) Teaching and Learning Support Stakeholder Perspectives: Senior Leaders
  11. 11. Academic Support, Technology & Innovation  Potential to increase retention and enhance performance but would need to show a return on investment  Varied responses to having personal access  With Analytics, data must become everyone’s responsibility  Academic Analytics could aid institutional decision-making  Enable Plymouth to offer something distinctive to its students Teaching and Learning Support Stakeholder Perspectives: Governors
  12. 12. Academic Support, Technology & Innovation Teaching and Learning Support Recommendations
  13. 13. Academic Support, Technology & Innovation  LA owned at a very high level  Plan for success e.g. fewer withdrawals  Define goal(s) and specify initial measures  Audit policies to identify amendments and gaps  Implement single version of truth for data & policies  Set and resource Institution- wide standards for responding  Build-in to future procurements  Consider offering ‘opt-out’ Teaching and Learning Support Recommendations: Policy
  14. 14. Academic Support, Technology & Innovation  Consent agreements and statements in line with planned use  Choice of Learning Analytics solution  Implement institution-wide standards for responding  Governance requires a multidisciplinary team including students  Digital literacy & training for staff  Be open and transparent, particularly with students  Be aware there will be false negatives/ false positives  Staff development to make responsibilities clear and support policy changes Teaching and Learning Support Recommendations: Process
  15. 15. Academic Support, Technology & Innovation  Bring silos together (e.g. data warehouse)  Single version of truth needed for chosen data  Specify data currently easily accessible  Establish ownership, stewardship and users of data  Agreements with 3rd party provider to reflect new use  Unique identifier work required  Work out synergies with other existing projects (e.g. S3, Mobile With Plymouth app) Teaching and Learning Support Recommendations: Technology
  16. 16. Academic Support, Technology & Innovation  A response to a ‘red flag’ should not be an automatic process but the start of a conversation  Implement single version of truth for data and policies  Policy changes needed, e.g. attendance monitoring  Support and training to understand responsibilities and support policy changes  Culture of respect for information and anonymity required  Establish ownership, stewardship and users of data  Policies and data need to be owned centrally (i.e. Academic Registry)  With Analytics, data must become everyone’s responsibility Teaching and Learning Support Institutional Checklist
  17. 17. Academic Support, Technology & Innovation  Senior Sponsorship is essential  Single Version of the truth for all data and policies  Have an “owner” for data and revised/updated/new policies  Use available solutions (i.e. Jisc toolkit, Mobile With Plymouth, S3)  Use analytics to support personal tutoring and institutional decision making  Learning Analytics is about Culture Change, not technology Teaching and Learning Support Making it so
  18. 18. Academic Support, Technology & Innovation Thank you Questions?

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