Able mc neil foster ucisa sero
•Download as PPTX, PDF•
0 likes•102 views
Presentation delivered at the UCISA event A-Z of learning analytics 28/06/2017. Ed Foster & Jane McNeil. A longer case study can be found at https://www.google.com/url?q=https://www.ucisa.ac.uk/-/media/Files/publications/truthaboutda/TheTruthAboutDA&sa=U&ved=0ahUKEwi8r-7W5_7eAhVKRBUIHf66CGEQFggMMAM&client=internal-uds-cse&cx=008281077274678676179:yulrfklwima&usg=AOvVaw17iuGZYPJPqFRCMGyBKLd0
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Able mc neil foster ucisa sero
Similar to Able mc neil foster ucisa sero (20)
More from Ed Foster
More from Ed Foster (20)
Recently uploaded
Recently uploaded (20)
Able mc neil foster ucisa sero
- 1. Progress with learning analytics: teaching quality & analytics ABLE Project 2015-1-BE-EPPKA3-PI-FORWARD STELA Project: 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
- 2. NTU progress with learning analytics • Already a data-rich institution • Data sub committee • Pilot in 2013-14 using Solutionpath’s Stream tool • Now • Wholesale adoption across the institution • 91% of students logged in (2015-16) • 2,056 staff logged in (2015-16) • Disruptive technology • Lever for institutional change • Data one of 4 main themes for TEF submission
- 3. Analytics bound within quality context (Clow, 2012) (Thomas, 2012)
- 4. Core Dashboard Processes Student data Engagement with learning Engagement score Risk alerts Referrals Notes Sense checking Passive useful information Active engagement data High Good Low Partial NFE !
- 6. Dashboard TutorStudent Metrics Raw data & engagement rating Student engagement with course Metrics & alerts Engagement with students presented to students students act presented to tutors more-informed interactions Embedded Learning Analytics Two agents of change model NTU is strongly interested in the potential of parallel work on course quality metrics & embedding data into management decision making
- 7. The importance of engagement for success • Av. engagement for the year = very strong predictor of progression Average engagement whole year (2015-16) Low HighGoodPartial Progression% 9.5% 81.3% 92.0% 94.9%
- 8. Risks associated with sustained low engagement • As the year progresses, sustained low engagement = increasing risk Impact of low average engagement over time (2015-16) Progression% Low av. engagement Welcome Week Low av. engagement 1st term Low av. engagement Whole year 64% 27% 9.5%
- 9. Students as change agents
- 10. How do students use the Dashboard? 0% 20% 40% 60% 80% 100% Spoke to someone providing specialist help (for example student support services/… Spoke to your tutor Increased the amount of time you spend studying Changed your behaviour to raise or maintain your engagement score (for example made… Checked your attendance % Of students who responded 'yes- very often, often or sometimes' Studentresponses Student actions after logging in to the Dashboard (n=753, Feb/Mar 2017)
- 11. Relationship between students logging in to the Dashboard and progression Progression% 72.4% 89.8%81.4% 1 log in 4-6 log ins 20+ log ins Total log ins for the year (2015-16)
- 12. Staff as change agents
- 13. Staff embedding the Dashboard into their working practices • Staff involved in one Dashboard pilot told us that they used the Dashboard in the following ways: • Overview monitoring their tutees • ”I use it to check the ‘academic health’ of students • Preparing for tutorials • If I am concerned about a student’s engagement, I will look at the Dashboard prior to meeting the student to enable me to triangulate evidence gleaned from other areas, e.g. a Module Leader’s feedback …” • “I access information pre-tutorial and have it available during the tutorial”
- 14. Using the Dashboard in tutorials • Framing the discussion • “The information within the Dashboard is a start point for discussion” • Check student self-perception • “show a visual representation of their engagement – this works well with art students it seems” • Coaching • “Looking at engagement with modules, referring to attendance, but in a positive and encouraging way. If there is a lot of good things to talk about, then I do so. If the picture does not look so good then I encourage more engagement, if it looks terrible, I try to understand what the problem is." • “[I] use this as a springboard to talk about engaging with course texts …” • Action planning/ referrals • “I use the Dashboard to update notes after the tutorial as I want to use the tutorial time listening to them and exploring issues/ priorities…” • ”We agree an action plan which is noted in the Dashboard”
- 15. Predictive Primarily 1-1 interventions Ethical debates about approach Descriptive Assess quality Curriculum design Learning analytics Learning metrics
- 16. Discussion points • Disruptive technology • Learning analytics without institutional change likely to lead to disappointing results • Resource, training, double loop change, LA, LM • Institutional data & data infrastructure not necessarily set up to enable this use of data • Two change agents • Not the tool, but institutional change • Engagement is a profoundly powerful predictor of success
Editor's Notes
- Important to say that this work sits within a wide programme of academic development and student support. Note TEF GOLD and the role played by Learning Analytics (LA) in that. One of our major themes was our systematic use of data to make evidence-based developments Analytics, metrics and qualitative data Parallel project to NTU Dashboard: Course quality metrics. Descriptive data. Good data in this context: meaningful proxies for quality, robust data, accessible to course leaders, recognised as valid by them> like NTU Dashboard project, this is about generating data, presenting it in an accessible way, developing the use of it. Emerging set of course quality metrics: related to Gibb’s work on factors that predict quality. Let’s call these Learning Metrics (LM). TEF is a great opportunity to move this up the agenda. (TEF is selected NSS + non-continuation+ )
- 64% find Dashboard useful when they log in themselves 80% reported that they found it useful when their tutors used it in tutorials
- SO, HOW ARE LA and LM connected? CLICK LA initially seen by staff as primarily about informing interventions with individual students. = 1-1 tutorial, support strategies. THEN as a way to INDIRECTLY TARGET students for wider, thematic interventions or support schemes (e.g. for equality groups in Success for All) CLICK LM are used to make judgements about programme quality. For “feedback and course correction”. Like the TEF. (Traditional Q metric vs Gibbs-based metrics is a story of another day!) HOWEVER, it is at the point of interaction between LA and LM that things get really interesting. WHY? Because engagement predicts everything else. Engagement as student behaviour (can de deficit) vs engagement as a programme strategy (by design). Story: new Data Sub-committee – importance of the committee in brining distributed expertise together to look at patterns across multiple data sources. Example: referrals – patterns. Can see across modules. Implications for course design. BUT Engagement predicts these earlier than assessment failure. Therefore, add LA on engagement to LM on course quality for use in monitoring and in periodic course review. = Deeper understanding of contextualised strategies for course design and LTA strategies to improve student success. E.g. our Festival: how to use course design to engender communities