Able mc neil foster ucisa sero

Able mc neil foster ucisa sero

• 1.
  Progress with learninganalytics: 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 withlearning 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 withinquality context (Clow, 2012) (Thomas, 2012)
• 4.
  Core Dashboard Processes Student data Engagement withlearning Engagement score Risk alerts Referrals Notes Sense checking Passive useful information Active engagement data High Good Low Partial NFE !
• 5.
  Engagement Information View
• 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 ofengagement 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 withsustained 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 changeagents
• 10.
  How do studentsuse 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 studentslogging 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 changeagents
• 13.
  Staff embedding theDashboard 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 Dashboardin 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 Ethicaldebates about approach Descriptive Assess quality Curriculum design Learning analytics Learning metrics
• 16.
  Discussion points • Disruptivetechnology • 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