Using academic data to personalise support in a large faculty

ADAM BRIDGEMANDIRECTOR OF FIRST YEAR STUDIES, SCHOOL OF CHEMISTRY

ASSOCIATE DEAN (LEARNING AND TEACHING), FACULTY OF SCIENCE

@adambridgeman

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A FUTURE OF PERSONALISEDLEARNING AND SUPPORT

Supporting students

transition to university

Personalised online learning

and support

Active and social learning with an

inquiry focus

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TRANSITION PEDAGOGY

• Starting first year can be a daunting experience ... Some adjust easily and thrive. As many as one third do not and think about leaving.

If first year goes well, it sets students up for successful university study and future careers. But if students struggle or become disengaged they can under-perform or just drop out completely.

Sally Kift, The Conversation, 14 February 2014

• Effective retention programs are committed to the education of all, not just some, of their students. … Contrary to popular belief, academic difficulty is not the major reason for attrition. Other reasons are adjustment difficulties, uncertain goals, weak commitments, financial inadequacies, and isolation

Vincent Tinto, Leaving College, 1993• Whole of institution approaches are needed which are “comprehensive,

integrated and coordinated”.

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COMPREHENSIVE, INTEGRATED AND COORDINATED SUPPORT

• To be effective, academic support needs to be personalised:o Available as early as possible ando Targeted according to needo Delivered at the right time and through the right mediumso Integrated across the faculty and with institutional support

• Faculty of Science:o Around 13000 first year enrolments students in semester 1

(BIOL – 2200, CHEM – 2200, GEOS – 350, HPS – 420, MATH – 5300, PSYC – 1700, PHYS – 1000)

o Students drawn from every faculty with ATARs from 70 – 99.5 and a wide range of attitudes and backgrounds to their units

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APPROACHES TO EARLY SUPPORT

• Students likely to be ‘at risk’ students can be identified according to demographic and incoming academic background:o TAFE entryo First in familyo Gendero Type of high school etco HSC and/or ATARo Level and performance in high school maths etc

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USING ACADEMIC DATA

• Purdue’s Signals Project:o Intervention messages and traffic light signals

available to institution and students from about week 2

o Some successes in improving retentiono Student success algorithms customized by

course

• Blackboardo ‘Retention Center’o Blackboard ‘Analytics’

(being trialled at Sydney in semester 2)

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• Trigger points week 4, 7, 13

• Upstream from Academic Risk and progression rules

• Every student in CHEM1, PHYS1, BIOL1, GEOS1, PSYC1 and ATHK1 and (from 2015) some in MATH1 sent a personalised email

• Selected students also sent an SMS text

• Based on whatever flag(s) the coordinator thinks is most appropriateo Early assessment datao Blackboard login statisticso Attendance

• Personalised to performance level (excellent, good, bad, none)

early warning system

USING APPROPRIATE ACADEMIC DATA

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EARLY WARNING SYSTEM

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EARLY WARNING SYSTEM

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TRACK AND CONNECT

• In generating emails, the EWS also generates lists of students grouped according to the level of (disengagement)

• Data fed to Student Services Track and Connect systemo Most “at risk” students receive phone calls from student mentorso Early linkage and identification with faculty and support services

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ATHK1001 - RESULTS

3%

16% 1%

80%

After week 7

Before Hecs

Before week 7

Continuing

3%

5%2%

90%

20132012

CHEM1001 - RESULTS

2008 2009 2010 2011 2012 2013 20140

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400

600

800

2008 2009 2010 2011 2012 2013 2014

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of st

uden

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CHEM1001 - RESULTS

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2012

2013

2014

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THANK YOU

adam.bridgeman@sydney.edu.au @adambridgeman

• Directors and first year coordinators in Science:o Bruce Burns (ATHK), Charlotte Taylor (Biological Sciences), Bill

Pritchard (Geosciences), Sharon Stephen (Maths & Stats), Helen Johnston (Physics), Caleb Owens (Psychology) and Dale Hancock (SMB)

• Zinnia Sahukar (Faculty of Science Student Experience Officer)

• STAR Team

• Danny Liu