Development of a student success framework A case with an academic student learning assistance unit Presentation outline Who are we Why did we do it What was the quest What does the student success framework look like ID: 830475
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Slide1
A quest for student success:
Development of a student success framework – A case with an academic student learning assistance unit
Slide2Presentation outline.
Who are we?
Why did we do it?
What was the quest?
What does the student success framework look like?
How did we do it?
Did it work?
Slide3Filip
Federowicz
, Puzzle, used under a Creative Common Attribution License
, from
http
://www.flickr.com/photos/filu/4899342532
Slide4Student Success.
What is Student Success?
Persistence
Progression
Timely Completion
Graduation
Slide5‘Happy Fourth of July 2014’ by
JD Hancock http://
photos.jdhancock.com
/photo/2014-07-04-213350-happy-fourth-of-july-2014.html
Slide6About USQ.
27,000 students
1,600 FTE staff
USQ Ipswich
Slide775% external
25% on-campus
23% low SES
60% over 25
yo
26 median age
First in family to attend
uni.
Work FT
Study PT
20% international student load
101 countries
Our students.
Slide8The Learning & Teaching Services.
A
centralised
, university-wide unit
(Current configuration - 3 years old)
Slide9Self
-directed
Online resources
Smarthinking
– 24/7, personalised feedback, Writing & Maths
Academic Success Planner – 24/7, personalised study plan - resources & learning activities for 7 domain knowledge
Peer
-
assisted learning
By students for students
Course-based – content based + study skills
Non-course based - general study skills
Advisor-facilitated
Individual learning consultations
Workshops & Drop-in clinics
Learning assisted programs (LAPs).
Slide10Why?
CC0 – No attribution needed.
Slide11Quest for quality: External drivers.
Slide12Source: TEQSA v2.1 (2016)
TEQSA RAF.
Slide13Risk
indicators for student success.
Cohorts completed
Student load
Attrition rate
Progress rate
Completions
Graduate satisfaction
Graduate destination (employment rate)
Source: TEQSA v2.1 (2016)
Slide14Personalised learning
Focused research
Enriched communities
Engaged enterprise
USQ Vision 2022
Quest for educational excellence: Internal drivers.
Slide15Direct challenges.
Efficiency savings – cost cutting
Reduced funding
Requests for funding
Show us the evidence
…
Slide16Pebbles on pebbles by Darren Hester
, http
://
openphoto.net
/gallery/image/view/6277
What was our quest?
Slide17What
and
how much
(the extent) are
the
impacts
of
LAPs on student success?
Progression
Academic performance
Slide18Every student
m
atters.
Slide19Chickering
& Reiser’s student identity.
Developmental ‘highways’ of learning
7 vectors
Developing competence
Managing emotions
Moving through autonomy toward interdependence
Developing mature interpersonal relationships
Establishing identity
Developing purpose
Developing integrity
Source: Chickering and Reiser (1993)
Slide20Source: Applied from
Bronfenbrenner’s (1979, 2006) bio-ecological development systems
Bronfenbrenner’s
human development.
MACRO-SYSTEM
Belief systems
Culture
Social interchanges
MICRO-SYSTEM
STUDENT
Classrooms
Teachers/ Tutors
Regulatory/TEQSA RAF
University policy, vision & mission
Faculty
/ department culture
Teachers
’
individual characteristics
EXO-SYSTEM
Library
Institutional co-curricular/ student development & support
Teachers
’
teaching approaches
Student Services
MESO-SYSTEM
Family
Work
Laboratory
Societies/
Clubs
Residential colleges/ halls
House/ room-mates
Study groups
Slide21Astin’s
I-E-O.Input-environment-output
Students develop (output) through interactivities between student background (input) and university context (environment)
Source:
Astin
(1984, 1985, 1993)
Slide22Reality.
‘Balancing act 3’ by Brandon Landis, https
://
www.flickr.com
/photos/qf8/578590012/
Slide23(Un)connected data.
Source:
Padró
& Kek, 2013.
AUSSE/UES
Grades
Graduation rate
Retention
Student demographics
Student satisfaction data
Transfer rates
Co-curricular student engagement activities data
LTS student support data
Other student learning support activities
data
Institutional emphasis for data collection & analysis:
Customer service (transactional
)
National policy
preference
Outcomes
(developmental)
Slide24Data strategy.
Weick’s
(1984) small wins – miniature experiments
Schuh
(2009) suggests starting modestly with a few assessment projects
Move beyond transactional evaluative data level to an outcomes level (Stake, 2004)
Slide25‘Vintage Lego blocks’ by
floodllama
, https
://
www.flickr.com
/photos/38446022@N00/
Student
Success
Framework.
Slide26Source: Adapted from
Kek & Huijser (2011),
Bronfenbrenner (1979),
Astin
(1984, 1985, 1993)
Institutional Level
(
Exo
Systems)
Individual Student Level
(Micro &
Meso Systems)
Personal / Demographic
Outcome
Input
Environment
Motivational
Family
+ Other factors – theoretical/ empirical
Student Success
Co-curricular Program / LAPs
/ Student Services
Academic Faculty/
Departments/
Units
Learning & Developing
University structural factors such as level of study, mode of study
Slide27Eight squares by Tseng Fang Ning, http
://
openphoto.net
/gallery/image/view/22458
How?
Slide28Student success outcomes
p
rogression
academic achievement
Multiple case studies - 3 LAPs
peer
assisted learning
(Meet-Up, N=1,526
)
learning
consultations (The Learning Centre, N=1,277)
Smarthinking (24/7 online, N=265)S1 2013 & S1 2014 dataConcurrent mixed-methods quantitative (archival
data)qualitative (structured interviews, N= 21)
Analysis
hierarchical linear regressionthematic analysis
Slide29Glass windows by Miroslav
Vajdic
, http
://
openphoto.net
/gallery/image/view/10871
What did we find?
Slide30Academic performance.
Learning
consultations
5 significant predictors:
Female
(
β
= -0.13)
Domestic
(
β
= 0.12)Distance study mode (β= 0.13) The number of courses enrolled (β= 0.25) Time spent (β= 0.07
)Total variance is 9% [F (9, 1098) = 12.11, p = 0.000]Final R is 30%
Small effect size (0.10)
Peer assisted learning7 significant predictors:Female (
β= -0.17); Domestic (β= 0.05); On-campus (β= -0.08); Faculty (β= 0.13); Total number of courses enrolled (
β= 0.25). Frequency of attendance (β= 0.16)
Number of
PAL courses attended (β= 0.06)Total variance is
17% [F (9, 1423) = 31.66, p = 0.000]Final R is 41%Moderate effect size (0.18)
Slide31Progression
rate.
Learning
consultations
3 significant predictors:
Female (
β
= -0.08)
Time
spent
(β
= 0.09) Total number of courses enrolled (β= 0.22)Total variance is 7% [F (9, 1098) = 8.6, p = 0.000]Final R is 26%Small effect size (0. 07)Peer assisted learning
4 significant predictors:Female (β= -0.11)
Faculty
(β= 0.08) Number of courses enrolled (β
= 0.30) Frequency of attendance (β= 0.16)Total variance is 15% [F (9, 1419) = 28.13, p = 0.000]Final R is 39%Moderate effect
size (0.18)
Slide32Progression
rate.
Smarthinking
1 significant predictor:
Time spent
(
β
=
0.14)
Total
variance is
8% [F (8, 255) = 2.73, p = 0.007]Final R is 28%Small effect size (0.09)
Slide33Student voice.
”I mean success at
uni
is it helps you
grow as a person
, your way of thinking changes. You become less judgemental, you become a lot more open-minded.
”
“
… the point of why i went to uni was to get
employment
...
But if it went another way ..it’s not like you’ve been to uni and it’s not contributed to any othe part of your life other than taking up four years of your life ... As long as you’ve learned something yeah”“ ...for an external student ...it’s like a lifeline”
“ ... m
ade me believe in me
at the time”
Slide34Integrating data and stories.
http://
www.brookestoneassociates.com
/puzzle-piece/
Slide35The story of far
…
Students who
engaged with LAPs did
achieve academic success and
progressed.
Students viewed success as being about more than just passing and progressing and getting that job.
Their confidence increased and persisted as they had somewhere and someone to be go to for learning support when they felt they needed to.
Slide36Did it work?
‘Dreams’ by Bjorn
Lindell
, https
://
www.flickr.com
/photos/
arctia
/
Slide37Having a student success framework is useful
no more guess work Essential to collect and analyse student
engagement data
time, effort & emotion
increased
confidence
persistence
student learning & developing
=
student successmind, heart & actionWidening access without support is not opportunity
multiple data – levels & typesnot just academic (in classrooms) X co-curricular/student services (outside classrooms) X institutional/administrators (policies)cognitive, affective, behaviour
Slide38https://
nonprofitquarterly.org
/2015/10/22/charter-school-oversight-a-missing-piece-of-the-educational-puzzle/
Conclusion.
Slide39LAPs are just one piece of the student success puzzle
every piece matters even though small impact
The whole is greater than the sum of parts
shared
responsibility
student success is everyone’s business
academic, professional, administrators, community,
employers
mind (cognitive), heart (affective) & action (behavior) data
Slide40Filip
Federowicz
, Puzzle, used under a Creative Common Attribution License
, from
http
://www.flickr.com/photos/filu/4899342532
Slide41Marry as many parts of learning together to form the
interconnections
that are more likely to produce
more
effective educational experiences for students.
(
Pascarella
&
Terenzini, 2005)
Slide42Thank you.
Q & A.
Slide43References
Astin
, A. W. (1984). Student involvement: A developmental theory of higher education.
Journal of College Student Personnel
, 25(4), 297-308.
Astin
, A. W. (1985).
Achieving educational excellence: A critical assessment of priorities and practices in higher education
. San Francisco:
Jossey
-Bass.Astin, A. W. (1993). What matters in college? Four critical years revisited
. San Francisco: Jossey-Bass.Bronfenbrenner, U. (1979). The ecology of human development. Cambridge, Massachusetts: Harvard University Press.Bronfenbrenner, U., & Morris, P. A. (2006). The bioecological model of human development. In W. Damon & R. M. Lerner (Eds.), Handbook of child psychology (6th ed., pp. 793-828). Hoboken, NJ: Wiley.Chickering, A. W. & Reiser, L. (1993). Education and identity. San Francisco: Jossey-Bass.Kek, M. Y. C. A. (2012). Integrated student learning journey initiative (ISLJI) Final Paper: The integrated student learning journey - Student Personalised Academic Road to Success (SPARS): A framework for the provision of adaptive and student-directed, on-line, on-demand, integrated study support to students. Paper submited to the Director, Learning and Teaching Support, Office of Pro-Vice Chancellor (Learning, Teaching and Quality): University of Southern Queensland
.Kek, M., & Huijser, H. (2011). Exploring the combined relationships of student and teacher factors on learning approaches and self-directed learning readiness at a Malaysian university. Studies in Higher Education, 36(2), 185-208.
doi: 10.1080/03075070903519210
Slide44References (continue)
Kuh
, G. D.,
Kinzie
, J., Buckley, J. A., Bridges, B. K., & Hayek, J. C. (2007). Special Issue: Piecing together the student success puzzle: Research, propositions and recommendations.
ASHE Higher Education Report, 32
(5).
Padro
, F. F. (2012). STEM and job churning: One influence impacting STEM career choice. In C. P.
Veenstra
, F. F. Padro & J. A. Furst
-Bowe (Eds.), Advancing STEM Agenda (pp. 193-204). Milwaukee: American Society for Quality, Quality Press. Padro, F. F., & Kek, M. Y. C. A. (2013). Student engagement and student satisfaction: Two measures arguing for independent review criteria of standards for student support services in national quality assurance schemes. Paper presented at the 2013 INQAAHE Bi-Annual Conference, Taipeh, Taiwan.Pascarella, E. T. & Terenzini, P. T. (2005). How college affects students: A third decade of research. San Francisco: Jossey-Bass. Schuh, J. H. (2009). Assessment as an essential dimension of contemporary student affairs practice. In J. H. Schuh & Associates. (Eds.),
Assessment Methods for Student Affairs (pp. 1-22). San Francisco: Jossey-Bass.Stake, R. E. (2004). Standards-Based and Responsive Evaluation. Thousand Oaks: SAGE.
Weick, K. E. (1984). Small wins: Redefining the scale of social problems.
American Psychologist, 39(1), 40-49.