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A Student Retention Model: Various Considerations

SRITEC-13 Convecticle
by

Amara Atif

on 17 November 2013

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Transcript of A Student Retention Model: Various Considerations

Conclusion & Future Work
The results are expected to indicate which of
the factors are most important in developing an early warning/alert systems
to predict at-risk students and
suggest interventions to improve retention

A Student Retention Model:
Analytics in HE
Development of an
Operational Framework
Implement rules as prescribed by the eclectic model for data collection
Measure the determinants of student retention by expressing them through commonly accepted scales
Provide a more in-depth view to administrators and practitioners with a menu of activities, policies and practices
Proposed Methodology
Two Phases
Quantitative
Survey
Qualitative
Focus Groups/Interviews
Quantitative and qualitative data are collected and analysed at the same time
Data analysis is separate
Integration occurs at the interpretation stage
Student Retention
Empirical, Theoretical and Pragmatic Considerations
Definition:
Ability of an institution to retain a student from admission through graduation or degree completion
Challenges:
Large body of international research
Massive & complex data
Data residing in different systems
Increasing complexity of different types of data
$$$ - more with less
A priority for Australian universities:
The Commonwealth Government has included retention, progression rates and student experience data on its list of indicators for funding of higher education (DEEWR 2009)
Measures of Retention
1. Retention rates
2. Graduation rates
Attrition
Student who stops attending their program or course of study at an institution in consecutive terms and does not respond to institutional intervention
Student Retention Behaviours
Too many variables involved
Overlapping terms
We have noted 16 terms
By identifying and distinguishing these terms has allowed us to be critical in terms of understanding the context of student retention and to define a set of categories of student retention or attrition behaviours.
1. The persister
2. The stop-out
3. The transfer
4. The attainer
5. The drop-out
6. The slow-down
Learning Analytics
Factors influencing Retention
Demographic variables & background characteristics
Academic factors
Institutional / environmental factors
Social / Campus integration factors
Psychological factors (attitudes & intention)
Personal / family factors
Financial / economical factors
Health factors
Eclectic Model
Cognitive Aspects
Social Aspects
Initial Commitments
Learning Environment
Institutional Aspects
Later Commitments
Decision Making
Strategies to Improve Retention
Theoretical Background
Pedagogy
Class structure
Nature of assignments
Activities that help students’ feeling part of the supportive learning community
Students’ informal interactions with peers and academic staff
Foundational Retention Theories
Vincent Tinto’s Theory of Student Departure (1975-updated in 2000)
John Bean’s Explanatory Theory of Student Retention (1980)
Alexander Astin’s Theory of Involvement (1985)
Swail Watson’s Geometric Model of Student Persistence and Achievement (1995-updated in 2004)
Determinants of Student Retention
Mixed-methods concurrent triangulation strategy
Retention should be everybody's business
Amara Atif
amara.atif@mq.edu.au
Department of Computing
Macquarie University
Opposite of Retention
Why retaining a student is important?
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs
(LAK, 2012)
HEI are using analytics to
Recruit students
Retain students and
To improve learning and teaching
Academic analytics (AA)
Learning analytics (LA)
LA refers to a subset of AA that
connects to student success and
retention in HE
Atif,A., Richards, D. and Bilgin, A. (2013). A student retention model: Empirical, theoretical and pragmatic considerations (ACIS 2013), December 4-6, 2013 at the city Campus, RMIT University, Melbourne. ACIS 2013 proceedings. http://aisel.aisnet.org/acis2013
1985 - 2013
Full transcript