Student throughput analysis using “What if” from a Bayesian Model : UP and WITS Case Study

Benjamin Ntshabele and Fezile Mduli

14 November 2018

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ROADMAP

Main objective

Research questions

Literature

Approach – Bayesian Networks

Data

Model

Results Institutional level (UP)

Institutional level (WITS)

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Main objective and research questionsMain objective

To analyse student throughput using “What If questions” from a Bayesian Model

Research questions

What is the profile of a student who finish in minimum time? o Gender, ethnicity, home language, quintile & funding

Are students who finished in minimum time more likely to have funding and less likely to have changed program? o What is their home language and quintile group?

Strong correlation between socio-economic status and educational outcomes (Moses, van der Berg & Rich, 2017)

Ball, Maguire & Macrae (2002) indicated that in families where one or more members have been to university, it is likely that others will follow- (First generation)

Personal aspirations, parents education and economic capital affect different choices about higher education ( Reay, David and Ball, 2005)

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Literature

University status is considered as the elephant in highereducation room ( Pitman, 2015) –better institutions are skilled toplay the access and admission, with their cultural capital;Universities might also look for students who are best fit.

Funding a hot topic – ( “Separate but equal” policy) Funding is adriver for realization of public policy objectives post-apartheidera – adequate funding for university and affordability especiallyfor students from lower socio-economic background.

University requires an upfront registration fee Progression to the following year requires students to pay

their balances or they are de-registered and exam resultswithheld.

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Literature

Lack of career guidance and poor schooling ( under- resources schools) – Quintile 1 to 3 and Quintile 4 to 5 (Former white schools).

Students struggle with the language proficiency necessary for them to be successful graduates ( Ratange, 2006)- English a proxy

Are there students unprepared/immature for demands of higher education or university a best fit for specific students ?

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Literature

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Approach – Bayesian networks

Graphical structure

Nodes

• A set of random variables

Directed Arcs

• Connect nodes, representing the direct dependencies between variables

• Strength of dependencies is quantified by conditional probability distributions associated with each node.

The study involved UP and WITS 2011, 2012 and 2013 students

First time entering students doing 3, 4, 5 and 6 years programs

The WITS data involved 4075 students in 2011, 4601 in 2012 and in 4655 in 2013

The UP data involved 7386 students in 2011, 7390 in 2012 and 8453 in 2013

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Data

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Model

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Explanatory variables

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Response variables

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Model (UP)

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Results UP

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Model (WITS)

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Results WITS

From UP

White females are more likely to complete in minimum time, they are likely to be

in quintile 4 or 5 and self funded while English is likely to be their home language.

From WITS

Black females are more likely complete in minimum time , while the home

language is other foreign language ( other African countries) while they are from

quintile 6 and IQ which private schools.

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Findings

Socio-economic status is interlinked with educational outcomes.

For example students from quintile 1- 3 are more likely to change program and stay longer in the system. First generation and parents education is associated with the career choice and influences the success.

Changing program- lack of career guidance

Implications of changing a program and not finishing in minimum time

Financial implications – spending more money in the university, and leaving university with a debt.

Stay longer in the system

Emotional

Increased drop- out probability

Economic implication - unemployment

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Conclusions

University invest more on:-

Career guidance- academic advising

Student support – tutors, mentors and faculty adviser

Funding programs not only tuition fees but meals ,

accommodation etc.

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Recommendations

Thank You

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