Advice from universities could confuse student UAC preferences

Students applying for university courses in NSW may be making poor choices when listing their order of course preferences with the Universities Admissions Centre (UAC) due to their own misunderstanding of the rules and confusing advice from universities, according to a new study from the School of Economics at the University of Sydney.

Making a poor choice in their university application may lead students to miss out on their preferred course, the study found. The students making the most mistakes – and most at risk of missing out – are from non-selective government schools.

At the time of the study, at least one university was advising students to place a course with a “guaranteed entry” in first preference place. Currently, several Australian universities advise applicants to place their “guaranteed entry” course as their “highest eligible preference” to get in.

This is confusing, the authors found, because an applicant should always place their ideal course first and any guaranteed courses second or third. If the guaranteed course is also their ideal course, the applicant should place it first.

If a student prefers other courses, they should be placed above the guaranteed course. But the advice of some universities effectively discourages this behaviour, the authors found, potentially affecting thousands of students each year.

The study, published in the prestigious journal Proceedings of the National Academy of Sciences of the United States of America Nexus (PNAS Nexus), found 75.5 percent of students in the experimental study made poor choices in listing their course preferences that could impact their career and earning potential.

“The findings indicate that many applicants fail to understand the UAC preference system and they follow advice from universities, and others, that may not be in their best interest. That is not good for them as they can end up studying a course they like less than another course they were eligible for,” said lead author Associate Professor Pablo Guillen Alvarez, who is an experimental economist in the School of Economics at the University of Sydney.

“If someone ends up studying Course A instead of Course B they could be losing hundreds of thousands of dollars over a lifetime in salary, not to mention they are doing something that was not their preferred choice.”

“We need to fix the advice we are giving potential university students,” Associate Professor Guillen Alvarez said.

Each year, over 40,000 graduating high school students in NSW apply to university through UAC. Applicants submit an ordered preference list of up to five degrees for which they wish to be considered. To generate offers to students, UAC applies a matching algorithm that accounts for a student’s individual assessment score and the university-determined, degree-specific, entry cut-off scores. Most school leavers in NSW are given an ATAR score so they can be ranked against other students when applying for a university course.

To reduce uncertainty around the ATAR ranking system, many universities offer “guaranteed entry” into particular courses. This means the university has publicly committed to a cut-off ATAR score for the course. Some universities then encourage students to place the guaranteed entry course first or in the “highest eligible” position.

Many potential students are attracted to the guaranteed entry courses and place them first in their preferences because it feels like a “safe” choice, the authors said. Applicants receive advice on how to list their degree preferences from multiple sources including UAC and individual universities. The researchers set out to test how students behaved after receiving different advice.

To evaluate the performance of the current system, Associate Professor Pablo Guillen Alvarez and his co-authors Professor Onur Kesten, Associate Professor Mark Melatos and Alexander Kiefer ran a field experiment with 832 former high school students. The students were asked to imagine they had an ATAR of 80 and to list their course preferences into a dummy UAC application based on different advice choices:

-        no advice (participants were told the experiment goes by UAC rules which they could look for on the internet)

-        UAC advice (participants were given specific UAC advice about how to rank their courses)

-        University advice (participants were given specific university from the time of the experiment)

-        Both UAC and university advice.

To remove bias regarding choice of university or program, the study listed courses using a unique identifying number and a proposed monetary pay-off for each course. Assuming applicants preferred more money to less, the pay-off created a preference system. The applicants were also told, one of the courses had guaranteed entry.

Overall, 75.5 percent of participants failed to behave in their best interest (the preferred course with the higher pay-off). High rates of applicant manipulation (70 percent) persisted even when applicants were provided with UAC’s advice. University advice made things worse, with manipulation rates significantly increasing to 80 percent.

Students who attended non-selective government high schools were even more prone to following misleading advice than those who attended academically selective government high schools and private high schools.

“We find that the accuracy and clarity of advice given by market actors may significantly affect applicant understanding and behaviour,” said Associate Professor Guillen Alvarez. “This can potentially translate into suboptimal assignments and subsequent suboptimal employment outcomes. We need to give clearer advice.”

Kim Paino, General Manager, Marketing and Engagement at UAC, said the current situation was frustrating. “We do know that many of our applicants find the process of ordering their preferences confusing. They’re also very anxious not to do the wrong thing and miss out on an offer. Clear and consistent information can really help them make the best choices.”

Declaration: The research was funded by the ARC Discovery Grant DP160103699. Banner image: Pixabay