Applying matching theory to academic advising

Last registered on June 28, 2023
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Pre-Trial

Trial Information

General Information

Title
Applying matching theory to academic advising
RCT ID
AEARCTR-0011568
Initial registration date
June 25, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
June 28, 2023, 4:54 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name
Siqi Pan
Affiliation
University of Melbourne
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Other Primary Investigator(s)

PI Name
Georgy Artemov
PI Affiliation
University of Melbourne
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PI Name
Ivan Balbuzanov
PI Affiliation
University of Melbourne
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Additional Trial Information

Status
In development
Start date
2023-06-26
End date
2026-12-12
Keywords
Behavior, Education
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The Academic Advising Program was introduced at the University of Melbourne in 2020. The goal of this program is to build connections between students and academics so that students can receive personalized guidance and support while navigating the opportunities and challenges of university. The current system randomly assigns students to academics. However, a great amount of feedback has indicated that participants care about whom they are assigned to. A better match between students and academics can make it easier for academics to provide useful and relevant advice and improve students' engagement and participation in the program. In this project, we aim to tackle this problem using insights from the matching theory and market design.
External Link(s)

Registration Citation

Citation
Artemov, Georgy , Ivan Balbuzanov and Siqi Pan. 2023. "Applying matching theory to academic advising." AEA RCT Registry. June 28. https://doi.org/10.1257/rct.11568-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We designed two matching algorithms to match advisors and students in the Academic Advising program.
Intervention Start Date
2023-06-26
Intervention End Date
2025-07-30

Primary Outcomes

Primary Outcomes (end points)
1. Submitted preferences: the number and range of selected characteristics, the ranking and score of selected characteristics.
2. Student welfare (mean and distribution) based on their (1) submitted preferences, (2) the interactions with the Academic Advising program (including meeting attendance, responses to the satisfaction surveys, etc.).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
1. Student welfare in correlation with demographic variables.
2. Long-term impact: students' academic performance and outcomes (individual study plans, subject completions, time-to-graduation, and participation in extracurricular activities).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design consists of two treatments. Each treatment includes a preference elicitation survey, a matching algorithm, and the collection of outcome variables
Experimental Design Details
Not available
Randomization Method
Each participant will be randomly assigned to one of the two treatments. We will use Qualtrics to conduct randomization and set the randomizer to equalize participation in each treatment.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Our target sample size is 1000 students.

We have limited control over the sample size because all members of the eligible cohort will be invited to participate. It is difficult to predict the exact sample size because (1) it is difficult to predict the take-up rate as the Office of Academic Advising has not done any programs like this in the past; (2) it is difficult to predict how many cohorts we can include in the study as this is determined by the Office of Academic Advising on the faculty and the university level.
Sample size: planned number of observations
1000 students
Sample size (or number of clusters) by treatment arms
1000 students treatment 1, 1000 students treatment 2
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
The Office of Research Ethics and Integrity (OREI), University of Melbourne
IRB Approval Date
2023-06-23
IRB Approval Number
2023-27090-41872-4
Analysis Plan

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