Preferences over Growth Mindset Embedded Teaching Strategies: A Discrete Choice Experiment

Last registered on November 17, 2025

View Trial History

Pre-Trial

Trial Information

General Information

Title

Preferences over Growth Mindset Embedded Teaching Strategies: A Discrete Choice Experiment

RCT ID

AEARCTR-0017196

Initial registration date

November 10, 2025

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

November 17, 2025, 6:58 AM EST

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Ashley Orr

Affiliation

The Ohio State University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Emily Marshall

PI Affiliation

Denison University

Contact Investigator

Additional Trial Information

Status

In development

Start date

2025-11-11

End date

2026-01-31

Keywords

Education, Labor

Additional Keywords

Growth mindset, academic mindset, teaching interventions

JEL code(s)

A2: Economics Education and the Teaching of Economics

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Educators who experiment with new and emerging teaching interventions and methods face an important tradeoff between costs (upfront learning and ongoing implementation) and sometimes uncertain returns (student learning and retention gains) to the intervention. Using an online survey and a choice experiment, we measure the current preferences of economics educators over instruction scenarios that employ different teaching strategies. We examine how choices vary with class size, intervention time cost, expected student performance, and expected retention. We also investigate how, if at all, preferences change with a hypothetical additional monetary payment that rewards the initial use and application of a new teaching strategy. We finally report descriptive results regarding instructors' ranking of growth mindset embedded educational interventions and self-reported willingness to experiment with academic/growth mindset-embedded teaching strategies, offering a direction for future research within the Economics Education Network for Experiments (EENE).

External Link(s)

Registration Citation

Citation

Marshall, Emily and Ashley Orr. 2025. "Preferences over Growth Mindset Embedded Teaching Strategies: A Discrete Choice Experiment." AEA RCT Registry. November 17. https://doi.org/10.1257/rct.17196-1.0

Sponsors & Partners

Partner

Name

The Economic Education Network for Experiments

Type

none

URL

https://eene.org/

Experimental Details

Interventions

Intervention(s)

We use a discrete choice experiment in a Qualtrics survey. Current instructors select their preferred class to teach between two classes with different attributes.

Intervention (Hidden)

We use a discrete choice experiment in a Qualtrics survey. Current instructors select their preferred class to teach between two classes with different attributes. Class attributes are randomized with equal probability in the Qualtrics survey. Attributes include class size, intervention time cost, expected student performance (in percentage point gains), hypothetical retention (measured in student retained or loss to the major), and payment (usual or with bonuses).

Intervention Start Date

2025-11-11

Intervention End Date

2026-01-31

Primary Outcomes

Primary Outcomes (end points)

Course selection conditional on stated course attributes.
Course selection is a 0/1 indicator, while course attributes are randomized.

Primary Outcomes (explanation)

The primary objective of our study is to document how course teaching preferences change, if at all, as the attributes of the course change and to estimate how instructors trade off teaching time costs with potential student learning gain. This information allows us to deduce the utility and “willingness to pay” estimates over class attributes.

Secondary Outcomes

Secondary Outcomes (end points)

Rankings over eight proposed teaching interventions.
Rankings are a discrete ordered categorical variable between 1 and 8.

Secondary Outcomes (explanation)

The secondary objective of our study is to learn how current economic instructors rank potential teaching interventions and how those rankings vary by the course level they primarily teach.

Experimental Design

Using a discrete choice experiment, current economics instructors select their most preferred hypothetical teaching assignment. Discrete choice experiments are common in labor economics, whereby survey participants select or rank job options with different attributes, allowing the researchers to learn about their most preferred option and ranking over options that are not chosen (Maestas et al., 2023).

Experimental Design Details

Each respondent (economics educator) is presented with five choices between hypothetical economics classes where the classes are defined by their randomly selected attributes including whether or not you have taught the material before, the class size, and expected student performance.

We assume instructors carefully review the attributes and select their most preferred option.

After collecting the data we estimate a conditional logit regression relating the class attributes to a 0/1 indicator of whether or not the class was selected.

Randomization Method

Class attributes will be randomly selected with equal probability from a pre-specified choice set. Randomization occurs for each individual at the attribute level. Instructor respondents complete 5 choices.

Randomization Unit

Classes - as defined by a set of random class attributes

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Sample size: planned number of observations

250-1500 choice observations

Sample size (or number of clusters) by treatment arms

50-300 economics instructors

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

There are over 200 instructors on the email list of EENE, so our maximum sample size is 300; however, we will target a sample size of 70 faculty/instructors. This is because the minimum sample size that will result in publishable research can be determined from a statistical power calculation. With a minimum of 70 responses, 5 tasks per response, 5 variable levels, a 0.05 effect size, we have 83% power to detect differences in preferences.

Supporting Documents and Materials

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

IRB