Realized reward, memory and prediction

Last registered on October 17, 2023
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Pre-Trial

Trial Information

General Information

Title
Realized reward, memory and prediction
RCT ID
AEARCTR-0012250
Initial registration date
October 10, 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
October 17, 2023, 11:47 AM 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
Yilei Luo
Affiliation
Renmin University of China
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Other Primary Investigator(s)

PI Name
Fangwen Lu
PI Affiliation
Peking University
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PI Name
Bin Miao
PI Affiliation
Renmin University of China
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PI Name
Haiyan Zhang
PI Affiliation
Renmin University of China
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Additional Trial Information

Status
In development
Start date
2023-10-10
End date
2025-08-31
Keywords
Behavior, Education
Additional Keywords
Realized reward, memory, prediction
JEL code(s)
D82 D83 D91
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We conduct an experiment at a Chinese middle school to examine how rewarding students for previous accurate predictions or recall regarding their exam performance affects their subsequent memory and predictions. Intervention occurs at both class and individual levels. Half the classes are rewarded for their previous correct recall, while the other half are rewarded for their previous correct predictions. Within each class, students who answered correctly are randomly chosen for either cash or gift rewards. After students complete a post-intervention exam, we administer a survey to evaluate the treatment effect in recalling exam performance and predicting future exam performance.
External Link(s)

Registration Citation

Citation
Lu, Fangwen et al. 2023. "Realized reward, memory and prediction." AEA RCT Registry. October 17. https://doi.org/10.1257/rct.12250-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Our team had previously conducted a survey where students were asked to recall their past exam scores and predict their future exam scores. For those students who answered correctly, the survey promised to randomly select a certain number of them for rewards. The specific intervention is based on this random reward system. Half of the classes are rewarded for their accurate recall, while the other half are rewarded for their correct predictions. In each class, we randomly reward some among students who answer correctly, where half of them receive cash rewards and the other half receive gift rewards.
Intervention Start Date
2023-10-10
Intervention End Date
2024-12-31

Primary Outcomes

Primary Outcomes (end points)
1） recall of mid-exam performance
2） prediction of final-exam performance
3） actual mid-exam performance
4） actual final-exam performance
5） the gap between recalled mid-exam performance and actual mid-exam performance
6） the gap between predicted final-exam performance and actual final -exam performance
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
1) Whether recall accurately
2) Whether predict accurately
3) The interest in learning
4) The effort exerted in learning
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our team had previously conducted a survey where students were asked to recall their past exam scores and predict their future exam scores. For those students who answered correctly, the survey promised to randomly select a certain number of them for rewards. The specific intervention is based on this random reward system. Half of the classes were rewarded for their accurate recall, while the other half were rewarded for their correct predictions. In each class, we randomly reward some among students who answer correctly, where half of them receive cash rewards and the other half receive gift rewards. Following the intervention, students will undergo another round of exams. After these exams, we will once again administer a survey, asking students to recall their exam scores and predict their future exam scores. Until the survey is conducted, students will remain unaware of any subsequent recall and prediction activities, allowing us to assess the impact of the intervention on their subsequent recall and prediction behaviors.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
2 levels of randomization:
class level: 16 classes
individual level: around 560 individuals
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
The recall-rewarding versus prediction-rewarding interventions are cluster based, and we have 16 classes in total.
Whether eligible students receive cash, gift or no reward is randomized at the student level, and we have around 560 individuals.
Sample size: planned number of observations
Around 560 students
Sample size (or number of clusters) by treatment arms
around 280 students in 8 recall-rewarding classes,
around 280 students in 8 prediction -rewarding classes,
within recall-rewarding classes: 32 students receiving cash rewards, 32 students receiving gift rewards, around 100 students are eligible for reward but not selected, around 120 students are not eligible for reward
within prediction-rewarding classes: 32 students receiving cash rewards, 32 students receiving gift rewards, around 100 students are eligible for reward but not selected, around 110 students are not eligible for reward
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
(1) Recall-rewarding versus prediction-rewarding interventions The interventions are clustered at the class level, so the correlation across students within the same class has to be taken into account. To circumvent such complexity, we take advantage of the data from previous survey, assume the future recall or prediction is comparable to those in the past if there was no intervention, and regress the past recall/prediction on the newly generated intervention variable as well as other control variable with standard error clustered at the class level. Using score recall as example, the estimated s.e. of the intervention variable is about 5.5, so the Minimum Detectable Effect (MDE) is expected to be 2.8*5.5=15.4, which is equivalent to an increase of 4.2 percent or 0.14 standard error over the recall score in control group. (2) Receiving reward (cash or gift) versus no rewards Also using data from previous survey and also using score recall as example, the Minimum Detectable Effect (MDE) is expected to be 14.8, 3.9 percent or 0.13 standard error. (3) Cash vs gift Also using data from previous survey and also using score recall as example, the Minimum Detectable Effect (MDE) is expected to be 23.8, 6.4 percent or 0.22 standard error.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB at Renmin University of China
IRB Approval Date
2023-10-07
IRB Approval Number
RUCecon-202310-1
Analysis Plan

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