How to battling learning decay in class

Last registered on September 01, 2020

Pre-Trial

Trial Information

General Information

Title
How to battling learning decay in class
RCT ID
AEARCTR-0006384
Initial registration date
August 31, 2020

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
September 01, 2020, 7:25 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
Tsinghua University

Other Primary Investigator(s)

PI Affiliation
University of California at Berkeley
PI Affiliation
University of Pittsburgh

Additional Trial Information

Status
In development
Start date
2020-09-13
End date
2021-09-13
Secondary IDs
Abstract
How to alleviate learning decay is a central challenging for both online and offline teaching. Prior studies have shown that asking students to finish some in-class activities is effective for battling such self-control problems. We are interested in examining whether the timing for doing tasks, e.g., early vs. late, has any impact on students' learning performances.
External Link(s)

Registration Citation

Citation
Liu, Tracy, Ulrike Malmendier and Stephanie Wang. 2020. "How to battling learning decay in class." AEA RCT Registry. September 01. https://doi.org/10.1257/rct.6384
Experimental Details

Interventions

Intervention(s)
We are going to randomly assign students to different lectures and ask them to finish an in-class activity.
Intervention Start Date
2020-09-13
Intervention End Date
2021-09-13

Primary Outcomes

Primary Outcomes (end points)
attendance and grades
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We are going to randomly assign students to different lectures and ask them to finish an in-class activity.
Experimental Design Details
Randomization Method
randomization done in office by a computer
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
500 students
Sample size: planned number of observations
500 students
Sample size (or number of clusters) by treatment arms
if we will randomly assign students to 20 lectures, each lecture will have 25 students.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials