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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
Last updated
September 01, 2020 7:25 AM EDT
Location(s)

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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.6384-1.0.
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
Not available
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