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Tackling the Gender Gap in Mathematics in Italy
Last registered on December 10, 2018

Pre-Trial

Trial Information
General Information
Title
Tackling the Gender Gap in Mathematics in Italy
RCT ID
AEARCTR-0003651
Initial registration date
December 06, 2018
Last updated
December 10, 2018 2:05 PM EST
Location(s)

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Request Information
Primary Investigator
Affiliation
University of Torino
Other Primary Investigator(s)
PI Affiliation
FBK-IRVAPP
PI Affiliation
University of Torino, Italy
Additional Trial Information
Status
On going
Start date
2017-11-01
End date
2020-04-30
Secondary IDs
Abstract
According to the last available PISA data, Italy is one of the countries with the highest gender gap in mathematics, and the gender gap in mathematics in Piedmont is even higher. Therefore, Piedmont represents a good case study to test new methods devised at tackling the math gender gap.
Many explanations have been proposed for the existence of a gender gap in mathematics. Among other factors affecting math performance, the educational methods and practices used in class matter. Many studies show that when mathematics’ teaching is centred upon problem solving, involving students in discussions and investigative work as opposed to traditional passive methods, the gender gap in math decreases and can even disappear.
The objective of the project is to devise a teaching method, designed incorporating recommendations from the existing literature and own analysis of existing data, that aims to narrow the gender gap in mathematics and to measure the impact of this method on Piedmont children in primary school. Due to the type of intervention, we also evaluate the impact of the applied teaching practices on the math skills of all children, which is likely to be the first and strongest effect.
We conduct a randomized control trial to evaluate the impact of the implemented teaching methodology on math skills and on the gender gap in mathematics. The intervention covers about 50 third grade classes in 25 schools, evenly divided into treatment and control classes, involving about 1000 students. The gap will be reduced if math skills improvement is larger for girls, thanks to the specific design of the treatment.
External Link(s)
Registration Citation
Citation
Contini, Dalit, Maria Di Tommaso and Daniela Piazzalunga. 2018. "Tackling the Gender Gap in Mathematics in Italy." AEA RCT Registry. December 10. https://doi.org/10.1257/rct.3651-1.0.
Former Citation
Contini, Dalit, Maria Di Tommaso and Daniela Piazzalunga. 2018. "Tackling the Gender Gap in Mathematics in Italy." AEA RCT Registry. December 10. https://www.socialscienceregistry.org/trials/3651/history/38645.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2019-01-15
Intervention End Date
2019-06-15
Primary Outcomes
Primary Outcomes (end points)
This study will provide evidence of the impact of a new teaching methodology described. The primary research questions are the following:
i. What is the impact of the treatment on children’s math abilities?
ii. How does the impact differ by gender?
iii. How does the impact differ by children’s prior achievements?
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The randomized control trial takes place in the primary schools of Torino province in the Piedmont region (North West of Italy), where there are about 180 primary schools. All primary schools in the province of Torino were informed about the project and the minimum conditions to participate.
Enrollment to the project is voluntary. Due to budget constraints, the plan was to enroll 50 classes and therefore at most 25 schools, implying approximately 1000-1250 pupils, half of which in the treated group. Among the applicant schools and classes, we randomly selected 25 participating schools and the two participating classes within each school. Finally, within each school we randomly assigned one class to the treatment group and the other to the control one.
Experimental Design Details
Not available
Randomization Method
Public lottery
Randomization Unit
Classes within the same school
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
50 classes
Sample size: planned number of observations
1,000 pupils
Sample size (or number of clusters) by treatment arms
25 classes control, 25 classes treated
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
Analysis Plan
Analysis Plan Documents
Pre_Analysis_ Tackling the gender gap in math

MD5: 94d47f1e8c34e506da1b25e950594ae9

SHA1: 375bc502e55a480ee89fe83b881dc557a6bd470c

Uploaded At: December 06, 2018