Understanding factors that influence Teaching at the Right Level’s effectiveness and generalisability: a Bayesian evidence synthesis

Programme of work

Enhancing evidence transferability

Principal investigator(s)

Noam Angrist

Host institution

Young 1ove Organization

Other institutions

London School of Economics

Dates

February 2020 to July 2021

Project type

Evidence synthesis

Country/ies

All the countries where the Teaching at the Right Level (TaRL) intervention has been rolled out.

Research question

This study is a systematic assessment of which programme compo-nents and contextual factors are most decisive for the success of TaRL, a remedial education intervention to improve learning.

Research design

This project will employ a Bayesian hierarchical approach to synthesise the evidence. This approach will assess generalisability across settings and unpack the relative importance of the differing features of the TaRL programmes and study contexts. The analysis will estimate the average treatment effect across all studies and the variance across contexts.

Data source

The study will draw on raw data from existing trials of TaRL interven-tions

Policy relevance

This study will:

  1. Advance knowledge on what works to improve learning outcomes;
  2. Develop methodology to synthesise and understand complex interventions through middle-range theory; and
  3. Test an innovative combination of methods to inform generalizability that can be applied to future interventions.

Study findings can inform efforts to scale up TaRL in several countries.

Project Outputs

« 2. Enhancing evidence transferability