In several LAC countries, the assignment of teachers to schools is also inefficient and not transparent. Teacher assignment systems often do not provide teachers with enough information on the available vacancies to allow them to make informed decisions. Teachers are more likely to be dissatisfied with their assigned school if they do not have enough information about their options, which can impact their effectiveness in the classroom (Jackson, 2012). Moreover, lack of information about vacancies also creates imbalances in supply and demand for teaching staff. For example, in Peru, more than one quarter of vacancies remain unfilled after the teacher selection process. Most of these vacancies are in disadvantaged schools.
To address these issues and improve equity, transparency, and efficiency in teacher allocation, some school systems around the world have adopted on-line centralized allocation systems (Elacqua et al., 2016). These centralized systems provide a unique opportunity to use new technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to improve the allocation process and its outcomes (Agrawal et al., 2018). Moreover, AI can be paired with behavioral insights to improve the outcomes of the allocation systems. Recent experimental evidence in Peru and Ecuador also suggests that behavioral strategies can be effective at attracting teachers to hard-to-staff and vulnerable schools (Ajzenman et al., 2020).
The general objective of this project is to strengthen the centralized teacher allocation system in Peru. This TC will finance: (i) assessments and improvements in the mechanisms for teacher assignment, (ii) improvements in the front-end technology used to assign teachers including a) further exploration of behavioral strategies to motivate teachers to work in more disadvantaged schools, b) introduction of changes in the teacher assignment platform to enhance user experience to increase transparency and efficiency in teacher assignment, and c) introduction of new technologies, such as artificial intelligence and machine learning, to improve equity and efficiency in the allocation process and increase teacher satisfaction with their final allocation.
Project Detail
Country
Peru
Project Number
PE-T1447
Approval Date
September 1, 2021
Project Status
Implementation
Project Type
Technical Cooperation
Sector
EDUCATION
Subsector
TEACHER EDUCATION &EFFECTIVENESS
Lending Instrument
-
Lending Instrument Code
-
Modality
-
Facility Type
-
Environmental and Social Impact Category (ESIC)
Category C: Likely to cause minimal or no negative environmental and associated social impacts
Total Cost
USD 556,000.00
Country Counterpart Financing
USD 56,000.00
Original Amount Approved
USD 500,000.00
| Operation Number | Lending Type | Reporting Currency | Reporting Date | Signed Date | Fund | Financial Instrument |
|---|---|---|---|---|---|---|
| ATN/JF-18796-PE | Sovereign Guaranteed | USD - United States Dollar | Japan Special Fund | Nonreimbursable |
- Lending Type: Sovereign Guaranteed
- Reporting Currency: USD - United States Dollar
- Reporting Date:
- Signed Date:
- Fund: Japan Special Fund
- Financial Instrument: Nonreimbursable
Can’t find a document? Request information
https://www.iadb.org/document.cfm?id=EZSHARE-957413516-18
TC Document
Procurement Plan_55483.pdf
Dec. 01, 2022
English
https://www.iadb.org/document.cfm?id=EZSHARE-957413516-19
TC Document
TC Document - Disclosure_80938.pdf
Dec. 01, 2022
English
https://www.iadb.org/document.cfm?id=EZSHARE-957413516-16
TC Document
Results Matrix_36216.pdf
Dec. 01, 2022
English
https://www.iadb.org/document.cfm?id=EZSHARE-957413516-17
TC Document
Terms of Reference_6153.pdf
Dec. 01, 2022
English
https://www.iadb.org/document.cfm?id=EZSHARE-957413516-15
TC Document
Request from the Client_82311.pdf
Dec. 01, 2022
English
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