Consultancy: Advanced Analysis of Learning Assessment Data (AMPL)

Organizational Context

The UNESCO Institute for Statistics (UIS) is the custodian agency for SDG 4 indicators, supporting countries in generating and utilizing data on student learning outcomes. The Assessment for Minimum Proficiency Levels (AMPL) is a key module for producing internationally comparable SDG 4.1.1 indicators. Many countries struggle to apply advanced analytical methods to their assessment data, leading to underutilization of valuable information for policy development.

Job Purpose

This consultancy aims to address the limitations countries face in analyzing learning assessment data. The primary goal is to generate policy-relevant analytical outputs on learning quality and equity using AMPL datasets, in collaboration with national teams. Additionally, the consultancy will focus on strengthening national capacity to independently apply advanced statistical methods to learning assessment data. This initiative is guided by UIS studies on learning divides and the use of data to inform educational policy, seeking to enhance the effective use of assessment data for targeted policy interventions and improved educational outcomes.

Responsibilities

The consultant will lead a six-month program involving structured training, hands-on application, and technical mentoring. Key responsibilities include assessing dataset readiness, developing an analytical framework for policy questions on learning outcomes and equity, and preparing training materials on advanced statistical methods like multilevel modeling. The consultant will conduct two in-person workshops to introduce methods, guide data analysis, and facilitate peer review. Inter-sessional support will be provided remotely for deepening analyses and developing draft country reports. The final phase involves finalizing country reports, creating a synthesis report of cross-country findings, and developing a practical toolkit for institutionalizing analytical practices within national systems.

Work Experience

Requires demonstrated expertise in analyzing large-scale assessment data, including familiarity with programs like AMPL or MILO. Strong experience in multilevel modeling and complex survey data analysis is essential. Proven experience in capacity building and training delivery, with at least five years of experience or three comparable assignments, is also necessary.

Skills

Proficiency in statistical software (e.g., R, Stata) for large-scale data analysis is required. The ability to design and deliver effective training and mentoring to national technical teams is crucial. Excellent drafting skills for analytical reports and policy briefs, along with the ability to work independently and manage multiple engagements to meet deadlines, are essential.

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