Call of Interest: Meta-Learning Assessment & Capacity Strengthening Consultant at Samuel Hall

We invite applications from suitably qualified candidates. SCOPE OF WORK

• The consultant will undertake the following activities across 5 months (February – June 2026): The consultant will design and implement an innovative, data-driven meta-learning assessment that goes beyond traditional qualitative tools. The emphasis is on how learning occurred, how it was applied, and how it generated value, using quantifiable metrics, ratios, and comparative indicators that can be shared with partners during restitution workshops.

• The consultant is expected to demonstrate creativity, propose fit-for-purpose methodologies, and articulate clearly how they define and operationalise meta-learning within LLEARN.

Activity 1: Inception, Conceptualisation & Methodological Design (February 2026)

• Review LLEARN project documentation: Implementation Manual, Data Collection Manual, PF reports, Cocreation City

• Notes, Country Reports, Validation Workshop outputs

• Provide a clear conceptualisation of meta-learning within the LLEARN context, explaining its relevance to multi-country, multi-stakeholder consortia.

Propose an innovative methodological approach that captures individual, organisational, and consortium-level meta-learning using:

• Quantitative and semi-quantitative tools

• Meta-learning scorecards

• Process-tracing indicators

• Behavioural markers of adaptation and self-regulation

• Learning transfer matrices

Deliverable: Inception Report (10-15 pages) with full methodology and indicator framework. Activity 2: Meta-Learning Measurement & Data Capture (March – April 2026) The consultant will employ creative, participatory, and quantifiable techniques, such as: Learning Analytics Dashboards:

• Analysis of project workflows, communication patterns, and decision logs to quantify adaptive behaviours.

Learning Behaviour Metrics:

• Ratios such as feedback uptake rates, time-to-adaptation, and cross-country knowledge transfer frequency.

Scenario-Based Assessments:

• Structured exercises allow partners to demonstrate decision-making, problem-solving, and reflexivity.

Meta-Learning Self-Assessment Scores:

• Numerical scoring tools co-developed with partners to measure their confidence, self-regulation, and learning strategy utilisation.

Synergy and Ripple-Effect Mapping:

• Quantitative mapping of how one activity influences others across cities or countries using network analysis or contribution scoring.

• Field anecdotes may be collected selectively as illustrative case examples, but all primary findings must be supported by figures, ratios, or comparative metrics.

Deliverable: Dataset and analytical summaries of all meta-learning indicators in Meta-Lear...