Learning Assessment and Evaluation for Education Programmes Training Course

Course overview

Education programmes are often judged by enrolment, attendance, completion rates and stakeholder satisfaction, while the harder questions remain unanswered: Did participants learn? Did teachers change practice? Did learning gains reach marginalised groups? Were results sustained after delivery? Programme managers and M&E teams need defensible evidence that connects inputs, teaching quality, learner assessment data and longer-term outcomes. This course addresses the practical challenge of designing and managing assessment and evaluation systems that support programme improvement, donor reporting and accountable investment decisions.

Participants learn to build an education-specific theory of change, develop measurable learning and performance indicators, select appropriate assessment methods, and construct an evaluation matrix. The course covers baseline, midline and endline design; criterion-referenced tests; rubric development; sampling; data quality assurance; disaggregation by gender, disability, location and other equity factors; and mixed-method evaluation. Participants also practise analysing learner assessment results in Microsoft Excel and Power BI, interpreting findings without overstating attribution, and presenting evidence against OECD DAC evaluation criteria.

Delivery combines instructor-led technical sessions with an extended education programme case study. Participants work with simulated learner assessment datasets, school observation records, teacher surveys and programme monitoring reports. They leave with an assessment and evaluation plan for an education intervention, including a results framework, indicator reference sheet, data collection plan, assessment blueprint, evaluation questions, analysis approach and reporting outline. This gives both participants and sponsoring managers a usable template that can be adapted to active literacy, teacher development, TVET, school improvement or non-formal education programmes.

The course is designed for professionals who already work with education projects, learning data, quality assurance or programme evaluation and need stronger technical judgement on how learning outcomes should be measured and used.

Course objectives

By the end of this course, participants will be able to:

• Develop an education programme theory of change linking activities, learning outputs, outcomes and longer-term impact
• Construct a results framework with SMART learning, equity and teaching-quality indicators
• Create an indicator reference sheet specifying definitions, disaggregation, sources, frequency and accountability
• Design criterion-referenced learner assessments using test blueprints, item specifications and scoring rubrics
• Select baseline, midline and endline sampling approaches appropriate to school and community education settings
• Apply data quality assessment procedures to verify completeness, accuracy, timeliness, integrity and confidentiality
• Analyse disaggregated learning assessment data in Microsoft Excel and Power BI to identify performance gaps
• Produce an education programme evaluation matrix and evidence-based findings report aligned to OECD DAC criteria

Benefits of attending

Target competencies

Who should attend

• Education Programme Managers — who need credible evidence to improve delivery models and defend programme investment
• Monitoring, Evaluation, Accountability and Learning Officers — who design indicator systems and manage education programme data
• Education Project Coordinators — who oversee school, teacher, learner or community-level implementation activities
• Learning Assessment Specialists — who need to connect test design and learner results to programme evaluation decisions
• Donor-Funded Project Managers — who must meet results-framework, reporting and evaluation commitments
• Government Education Planning and Quality Assurance Officers — who use learning evidence to monitor policy and service-delivery performance

Requirements and prerequisites

Participants should have practical experience working on, managing or supporting an education programme and understand basic project-cycle concepts such as objectives, activities, outputs, outcomes and indicators. Familiarity with routine monitoring data, learner assessments, school records or donor reporting is helpful. Participants should be comfortable using spreadsheets for sorting, filtering and basic formulas; prior Power BI experience is not required. The course does not require advanced statistics, psychometrics, coding, prior use of KoboToolbox or formal evaluation certification. Those without prior M&E experience should expect a demanding week and should first review basic logical frameworks and indicator terminology.

Training methodology

The instructor uses short technical briefings followed by worked examples from literacy, teacher professional development and school improvement programmes. Participants develop components of one running case: a results framework, learner assessment blueprint, sampling plan, data collection workflow and evaluation matrix. Hands-on labs use KoboToolbox forms, Microsoft Excel and Power BI to inspect data, calculate learning results and visualise disaggregation. Facilitated peer review tests the feasibility of each design choice. On day five, participants complete an application plan identifying how they will adapt their course deliverable for a live programme.

Course outline

Day 1: Education programme results and learning evidence

• Distinguishing access, participation, completion, learning and education-system outcomes
• Education programme theories of change and causal assumptions
• Results frameworks for literacy, teacher development and school improvement interventions
• SMART indicator design for learner, teacher and school-level results
• Indicator reference sheets and indicator performance tracking tables
• Equity-focused disaggregation by gender, disability, location, language and socioeconomic status
• OECD DAC criteria applied to education programme evaluation

Workshop: Participants build a theory of change and draft a results framework for a fictional remedial learning programme.

Day 2: Learning assessment design and data collection

• Formative, summative, diagnostic and outcome assessment purposes
• Criterion-referenced versus norm-referenced assessment decisions
• Assessment blueprints linking curriculum objectives, cognitive demand and test items
• Item specifications, distractors and basic item-writing quality checks
• Analytic and holistic rubric design for performance-based learning tasks
• Baseline, midline and endline study designs for education programmes
• Sampling frames, sample-size considerations and cluster sampling in school settings

Workshop: Participants create a test blueprint, draft assessment items and score a sample learner task using a rubric.

Day 3: Field implementation and data quality assurance

• KoboToolbox form design for learner, teacher, classroom observation and school surveys
• Informed consent, child safeguarding and data protection in education research
• Enumerator training, standardisation and assessor reliability procedures
• Data collection protocols for classroom observation and learner testing
• Data quality assessment dimensions: validity, reliability, completeness, timeliness and integrity
• Spot checks, back checks and verification of school administrative records
• Data cleaning rules, codebooks and secure data management workflows

Workshop: Participants configure a KoboToolbox data-collection form and complete a data quality assessment checklist for a school survey.

Day 4: Analysis, interpretation and evaluation judgement

• Microsoft Excel cleaning, validation and learner-level data preparation
• Descriptive statistics for scores, proficiency levels, attendance and completion
• Pre-post learning gain calculations and interpretation limitations
• Disaggregation analysis and equity-gap identification
• Power BI dashboards for cohort, school and subgroup learning results
• Mixed-method triangulation of tests, observations, interviews and monitoring records
• Evaluation matrices, evidence rating and contribution-focused conclusions

Workshop: Participants analyse a learner assessment dataset in Excel and Power BI and produce an equity-focused findings dashboard.

Day 5: Reporting, use and implementation planning

• Writing evaluation questions for relevance, effectiveness, efficiency, impact and sustainability
• Structuring findings, conclusions and actionable recommendations
• Communicating learning evidence to donors, school leaders and community stakeholders
• Data visualisation choices for technical and non-technical audiences
• Learning review meetings and adaptive management decision logs
• Evaluation terms of reference, timelines, roles and budget assumptions
• Assessment and evaluation plan quality review against implementation constraints

Workshop: Participants finalise and peer-review an assessment and evaluation plan, then present the management decisions their evidence will support.

Tools & standards covered

KoboToolbox, Microsoft Excel, Microsoft Power BI, OECD DAC evaluation criteria

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions