Demonstrating learning in practice

How do you distinguish between what is learnt during training and what is actually applied again at work?

1960 € per session, excl. VAT

In short

This 7-hour day, excluding breaks, enables you to build an evaluation framework linking learning objectives, evidence of learning and observations in the workplace. Generative AI helps prepare the instruments and analyse the results, under human supervision. You leave with a usable portfolio and explicit quality rules.

Why this training?

High satisfaction demonstrates neither mastery of a competence nor its use in the workplace. Without a baseline, observable criteria and follow-up data collection, training teams struggle to identify what needs strengthening. AI can suggest questions, clarify a marking scheme and summarise observations. Its suggestions must nevertheless be checked to avoid ambiguous questions, unsupported conclusions and confusion between learning and working conditions. Ongoing case study: The fictitious organisation Avelune Assistance trains its advisers to handle written complaints. Despite favourable satisfaction questionnaires, the quality of responses remains variable. Using fictitious work produced before and after training, followed by later observations, you must establish what has been learnt and examine its transfer. Transfer to the workplace: In the following month, apply the matrix to a training course within your remit, have a colleague review the marking scheme and agree a follow-up observation with an operational manager. Adjust the instruments after comparing the initial ratings.

Objectives of this training

  • Distinguish between satisfaction, learning, transfer and organisational effects
  • Link each competence to observable evidence
  • Develop comparable assessments before and after training
  • Check the consistency of a marking scheme through double marking
  • Analyse the results and propose evidence-based adjustments

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Training managers and instructional designers responsible for evaluation
  • Trainers and subject-matter leads who observe competences in the workplace

Prior skills

  • Be able to formulate a structured request to a generative AI assistant.
  • Know the objectives and delivery methods of a vocational training course.
  • Be able to calculate scores and filter a simple table.

What you will work on

  1. Define what constitutes evidence

    Objective: Link competences to instructional decisions. Evaluation levels, expected behaviours, baseline and limits of attribution.

  2. Develop assessments of learning

    Objective: Compare performance on a fair basis. Parallel simulated tasks, instructions, model answers and the risk of memorisation.

  3. Make judgements reproducible

    Objective: Reduce divergent interpretations. Descriptive criteria, proficiency levels, weighting and justified thresholds.

  4. Observe transfer to the workplace

    Objective: Collect evidence of use in professional practice. Follow-up observation, opportunities for application, managerial support, data minimisation and retention period.

  5. Analyse without overinterpreting

    Objective: Produce traceable findings. Individual comparisons, missing data, small sample sizes and contextual factors.

  6. Decide on adjustments

    Objective: Turn findings into targeted instructional actions. Remedial support, workplace coaching and tailored reporting to stakeholders.

What you take away

  • Matrix of competences, evidence and measurement points
  • Initial and final assessment kit with marking scheme
  • Follow-up observation grid and data collection protocol
  • Table of verified results and recommendations note

Method and assessment

  • Short input sessions followed by immediate practice using the ongoing case study.
  • Individual work followed by work in pairs, with reciprocal review of the outputs.
  • Facilitated group discussion: what works, what needs correcting and what remains a human decision.
  • Preparation: Bring anonymised learning objectives and an existing evaluation; a fictitious portfolio can replace them. Check access to the tools authorised by the organisation; no personal data will be needed.

Observable criteria: Each competence has an observable indicator and a source of evidence. ; The initial and final assessments evaluate the same competences at comparable levels of difficulty. ; Marking discrepancies are documented and lead to clarification of the marking scheme. ; Each aggregate score is recalculated independently of AI. ; Conclusions distinguish between learning outcomes, observed transfer and contextual factors..

Frequently asked questions

Is a questionnaire enough?

It provides information about certain knowledge, but an applied competence requires a piece of work or an observation.

Can AI mark on its own?

Marks remain subject to human validation; AI can help clarify the criteria.

When should transfer be observed?

After a genuine opportunity to apply learning, according to the rhythm of the role, rather than at a uniform deadline.

Can a causal effect be inferred?

Improvement from before to after training is not enough: context, selection and other changes must be examined.

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