Prototype 6: Media Literacy Production competences


This evaluation test provide you a simple procedure to evaluate the level of the trainees media literacy production competences. The test allows to evaluate each trainee individually before and after the training, and to assess the progression during the training by comparison between the two tests. The test is based on the idea that at most the trainees are competent, at the most they are able to describe a production activity.


The trainees conceive (describing) a scenario presenting a media about a given topic to a specific audience. At most they are able describe the sequence in a coherent way, at most they are considered as competent.

The topic and the audience are given by the trainer. Another elements may be given by the trainer or be left free to the trainees appreciation.

Method 1 – Free page for responding: “Describe a sequence about [topic] addressed to [specific audience].”

Method 2 – Responding in two steps: (i) collecting and analysing existing presentation, (ii) developing his/her own.


Each answer is individually coded with the following scale:

Level Criteria Interpretation
0 The trainee is unable to conceive a coherent production. The trainee has no ML production competences.
1 The trainee is able to conceive a basic sequence. His/her answer counts a few keywords indicating that he/she makes the difference between content, objectives and method (“objective”, “method”, etc.). The trainee has basic ML production competences.
2 The trainee is able conceive a coherent production sequence and to plan it in different aspects. His/her analysis counts most keywords indicating that he/she masters the whole process: objectives, task, ressources, evaluation, timing, etc. The trainee has good ML production competences.
3 The trainee is able conceive and plan a complete ML production sequence and to justify interest (or limitations) of each element. His/her answer counts all keywords indicating that he/she masters the whole process (objectives, task, ressources, evaluation, timing, etc.) and makes links between these elements showing their coherence/alignment. The trainee has excellent ML production competences and may be viewed as an “expert” in these compétences.

The progression is evaluated by comparison between the scores before and after the training. You should expect that the final score is (individually and/or globally) higher at the end of the training that at the beginning.

Extra comments

It is recommended that the same evaluator does the scoring in the pre-training and post-training tests to ensure that the interpretation of the scoring indicators are constant.

Examples in eMEL