Duration
20h Th, 20h Pr
Number of credits
| Master in education (120 ECTS) | 3 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The course includes the production and analysis of activities aiming at the development of computational thinking as well as their experimentation in the field.
- Definitions and Characteristics of Computational Thinking
- Competency framework
- Concepts/notions (algorithm, variable, loop, sequence of actions...)
- Activities that are connected (e.g. use of robots, programming languages) and disconnected (without the use of technological tools)
- Pedagogical scenario respecting the triple concordance between targeted competencies, learning activities and evaluation
- Problem solving approaches
- Reflexivity
- ...
Learning outcomes of the learning unit
The main course's objectives are (for the students):
- learning how to build, implement and analyze teaching-learning devices for the development of Computational Thinking (CT) among students from kindergarten to high school.
- reflecting on the training of teachers/trainers in the field of CT.
- define "Computational Thinking"
- appropriate a competency framework (for young people of different age groups)
- acquire (part of) the competency framework
- understand complex problems, break them down into sub-problems, propose solutions that can be partially or fully automated
- link a (series of) learning activities to one or more competencies
- analyze various existing activities, particularly in terms of the triple alignment between target competencies, learning activities and evaluation
- design a pedagogical scenario for the development of CT
- implement their scenario in a given context
- make links between these activities and teacher/trainer training
- take a reflexive look at their learning process and their productions
Prerequisite knowledge and skills
Planned learning activities and teaching methods
- Collect learner's conceptions/representations of Computational Thinking (CT)
- Brief presentation of the competency framework "PIAF" (origin, comparison)
- Pluged and unpluged activities in sub-groups (+ note taking)
- Presentation of the pedagogical scenario framework
- In sub-groups, complete part of the canvas based on the experience of a workshop
- Group debriefing
- Intervention of external expert(s)
- Analysis of existing activities or scenarios (observation grids)
- Online resource exploration
- Testing of different hardware (e.g. different types of robots or languages)
- Co-creation in duo, mainly conducted and tutored at a distance, of a pedagogical scenario (personal choice of the learners validated by the teacher) based on a provided framework
- Experimentation (of part) of the scenario in the field
- Filling a reflective roadbook throughout the process
Mode of delivery (face to face, distance learning, hybrid learning)
Mainly on face-to-face.
Organisational adjustments related to the current health context
Recommended or required readings
All the documents will be put online progressively on eCampus.
Assessment methods and criteria
Below you will find information on the evaluation methods planned for in-person and remote exams as well as those planned for hybrid sessions. Depending on how the health crisis evolves, the chosen method will be communicated to you no later than one month before the start of the exam session.
Any session :
- In-person
written exam
- Remote
written work
- If evaluation in "hybrid"
preferred remote
Additional information:
The evaluation is based on :
- the design of a pedagogical scenario aimed at developing computer and algorithmic thinking in young people or in (future) supervisors
- keeping a reflexive roadbook throughout the learning activities, including a brief report on the partial or total experimentation of the scenario including possible outlooks
Work placement(s)
Organizational remarks
Contacts
Profesor :
Brigitte Denis, b.denis@uliege.be
Teacher's assistant :
Sarah Higuet, sarah.higuet@uliege.be