cookieImage
2026-2027 / ZENS4327-1

Algorithm optimisation and complexity

Duration

24h Th

Number of credits

 Master of Education, Section 3: Mathematics and Digital Training (HECh)4 crédits 
 Master of Education, Section 3: Mathematics and Digital Training (HELMO)4 crédits 
 Master of Education, Section 3: Mathematics and Digital Training (HERS)4 crédits 
 Master of Education, Section 3: Mathematics and Digital Training (HEL)4 crédits 
 Master of Education, Section 3: Manual, Technical and Technological Training and Ditital Training (HELMO)4 crédits 
 Master of Education, Section 3: Manual, Technical and Technological Training and Ditital Training (HERS)4 crédits 
 Master of Education, Section 3: Manual, Technical and Technological Training and Ditital Training (HEL)4 crédits 

Lecturer

Adeline Massuir

Coordinator

Alix Dassargues

Language(s) of instruction

French language

Organisation and examination

Teaching in the second semester

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The aim of this course is to develop a critical perspective on the rationale underlying algorithms and on the consequences they entail. To this end, several areas may be explored, including:

  • Fundamental algorithmics: understanding what an algorithm does, constructing algorithms, determining whether an algorithm terminates, comparing algorithms, evaluating their efficiency, and rewriting an algorithm based on the results of the analysis, among other topics.
  • Applications: machine learning, cryptography, computability, etc.
  • Epistemological and didactic analysis of the concept of an algorithm and of algorithmics: different dimensions of algorithms, the tool-object duality, algorithmic thinking, paradigms, etc.
  • Analysis of didactic transpositions.
  • Design of teaching and learning environments.

Learning outcomes of the learning unit

Upon completion of this course unit, students will be able to:

  • Examine and analyse the potential of an algorithm.
  • Read, compare, and evaluate the efficiency of an algorithm.
  • Modify and rewrite an algorithm based on the results of an analysis.
  • Apply the concepts covered to various contexts and applications (machine learning, cryptography, computability, etc.).
  • Analyse and design teaching and learning environments.

Prerequisite knowledge and skills

The concepts covered in bloc 2 in the course unit "ZENS0011-1 Didactique discplinaire - numérique" provide useful background knowledge for this Master's-level course. In particular, the knowledge acquired in this context, including graph theory, will be drawn upon in the activities and course content covered in this module.

Planned learning activities and teaching methods

A variety of learning activities will be offered, including:

  • Lectures
  • Discussions and debates

  • Exercises

  • Small-group work

Mode of delivery (face to face, distance learning, hybrid learning)

Face-to-face course

Course materials and recommended or required readings

Platform(s) used for course materials:
- eCampus
- MyULiège


Further information:

Course materials will be made available on the institutional platform as the course progresses.

Exam(s) in session

Any session

- In-person

written exam ( multiple-choice questionnaire, open-ended questions )


Further information:

Depending on the number of students enrolled in the course, the assesment procedures may be subject to modification. Students will be informed by the course instructor, during the course of the semester, of any specifuc arrangements regarding the assesment procedures.

Work placement(s)

Organisational remarks and main changes to the course

Contacts

Massuir Adeline

 

Institut de mathématique (B37)

Allée de la découverte, 12

4000 Liège

 

Mail : A.Massuir@uliege.be

Association of one or more MOOCs