Seminar on Relational and Managerial Competences

llsmd2090  2024-2025  Louvain-la-Neuve

Seminar on Relational and Managerial Competences
3.00 credits
45.0 h + 7.5 h
Q1 and Q2
Language
English
Prerequisites
None
Main themes
The main themes as a process
  1. Self-diagnosis of the students’ relational and emotional competences.
  2. Development of priority competences, within the framework of an experiential seminar.
  3. Construction of a personal development plan.
  4. Learning actions linked to two chosen competences.
  5. Reporting of learning outcomes.
Learning outcomes

At the end of this learning unit, the student is able to :

1 During their programme, students of the LSM Master's in management or Master's in Business engineering will have developed the following capabilities:
  1. Being aware and managing his/her emotions, being able to be objective about his/her work and behaviour, recognizing his/her own strengths and weaknesses, accepting them and using them in a professional manner.
  2. Being capable of creating a project in line with his/her own values and aspirations, confident and motivated in managing the implementation of the project, and persevere in difficult situations.
  3. Interacting and discussing effectively and respectfully with diverse stakeholders in face-to-face and group contexts, using both verbal and nonverbal communication skills : demonstrating the ability to listen, empathizing, being assertive, providing and accepting constructive feedback.
  4. Working in a team : joining in and collaborating with team members. Being open and taking into consideration the different points of view and ways of thinking, managing differences and conflicts constructively, accepting diversity.
  5. Exercising enlightened leadership skills : uniting and motivating different team members, identifying, drawing on and developing skills and talents, achieving a common goal, while adapting to time constraints and the changing environment.
At the end of this learning, students will be able to :
  1. Initial self-diagnosis of the skills using the LSM reference system.   The students will be led to question themselves on their achievements and strengths and on their areas of discomfort and their potential for development. 
  2. Development of priority competences, within the framework of an experiential seminar.
  3. Construction of a personal development plan.
  4. Learning actions linked to two chosen competences.
  5. Reporting of learning outcomes.
 
Content
The course is structured in 3 modules
1. The first module begins with three plenary lectures introducing values-driven and collaborative management as the key theme of the course.
2. In the second module, the course continues with workshops focusing on several foundational competencies that will serve as a basis for more advanced skills in the future. These include time management and prioritization listening and giving feedback, conflict resolution and negotiation, cultural competencies, motivation, as well as others proposed by our corporate partners.
3. The third module, which runs parallel to the first two, includes autonomous teamwork supported by the teaching team through online coaching sessions.
Teaching methods
The course is based on group experiential learning and incorporate 'hands-on' experience, 'minds-on' exercises, peer and self-instruction. Throughout the course, students will work in randomly organized groups and produce a series of assignments. Regular contact points with the teaching team will be organized.
Teaching activities in this course include:
  • Lectures
  • Workshops: 
  • Peer-instruction
  • Self-instruction
Evaluation methods
The assessment plan in this course is based solely on team-based continuous evaluation:
1. Giving Voice to Values (GVV) Project– 40%
  • Team exercise and case analysis on managing values conflicts
2. From Group to TeamWork Project – 30%
  • Developing recommendations for effective teamwork based on real life cases.
  • Peer-evaluation 
3. TeamWork Reflections – 30%
  • Report reflecting on the team dynamics experienced throughout the duration of this course.

A minimum grade of 10 applies for all parts of the assessment.

Late submission policy:
Late submissions of assignments will result in a grade deduction. Please follow the policy posted on Moodle.

Free-riding:
Free-riding will result in a full grade deduction on the assignment for the student in question. Please adhere to the guidelines for reporting free-riding instances published on Moodle.

Plagiarism:
Plagiarism is a form of free-riding, where you present someone else's intellectual efforts as your own. Reusing parts of a paper written for one course in another course is also considered plagiarism.
By submitting an assignment for evaluation:
  • you assert that all your sources that go beyond common knowledge are suitably attributed (please follow the referencing guidelines posted on Moodle);
  • you assert that you have respected all specific requirements of your assigned work, in particular requirements for transparency and documentation of process, or have explained yourself where this was not possible.
If any of these assertions are not true, whether by intent or negligence, you have violated your commitment to academic integrity. This constitutes academic misconduct.

Use of Generative AI Policy:
  • The use of generative AI for the course assessments is allowed.
  • You bear full responsibility for what you present or submit. By submitting your assignment, you affirm that you have verified it accurately reflects the facts.
  • If you have used AI in preparing your assignment, you must acknowledge it by clearly indicating which AI tools were used and how they assisted you, including specific functionalities or tasks. Be sure to mention the prompts used and how you applied the AI-generated output.
  • Your submission must represent your own original work. AI tools should only be used to support your work, not to produce it entirely. Ensure that your own thoughts, analysis, and writing are evident in the final submission, so it allows the teacher to evaluate the skills and competencies acquired.
  • Failure to disclose AI use or misuse of AI tools will be considered as fraud, as defined in articles 107-114 of the RGEE.

Acknowledgment:
  • This course description was reviewed and refined with the assistance of ChatGPT, which helped improve clarity, grammar, and language consistency.
Other information
The workshops will be organized in cooperation with experts from companies and public institutions
Online resources
Moodle
Bibliography
Slides and readings posted on Moodle
Faculty or entity


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] : Business Engineering

Master [120] in Management