Teacher(s)
Language
English
Prerequisites
None
Main themes
The main themes as a process
- Self-diagnosis of the students’ relational and emotional competences.
- Development of priority competences, within the framework of an experiential seminar.
- Construction of a personal development plan.
- Learning actions linked to two chosen competences.
- 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:
|
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.
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:
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%
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:
Use of Generative AI Policy:
Acknowledgment:
1. Giving Voice to Values (GVV) Project– 40%
- Team exercise and case analysis on managing values conflicts
- Developing recommendations for effective teamwork based on real life cases.
- Peer-evaluation
- 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.
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