Perfilado de sección

  • Learning objectives

    The course aims at providing students with a broad and solid grasp of the challenges and opportunities raised by datafication (the transformation of social action into data) and digitization (the conversion of information into a digital format). As last year, the course is jointly organized LLSMS2903-Digital Transformation Management - LLSMF2903 Digital Business Systems.

    At the end of the course, students will (i) understand how meaningful information and knowledge can be extracted from data; (ii) be exposed to methods for analyzing and interpreting the obtained results to support decision-making; (iii) have the means to evaluate the scope, risks, and opportunities of data analytics within an organization; (iv) identify the implications and ethics of the digital transformation for organizations and their stakeholders; and (v) have a grasp of the different steps to conduct a digital transformation process.

    Teaching methods

    The course follows a Project-Based Learning method. Students learn by actively engaging in a specific project, which they achieve in groups (see below for the description). Thereby, students develop deep content knowledge and acquire important skills (critical thinking, teamwork, creativity, and communication abilities). 

    To provide students with sufficient time to work on their project, lectures are kept to a minimum (an average of 2 hours per week). Also, rooms have been booked on Tuesdays to allow students to meet and work (see ADE for the lists of rooms for each week and time slot). The instructors and the teaching assistants are available to answer your questions on Tuesdays (or by appointment at any other time that suits them).

    Grading and Assessment

    The final grade in this course will be based on marks in individual coursework, group coursework, and a final exam.

    • The individual coursework counts toward 16% of the final grade. It consists of a combination of 8 activities that are specified on a weekly basis.
    • The group coursework counts toward 47% of the final grade. The contents and guidelines are specified below (see the 'Group coursework' section). Groups will first present their work in progress in an interim report (4% of the final grade); they will then present their final work in a written report (33% of the final grade) and in an oral online video presentation (10% of the final grade).
    • The final exam counts toward 33% of the final grade. It is a closed-book exam, with multiple-choice questions.
    • The Peer and Self Assessment of groupwork counts toward 4%. It consists of an auto-evaluation of your group project commitment and peer review of other groups online presentations. 

    Important Notes

    1. The marks for all individual courseworks are set once and for all (this part of the assessment cannot be retaken). However, students have the possibility to retake the final exam in August and, if the mark of the group coursework is below 10/20, to replace it with a corresponding individual coursework (to be handed in August).

    2. Students compose their groups by themselves (max 6 students/group) by the end of Week2 (Sept 27) since the project draft is to be submitted in Week3. The group can be a mix of GEST and INGE students. Students  also agree to be committed to the group. Students not enrolled in a group by 28/9 will only have the possibility to submit an individual project for the August session.

    3. By submitting an assignment for evaluation:
    - you assert that it accurately reflects the facts and to do so you need to have verified the facts, especially if they 
    originate from generative AI resources;
    - you assert that all your sources that go beyond common knowledge are suitably attributed. Common knowledge
    is what a knowledgeable reader can assess without requiring confirmation from a separate source;
    - 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 
    truth, and possibly other aspects of academic integrity. This constitutes academic misconduct.