Assessment

Continous Assessment Activities

Continuous Assessment

Evaluation will be based on a continuous assessment approach. Criteria such as participation, the ability to integrate and relate conceptual and procedural content, as well as the capacity to critically analyze each case while respecting the ethical values of the profession, will be taken into account.

The assessment will consist of three activities:

• Case study in small groups (45%): Students will form interdisciplinary groups and work collaboratively on the resolution of a practical case related to the course content. The case resolution will be carried out during the module. This activity will allow for the application of theoretical knowledge to real or simulated situations and must be submitted two weeks after the module ends (November 12).

• Individual theoretical-practical test (45%): A test will be administered combining theoretical questions with practical applications of the concepts covered during the course. This test will take place at the end of the course's teaching period (November 5).

• Individual critical self-assessment (10%): Each student will write a critical reflection on their own learning process throughout the course, evaluating their achievements, challenges, and contributions. This self-assessment must be submitted one week after the module ends.

Conditions for being assessed: Active participation in class sessions and group work, as well as submission of all individual assignments.

Requirements for passing the course: To pass the course, all three assessment activities must be successfully completed. A minimum grade of 5 out of 10 is required in each type of assessment activity.

Resit (Recovery): If a student does not pass one or more of the assessment activities, they will have the opportunity to resubmit them by December 12. The theoretical-practical test may also be retaken on this date.

Single Assessment: The single assessment consists of an individual theoretical-practical test (45%), the individual resolution of a case study (45%), and a theoretical review of a book from the recommended bibliography (10%). These three activities will take place on November 5. The same resit policy applies as for continuous assessment: the activities may be submitted and/or reassessed on December 12.

Important Notes:

• Attendance at face-to-face sessions is mandatory, with a minimum requirement of 80%. Otherwise, the evaluation will be marked as “Not Presented” (NP).

• Feedback, grading, or return of assignments will be provided within a maximum of 20 working days.

• If a student fails the module, they must re-enroll in the next edition of the master’s program. In this case, no previously earned grades will be retained.

• According to UAB regulations, plagiarism or copying of any written work or test will be penalized with a grade of 0 for the assignment, with no possibility of resubmission. This applies to both individual and group work (in the case of group work, all group members will receive a 0).

Use of AI Technologies:
The use of Artificial Intelligence (AI) tools is permitted in this course exclusively for support tasks, such as text correction, translations, idea generation, initial content organization, or preliminary conceptual exploration. These tasks may not, under any circumstances, replace the systematic processing or regular handling of data, nor the student’s primary responsibilities as the author of the work, such as original writing, data management and processing, critical analysis, or discussion of results.

Students must clearly identify which parts of their work were generated using AI technologies, specify the tool used (including version and specific function), and include a critical reflection on how AI influenced both the process and the final outcome of their work. This reflection may be included in an appendix or in the methodology section, as appropriate.

Lack of transparency regarding the use of generative AI tools will be considered academic dishonesty and may result in partial or total penalties to the course grade, as well as more severe sanctions in serious cases, in accordance with institutional regulations.

Furthermore, students must demonstrate that the AI tools used meet appropriate standards for confidentiality, data security, copyright protection, and content reliability, in line with ethical and editorial best practices in academic research.