Methodology

- Directed activities

Lectures with support of ICT.

Workshops for analyzing and discussing specialized papers and cases. 

- Supervised activities

Attention to students

- Autonomous activities

Reading papers 

Individual study and homework preparation

Autonomous search of information

In the case of requesting the exam in Spanish, THE TRANSLATION OF IN-PERSON ASSESSMENT TESTS WILL BE PROVIDED ONLY IF THE REQUIREMENTS SET OUT IN ARTICLE 263 ARE MET (all the information is available on the Faculty website).

The competences of this course will be assessed through: written exams, participation in the discussions that take place during Seminar classes, and the submission of reports/assignments during the Seminar sessions.

The assessment system is organized into two modules, each of which carries a specific weight in the final grade:

• Report/Assignment Submission Module: This module evaluates the reports and assignments submitted during the seminars, with an overall weight of 60% of the final grade.
• Written Test Module: This module carries an overall weight of 40% of the final grade.

Specifically, the following learning evidences will be presented:

• EV1: Presentation of assignments and participation in Seminar discussions (group and individual) (25% weight) (in-person).
• EV2: Group written assignment and oral defense (35% weight) (second assessment period) (written assignment submitted virtually, oral defense in person).
• EV3: Individual written test (40% weight) (second assessment period) (in-person).

To pass the course, students must obtain a minimum overall average of 5 (on a scale from 0 to 10) across all evidences, and must have passed at least two out of the three evidences (with a score of 5.0 or higher in at least two evidences). If these requirements—and any others established by the course—are not met, the maximum grade recorded on the academic transcript will be 4.5 points

A student who has submitted learning evidences with a total weight equal to or greater than 4 points (40%) cannot be recorded as \"not assessable.\"

A make-up test is planned for the learning evidences EV2 and EV3 in the case that satisfactory performance has not been demonstrated. Students who meet the following conditions may take the make-up test:

(a) their overall course grade is equal to or higher than 3.5 but lower than 5.0 (scale 0–10), and

(b) they have previously been assessed in a set of activities whose combined weight equals at least two-thirds of the total course grade.

The maximum grade that can be obtained for a recovered evidence is 5.0.

This course does not offer a single assessment option.

Link to the Faculty's assessment guidelines:

https://www.uab.cat/web/estudiar/graus/graus/avaluacions-1345722525858.html

It is not expected that second-time or subsequent enrollees will be assessed through a single, non-recoverable synthesis exam.

Important: Exams will be in Catalan. If a student wishes to take the exams in Spanish and meets the requirements set out in Article 263, they must submit the request electronically during week 4 (using the e-form; more information is available on the Faculty website). Requests submitted under other conditions or outside of the stated deadline will not be accepted.

Attendance

Attendance at seminars is mandatory.

Feedback

• For EV1, global feedback will be provided in class and on the virtual campus through digital tools two weeks after each submission.
• For EV2, feedback will be provided in class and on the virtual campus through digital tools (week 15).
• For EV3, feedback will be given during an individual office-hour session upon student request (week 20).

Use of AI

In this course, the use of Artificial Intelligence (AI) technologies is allowed only for support tasks, such as bibliographic or information searches, text correction, or translations. Students must clearly identify which parts have been generated using AI, specify the tools used, and include a critical reflection on how these tools influenced the process and the final outcome of the activity. Lack of transparency in the use of AI in this graded activity will be considered academic dishonesty and may result in partial or full penalization of the grade, or more severe sanctions in serious cases.