Master's Degree Dissertation
Code: 43217 ECTS Credits: 15| Degree | Type | Year |
|---|---|---|
| Research in Education | TFE | 1 |
Contact
- Name:
- David Rodriguez Gomez
- Email:
- david.rodriguez.gomez@uab.cat
Teachers
- David Rodriguez Gomez
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
This module is compulsory for the master's degree. The theme of the Final Master's Project will be related to the specialty that the student is studying.
It is not possible to enroll in it if the rest of the modules have not been enrolled.
Objectives and Contextualisation
Master's dissertation aims to evaluate the integration of skills acquired in the Master.
Master's dissertation is established as a research in the field of education developed with empirical data. It is a work that the student develops individually and presented in a commission
Learning Outcomes
- CA07 (Competence) Adopt criteria of methodological quality when producing a Master's final project.
- CA08 (Competence) Contrast the data obtained in the Master's final project with the goals that had been set and the corpus of available knowledge in order to draw conclusions.
- CA09 (Competence) Make proposals for improvement and/or innovation projects based on the evidence obtained in the Master's final project.
- KA07 (Knowledge) Delimit a research problem that is addressed in the Master's final project.
- KA08 (Knowledge) Enunciate the ethical principles adopted in producing the Master's final project.
- SA07 (Skill) Design and develop field work consistent with the research goals and questions of the Master's final project.
- SA08 (Skill) Analyse data using methodologies and techniques that are suited to the nature thereof and the research goals.
- SA60 (Skill) Present the Master's degree dissertation, adapting the register to the type of scientific community.
Content
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Group introduction to research work | 10 | 0.4 | CA07, CA08, CA09, KA07, KA08, SA07, SA08, SA60, CA07 |
| Type: Supervised | |||
| Individual or group tutoring | 65 | 2.6 | CA07, CA08, CA09, KA07, KA08, SA07, SA08, SA60, CA07 |
| Type: Autonomous | |||
| Development of the study, report writting and presentation | 300 | 12 | CA07, CA08, CA09, KA07, KA08, SA07, SA08, SA60, CA07 |
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Monitoring tutorials | 20% | 0 | 0 | CA07, CA08, CA09, KA07, KA08, SA07, SA08, SA60 |
| Presentation/oral presentation | 20% | 0 | 0 | CA07, CA08, CA09, KA07, KA08, SA07, SA08, SA60 |
| Report | 60% | 0 | 0 | CA07, CA08, CA09, KA07, KA08, SA07, SA08, SA60 |
The module will be assessed through the activities indicated.
The Master's Thesis is based on tutorial work. Each student will have a tutor who will be a doctor from the department responsible for the speciality that the student is studying in the Master's programme.
The student and the tutor will agree on a schedule of tutorials to monitor the work.
The project will be presented and defended publicly before a panel during the week of 13 July 2026. The specific date for each panel will be determined by the specialisations during the month of June. In order to defend the Master's Thesis, it is essential that the tutor has monitored the project to ensure not only the quality of the process, but also that the thesis has been completed during the academic year.
In order to submit the TFM, the student must have completed all the partial monitoring submissions and have the approval of the tutor.
The final grade will be the weighted average of the planned activities. To pass, all three activities must be passed. If the student does not pass the final project defence and/or the tutor's assessment, there is no possibility of retaking it within the same academic year.
In order to pass the Master's Thesis, students must demonstrate good general communication skills, both orally and in writing, and a good command of the language or languages listed in the teaching guide. In all activities (individual and group), linguistic correctness, writing and formal aspects of presentation will be taken into account. Students must be able to express themselves fluently and correctly and must demonstrate a high degree of comprehension of academic texts. An activity may be returned (not assessed) or failed if the teacher considers that it does not meet these requirements.
Copying or plagiarism, both in the case of assignments and exams, constitutes an offence that will result in the failure of the Master'sThesis:
- An assignment, activity or exam will be considered ‘copied’ when it reproduces all or part of the work of another student.
- An assignment or activity will be considered ‘plagiarised’ when part of an author's text is presented as one's own without citing the sources, regardless of whether the original sources are in paper or digital format. (more information on plagiarism at http://wuster.uab.es/web_argumenta_obert/unit_20/sot_2_0 1.html).
In this course, the use of Artificial Intelligence (AI) technologies is permitted as an integral part of the development of the work, provided that the final result reflects a significant contribution by the student in terms of analysis and personal reflection. The student must clearly identify which parts have been generated using this technology, specify the tools used, and include a critical reflection on how these have influenced the process and the final result of the activity. Failure to be transparent about the use of AI will be considered a lack of academic honesty and may result in a penalty on the activity grade, or more severe sanctions in serious cases. Specific checks may be carried out to ensure authorship and the acquisition of skills in cases of suspected academic fraud.
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Software
Data qualitative analysis: Atlas.ti; nvivo
Data quantitative analysis: Jasp; Jamovi
Groups and Languages
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.