All UDC centers have a Internal Quality Assurance System (IQAS), verified by the Agency to Calidade do Sistema Universitario de Galicia (ACSUG) and ANECA in FIDES programs and AUDIT, respectively, and in accordance with the standards and Guidelines for Quality Assurance in the EHEA. Within IQAS centers integrate all their degrees and masters. IQAS procedures established who and how to follow up and monitoring of the results and the student's learning process in the titration, establishing coordinating bodies and mechanisms, evaluation and continuous improvement of the studies, which are the academic comittee and the quality assurance commision of center.
The IQAS establish how UDC centers measure and analyze the learning outcomes. To do the IQAS uses rates and global indicators for assessing the quality of training provided. Included among the results, in addition to the fees included in the title verification reports (efficiency rates, graduation and dropout), assessment rates, success and performance. We compare the results obtained in the title with the center's results for each indicator.
To ensure that the title is developed in accordance with the verification report presented the center conducts an annual monitoring report in accordance with the procedures of the center IQAS and results. This report is reviewed by the ACSUG, external evaluation agency.
The outcome of this analysis will emerge corrective actions and improvement proposals that achieve the intended objectives and the improvement of the degree.
In order to ensure proper standards and quality at UDC, an academic management committee and a quality assurance committee are appointed for each faculty/school. In addition to, each faculty/school will be able to establish a specific academic management committee of the study.
| SEGUIMIENTO rates | Period | Value |
|---|---|---|
| Leaving rate | 2022/2023 | 44.44 |
| Student enrollment | Period | Value |
|---|---|---|
| Students count | 2024/2025 | 1,740 |
| Men | 2024/2025 | 1,403 |
| Women | 2024/2025 | 337 |
| Credits enrolled | Period | Value |
|---|---|---|
| Total enrolled credits | 2024/2025 | 96,500 |
| Credits on 1st enrollment | 2024/2025 | 78,390 |
| Credits on 2nd enrollment | 2024/2025 | 12,170 |
| Credits on 3rd enrollment and above | 2024/2025 | 5,934 |
| % credits repeated | 2024/2025 | 18.77 |
| Academics results | Period | Value |
|---|---|---|
| Average credits by student | 2024/2025 | 55.46 |
| Students enrollment | Period | Value |
|---|---|---|
| Students count | 2023/2024 | 3 |
| Men | 2023/2024 | 3 |
| Women | 2023/2024 | 0 |
| Credits enrolled | Period | Value |
|---|---|---|
| Total enrolled credits | 2023/2024 | 162 |
| Credits on 1st enrollment | 2023/2024 | 120 |
| Credits on 2nd enrollment | 2023/2024 | 42 |
| Credits on 3rd enrollment and above | 2023/2024 | 0 |
| % credits repeated | 2023/2024 | 25.93 |
| Academic results | Period | Value |
|---|---|---|
| Average credits by student | 2023/2024 | 54 |
| Evaluation rate | 2023/2024 | 0 |
| Success rate | 2023/2024 | 0 |
| Performance rate | 2023/2024 | 0 |
| SEGUIMIENTO rates | Period | Value |
|---|---|---|
| Graduation rate | 2022/2023 | 50 |
| Leaving rate | 2021/2022 | 22.73 |
| Student enrollment | Period | Value |
|---|---|---|
| Students count | 2023/2024 | 1,788 |
| Men | 2023/2024 | 1,457 |
| Women | 2023/2024 | 331 |
| Credits enrolled | Period | Value |
|---|---|---|
| Total enrolled credits | 2023/2024 | 94,570 |
| Credits on 1st enrollment | 2023/2024 | 75,280 |
| Credits on 2nd enrollment | 2023/2024 | 12,870 |
| Credits on 3rd enrollment and above | 2023/2024 | 6,426 |
| % credits repeated | 2023/2024 | 20.4 |
| Academics results | Period | Value |
|---|---|---|
| Average credits by student | 2023/2024 | 52.89 |
| Evaluation rate | 2023/2024 | 84.76 |
| Success rate | 2023/2024 | 82.21 |
| Performance rate | 2023/2024 | 69.68 |
| Students enrollment | Period | Value |
|---|---|---|
| Students count | 2022/2023 | 26 |
| Men | 2022/2023 | 25 |
| Women | 2022/2023 | 1 |
| Credits enrolled | Period | Value |
|---|---|---|
| Total enrolled credits | 2022/2023 | 1,068 |
| Credits on 1st enrollment | 2022/2023 | 954 |
| Credits on 2nd enrollment | 2022/2023 | 99 |
| Credits on 3rd enrollment and above | 2022/2023 | 15 |
| % credits repeated | 2022/2023 | 10.67 |
| Academic results | Period | Value |
|---|---|---|
| Average credits by student | 2022/2023 | 41.08 |
| Evaluation rate | 2022/2023 | 79.49 |
| Success rate | 2022/2023 | 97.17 |
| Performance rate | 2022/2023 | 77.25 |
| SEGUIMIENTO rates | Period | Value |
|---|---|---|
| Efficiency rate | 2022/2023 | 90.91 |
| Graduation rate | 2021/2022 | 77.27 |
| Leaving rate | 2020/2021 | 50 |
| Student enrollment | Period | Value |
|---|---|---|
| Students count | 2022/2023 | 1,833 |
| Men | 2022/2023 | 1,506 |
| Women | 2022/2023 | 327 |
| Credits enrolled | Period | Value |
|---|---|---|
| Total enrolled credits | 2022/2023 | 94,360 |
| Credits on 1st enrollment | 2022/2023 | 75,220 |
| Credits on 2nd enrollment | 2022/2023 | 12,700 |
| Credits on 3rd enrollment and above | 2022/2023 | 6,441 |
| % credits repeated | 2022/2023 | 20.28 |
| Academics results | Period | Value |
|---|---|---|
| Average credits by student | 2022/2023 | 51.48 |
| Evaluation rate | 2022/2023 | 87.52 |
| Success rate | 2022/2023 | 82.83 |
| Performance rate | 2022/2023 | 72.49 |
These are the master thesis works that have been read in recent academic years. For more information, visit our search engine of final master thesis
Creation of an activity report on the Twitter social network
Distributed on-the-fly Data Analysis for Parallel Numerical Simulations using Ray
Enhancements implementation in Open OnDemand applied to science
Execution and synchronization model in multiple heterogeneous devices
High-performance data analytics applied to stock price forecasting
High-performance data analytics applied to the labour market
Multi-resolution out-of-core system for massive point cloud processing in HePNP tool
Parallelization of motif discovery in genetic sequences with the Zagros algorithm
Performance analysis of inference in deep learning networks on high performance architectures
Performance characterization of DPUs from the perspective of HPC and DL
Predicting power consumption in an HPC infrastructure using machine Learning
Quantum Annealing based Hyperspectral Image Segmentation
Tool for modeling processor power consumption using software
Offer available places last year and, for degree studies, you can also check the cut notes of those admitted by PAAU.
| academic year 2023/2024 | |
|---|---|
| General offer | 13 |