DSpace 9
DSpace is the world leading open source repository platform that enables organisations to:
- easily ingest documents, audio, video, datasets and their corresponding Dublin Core metadata
- open up this content to local and global audiences, thanks to the OAI-PMH interface and Google Scholar optimizations
- issue permanent urls and trustworthy identifiers, including optional integrations with handle.net and DataCite DOI
Join an international community of leading institutions using DSpace.
The test user accounts below have their password set to the name of this software in lowercase.
- Demo Site Administrator = dspacedemo+admin@gmail.com
- Demo Community Administrator = dspacedemo+commadmin@gmail.com
- Demo Collection Administrator = dspacedemo+colladmin@gmail.com
- Demo Submitter = dspacedemo+submit@gmail.com
Communities in DSpace
Select a community to browse its collections.
- W-STEM
- Supporting Culturally Responsive Leadership and Evaluation in Schools
- A Digital Ecosystem Framework for an Interoperable NEtwork-based Society (DEFINES)
- Evaluation environment for fostering intercultural mentoring tools and practices at school
Recent Submissions
Model of Characterization of Teamwork Competence Based on Three Types of Capabilities
(Springer, 2025-07-15) Fidalgo-Blanco, Ángel; Sein-Echaluce, María Luisa; Fonseca, David; García-Peñalvo, Francisco José
here are various teamwork models with different orientations regard-ing the conceptual model, member involvement, evidence management, and even the training process for acquiring teamwork-related skills. This research defines a hybrid model that integrates the two main theoretical models (focused on group achievements and team member involvement) and an open-box method (with continuous generation and verification of both group and individual evidence). Therefore, teamwork competence is associated with a set of capabilities of dif-ferent types, classified into three main categories: group, individual, and general (soft skills), which are related to teamwork but not exclusive to it. This paper also presents the evidence that allows for continuous and transparent training and evaluation of these three types of capabilities.
Integrating Individual and Collective Skills: A Rubric-Based Model for Teamwork Competence Assessment
(Springer, 2024-06-29) Sein-Echaluce, María Luisa; Fidalgo-Blanco, Ángel; García-Peñalvo, Francisco José; Fonseca Escudero, David
The competence of teamwork comprises a set of skills that enable the assessment of teamwork evolution (collective skills) and the involvement of each team member (individual skills). In most research works, these skills are grouped without making this distinction between collective and individual skills. In this study, collective skills are associated with the different phases that constitute the evolution of teamwork, allowing for the identification of the precise moment when such a skill should be applied. Individual skills are applied in all phases of teamwork, as they measure individual involvement and responsibility, along with the competencies necessary at an individual level to develop teamwork. This work presents a rubric that associates phases, evidence, technology, and indicators and allows educators to measure the degree of acquisition of each and collective skill. The method used for the development of teamwork has been the Comprehensive Training Model of the Teamwork Competence, which supports both the continuous and transparent creation of evidence of teamwork development by the teams and each of their members, as well as the continuous monitoring of this development by educators.
Enhancing Learning Assistant Quality Through Automated Feedback Analysis and Systematic Testing in the LAMB Framework
(Springer, 2025-06-22) Alier-Forment, Marc; Pereira-Valera, Juanan; Casañ-Guerrero, María José; García-Peñalvo, Francisco José
he Learning Assistant Manager and Builder (LAMB) is an open-source software framework that lets educators build and deploy AI learning assis-tants within institutional Learning Management Systems (LMS) without cod-ing expertise. It addresses critical challenges in educational AI by providing privacy-focused integration, controlled knowledge bases, and seamless deploy-ment through standard protocols. This paper presents major enhancements that enable systematic quality assurance and continuous improvement of these learning assistants.
The new LAMB includes mechanisms for structured feedback on real-world assistant behavior, transforming it into a test suite with curated prompts and expected correct or incorrect responses. When changes are made—such as prompt engineering, retrieval-augmented generation optimization, or knowledge base expansions—this suite enables automated validation of their impact.
A key innovation is using frontier large language models (LLMs) to evaluate responses automatically, generating detailed reports that reveal improvement areas and confirm performance gains. This systematic feedback-driven testing fosters continuous refinement while preserving quality standards.
Validation studies show measurable boosts in reliability and consistency. In various educational contexts, the framework identifies edge cases, maintains con-sistency across iterations, and provides actionable insights. Automated testing is especially beneficial for assistants with extensive knowledge bases and complex interaction patterns.
This work advances educational AI by providing a robust methodology for quality assurance and ongoing improvement of learning assistants. Its structured feedback and automated evaluations ensure alignment with educational goals while refining assistants over time. The enhanced LAMB framework offers a scalable and reliable solution for educators aiming to integrate AI-driven support into their LMS environments.
Refactoring User Interfaces Through a Data-Driven Framework: a Case Study in the Health Domain
(IEEE, 2023-10-16) Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; García-Peñalvo, Francisco José; Pérez-Sánchez, Pablo; Antúnez-Muiños, Pablo; Sánchez-Puente, Antonio; Vicente-Palacios, Víctor; Dorado-Díaz, Pedro Ignacio; Sánchez, Pedro Luis
User interfaces (UIs) play a crucial role in defining user experiences and influencing the success of software products. While UI design has traditionally been subjective and iterative, data-driven approaches are becoming increasingly popular to ensure that Uis meet user needs and expectations. However, contextual factors such as the application domain can present challenges for designing Uis that are both effective and efficient. This is particularly true in the health domain, where Uis must be adapted to specific tasks and user expertise to maximize the support provided by software systems. Moreover, the urgency of delivering fully functional systems in short periods can relegate UI design to a second plane. This paper presents a framework proposal for refactoring and improving Uis using a data-driven approach, providing an efficient and systematic methodology to address not solved UI issues introduced during previous software development processes. The proposed framework has been successfully applied to two medical platforms, demonstrating the importance of data-driven approaches for UI refactoring in domains with particular necessities.
D-AI-COM: A DICOM Reception Node to Automate the Application of Artificial Intelligence Scripts to Medical Imaging Data
(Springer, 2024-05-01) Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; García-Peñalvo, Francisco José; Pérez-Sánchez, Pablo; Sánchez-Puente, Antonio; Vicente-Palacio, Víctor; Dorado-Díaz, Pedro Ignacio; Sánchez, Pedro Luis
Artificial Intelligence (AI) has proven to be useful in several fields. The medical domain is one of the fields that benefits from the application of AI methods to automate and ease complex tasks including disease detection, segmentation, assessment of organ functions, etc. However, applying these kinds of methods to the variety of data formats involved in health contexts is not trivial. It is necessary to provide technologies that enable non-expert users to benefit from AI applications. This work presents a platform that acts as a DICOM reception node with the goal of automating the application of AI algorithms to medical imaging data. This platform is set to ease the process applying AI to their DICOM images by making the whole process transparent and straightforward for users without AI-related or programming skills.