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Publicações

Publicações por CRACS

2024

Computing Motifs in Hypergraphs

Autores
Nóbrega, D; Ribeiro, P;

Publicação
COMPLEX NETWORKS XV, COMPLENET 2024

Abstract
Motifs are overrepresented and statistically significant sub-patterns in a network, whose identification is relevant to uncover its underlying functional units. Recently, its extraction has been performed on higher-order networks, but due to the complexity arising from polyadic interactions, and the similarity with known computationally hard problems, its practical application is limited. Our main contribution is a novel approach for hyper-subgraph census and higher-order motif discovery, allowing for motifs with sizes 3 or 4 to be found efficiently, in real-world scenarios. It is consistently an order of magnitude faster than a baseline state-of-art method, while using less memory and supporting a wider range of base algorithms.

2024

Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs

Autores
Eddin, AN; Bono, J; Aparício, D; Ferreira, H; Ribeiro, P; Bizarro, P;

Publicação
CoRR

Abstract

2024

GERF - Gamified Educational Virtual Escape Room Framework for Innovative Micro-Learning and Adaptive Learning Experiences

Autores
Queirós, R;

Publicação
Communications in Computer and Information Science

Abstract
This paper introduces GERF, a Gamified Educational Virtual Escape Room Framework designed to enhance micro-learning and adaptive learning experiences in educational settings. The framework incorporates a user taxonomy based on the user type hexad, addressing the preferences and motivations of different learners profiles. GERF focuses on two key facets: interoperability and analytics. To ensure seamless integration of Escape Room (ER) platforms with Learning Management Systems (LMS), the Learning Tools Interoperability (LTI) specification is used. This enables smooth and efficient communication between ERs and LMS platforms. Additionally, GERF uses the xAPI specification to capture and transmit experiential data in the form of xAPI statements, which are then sent to a Learning Record Store (LRS). By leveraging these learning analytics, educators gain valuable insights into students’ interactions within the ER, facilitating the adaptation of learning content based on individual learning needs. Ultimately, GERF empowers educators to create personalized learning experiences within the ER environment, fostering student engagement and learning outcomes. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2024

Work in progress: Leveraging Virtual Escape Rooms for Innovative Computer Programming Learning Environments

Autores
Queirós, R; Pinto, CMA; Cruz, M;

Publicação
VIII IEEE WORLD ENGINEERING EDUCATION CONFERENCE, EDUNINE 2024

Abstract
This paper explores the integration of virtual escape rooms as innovative educational tools in the realm of computer programming. Recognizing the need to engage and motivate learners in this complex domain, we investigate the use of virtual escape rooms in a typical educational setting where Learning Management Systems play a pivotal role. The paper starts by surveying existing escape rooms designed for teaching programming and related domains, considering factors such as interactivity, educational efficacy, and learner engagement. Additionally, it is emphasized the role of standards in creating interoperable learning environments, introducing IMS LTI for seamless integration with learning management systems and xAPI for tracking learner activities within escape rooms. By leveraging these standards and a Learning Record Store (LRS) as a central repository, an architectural framework is presented which enables personalized learning experiences and data-driven insights, catering to the diverse needs and preferences of the new generation of learners.

2024

Reconfiguring Teacher Professionality in Higher Education in Portugal: A Case Study on Pedagogical Innovation and Hybrid Learning

Autores
Cruz, M; Mascarenhas, D; Pinto, CMA; Queirós, R;

Publicação
VIII IEEE WORLD ENGINEERING EDUCATION CONFERENCE, EDUNINE 2024

Abstract
The teaching and learning process in higher education needs continuous cultivation of pedagogical expertise, encompassing subject mastery and pedagogical methodologies. This article explores the transformation of higher education institutions (HEIs) into hybrid campuses and the importance of pedagogical innovation, highlighting the need for training in hybrid/e-learning environments, and emphasizing the potential of mobile technologies. Furthermore, it presents a case study on two professional development courses offered to faculty members, working in the field of Engineering in Portugal, aiming to reconfigure their professionality. The research adopts an ethnographic methodology, integrating quantitative methods and utilizing a variety of data collection tools, including field notes and self-reflection sheets, to analyze the teachers' reconfiguration of their professional practices. The main findings of the study reveal that the majority of faculty members reported significant gains in transforming traditional courses to digital formats, mastering various online platforms and tools, and developing skills in online communication.

2024

GAMAI, an AI-Powered Programming Exercise Gamifier Tool

Autores
Montella, R; De Vita, CG; Mellone, G; Ciricillo, T; Caramiello, D; Di Luccio, D; Kosta, S; Damasevicius, R; Maskeliunas, R; Queiros, R; Swacha, J;

Publicação
ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS, DOCTORAL CONSORTIUM AND BLUE SKY, AIED 2024, PT I

Abstract
This paper presents GAMAI, an AI-powered exercise gamifier, enriching the Framework for Gamified Programming Education (FGPE) ecosystem. Leveraging OpenAI APIs, GAMAI enables the teachers to leverage the storytelling approach to describe the gamified scenario. GAMAI decorates the natural language text with sentences needed by OpenAI APIs to contextualize the prompt. Once the gamified scenario has been generated, GAMAI automatically produces the exercise files for the FGPE AuthorKit editor. We present preliminary results in AI-assessed gamified exercise generation, showing that most generated exercises are ready to be used with none or minimum human effort needed.

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