Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state‐of‐the‐art and future directions on this area of research.

Irony is nowadays a pervasive phenomenon in social networks. The multimodal functionalities of these platforms (i.e., the possibility to attach audio, video, and images to textual information) are increasingly leading their users to employ combinations of information in different formats to express their ironic thoughts. The present work focuses on the study of irony detection in social media posts involving image and text. To this end, a transformer architecture for the fusion of textual and image information is proposed. The model leverages disentangled text attention with visual transformers, improving F1-score up to 9% over previous existing works in the field and current state-of-the-art visio-linguistic transformers. The proposed architecture was evaluated in three different multimodal datasets gathered from Twitter and Tumblr. The results revealed that, in many situations, the text-only version of the architecture was able to capture the ironic nature of the message without using visual information. This phenomenon was further analysed, leading to the identification of linguistic patterns that could provide the context necessary for irony detection without the need for additional visual information.

Objectives . This study investigates gender biases in AI perceptions among university students. It focuses on assessing self-perceptions regarding knowledge, impact, and support, with a specific emphasis on identifying any significant gender differences. The main hypotheses are focused on the existence of gender disparities in AI awareness, perceptions, and attitudes among higher education students. Participants . The study involves 380 participants, enrolled in undergraduate courses across various academic disciplines. Participants are university students with diverse backgrounds in terms of age, academic majors, and prior exposure to AI technologies. Study Methods . This research employs an observational study design. The sample size includes 380 participants. The study utilizes a structured questionnaire as the primary instrument for data collection. Outcome measures focus on variables such as perceived knowledge of AI, perceived impact of AI, and levels of support or apprehension towards AI technologies. Findings . The findings reveal significant gender differences, with females exhibiting lower levels than their male counterparts in the level of perceived knowledge about AI ( \(\text{p} \!\lt\! 0.005\) ), exposure awareness (p = 0.001), perceived ability to apply AI (p = 0.004), sensitivity towards AI use of private data (p = 0.004), positive impact on society (p = 0.002), support for AI development ( \(\text{p}\!\lt\!0.005\) ), and positive expectations towards AI ( \(\text{p}\!\lt\!0.005\) ). Statistical analysis, including nonparametric tests, was used to validate these observations. Conclusions . There are notable gender biases in the knowledge and perception of AI among university students. These biases have implications for the future development and adoption of AI technologies, suggesting a need for more gender-inclusive educational strategies in AI. The findings underscore the importance of addressing gender disparities in AI education to ensure equitable access and understanding of these technologies. It is important to integrate gender perspectives in AI curriculum and policy-making to mitigate potential biases and enhance inclusivity in the field of AI.

The Internet of Things generates vast data volumes via diverse sensors, yet its potential remains unexploited for innovative data-driven products and services. Limitations arise from sensor-dependent data handling by manufacturers and user companies, hindering third-party access and comprehension. Initiatives like the European Data Act aim to enable high-quality access to sensor-generated data by regulating accuracy, completeness, and relevance while respecting intellectual property rights. Despite data availability, interoperability challenges impede sensor data reusability. For instance, sensor data shared in HTML formats requires an intricate, time-consuming processing to attain reusable formats like JSON or XML. This study introduces a methodology aimed at converting raw sensor data extracted from web portals into structured formats, thereby enhancing data reusability. The approach utilises large language models to derive structured formats from sensor data initially presented in non-interoperable formats. The effectiveness of these language models was assessed through quantitative and qualitative evaluations in a use case involving meteorological data. In the proposed experiments, GPT-4, the best performing LLM tested, demonstrated the feasibility of this methodology, achieving a precision of 93.51% and a recall of 85.33% in converting HTML to JSON/XML, thus confirming its potential in obtaining reusable sensor data.