This paper presents the basis of the GENIE Learn project, a coordinated three-year research project funded by the Spanish Research Agency. The main goal of GENIE Learn is to improve Smart Learning Environments (SLEs) for Hybrid Learning (HL) support by integrating Generative Artificial Intelligence (GenAI) tools in a way that is aligned with the preferences and values of human stakeholders. This article focuses on analyzing the problems of this research context, as well as the affordances that GenAI can bring to solve these problems, but considering also the risks and challenges associated with the use of GenAI in education. The paper also details the objectives, methodology, and work plan, and expected contributions of the project in this context.

As we move from the Information Age to the Intelligence Age, universities must redefine themselves taking into account recent challenges. Two of these challenges are Micro-credentials and Artificial Intelligence (AI). Micro-credentials certify the learning outcomes of short-term learning experiences, which are typically more flexible and tailored to specific, job-relevant skills, meeting the increasing demand for continuous education. These learning experiences are normally referred to as micro-credential courses or micro-credential programs and disrupt traditional degree models. On the other hand, Artificial Intelligence is disrupting every single aspect of our life and work. Artificial Intelligence tools can work as assistants helping in all kinds of tasks that were previously reserved for humans. Artificial Intelligence can be used in education in many ways, such as to create personalized learning paths, create and optimize educational multimedia content such as text, voice, image, or video, tutor students, and monitor student progress in real-time, contributing to the acquisition of learning outcomes. Putting the two challenges together, Artificial Intelligence can also contribute to generate micro-credentials by ensuring learners demonstrate competence in practical, job-specific skills, enhancing their credibility for employers. Therefore, Artificial Intelligence can playa pivotal role in advancing micro-credential courses and programs, helping universities redefine their offerings to provide personalized, adaptable, and industry-relevant learning experiences. Artificial Intelligence can help in designing the content and the learning experience as with courses in general, but it can also help in designing the specificities of micro-credential courses. The specific “skills” provided by Artificial Intelligence for defining micro-credentials range from clerical work related to mastering formats such as ELM or OpenBadges that are needed to construct digital credentials to creative sug-gestions that aid in designing the details of the micro-credential course. Alternatively, the micro-credentials designed can include topics about Artificial Intelligence, helping the workforce on this important area. This paper analyzes the interplay of these two challenges and how they can help each other, making opportunities out of challenges.

Prediction of student performance is one of the main research lines in learning analytics, whose aim is to detect students at risk to support them. Nevertheless, current literature often relies on a single platform to analyze data. However, some courses use several platforms and it is relevant to know which type of platform/resources is more relevant for prediction. In this line, the objective of this work is to analyze the effect of multiple sources when conducting performance predictive models. Particularly, data are collected from a spreadsheets course where there is information about activity in the Learning Management System (LMS, Moodle in this case), a supporting Small Private Online Course (SPOC), class attendance registered in the Blackboard Collaborate online tool, and academic results. Results show that it is possible to obtain accurate predictions in both final test and final grade, and Moodle interactions and academic results stand out for final grade predictions, while class attendance activity is a strong predictor of the final test. Conversely, the SPOC formative interactions show a worse predictive power in comparison to other variables, which may reflect that students do not use the supporting videos if they grasp the contents using the materials provided in Moodle.

This work explores the integration of Artificial Intelligence (AI) to improve research skills in engineering education, specifically at the time of conducting a Systematic Literature Review (SLR). While AI has previously been employed for basic tasks related to SLRs, such as relevant paper screening and classification within scope, there is still limited research on information retrieval from the full texts of scientific papers. This study focuses on the next phase in the SLR, which involves scientific documentary analysis. The methodology presented in this work compares the AI tools in the market to facilitate scientific documentary analysis and enhance and refine research skills in engineering students. Also, this paper addresses the challenges to automatically run documentary analysis during a SLR. To do so, several techniques to extract information from AIs are presented, including Application Programming Interfaces (API) or Graphical User Interfaces (GUI). According to the results, most of the AI tools have a limitation in the operations students can perform per day. Only PDFgear offers a no-cost solution with unlimited usage, while some other AI tools allow limited usage for not so high prize. In conclusion, this paper presents a contribution during the documentary analysis of a SLR. It is important to keep in mind the constraints associated with the limited usage of AIs, the accuracy of AI tools, or the complexity of the developed scripts for automating the process. Due to the rapidly changing market for artificial intelligence, the validity of this study is limited to the current state of the tools. Similar studies will be necessary as AI tools continue to evolve.

The possible applications of GenAI (Generative AI) in education alone are so manyfold and overwhelming that it is useful to have an overview of the many possibilities that are opening up. In this paper, we try to organize some of the low-hanging fruits that can help instructors, learners, and educational managers use GenAI applications to improve educational performance. For instructors, GenAI can help in gaining a deeper understanding of the topics to be taught, preparing educational materials, and facilitating the enactment phase in class. Learners can be assisted in getting personalized content and feedback, having GenAI as a participant in forums, or for self-reflection and emotions detection. Managers and other stakeholders can profit from Academic Analytics, bias detection, course repurposing, and many other uses. In this paper, we also present a use case detailing some initial actions we are implementing for a Programming with Java course. One action is to explicitly identify, in each problem set, the competencies being developed. Another one is the development of a chatbot, fine-tuned with the course material, which can be used by students as a tutor. The third action is to use a GenAI tool to generate questions aimed at assessing whether students truly grasp the programming project they have supposedly developed. AI is here to stay, in spite of the issues it opens up, and therefore it is never too early to start experimenting with it in practice.

If learning at the side of the student is important, it is also recognition of the learning happened. This occurs at different levels and through different instruments. It can be informal, for motivation purposes or formal, to show outside. In this paper, we analyse the concept of recognition from several vantage points.

This paper presents the H2O Learn (Hybrid and Human-Oriented Learning) project, a coordinated research project funded by the Spanish Research Agency, which just started in 2021 and will last for three years. The main goal of the H2O Learn project is to build Trustworthy and Human-Centered Learning Analytics (TaHCLA) solutions to support human stakeholders when designing, orchestrating and (self-,socially- or co-) regulating learning in Hybrid Learning (HL). The contributions of H2O Learn consider key requirements for trustworthy Artificial Intelligence (AI), as defined by the European Commission: 1) fostering human (i.e., teachers, learners…) agency; 2) enabling transparency of the Learning Analytics (LA) systems; 3) seeking socio-emotional and inclusive wellbeing; and 4) promoting accountability by enabling the assessment of algorithms and design processes.

There are many applications that help orchestrate face-to-face, online, or hybrid classes through the cloud. Examples are polling apps like Wooclap or Mentimeter, digital boards like Jamboard or Miro, or diagramming tools like diagrams.net, Coggle, or Kialo. However, one can go one step further and integrate some of these tools in order to use the most appropriate tool for each teaching moment and have information flow across tools preserving consistency without having to do this manually. In this paper, we present a specific experience and extrapolate it to highlight the power of programming the teaching interaction.

Selenium WebDriver is a library that allows controlling web browsers (e.g., Chrome, Firefox, etc.) programmatically.It provides a cross-browser programming interface in several languages used primarily to implement end-to-end tests for web applications.JUnit is a popular unit testing framework for Java.Its latest version (i.e., JUnit 5) provides a programming and extension model called Jupiter.This paper presents Selenium-Jupiter, an open-source JUnit 5 extension for Selenium WebDriver.Selenium-Jupiter aims to ease the development of Selenium WebDriver tests thanks to an automated driver management process implemented in conjunction with the Jupiter parameter resolution mechanism.Moreover, Selenium-Jupiter provides seamless integration with Docker, allowing the use of different web browsers in Docker containers out of the box.This feature enables cross-browser testing, load testing, and troubleshooting (e.g., configurable session recordings).This paper presents an example case in which Selenium-Jupiter is used to evaluate the performance of video conferencing systems based on WebRTC.This example case shows that Selenium-Jupiter can build and maintain the required infrastructure for complex tests effortlessly.

The coronavirus pandemic has accelerated the digital transformation of society, and in particular of university teaching. Professors who were reluctant or unwilling to take advantage of what digital technologies offered suddenly were forced to teach with technologies. In this paper, we describe teaching during the two main phases during the pandemic at Universidad Carlos III de Madrid (UC3M): the first phase during the complete lockdown and the second where students were allowed on campus with limitations. We also explain how the investment done during these two phases influences teaching in the future and how digital cloud-based technologies promote active learning at the university.

Interfaces are what separates us from the tools we use. Interfaces are presently becoming more natural and human-oriented. We are moving from keyboard-oriented tools to those that exploit other human senses, such as sight and sound. In this paper, we explore how voice recognition and generation, augmented reality, and unperceivable sensors help in making educational technology more invisible.

Technology is advancing at an ever-increasing speed. The backend capabilities and the frontend means of interaction are revolutionizing all kinds of applications. In this paper, we analyze how the technological breakthroughs seem to make educational interactions look smarter and more human. After defining Education 4.0 following the Industry 4.0 idea, we identify the key breakthroughs of the last decade in educational technology, basically revolving around the concept cloud computing, and imagine a new wave of educational technologies supported by machine learning that allows defining educational scenarios where computers interact and react more and more like humans.

Education has undergone numerous changes over the last few years. The number and variety of technologies that can be used now to access education and enrich teaching and learning scenarios, as well as the number and variety of people that are connected to education entail that we face increasingly complex educational settings. This paper reflects on different aspects of current education and how these relate to multiple factors, which lead to the definition of multi-education. To this end, several terms related to multi-education, such as multi-media, multi-channel, multi-scale, multi-modal, and multi-intelligence will be analyzed, presenting related works, and trying to find out where we are heading.

Forum messages in MOOCs (Massive Open Online Courses) are the most important source of information about the social interactions happening in these courses. Forum messages can be analyzed to detect patterns and learners' behaviors. Particularly, sentiment analysis (e.g., classification in positive and negative messages) can be used as a first step for identifying complex emotions, such as excitement, frustration or boredom. The aim of this work is to compare different machine learning algorithms for sentiment analysis, using a real case study to check how the results can provide information about learners' emotions or patterns in the MOOC. Both supervised and unsupervised (lexicon-based) algorithms were used for the sentiment analysis. The best approaches found were Random Forest and one lexicon based method, which used dictionaries of words. The analysis of the case study also showed an evolution of the positivity over time with the best moment at the beginning of the course and the worst near the deadlines of peer-review assessments.

The learning process in a MOOC (Massive Open Online Course) can be improved from knowing in advance learners’ grades on different assignments. This would be very useful to detect problems with enough time to take corrective measures. In this work, the aim is to analyse how different course scores can be predicted, what elements or variables affect the predictions and how much and in which way it is possible to anticipate scores. To do that, data from a MOOC about Java programming have been used. Results show the importance of indicators over the algorithms and that forum-related variables do not add power to predict grades, unlike previous scores. Furthermore, the type of task can vary the results. Regarding the anticipation, it was possible to use data from previous topics but with worse performance, although values were better than those obtained in the first seven days of the current topic.

The MOOC movement has helped faculty in focusing on how to lecture. However, once this is done, it would not make sense not to use this content for on-campus classes. In this paper, we will explain how to harness top content created for MOOCs to improve on-campus classes, where the personal interaction is a key added feature. Interactive practices and on-site interaction, especially in-class interaction, are of particular relevance in the evolution of Higher Education towards a more effective learning.

The appearance of MOOCs has contributed to the use of educational technology in new contexts. As a consequence, many teachers face the challenge of creating educational content (courseware) to be offered in MOOCs. Although some best practices exist, it is true that most of the content is being developed without much thought about adequacy, reusability, maintainability, composability, etc. The main thesis at this paper is that we are facing a "courseware crisis" in the same way as there was a "software crisis" 50 years ago, and that the way out is to identify good engineering discipline to aid in the development of courseware. We need Courseware Engineering in the same way as at those times we needed Software Engineering. Therefore, the challenge is now to define and develop fundamentals, tools, and methods of Courseware Engineering, as an analogy to the fundamentals, tools, and methods that were developed in Software Engineering.

The emergence of MOOCs has boosted the use of educational technology in all possible contexts. How can MOOC affordances, such as rich multimedia content, interactive exercises, and social networks, be harnessed in face-to-face education? In this paper, we present some experiences carried out at Universidad Carlos III de Madrid that combine MOOC-like content in on-campus courses. We believe that this is the most promising and useful way to use the affordances of technology in education.

MOOCs represent a new medium for education. In this paper, we describe some of the best practices identified after having produc- ed and run a MOOC in 2013 for the first run of MiríadaX, having set up a service unit at Univ. Carlos III de Madrid (UC3M) for the production of MOOCs (mainly for edX) and SPOCs for internal use, and continuing to be active in MOOCs and MOOC-related production at both sides of the Atlantic (in Spain and Chile).

The appearance of MOOCs has boosted the use of educational technology in all possible contexts. Universities are trying to understand this new phenomenon, while carrying out the first trials. Best practices are still scarce and will be developed in the coming months. In this paper, we present first experiences carried out at Universidad Carlos III de Madrid, both with MOOCs (Massive Open Online Courses) and with SPOCs (Small Private Online Courses), which are MOOC counterparts for internal use.

This paper summarizes some of the work that has been carried out within the eMadrid research network in the last four years in the area of educational technology at Universidad Carlos III de Madrid. In order to organize the findings and identify future trends and possibilities, a framework space is used with three dimensions: physical-digital, local-global, and formal-informal. Research results are presented along the three dimensions and possible future developments are suggested in this framework.

Linked Data is a methodology to publish data on the Web that facilitates the federation of data sources. Following this methodology, a huge quantity of data is being published on the Web of Data, but very little is currently consumed by educational applications. This paper proposes to overcome this situation by relating educational vocabularies to the ones already used on the Web of Data. This way, educational applications can take advantage of the data published on the Web even if such data was not published for educational purposes. As a case study, the vocabulary used by an educational tool search system, called Ontoolsearch, is related to ICT tool descriptions published by DBpedia and Factforge. Evaluation results show that 2905 tool descriptions can be retrieved from the Web of Data to be consumed by Ontoolsearch. In addition, most of these descriptions have a similar quality to the ones manually published by an educational tool expert.

The integration of existing educational computer-based tools is a current research trend aimed at increasing the range of learning situations that can be supported by most widely adopted Virtual Learning Environments (VLEs). Nevertheless, due to the technological and functional heterogeneity of both tools and VLEs, there is an integration cost that must be assumed and that varies depending on several design issues. This paper identifies the issues to be tackled when designing a VLE or a tool and the effects that the different alternatives may have in the integration cost. Besides, the paper discusses the advantages and drawbacks of previous research proposals when dealing with such issues. The final goal is to understand the impact of design decisions in the chances for acceptance of the adopted integration approaches.