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.

Abstract Artificial intelligence (AI) algorithms are important in educational environments, and the use of machine learning algorithms to evaluate and improve the quality of education. Previous studies have individually analyzed algorithms to estimate item characteristics, such as grade, number of attempts, and time from student interactions. By contrast, this study integrated all three characteristics to discern the relationships between attempts, time, and performance in educational exercises. We analyzed 15 educational assessments using different machine learning algorithms, specifically 12 for regression and eight for classification, with different hyperparameters. This study used real student interaction data from Zenodo.org, encompassing over 150 interactions per exercise, to predict grades and to improve our understanding of student performance. The results show that, in regression, the Bayesian ridge regression and random forest regression algorithms obtained the best results, and for the classification algorithms, Random Forest and Nearest Neighbors stood out. Most exercises in both scenarios involved more than 150 student interactions. Furthermore, the absence of a pattern in the variables contributes to suboptimal outcomes in some exercises. The information provided makes it more efficient to enhance the design of educational exercises.

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.

The content model should be tested using student interactions with the content, and student behavior plays a fundamental role in model evaluation. Using simulations allows the recreation of different educational exercises, and researchers can obtain valuable information before implementing them in a real scenario. This paper presents seven educational exercise scenarios that illustrate different exercise typologies and exercise model visualizations with three characteristics: the number of attempts, grade, and time spent. The results could help detect possible problems in formulating the questions in the proposed scenarios. Additionally, the visualizations show information on the three exercise characteristics, allowing teachers to obtain exercise characteristics and make decisions such as reordering and redesigning to improve the learning process.

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.

Learning analytics (LA) employs educational data to improve the timeliness of support for learners. Apart from technical aspects, there is a need to understand social complexities brought about by different stakeholders, so as to systematise the adoption of LA in Higher Education (HE). We present an analysis of the situation, needs and challenges of LA in the context of Spanish HE, considering managers’, teachers’ and students’ perspectives. Qualitative research is employed using recursive abstraction. Results reveal that the level of institutional adoption is low and none of the analysed institutions had an LA policy. Furthermore, only two of these institutions had an initial LA strategy. While the institutions shared some commonalities in their objectives for LA, chosen tools and adoption challenges, the distinct differences in the political contexts and institutional practices among the institutions reaffirmed that LA solutions and services cannot be implemented in the same manner. Moreover, different needs for LA and concerns are identified about its adoption among managers, students and teachers. These observations lead to our conclusion that the main challenges to implement LA in Spain are not related to technological issues but to the social and cultural issues rooted in institutions and those associated with different stakeholders.

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.

This work in progress paper investigates if timely feedback increases learners' passing rate in a MOOC. An experiment conducted with 2,421 learners in the Coursera platform tests if weekly messages sent to groups of learners with the same probability of dropping out the course can improve retention. These messages can contain information about: (1) the average time spent in the course, or (2) the average time per learning session, or (3) the exercises performed, or (4) the video-lectures completed. Preliminary results show that the completion rate increased 12% with the intervention compared with data from 1,445 learners that participated in the same course in a previous session without the intervention. We discuss the limitations of these preliminary results and the future research derived from them.

Time management strategies and self-regulated learning have received much attention. Nevertheless, there is a lack of research in terms of student self-awareness related to their own self-regulation and autonomy. This study aims to validate whether students are self-conscious about their working patterns and their time management. In order to do so, several parameters like work sessions regularity or invested time have been computed and analyzed. This invested time is measured thanks to a data-gathering tool that collects events generated by students. Results show that students are, in general, self-aware of their own working patterns and that the regularity of their weekly work time is correlated with their final marks.

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.

Visualization of electromagnetism phenomena is difficult to achieve for learners with low spatial abilities. We are addressing this difficulty by using augmented reality interfaces to create 3D magnetic field representations as aid to student conceptualization about electromagnetic phenomena. Results of the study suggest that students with low spatial abilities perceive more enjoyment when using the AR application than those who had slightly better spatial abilities.

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.

The 2030 Agenda for Sustainable Development of UNESCO establishes goals and targets in areas of critical importance for humanity. Recently, members of the eMadrid network have organized the Conference Learning with MOOCs (LWMOOCS 2018), the leading worldwide global conference, inspired by this year's theme “MOOCs for All - A Social and International Approach”. This paper analyzes the main outcomes of this conference organized around the “Quality Education”, the Sustainable Development Goal 4.

This work aims to define and present some scenarios to use learning analytics with flipped classroom models. We selected some important indicators and visualizations widely used in learning analytics and analyze how to apply them in different types of flipped classroom. Besides, we specify some activities that students and teachers can do in these types of flipped classroom based on the use of learning analytics in order to improve the learning process. We also discuss the advantages of the combination of learning analytics with the flipped classroom, in comparison with only using the flipped classroom.

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.

Abstract One of the most investigated questions in education is to know which factors or variables affect learning. The prediction of learning outcomes can be used to act on students in order to improve their learning process. Several studies have addressed the prediction of learning outcomes in intelligent tutoring systems environments with intensive use of exercises, but few of them addressed this prediction in other web‐based environments with intensive use not only of exercises but also, for example, of videos. In addition, most works on prediction of learning outcomes are based on low level indicators such as number of accesses or time spent in resources. In this paper, we approach the prediction of learning gains in an educational experience using a local instance of Khan Academy platform with an intensive use of exercises and taking into account not only low level indicators but also higher level indicators such as students' behaviours. Our proposed regression model is able to predict 68% of the learning gains variability with the use of six variables related to the learning process. We discuss these results providing explanation of the influence of each variable in the model and comparing these results with other prediction models from other works.

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.

Educational technology is very powerful. It allows not only to overcome distance and time, but also to enhance interaction in face-to-face settings. In this session of the eMadrid network, we will present how educational technology can boost education, not only in the large, but especially in the small, in the classroom. We will also show how one can influence and improve the other.

The decade that we have embarked upon presents enormous challenges for Europe. The 2020 strategy for smart, sustainable and inclusive growth recognises the key role higher education must play if the ambitions for Europe in a fast-changing global reality are to be realised. This implies widening access to lifelong learning to as many European citizens as possible and it is vital that measures are implemented to transform our reality towards this direction. Notably, labour markets increasingly require more graduates with specialized knowledge and competences and substantial investment has to be made in education systems to ensure that this demand is met. The COMPASS project aims to the Composition of Lifelong Learning Opportunity Pathways through Standards-based Services investing on the establishment of a cohesive, strategic partnership for the longterm promotion of LLL-related European strategies and the development of instruments that will raise the awareness of both learning opportunity providers and learning opportunity seekers and stimulate the design of policies for enhancing ET with alternative, flexible pathways on the basis of easy, technology-enhanced access to learning opportunities.

This paper reports empirical evidence on using an augmented reality based simulator in a face-to-face learning environment. The simulator was used in a Physics course to allow students (N=112) to gain insight into the way in which magnetic fields interact with charged particles by watching in 3D the corresponding trajectories. Results suggest that augmented reality technology has a positive impact on students' motivation. It would, however, be necessary to consider reinforcement activities to improve learning effectiveness.

The emergence of massive open online courses (MOOCs) has caused a major impact on online education. However, learning analytics support for MOOCs still needs to improve to fulfill requirements of instructors and students. In addition, MOOCs pose challenges for learning analytics tools due to the number of learners, such as scalability in terms of computing time and visualizations. In this work, we present different visualizations of our “Add-on of the learNing AnaLYtics Support for open Edx” (ANALYSE), which is a learning analytics tool that we have designed and implemented for Open edX, based on MOOC features, teacher feedback, and pedagogical foundations. In addition, we provide a technical solution that addresses scalability at two levels: first, in terms of performance scalability, where we propose an architecture for handling massive amounts of data within educational settings; and, second, regarding the representation of visualizations under massiveness conditions, as well as advice on color usage and plot types. Finally, we provide some examples on how to use these visualizations to evaluate student performance and detect problems in resources.

The appearance of MOOCs has boosted the creation of educational material for use in all possible contexts. However, this material is often created without much thought and without proper tools and methodologies. Much more attention should be placed on the design of educational material, since this is the basis for a successful learning experience. In this special session, we report about several interesting approaches towards the design and creation of educational material that range from the call to the definition of a discipline of Courseware Engineering, to a methodology for video recording, another one for the creation of smart adaptive content, tools metrics for teaching computational thinking, the integration of OERs, and the design and application of game-based material.

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 use of computer based games for education can improve the learning process. The design of computer based games and the way the users might interact with them is an important and open issue as there are many possibilities and variables to take into account. This paper describes the design and evaluation of shopC, an educational computer board game driven by technology, based on the traditional well-known Monopoly game. Three different dimensions are considered: motivation, learning and gaming. We show the introduction and the evaluation of the game in a pilot study with 58 students from primary school interacting with the computer based game. This evaluation validates the design of the computer based game regarding the three dimensions (motivation, learning and gaming) and suggests a positive effect in the learning process.

The use of badges in educational contexts its starting to gain popularity. However many studies do not offer an extensive analysis of the results regarding the use of badges after the educational experiment is finished. In this work we offer an evaluation of the results of three courses (physics, chemistry and mathematics) that we have conducted using Khan Academy with a wide badge system and 291 different students. We analyze these results regarding the distribution of badges per student, analyzing also the different badge types and which of them were delivered more often. We also explore the influence of factors such as the difficulty of problems or video length in the amount of badges triggered by exercises and videos respectively. We compare the results among the three courses trying to find possible explanations to these differences. We also put the lessons learned into context and give recommendations so that our findings can be used by instructional designers and other researchers.

The aim of this study was to investigate the attitude of learners toward an augmented reality learning activity designed to help engineering students to solve an electromagnetic problem. The sample was 122 students. Students were asked to complete a survey questionnaire based on the Technology Acceptance Model (TAM) enhanced with perceived enjoyment items. The results of the evaluation show that intention of use the system is dependent of perceived enjoyment but not from perceived usefulness of the learning tool.

One of the most common gamification techniques in education is the use of badges as a reward for making specific student actions. We propose two indicators to gain insight about students' intentionality towards earning badges and use them with data from 291 students interacting with Khan Academy courses. The intentionality to earn badges was greater for repetitive badges, and this can be related to the fact that these are easier to achieve. We provide the general distribution of students depending on these badge indicators, obtaining different profiles of students which can be used for adaptation purposes.

Education is being powered by technology in many ways. One of the main advantages is making use of data to improve the learning process. The massive open online course (MOOC) phenomenon became viral some years ago, and with it many different platforms emerged. However most of them are proprietary solutions (i.e. Coursera, Udacity) and cannot be used by interested stakeholders. At the moment Open edX is placed as the primary open source application to support MOOCs. The community using Open edX is growing at a fast pace with many interested institutions. Nevertheless, the learning analytics support of Open edX is still in its first steps. In this paper we present an overview and demonstration of ANALYSE, an open source learning analytics tool for Open edX. ANALYSE includes currently 12 new visualizations that can be used by both instructors and students.

The emergence of Massive Open Online Courses (MOOCs) has caused a high disrupting effect on online education. One of the most extended MOOC platforms is Open edX. There is a demanding necessity by the instructors and students of these courses to provide timely analytics tools that can help understand the learning process at any moment. In this direction we have developed the Add-on of learNing AnaLYtics Support for open Edx (ANALYSE), which is our learning analytics contribution for Open edX. In this demonstration paper we will provide guidelines on how to use some of the ANALYSE video visualizations in order to detect problems in video resources, so that the learning process can be improved.

Augmented reality is a technology that can be used to deploy Problem Based Learning activities that immerse students into meaningful learning activities by overlaying digital information; such as audio, video, 3D simulations, etc., to the vision of the real world. This paper explores the integration of augmented reality based activities in web courses providers and proposes a communication flow between an augmented reality application and a web based learning course provider.

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.

This paper presents how to take advantage of the interactive capabilities of augmented reality (AR) technology to deploy assessment activities into AR-based learning environments. It shows how to combine physical and digital spaces to design authentic, meaningful tasks where students can put into practice their skills for solving problems. The use of the framework in real scenarios illustrates its suitability for the deployment of assessment activities into AR-based learning environments.

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.

Feedback is important for any activity, and learning is no exception. Whereas assessment can give summative feedback about the proficiency of the learning, learning analytics can give a much finer level of feedback about the learning process. Learning analytics can help in identifying the effectiveness of learning elements, can help in engaging students, can guide teachers in the preparation and deployment of the teaching activity. In this paper, we present a number of different initiatives carried out at UC3M that include elements of learning analytics for different purposes.

To process low level educational data in the form of user events and interactions and convert them into information about the learning process that is both meaningful and interesting presents a challenge. In this paper, we propose a set of high level learning parameters relating to total use, efficient use, activity time distribution, gamification habits, or exercise-making habits, and provide the measures to calculate them as a result of processing low level data. We apply these parameters and measures in a real physics course with more than 100 students using the Khan Academy platform at Universidad Carlos III de Madrid. We show how these parameters can be meaningful and useful for the learning process based on the results from this experience.

In this paper, an overview is given about the research carried out in the area of mobile teaching and learning by Universidad Carlos III de Madrid, a member of the eMadrid Excellence Network. Mobile learning is raising growing expectations and is considered by some authors the next disruptive revolution in education. Cognitive and pedagogical theories supporting this prospect are reviewed. How these theories can be translated into meaningful educational practices is analysed by exploring ways of usage of mobile devices for supporting learning and teaching. Finally, a portfolio of experiments and case studies carried out by the Gradient group of the Universidad Carlos III de Madrid testing the application and effects of mobile learning are reported. These experiments show that the use of these devices is changing educational practices in a fundamental way.

Can mobile learning disrupt methods and practices in the educational sector? This is the question we want to answer in this special session. Several examples of mobile educational applications are presented that have been carried out within the eMadrid excellence network.

This paper presents the integration of contextual learning activities on the platform. LRN through Android mobile devices with NFC support, from the Internet of Things model, as an alternative to the currently active methodologies on the LMS systems. For this purpose, the concept of learning space has been extended, enabling new scenarios, focusing on user experience.

The use of technology in every day tasks enables the possibility to collect large amounts of observations of events taking place in different environments. Most tools are capable of storing a detailed account of the operations executed by users in certain files commonly known as logs. These files can be further analyzed to infer information that is not directly visible such as the most popular applications, times of the day with highest activity, calories burnt after a running session, etc. Graphic visualizations of this data can be used to support this type of analysis as shown in [1]. Visualization can also be applied in the domain of learning experiences to track and analyse the data obtained from both learners and instructors. There are several tools that have been proposed in specific environments such as, for example, in personal learning environments [5], to foster self-reflection and awareness [2], and to support instructors in web-based distance learning [3]. These visualizations need to take into account aspects such as how to access and protect personal data, filter management, multi-user support and availability. In this paper, the web-based visualization platform GLASS (Gradient's Learning Analytics System) is presented. The architecture of the tool has been conceived to support a large number of modular visualizations derived from a common dataset containing a large number of recorded events. The tool was developed following a bottom-up methodology to provide a set of basic operations required by any visualization. The design goal is to provide a highly versatile, modular platform that simplifies the implementation of new visualizations.

IMS Learning Design is considered by many authors the "de facto" standard in educational modeling languages. The versatility of the framework enables its use in very different situations. However, such versatile framework is usually hidden by its complex management. One handicap identified in practical experiences is the lack of flexibility of scripted courses during the enactment phase. The activity sequence and learning resources are rigidly defined during authoring. This fact makes difficult to react to unexpected events that may happen in live courses. Also, this rigidness does not allow instructors to give "their personal touch" to courses. This paper presents the improvements made on GRAIL - an IMS LD compliant player-aimed at the support of a flexible enactment phase. Two types of modifications are considered: the modification of the learning flow and the management of course content with a wiki engine. Finally, this paper discusses how the integration of third party services in the activity sequence relaxes the rigidness of scripted learning flows. Experiences deployed in real scenarios allowed analyzing how such integration offered flexibility in practical situations.

Most of the current learning analytic techniques have as starting point the data recorded by Learning Management Systems (LMS) about the interactions of the students with the platform and among themselves. But there is a tendency on students to rely less on the functionality offered by the LMS and use more applications that are freely available on the net. This situation is magnified in studies in which students need to interact with a set of tools that are easily installed on their personal computers. This paper shows an approach using Virtual Machines by which a set of events occurring outside of the LMS are recorded and sent to a central server in a scalable and unobtrusive manner.

Abstract Computer‐supported collaborative learning is a paradigm that uses technology to support collaborative methods of instruction. When combining collaborative learning with the need to exchange documents between students and the teaching staff in a blended learning scenario, version control systems (VCSs) greatly simplify this collaboration. Furthermore, these tools need to be adopted in regular classes as they are used in industrial environments. But deploying a collaborative environment in which version control is used does not scale for large classes. This paper presents SubCollaboration, a platform that uses the VCS Subversion to manage a large number of work spaces in a collaborative learning environment. The tool maintains a reference workspace where teaching staff introduces new material that is then synchronized with the team repositories. Two case studies are presented showing that students easily learn the use of version control and its deployment in large classes is feasible. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we describe a 3D virtual collaborative system designed for the learning of Spanish as a second language. Several initiatives for second language learning in 3D virtual worlds exploiting immersive, interactive and motivating features of these worlds have been carried out successfully during the last years. However, these systems tend to be sometimes too rigid from a pedagogical point of view, requiring the presence of a teacher. We have used the Wonderland development toolkit to deploy a 3D virtual learning environment, which is flexible enough to allow learners to improve their language skills with minimum teacher’s help, setting up an instructional sequence in which fostered, motivating, and pre-designed collaboration is the key for self-learning. The environment includes technical issues such as natural text chatting with synthetic characters, textual tagging of virtual objects, automatic reading of texts, and the integration of a 3D mouse in learning sequences in order to exploit the capabilities of 3D virtual worlds.

The generation of Contextualized Attention Metadata (CAM) allows to retrieve information about the different actions that users execute over different resources in a specific context. This paper presents how CAM is used within a learning system to personalize help provided to students while working on online exercises. We outline our approach and present two application examples within this framework for the personalization of exercises with hints.

The introduction of mobile technologies in class provide instructors with tools for contextualized, active, situated, any-time any-where learning. In fact, the role of the instructor can be partially delegated to the student by the use of a mobile device. This paper assesses if this delegation can be brought to the limit of eliminating the need of the physical presence of the instructor in the particular context of a situated learning environment consisting of a server room where third year Telecommunication Engineering students learn how to configure network services such as DNS, SMTP and HTTP. The paper presents the results of two experiments inside the “advanced telematic applications” course at the Carlos III University of Madrid. Two groups of students participated in the experiments, one following traditional instructor based classes and the other using NFC enabled mobile phones. The paper analyzes both learning increments and motivational aspects.

In this paper, a case study of web usage mining for analyzing student activity is presented. Web sites supporting three traditional, face-to-face and essentially practical CS2 courses are audited using specific software. The courses web sites include organizational information about the course, theoretical resources, notes, laboratory materials and a web forum for queries and interaction among the students supervised by the teaching staff. Collected data provide information about navigation patterns. In this paper, the analysis performed is described, as well as the main conclusions reached. Results include information about studying habits as well as web content design; effectiveness of changes in course methodology is analyzed too. Finally, conclusions and main lessons learned are presented.

The deployment of mobile and ubiquitous computing in smart objects introduces the concept of an Internet of things which will offer new scenarios for learning processes. This paper presents this concept in relation with some other new alternatives of spaces for learning. A basic architecture for interaction is proposed and a set of prototypes that we have developed are also explained as part of an early infrastructure.

In this paper, we provide an overview of the overall process of authoring of educational resources in a semantic Web application that we are developing about adaptive hints in assessments. In this framework, we present some examples of semantic Web annotations about course topics, exercises and hints in notation 3 (N3). The current different roles and actions for the authoring process are explained, and some initial ideas of an authoring tool are presented.

Flexibility is an intrinsic property present in any learning environment. When capturing the structure of a learning experience with conventional languages such as learning design, this flexibility can be seriously compromised. In this document two mechanisms are proposed to avoid this pitfall. On one hand, flexibility needs to be considered from the early stages of the design phase. Editors need to include default constructions to easily accommodate changes. On the other hand, run-time environments need to offer the possibility of changing most of the execution parameters with an intuitive interface available to the teaching staff.

Collaborative learning models are widely used in educational institutions. These models require a high interaction level among students and are mainly oriented towards in-class scenarios. But when collaborative models are deployed in a distant scenario, user expressiveness is significantly reduced thus creating a gap that hinders the effectiveness of this collaboration. A computer-supported model provides a set of tools to compensate for the distant scenario and reduce this gap. This paper presents the issues and solutions derived from the design and deployment of a complex collaborative model in a distant scenario. The course structure was captured using the Learning Design specification, and an architecture based on virtual network computing was used to provide the required collaborative tools. The course was included as part of a regular undergraduate program in three higher educational institutions.

This demo presents the design of the NFC Interactive Panel. This is a touchable surface with which mobile phones can interact. The surface represents a display for interactive and adaptive information controlled by people using NFC (Near Field Communication) enabled phones. The mobile phone is used

This paper describes a possible application of swarm intelligence techniques in e-learning: an auditing tool for pedagogical planning. Swarm intelligence techniques can be applied to a web system thanks to the fact that the available online material can be organized in a graph by means of hyperlinks. In this case, the swarm that moves on the graph is composed of students who unconsciously leave pheromones in the environment depending on their success or failure. The paper presents the system and shows its capacity to serve as an auditing tool for courses designed by a pedagogical team.

Technological innovations can be important factors for change. The Internet is one such and it has modified the way people live and interact. In education, the effect of this change is visible in the way educational materials are designed, developed and distributed, and in the roles played by the teacher/designer, the student and the content provider. Learning objects technology and supporting tools now permits the generation of on-line educational materials. We present a framework for the generation, integration and reuse of learning objects, intended to (a) resolve the limitations of existing tools (b) provide interoperability between learning objects, and (c) provide mechanisms to compose new learning objects from old.

Programming automated web navigation assistants, i.e., applications that automatically navigate the web performing specific tasks for the user, is far from easy. Since HTML pages offered by legacy web-based applications, which are designed to be manipulated only by people using browsers, web pages contain semi-structured information [P. Atzeni, et al., 1997] whose data schema can easily be changed, the creation and even the maintenance of this kind of applications, is very expensive. However, an increasing amount of information sources and online applications have been added to the web during the last few years, so assistants for automating tasks over those web-enabled applications are more and more needed. These assistants may automate tasks by filling in forms, following links, analyzing data embedded in web pages and performing computations over those data on behalf of the user. Software engineering techniques are clearly needed to reduce the cost, not just creating these programs (by significantly reducing their time-to-market), but even more importantly, maintaining them working properly, reducing the cost of readapting them to web site pages whose structure or navigation schemes are frequently changed. This paper proposes the well known formal method message sequence charts (MSC) [ITU-T, 1997] as a base for defining a language for programming web navigation assistants which may navigate a web site according to the user's aims. This specification language, called XPlore, is specially suited for both requirements engineering and automatic generation of an executable, and has been successfully tested on several well known web sites.

This paper introduces a denotational semantics for a core of the draft IEEE standard analog and mixed signal design language VHDL-AMS, and derives general results about the behaviour of VHDL-AMS programs from it. We include, for example, a demonstration that VHDL-AMS parallelism is benign in the absence of shared initializations. As proof of concept we have built an interpreted simulator that prototypes the semantics and which runs multi-process mixed analog and digital descriptions correctly.

A formal refinement calculus targeted at system-level descriptions in the IEEE standard hardware description language VHDL is described here. Refinement can be used to develop hardware description code that is “correct by construction”. the calculus is closely related to a Hoare-style programming logic for VHDL and real-time systems in general. That logic and a semantics for a core subset of VHDL are described. The programming logic and the associated refinement calculus are shown to be complete. This means that if there is a code that can be shown to implement a given specification, then it will be derivable from the specification via the calculus.