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.

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.

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.

Collaboration is considered as one of the main drivers of learning and it has been broadly studied across numerous contexts, including Massive Open Online Courses (MOOCs). The research on MOOCs has risen exponentially during the last years and there have been a number of works focused on studying collaboration. However, these previous studies have been restricted to the analysis of collaboration based on the forum and social interactions, without taking into account other possibilities such as the synchronicity in the interactions with the platform. Therefore, in this work we performed a case study with the goal of implementing a data-driven approach to detect and characterize collaboration in MOOCs. We applied an algorithm to detect synchronicity links based on their submission times to quizzes as an indicator of collaboration, and applied it to data from two large Coursera MOOCs. We found three different profiles of user accounts, that were grouped in couples and larger communities exhibiting different types of associations between user accounts. The characterization of these user accounts suggested that some of them might represent genuine online learning collaborative associations, but that in other cases dishonest behaviors such as free-riding or multiple account cheating might be present. These findings call for additional research on the study of the kind of collaborations that can emerge in online settings.

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.

This paper introduces the Spanish Chapter of the IEEE Education Society, the CESEI, and its main activities. Along the years since its appearance, the CESEI has developed several initiatives devoted to promote the development of engineering education using new information and communication technologies. Publications and participation in relevant conferences have attracted the main efforts, but also the awards provided to the best student academic works is a remarkable action. It deserves special attention the activities performed in cooperation with the student branches, because they have been very fruitful. The paper describes all these initiatives and discusses about future actions.

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.

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.

SNOLA is a Thematic Network of Excellence recognized by the Spanish Ministry of Economy and Competitiveness composed of the main Spanish researchers in the field of learning analytics. This network emerged in 2013 focusing mainly on the technology underlying learning analytics development, but also interested on the integration of other views and disciplines that give the network a wider scope. During these years, SNOLA has carry out numerous actions with the goal of promoting the collaboration on this field, the diffusion of knowledge and initiatives, the collection of resources and the provision of broad support. Remarkable examples of these efforts have been LASI Spain events, workshops at TEEM conferences or the numerous webinars. In this case, the EDUCON 2018 has given us the opportunity to organize this special session where 5 papers have been selected for presentation.

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.

This workshop aims to promote strategic planning for learning analytics in higher education through developing institutional policies. While adoption of learning analytics is predominantly seen in small-scale and bottom-up patterns, it is believed that a systemic implementation can bring the widest impact to the education system and lasting benefits to learners. However, the success of it highly depends on the adopted strategy that meets the needs of various stakeholders and systematically pushes the institution towards achieving its targets. It is imperative to develop a learning analytics policy that ensures a practice that is valid, effective and ethical.

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 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.

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.

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.

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.

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, 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.