ABSTRACT In the digital era, the internet and social media have transformed communication but have also facilitated the spread of hate speech and disinformation, leading to radicalization, polarisation and toxicity. This is especially concerning for media outlets due to their significant role in shaping public discourse. This study examines the topics, sentiments, and hate prevalence in response messages (website comments and tweets) to news from five Spanish media outlets (La Vanguardia, ABC, El País, El Mundo and 20 Minutos) in January 2021. These public reactions were originally labelled as distinct types of hate by experts following an original procedure, and they are now classified into three sentiment values (negative, neutral, or positive) and main topics. The BERTopic unsupervised framework was used to extract topics, manually named with the help of large language models (LLMs) and grouped into nine primary categories. Results show social issues (), expressions and slang (), and political issues () as the most discussed. Content is mainly negative () and neutral (), with low positivity (). Toxic narratives relate to conversation expressions, gender, feminism and COVID‐19. Despite low levels of hate speech (), the study confirms high toxicity in online responses to social and political topics.

Oral communication is a crucial skill in modern society. Nevertheless, it requires sustained practice and constructive feedback. Consequently, several studies have explored the development of oral communication trainers powered by Artificial Intelligence (AI). However, what characterizes expert speech remains unclear, especially given the need to adapt speech to contextual factors. In instructional environments, the speaker’s communication proficiency is a key determinant of audience learning outcomes. For this reason, we have analyzed 1250 speeches from five types of instructional discourses: in-person college classes ( Lectures ), online learning lessons ( Online Courses ), instructional animations ( Animated Lessons ), supplementary materials for school and high school ( Supplementary Lessons ), and public presentations ( Public Talks ). We extracted 16 speech metrics, including six additional multiple-participant metrics for Lectures . We obtained 250 videos of each discourse type, ensuring a minimum length of five minutes. Our analysis revealed expert values for each speech metric and showed how speech metrics vary across discourse types. We also developed an AI speech classifier that achieved an F1 score of 0.78. The model struggled to identify Online Courses , which is consistent with the Uniform Manifold Approximation and Projection analysis, showing that Online Courses are closely interjected with the speech of other instructional discourses. Furthermore, we identified distinct speech profiles in Lectures, Public Talks , and Online Courses , highlighting variations in speaking styles. This research provides valuable insights into expert speech in instructional discourses by offering reference values that can help speakers refine their delivery and support researchers in developing more effective speech training systems.

ABSTRACT The quality of education hinges on the proficiency and training of educators. Due to the importance of teacher training, the innovative platform Teacher Moments creates simulated classroom scenarios. In this scenario‐based learning, confusion is an important indicator to detect users who struggle with the simulations. Through Teacher Moments, we gathered 7975 audio recording responses from participants who self‐labelled their recordings according to whether they sounded confused. Our dataset stands out for its size, for not including actor‐generated audio, and for measuring confusion, a neglected emotion in artificial intelligence (AI). Our experiments tested unimodal approaches and feature‐level, model‐level and decision‐level fusion. Feature‐level fusion demonstrated superior performance to unimodal methods, achieving a balanced accuracy of 0.6607 on the test set. This outcome highlights the necessity for further investigation in the overlooked area of confusion detection, particularly employing realistic datasets like the one used in this study and exploring new methods. Beyond teacher training, the insights of this research also extend to other directions, such as other professionals making critical decisions, user interface design or adaptive learning systems.

ABSTRACT In recent years, serious games (SGs) have emerged as a powerful tool in education by combining pedagogy and entertainment, facilitating the acquisition of knowledge and skills in engaging environments. SGs enable the collection of valuable interaction data from students, allowing for the analysis of student performance, with artificial intelligence (AI) playing a key role in processing this data to make informed inferences about their knowledge and skills. However, the lack of explainability in AI models represents a significant challenge. This research aims to develop an interpretable model for predicting students' performance in real‐time while playing an SG by: (1) calculating the performance of an interpretable prediction model of task completion in an SG and (2) demonstrating the application of the interpretable model for just‐in‐time (JIT) classroom interventions. Our results show that we are able to predict students' task completion in real‐time with a balanced accuracy result of 77.21% after a short playtime has elapsed. In addition, an explainable artificial intelligence (XAI) approach has been applied to ensure the interpretability of the developed models. This approach supports personalised learning experiences, unlocks AI benefits for non‐technical users, and maintains transparency in education.

Serious Games (SGs) have gained attention as powerful educational tools because of their potential to provide reliable assessments and evaluate hard-to-measure constructs and competencies that are difficult to capture using traditional forms of assessment. Specifically, this study presents a human-centered approach to model and detect persistence—a key component of successful learning outcomes—in the context of SGs. With this purpose in mind, we developed a comprehensive rubric to characterize persistence behaviors in SGs. To design the rubric, we identified a set of persistence profiles and characteristics from previous literature and elaborated a general rubric for identifying persistence behaviors at the level of individual attempts. These characteristics were then mapped onto measurable features within Shadowspect, the SG used for data collection. Following this rubric, two annotators manually labeled 1,374 level attempts from 64 students using two visualization methods: in-game and text replays. With a comprehensive dataset of 2,748 labeled attempts, we trained and evaluated Machine Learning (ML) models for each type of replay to classify persistence behaviors across four categories: Persistence , Non-persistence , Unproductive persistence , and No behavior . Our results indicate that while text-based replays enable efficient annotation with promising performance, in-game replays may provide finer detail for certain complex behaviors, highlighting the strengths and limitations of each visualization method. This work contributes the use of SGs for assessment, illustrating a transparent and adaptable AI-driven approach that enhances reliability and user-centered insights, highlighting the complementary role of human input in optimizing AI-based models to achieve meaningful, user-centered assessments in education.

As cyber threats continue to evolve, the need for highly skilled cyber defence operators becomes increasingly critical. In this work, we aim to provide a multidisciplinary exploration into the current educational landscape, focusing on the following pivotal areas: cyber defence educational initiatives, digital skills, technological enablers, and ethical considerations. First, we present the current landscape of cyber defence educational initiatives. Then, we examine the required digital skills, virtual reality and augmented reality initiatives for immersive learning experiences and delve into the advantages of game-based learning for skill acquisition in order to finally provide a holistic evaluation of the complexities involved in cyber defence training. We underscore the importance of standardising training modules tailored to diverse roles within cyber defence. The discussion emphasises the need for ethical and legal guidelines, especially concerning privacy and bias in AI-driven educational tools. Finally, we highlight the importance of dynamic curricula that include technical, legal, and soft skills, along with hands-on training through simulations to prepare operators for real-world cyber threats. This work will serve as a foundation for academics, industry professionals, and policy-makers interested in elevating the standards and effectiveness of cyber defence training suggesting ideas for more specialised, adaptable, and ethically responsible programs.

Abstract In our generation, there is an undoubted rise in the use of social media and specifically photo and video sharing platforms. These sites have proved their ability to yield rich data sets through the users’ interaction which can be used to perform a data‐driven evaluation of capabilities. Nevertheless, this study reveals the lack of suitable data sets in photo and video sharing platforms and evaluation processes across them. In this way, our first contribution is the creation of one of the largest labelled data sets in Flickr with the multimodal data which has been open sourced as part of this contribution. It incorporates multimodal data, combining information from various sources such as user profiles, photo metadata, and crowdsourced features. Predicated on these data, we explored machine learning models and concluded that it is feasible to properly predict whether a user is a professional photographer or not based on self‐reported occupation labels and several feature representations out of the user, photo and crowdsourced sets. We also examined the relationship between the aesthetics and technical quality of a picture and the social activity of that picture. Finally, we depicted which characteristics differentiate professional photographers from non‐professionals. As far as we know, the results presented in this work represent an important novelty for identifying expertise in the domain of photography, which researchers from various domains can utilise for related applications.

Managing stress is a crucial soft skill that affects cognitive performance and health. Stress detection through biometrics can be used to improve and evaluate stress management. However, measuring the effects of stress on biometrics and executive functions is difficult and dependent on the individual. Despite these challenges, this paper presents a case study that collects a comprehensive multimodal dataset with two stress metrics, four biometric signals, and twenty-two executive function metrics from Game-based Assessment (GBA) trace data specifically designed for this purpose. The experiments suggest that biometrics, especially the heart rate and skin temperature, are effective predictors of stress. Additionally, noteworthy correlations were observed between heart rate and certain executive function variables. The levels of GBA that measured shifting and processing speed showed a higher heart rate than the response inhibition levels. This case study, together with the developed stress detectors, enables the detection of persons who struggle to manage stress and measure their executive function performance under stressful situations.

Video games have assumed an important place in our daily lives. This has led to an increasing interest on the use of games for non-entertainment purposes, introducing the concept of Serious Games (SGs). In particular, SGs are being explored because of their potential to provide reliable assessments, but also because they can measure competences that would be difficult to measure using traditional forms of assessment. However, one of the key issues is that assessment machinery has to be designed specifically for each game, increasing the time and effort when designing and implementing Game-Based Assessments (GBAs). In this research, we introduce a novel approach to develop interoperable GBAs by: (1) designing and creating an ontology that can standardize the GBA area; (2) conducting a validation study on literature metrics to replicate them and designing novel metrics using data from different SGs; (3) conducting a case study that illustrates how our approach can be used in a real life scenario with real data. Our results confirm that the designed ontology can be used to effectively perform GBAs, along with the metrics replicated and designed in the system. We expect our work to solve the current limitations regarding GBA interoperability, thus allowing the deployment of Game-Based Assessments as a Service (GBAaaS).

The rapid technological evolution of the last years has motivated students to develop capabilities that will prepare them for an unknown future in the 21st century. In this context, many teachers intend to optimise the learning process, making it more dynamic and exciting through the introduction of gamification. Thus, this article focuses on a data-driven assessment of geometry competencies, which are essential for developing problem-solving and higher-order thinking skills. Our main goal is to adapt, evaluate and compare Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), Elo, and Deep Knowledge Tracing (DKT) algorithms applied to the data of a geometry game named Shadowspect, in order to predict students’ performance by means of several classifier metrics. We analysed two algorithmic configurations, with and without prioritisation of Knowledge Components (KCs) – the skills needed to complete a puzzle successfully, and we found Elo to be the algorithm with the best prediction power with the ability to model the real knowledge of students. However, the best results are achieved without KCs because it is a challenging task to differentiate between KCs effectively in game environments. Our results prove that the above-mentioned algorithms can be applied in formal education to improve teaching, learning, and organisational efficiency.

This paper describes the organization and results of several MOOCs delivered about technical topics (i.e., digital electronics, VHDL design on FPGAs, open education and OERs repositories and the use of STEAM technologies to encourage diversity and inclusion), where online laboratories have been used in some of them. An analysis of the enrollment, students participating on tasks or quizzes, drop-out rate and certifications requests have been done.

Games are increasingly being recognized as valuable tools for learning. In addition, they are also being explored for their potential to provide valid and reliable assessments, as they allow to create authentic and engaging assessment contexts through interactive and immersive environments. However, there are challenges to enable Game-based Assessment (GBA) at scale, including the need for interoperability between assessment models and machinery, and the complexity of managing and processing large amounts of data generated by users' interaction with games. In this study, we propose a novel approach that combines the use of ontologies and Big Data technologies for developing interoperable GBAs. The architecture enables assessments to be performed using data from different games, and we also designed and implemented a service API that facilitates the Game-Based Assessment as a Service (GBAaaS) paradigm. GBAaaS simplifies the GBA development process and enables its adoption at scale, making it a promising approach for future developments in this field.

During the past decade, there has been growing interest among researchers in informal learning at scale, particularly in the area of expert finding. These platforms have played a fundamental role in facilitating informal learning at scale, by providing access to diverse expertise and knowledge resources that might not otherwise be available to learners. Based on the encountered gaps in expert identification in Question & Answer (Q&A) portals, we inspect the feasibility of identifying data science experts in Reddit using the activity behaviour of every user, including Natural Language Processing (NLP), crowdsourced and user features sets. We also examine the impact of using only expert and non-expert classes versus three classes additionally including the out-of-scope class. Our findings can be used for distinguishing different types of users in Reddit, creating a recommendation system, identifying unreliable users or social bots in the early stage and reducing their influence.

Abstract During the last few years, there has been increasing attention paid to serious games (SGs), which are games used for non‐entertainment purposes. SGs offer the potential for more valid and reliable assessments compared to traditional methods such as paper‐and‐pencil tests. However, the incorporation of assessment features into SGs is still in its early stages, requiring specific design efforts for each game and adding significant time to the design of Game‐based Assessments (GBAs). In this research, we present a completely novel framework that aims to perform interoperable GBAs by: (a) integrating a common GBA ontology model to process RDF data; (b) developing in‐game metrics to infer useful information and assess learners; (c) integrating a service API to provide an easy way to interact with the framework. We then validate our approach through performance evaluation and two use cases, demonstrating its effectiveness in real‐world scenarios with large‐scale datasets. Our results show that the developed framework achieves excellent performance, replicating metrics from previous literature. We anticipate that our work will help alleviate current limitations in the field and facilitate the deployment of GBAs as a Service.

The recent pandemic has changed the way we see education. During recent years, Massive Open Online Course (MOOC) providers, such as Coursera or edX, are reporting millions of new users signing up on their platforms. Though online review systems are standard among many verticals, no standardized or fully decentralized review systems exist in the MOOC ecosystem. In this vein, we believe that there is an opportunity to leverage available open MOOC reviews in order to build simpler and more transparent reviewing systems, allowing users to really identify the best courses out there. Specifically, in our research we analyze 2.4 million reviews (which is the largest MOOC reviews dataset used until now) from five different platforms in order to determine the following: (1) if the numeric ratings provide discriminant information to learners, (2) if NLP-driven sentiment analysis on textual reviews could provide valuable information to learners, (3) if we can leverage NLP-driven topic finding techniques to infer themes that could be important for learners, and (4) if we can use these models to effectively characterize MOOCs based on the open reviews. Results show that numeric ratings are clearly biased (63% of them are 5-star ratings), and the topic modeling reveals some interesting topics related with course advertisements, the real applicability, or the difficulty of the different courses.

Computational Thinking is a competence that is developed more in the last few years. This is due to the multitude of benefits it has in the classroom. Throughout this article we show a series of activities that promote the development of Computational Thinking using tools that allow visual block programming. The particularity of these activities is that some of them were performed during the confinement due to the COVID-19 and other activities were performed later, in the period known as the new normality. Throughout the article, details are provided about the different sessions. The different visual programming tools by blocks and the educational scenarios used are also indicated. In addition, the results obtained are shown.

This paper presents findings on participants of a massive open online course named "Educational Robotics for all" developed under Open edX platform. The document describes the organization and structure of the MOOC and some of its preliminary results. As an example, Module 2 about Gender and STEAM is presented and discussed.

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.

Learner-centered pedagogy highlights active learning and formative feedback. Instructors often incentivize learners to engage in such formative assessment activities by crediting their completion and score in the final grade, a pedagogical practice that is very relevant to MOOCs as well. However, previous studies have shown that too many MOOC learners exploit the anonymity to abuse the formative feedback, which is critical in the learning process, to earn points without effort. Unfortunately, limiting feedback and access to decrease cheating is counter-pedagogic and reduces the openness of MOOCs. We aimed to identify and analyze a MOOC assessment strategy that balances this tension between learner-centered pedagogy, incentive design, and reliability of the assessment. In this study, we evaluated an assessment model that MITx Biology introduced in a MOOC to reduce cheating with respect to its effect on two aspects of learner behavior - the amount of cheating and learners' engagement in formative course activities. The contribution of the paper is twofold. First, this work provides MOOC designers with an 'analytically-verified' MOOC assessment model to reduce cheating without compromising learner engagement in formative assessments. Second, this study provides a learning analytics methodology to approximate the effect of such an intervention.

Massive Open Online Courses (MOOCs) have opened new educational possibilities for learners around the world. Most of the research and spotlight has been concentrated on a handful of global, English-language providers, but there are a growing number of regional providers of MOOCS in languages other than English. In this work, we have partnered with thirteen MOOC providers from around the world. We apply a multi-platform approach generating a joint and comparable analysis with data from millions of learners. This allows us to examine learning analytics trends at a macro level across various MOOC providers, with a goal of understanding which MOOC trends are globally universal and which of them are context-dependent. The analysis reports preliminary results on the differences and similarities of trends based on the country of origin, level of education, gender and age of their learners across global and regional MOOC providers. This study exemplifies the potential of macro learning analytics in MOOCs to understand the ecosystem and inform the whole community, while calling for more large scale studies in learning analytics through partnerships among researchers and institutions.

This workshop proposes specifically soliciting contributions and presentations from initiatives, programs, and platforms around the world. While many of these may already be presented at the full conference, we are also interested in more casual experience reports, case studies, and background presentations from individuals more closely acquainted with how learning at scale initiatives-including MOOCs, for-credit degree programs, informal learning environments, government initiatives, and so on-have unique needs and opportunities based on their local context. We refer to this as Global Learning @ Scale. For the purposes of this workshop, we take two views of Global Learning @ Scale.

The relationship between pricing and learning behavior is an increasingly important topic in MOOC (massive open online course) research. We report on two case studies where cohorts of learners were offered coupons for free-certificates to explore price reductions might influence user behavior in MOOC-based online learning settings. In Case Study #1, we compare participation and certification rates between courses with and without coupons for free-certificates. In the courses with a free-certificate track, participants signed up for the verified certificate track at higher rates and completion rates among verified students were higher than in the paid-certificate track courses. In Case Study #2, we compare the behaviors of learners within the same courses based on whether they received access to a free-certificate track. Access to free-certificates was associated with somewhat lower certification rates, but overall certification rates remained high particularly among those who viewed the courses. These findings suggests that some other incentives, other than simply the sunk-cost of paying for a verified certificate-track, may motivate learners to complete MOOC courses.

While global massive open online course (MOOC) providers such as edX, Coursera, and FutureLearn have garnered the bulk of attention from researchers and the popular press, MOOCs are also provisioned by a series of regional providers, who are often using the Open edX platform. We leverage the data infrastructure shared by the main edX instance and one regional Open edX provider, Edraak in Jordan, to compare the experience of learners from Arab countries on both platforms. Comparing learners from Arab countries on edX to those on Edraak, the Edraak population has a more even gender balance, more learners with lower education levels, greater participation from more developing countries, higher levels of persistence and completion, and a larger total population of learners. This "apples to apples" comparison of MOOC learners is facilitated by an approach to multiplatform MOOC analytics, which employs parallel research processes to create joint aggregate datasets without sharing identifiable data across institutions. Our findings suggest that greater research attention should be paid towards regional MOOC providers, and regional providers may have an important role to play in expanding access to higher education.

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 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 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 study presented in this paper deals with copying answers in MOOCs. Our findings show that a significant fraction of the certificate earners in the course that we studied have used what we call harvesting accounts to find correct answers that they later submitted in their main account, the account for which they earned a certificate. In total, around 2.5% of the users who earned a certificate in the course obtained the majority of their points by using this method, and around 10% of them used it to some extent. This paper has two main goals. The first is to define the phenomenon and demonstrate its severity. The second is characterizing key factors within the course that affect it, and suggesting possible remedies that are likely to decrease the amount of cheating. The immediate implication of this study is to MOOCs. However, we believe that the results generalize beyond MOOCs, since this strategy can be used in any learning environments that do not identify all registrants.

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 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.

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.