Rapid growth in the generation of data from various sources has made data visualisation a valuable tool for analysing data. However, visual analysis can be a challenging task, not only due to intricate dashboards but also when dealing with complex and multidimensional data. In this context, advances in Natural Language Processing technologies have led to the development of Visualisation-oriented Natural Language Interfaces (V-NLIs). In this paper, we carry out a scoping review that analyses synergies between the fields of Data Visualisation and Natural Language Interaction. Specifically, we focus on chatbot-based V-NLI approaches and explore and discuss three research questions. The first two research questions focus on studying how chatbot-based V-NLIs contribute to interactions with the Data and Visual Spaces of the visualisation pipeline, while the third seeks to know how chatbot-based V-NLIs enhance users’ interaction with visualisations. Our findings show that the works in the literature put a strong focus on exploring tabular data with basic visualisations, with visual mapping primarily reliant on fixed layouts. Moreover, V-NLIs provide users with restricted guidance strategies, and few of them support high-level and follow-up queries. We identify challenges and possible research opportunities for the V-NLI community such as supporting high-level queries with complex data, integrating V-NLIs with more advanced systems such as Augmented Reality (AR) or Virtual Reality (VR), particularly for advanced visualisations, expanding guidance strategies beyond current limitations, adopting intelligent visual mapping techniques, and incorporating more sophisticated interaction methods.

Geometry is a basic discipline in STEM education. Recent educational reports, however, suggest that geometry is one of the subjects that sees the lowest levels of performance in the math curriculum. This paper presents a gamified itinerary through digital activities designed to teach geometry. Our aim is to attract Primary School children into the world of geometry. First, we scaffold geometry outcomes to take students progressively from basic geometric shapes to a stronger understanding of complex three‐dimensional (3D) properties. Second, we use gamification to introduce fun into the learning process. Third, we propose digital activities in virtual environments, with which students can, in particular, develop 3D spatial perception. Concretely, children are immersed in a two‐dimensional (2D)–3D world in which they play the role of architects who manipulate 2D and 3D shapes to create buildings. Results obtained in the evaluation of the system with 60 children showed an improvement in both their learning and their interest in math. Both children and teachers rated the experience very positively.

The design of gamified experiences following the one-fits-all approach uses the same game elements for all users participating in the experience. The alternative is adaptive gamification, which considers that users have different playing motivations. Some adaptive approaches use a (static) player profile gathered at the beginning of the experience; thus, the user experience fits this player profile uncovered through the use of a player type questionnaire. This paper presents a dynamic adaptive method which takes players’ profiles as initial information and also considers how these profiles change over time based on users’ interactions and opinions. Then, the users are provided with a personalized experience through the use of game elements that correspond to their dynamic playing profile. We describe a case study in the educational context, a course integrated on Nanomoocs, a massive open online course (MOOC) platform. We also present a preliminary evaluation of the approach by means of a simulator with bots that yields promising results when compared to baseline methods. The bots simulate different types of users, not so much to evaluate the effects of gamification (i.e., the completion rate), but to validate the convergence and validity of our method. The results show that our method achieves a low error considering both situations: when the user accurately (Err = 0.0070) and inaccurately (Err = 0.0243) answers the player type questionnaire.

Several reasons underlie the low retention rates in MOOCs. These reasons can be analysed from different perspectives, either in terms of the course design or the enrolled students. On the student side, we find little social interaction, boredom, tiredness, and a lack of motivation and time. These challenges can be addressed by adaptive gamification that proposes the design of personalised, hedonic learning experiences. Studies to date have adopted either the one-fits-all approach or the adaptive approach. Nevertheless, the adaptive solutions have considered a static player profile throughout the entire experience. This paper presents the design and evaluation of a dynamic adaptive gamification approach which—based on students’ interactions with game elements and also their opinions about these elements—dynamically updates the students’ player profile to better figure out which game elements suit them. We evaluated the engagement of students with gamification elements by means of a course composed of a knowledge "pill" related to the topic of “recycling plastics from the sea”, offered through the nanoMOOCs learning platform. We propose metrics such as the mean number of interactions with the gamification dashboard, the time spent by participants with game elements, and the opinions of students about these elements to compare the Dynamic Adaptive Gamification (DynamicAG) and the Static Adaptive (StaticAG) approaches. An experimental study with 66 high school students showed significant differences between both approaches. Specifically, the DynamicAG group spent twice as much time with the Dashboard than the StaticAG group. Moreover, students in the DynamicAG group were more engaged with game elements (mean number of interactions = 12.13) than those in the StaticAG group (mean number of interactions = 3.21).

Rock slope monitoring using 3D point cloud data allows the creation of rockfall inventories, provided that an efficient methodology is available to quantify the activity. However, monitoring with high temporal and spatial resolution entails the processing of a great volume of data, which can become a problem for the processing system. The standard methodology for monitoring includes the steps of data capture, point cloud alignment, the measure of differences, clustering differences, and identification of rockfalls. In this article, we propose a new methodology adapted from existing algorithms (multiscale model to model cloud comparison and density-based spatial clustering of applications with noise algorithm) and machine learning techniques to facilitate the identification of rockfalls from compared temporary 3D point clouds, possibly the step with most user interpretation. Point clouds are processed to generate 33 new features related to the rock cliff differences, predominant differences, or orientation for classification with 11 machine learning models, combined with 2 undersampling and 13 oversampling methods. The proposed methodology is divided into two software packages: point cloud monitoring and cluster classification. The prediction model applied in two study cases in the Montserrat conglomeratic massif (Barcelona, Spain) reveal that a reduction of 98% in the initial number of clusters is sufficient to identify the totality of rockfalls in the first case study. The second case study requires a 96% reduction to identify 90% of the rockfalls, suggesting that the homogeneity of the rockfall characteristics is a key factor for the correct prediction of the machine learning models.