Analysing, visualising and supporting collaborative learning using interactive tabletops

The key contribution of this thesis is a novel approach to design, implement and evaluate the conceptual and technological infrastructure that captures student’s activity at interactive tabletops and analyses these data through Interaction Data Analytics techniques to provide support to teachers by enhancing their awareness of student’s collaboration. To achieve the above, this thesis presents a series of carefully designed user studies to understand how to capture, analyse and distil indicators of collaborative learning. We perform this in three steps: the exploration of the feasibility of the approach, the construction of a novel solution and the execution of the conceptual proposal, both under controlled conditions and in the wild. A total of eight datasets were analysed for the studies that are described in this thesis. This work pioneered in a number of areas including the application of data mining techniques to study collaboration at the tabletop, a plug-in solution to add user-identification to a regular tabletop using a depth sensor and the first multi-tabletop classroom used to run authentic collaborative activities associated with the curricula. In summary, while the mechanisms, interfaces and studies presented in this thesis were mostly explored in the context of interactive tabletops, the findings are likely to be relevant to other forms of groupware and learning scenarios that can be implemented in real classrooms. Through the mechanisms, the studies conducted and our conceptual framework this thesis provides an important research foundation for the ways in which interactive tabletops, along with data mining and visualisation techniques, can be used to provide support to improve teacher’s understanding about student’s collaboration and learning in small groups

Human-Centred Learning Analytics

The design of effective learning analytics (LA) extends beyond sound technical and pedagogical principles. If analytics are to be adopted and used successfully to support learning and teaching, their design process needs to take into account a range of human factors, including why and how they will be used. In this editorial, we introduce principles of human-centred design developed in other, related fields that can be adopted and adapted to support the development of human-centred learning analytics (HCLA). We draw on the papers in this special section, together with the wider literature, to define human-centred design in the field of LA and to identify the benefits and challenges that this approach offers. We conclude by suggesting that HCLA will enable the community to achieve more impact, more quickly, with tools that are fit for the purpose and a pleasure to use

NDT to identify biological damage in wood

Nondestructive techniques are widely used to assess existing timber structures. The models proposed for these methods are usually performed in the laboratory using small clear wood specimens. But in real situations many anomalies, defects and biological damage are found in wood. In these cases the existing models only indicate that the values are outside normality without providing any other information. To solve this problem, a study of non-destructive probing methods for wood was performed, testing the behaviour of four different techniques (penetration resistance, pullout resistance, drill resistance and chip drill extraction) on wood samples with different biological damage, simulating an in-situ test. The wood samples were obtained from existing Spanish timber structures with biotic damage caused by borer insects, termites, brown rot and white rot. The study concludes that all of the methods offer more or less detailed information about the degree of deterioration of wood, but that the first two methods (penetration and pullout resistance) cannot distinguish between pathologies. On the other hand, drill resistance and chip drill extraction make it possible to differentiate pathologies and even to identify species or damage location. Finally, the techniques used were compared to characterize their advantages and disadvantages

Towards Supporting Awareness for Content Curation: The case of Food Literacy and Behavioural Change

ABSTRACT This paper presents a theoretical grounding and a conceptual proposal aimed at providing support in the initial stages of sustained behavioural change. We explore the role that learning analytics and/or open learner models can have in supporting lifelong learners to enhance their food literacy through a more informed curation process of relevant-content. This approach grounds on a behavioural change perspective that identifies i) knowledge, ii) attitudes, and iii) self-efficacy as key factors that will directly and indirectly affect future decisions and agency of life-long learners concerning their own health. The paper offers some possible avenues to start organising efforts towards the use of learning analytics to enhance awareness in terms of: knowledge curation, knowledge sharing and knowledge certainty. The paper aims at triggering discussion about the type of data and presentation mechanisms that may help life-long learners set a stronger basis for behavioural change in the subsequent stages

Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (e.g., question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency, and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (e.g., GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models

MAPEO DE LA DISTRIBUCIÓN ESPACIAL DE TRIPS (INSECTA: THYSANOPTERA) EN PARCELAS COMERCIALES DE AGUACATE VAR. HASS EN COATEPEC HARINAS ESTADO DE MÉXICO

SE ESTABLECIÓ LA DISTRIBUCIÓN ESPACIAL DE LOS TRIPS EN CULTIVO DE AGUACATE EN UNA LOCALIDAD DEL ESTADO DE MÉXICO.El presente estudio permitió conocer la distribución espacial de trips mediante mapas de densidad en tres parcelas comerciales de aguacate VarHass en Coatepec Harinas, Estado de México. Los resultados mostraron que para el periodo comprendido de julio a diciembre de 2015, la distribución espacial de trips se ajustó a modelos gaussianos en su totalidad con una superficie infestada que osciló entre el 90 y 100% mostrando undistribución espacial agregada; el efecto pepita obtenido para todos los muestreos corroboró la agregación de las poblaciones de trips y refiere umínimo error de muestreo para los doce muestreos realizados. Trabajos como este, en que se aplican métodos geoestadísticos, coadyuvan al conocimiento de los problemas fitosanitarios de los cultivos de importancia económica y a la aplicación de medidas de control (químico, biológicocultural o mecánico) dirigidas a zonas específicas de la parcela

The Question-driven Dashboard: How Can We Design Analytics Interfaces Aligned to Teachers’ Inquiry?

One of the ultimate goals of several learning analytics (LA) initiatives is to close the loop and support students’ and teachers’ reflective practices. Although there has been a proliferation of end-user interfaces (often in the form of dashboards), various limitations have already been identified in the literature such as key stakeholders not being involved in their design, little or no account for sense-making needs, and unclear effects on teaching and learning. There has been a recent call for human-centred design practices to create LA interfaces in close collaboration with educational stakeholders to consider the learning design, and their authentic needs and pedagogical intentions. This paper addresses the call by proposing a question-driven LA design approach to ensure that end-user LA interfaces explicitly address teachers’ questions. We illustrate the approach in the context of synchronous online activities, orchestrated by pairs of teachers using audio-visual and text-based tools (namely Zoom and Google Docs). This study led to the design and deployment of an open-source monitoring tool to be used in real-time by teachers when students work collaboratively in breakout rooms, and across learning spaces

Transmission of SARS-CoV-2 in a primary school setting with and without public health measures using real-world contact data:A modelling study

BACKGROUND: Stringent public health measures have been shown to influence the transmission of SARS-CoV-2 within school environments. We investigated the potential transmission of SARS-CoV-2 in a primary school setting with and without public health measures, using fine-grained physical positioning traces captured before the COVID-19 pandemic. METHODS: Approximately 172.63 million position data from 98 students and six teachers from an open-plan primary school were used to predict a potential transmission of SARS-CoV-2 in primary school settings. We first estimated the daily average number of contacts of students and teachers with an infected individual during the incubation period. We then used the Reed-Frost model to estimate the probability of transmission per contact for the SARS-CoV-2 Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron variant (B.1.1.529). Finally, we built a binomial distribution model to estimate the probability of onward transmission in schools with and without public health measures, including face masks and physical distancing. RESULTS: An infectious student would have 49.1 (95% confidence interval (CI) = 46.1-52.1) contacts with their peers and 2.00 (95% CI = 1.82-2.18) contacts with teachers per day. An infectious teacher would have 47.6 (95% CI = 45.1-50.0) contacts with students and 1.70 (95% CI = 1.48-1.92) contacts with their colleague teachers per day. While the probability of onward SARS-CoV-2 transmission was relatively low for the Alpha and Delta variants, the risk increased for the Omicron variant, especially in the absence of public health measures. Onward teacher-to-student transmission (88.9%, 95% CI = 88.6%-89.1%) and teacher-to-teacher SARS-CoV-2 transmission (98.4%, 95% CI = 98.5%-98.6%) were significantly higher for the Omicron variant without public health measures in place. CONCLUSIONS: Our findings illustrate that, despite a lower frequency of close contacts, teacher-to-teacher close contacts demonstrated a higher risk of transmission per contact of SARS-CoV-2 compared to student-to-student close contacts. This was especially significant with the Omicron variant, with onward transmission more likely occurring from teacher index cases than student index cases. Public health measures (eg, face masks and physical distance) seem essential in reducing the risk of onward transmission within school environments