Información semántica e implicación lógica

El concepto de “implicación lógica”, uno de los más importantes de la lógica, está estrechamente relacionado con los conceptos de “información” y “cantidad de información”. En este informe se estudia dicha relación basándose para ello en la concepción semántica de la información y la polémica sobre la creencia, no por todos aceptada, de que la información es independiente de su valor de verdad. Tal como se explica a lo largo del informe, aceptar o no este punto de vista supone aceptar distintas concepciones de los tres conceptos anteriores

EFAR-MMLA: An evaluation framework to assess and report generalizability of machine learning models in MMLA

Producción CientíficaMultimodal Learning Analytics (MMLA) researchers are progressively employing machine learning (ML) techniques to develop predictive models to improve learning and teaching practices. These predictive models are often evaluated for their generalizability using methods from the ML domain, which do not take into account MMLA’s educational nature. Furthermore, there is a lack of systematization in model evaluation in MMLA, which is also reflected in the heterogeneous reporting of the evaluation results. To overcome these issues, this paper proposes an evaluation framework to assess and report the generalizability of ML models in MMLA (EFAR-MMLA). To illustrate the usefulness of EFAR-MMLA, we present a case study with two datasets, each with audio and log data collected from a classroom during a collaborative learning session. In this case study, regression models are developed for collaboration quality and its sub-dimensions, and their generalizability is evaluated and reported. The framework helped us to systematically detect and report that the models achieved better performance when evaluated using hold-out or cross-validation but quickly degraded when evaluated across different student groups and learning contexts. The framework helps to open up a “wicked problem” in MMLA research that remains fuzzy (i.e., the generalizability of ML models), which is critical to both accumulating knowledge in the research community and demonstrating the practical relevance of these techniques.Fondo Europeo de Desarrollo Regional - Agencia Nacional de Investigación (grants TIN2017-85179-C3-2-R and TIN2014-53199-C3-2-R)Fondo Europeo de Desarrollo Regional - Junta de Castilla y León (grant VA257P18)Comisión Europea (grant 588438-EPP-1- 2017-1-EL-EPPKA2-KA

Multimodal Data Value Chain (M-DVC): A Conceptual Tool to Support the Development of Multimodal Learning Analytics Solutions

Producción CientíficaMultimodal Learning Analytics (MMLA) systems, understood as those that exploit multimodal evidence of learning to better model a learning situation, have not yet spread widely in educational practice. Their inherent technical complexity, and the lack of educational stakeholder involvement in their design, are among the hypothesized reasons for the slow uptake of this emergent field. To aid in the process of stakeholder communication and systematization leading to the specification of MMLA systems, this paper proposes a Multimodal Data Value Chain (M-DVC). This conceptual tool, derived from both the field of Big Data and the needs of MMLA scenarios, has been evaluated in terms of its usefulness for stakeholders, in three authentic case studies of MMLA systems currently under development. The results of our mixed-methods evaluation highlight the usefulness of the M-DVC to elicit unspoken assumptions or unclear data processing steps in the initial stages of development. The evaluation also revealed limitations of the M-DVC in terms of the technical terminology employed, and the need for more detailed contextual information to be included. These limitations also prompt potential improvements for the M-DVC, on the path towards clearer specification and communication within the multi-disciplinary teams needed to build educationally-meaningful MMLA solutions.Junta de Castilla y León (Project VA257P18)Ministerio de Ciencia, Innovación y Universidades (Project TIN2017-85179-C3-2-R)CEITER (grant agreements no. 669074

From face-to-face to distance LMS-mediated collaborative learning situations with GLUE!

GLUE! is an integration architecture that allows teachers to easily set up an LMS environment with several external tools to carry out complex collaborative learning situations in distance settings. Though its effectiveness in alleviating the burden on teachers of deploying and enacting such situations has been studied elsewhere, there are no studies in the literature analyzing the impact, in terms of learning achievement, of turning traditional face-to-face collaborative learning situations into distance LMS-mediated ones with the support of integration approaches such as the GLUE! architecture. This paper compares the learning achievement in a distance LMS-mediated collaborative learning situation supported by GLUE! and in the equivalent face-to-face in a non-technological setting. The conclusions of this comparison, along with the fact that GLUE! significantly reduces the set up effort, suggest that GLUE! is a good choice for turning traditional face-to-face collaborative learning situations into distance LMS-mediated ones without significant negative effects in the learning achievement.This work has been funded by the Spanish Ministry of Economy and Competitiveness projects TIN2011-28308-C03-01 andTIN2011-28308-C03-02, and the postdoctoral fellowship Alliance 4 Universities.Publicad

Supporting contextualized learning with linked open data

Producción CientíficaThis paper proposes a template-based approach to semi-automatically create contextualized learning tasks out of several sources from the Web of Data. The contextualization of learning tasks opens the possibility of bridging formal learning that happens in a classroom, and informal learning that happens in other physical spaces, such as squares or historical buildings. The tasks created cover different cognitive levels and are contextualized by their location and the topics covered. We applied this approach to the domain of History of Art in the Spanish region of Castile and Leon. We gathered data from DBpedia, Wikidata and the Open Data published by the regional government and we applied 32 templates to obtain 16K learning tasks. An evaluation with 8 teachers shows that teachers would accept their students to carry out the tasks generated. Teachers also considered that the 85% of the tasks generated are aligned with the content taught in the classroom and were found to be relevant to learn in other informal spaces. The tasks created are available at https://casuallearn.gsic.uva.es/sparql.Junta de Castilla y León (grant VA257P18)Fondo Europeo de Desarrollo Regional - Agencia Nacional de Investigación (grant TIN2017-85179-C3-2-R

7th International Conference on Technological Ecosystems for Enhancing Multiculturality, TEEM 2019

Producción CientíficaProceedings of the 7th International Conference on Technological Ecosystems for Enhancing Multiculturality, TEEM 2019, León, Spain, October 16-18, 2019.Smart Education promises personalized learning experiences that bridge formal and informal learning. Our proposal is to exploit the Web of Data to automatically create learning resources that can be, later on, recommended to a learner based on her learning interests and context. For example, a student enrolled in an arts course can get recommendations of learning resources (e.g., a quiz related to a monument she passes by) by exploiting existing geolocalized descriptions of historical buildings in the Web of Data. This paper describes a scenario to illustrate this idea and proposes a software architecture to support it. It also provides some examples of learning resources automatically created with a first prototype of a resource-generator module.Ministerio de Ciencia, Innovación y Univerisades (project grant TIN2017-85179-C3-2-R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. Project VA257P18

Towards a Living Lab to support evidence-based educational research and innovation

Living Labs represent a promising approach to bridge the gap between evidence-based educational research and sustained innovation. This position paper presents our initial work related to educational Living Labs. It describes a model of the research and innovation processes that we aim to support. It also presents the preliminary results of a pilot study in which a Living Lab supported a researcher and two teachers to introduce Learning Analytics in their classroom

Casual Learn: A Smart Application to Learn History of Art

Producción CientíficaSmart Learning promises the connection between formal and informal learning, but how to offer informal learning tasks related to formal learning is still a challenge. This demonstration paper presents CasualLearn, a smart learning application that bridges formal and informal learning to learn History of Art in the Spanish region of Castile and Leon. CasualLearn uses a dataset of 16,221 contextualized informal learning tasks that were semi-automatically created exploiting Open Data from the Web. CasualLearn offers these tasks to students based on their context: their geolocation, the activity they do and the topics covered in their formal education. A demo application is currently available for Android devices.Junta de Castilla y León (Project VA257P18)Ministerio de Ciencia, Innovación y Universidades (Project TIN2017-85179-C3-2-R)European Commission (Project grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

The potential of Open Data to automatically create learning resources for smart learning environments

Producción CientíficaSmart Education requires bridging formal and informal learning experience. However, how to create contextualized learning resources that support this bridging remains a problem. In this paper, we propose to exploit the open data available in the Web to automatically create contextualized learning resources. Our preliminary results are promising, as our system creates thousands of learning resources related to formal education concepts and physical locations in the student’s local municipality. As part of our future work, we will explore how to integrate these resources into a Smart Learning Environment.Ministerio de Ciencia e Innovación - Fondo Europeo de Desarrollo Regional (grant TIN2017-85179-C3-2-R)Junta de Castilla y León - Fondo Europeo de Desarrollo Regional (grant VA257P18

CIMLA: A Modular and Modifiable Data Preparation, Organization, and Fusion Infrastructure to Partially Support the Development of Context-aware MMLA Solutions

Multimodal Learning Analytics (MMLA) solutions aim to provide a more holistic picture of a learning situation by processing multimodal educational data. Considering contextual information of a learning situation is known to help in providing more relevant outputs to educational stakeholders. However, most of the MMLA solutions are still in prototyping phase and dealing with different dimensions of an authentic MMLA situation that involve multiple cross-disciplinary stakeholders like teachers, researchers, and developers. One of the reasons behind still being in prototyping phase of the development lifecycle is related to the challenges that software developers face at different levels in developing context-aware MMLA solutions. In this paper, we identify the requirements and propose a data infrastructure called CIMLA. It includes different data processing components following a standard data processing pipeline and considers contextual information following a data structure. It has been evaluated in three authentic MMLA scenarios involving different cross-disciplinary stakeholders following the Software Architecture Analysis Method. Its fitness was analyzed in each of the three scenarios and developers were interviewed to assess whether it meets functional and non-functional requirements. Results showed that CIMLA supports modularity in developing context-aware MMLA solutions and each of its modules can be reused with required modifications in the development of other solutions. In the future, the current involvement of a developer in customizing the configuration file to consider contextual information can be investigated