Neuropathic pruritus: an early indicator of neuromyelitis optica spectrum disorders

Neuromyelitis optica is a relapsing, inflammatory astrocytopathic disorder, affecting predominantly the optic nerves and spinal cord. It is associated with antiaquaporin-4 immunoglobulin G (AQP4-IgG) in up to 70% of patients. Spinal cord involvement typically presents as a longitudinally-extensive transverse myelitis, with associated sensorimotor and sphincter dysfunction. Sensory symptoms such as numbness, dysaesthesia, pain and tonic spasms are common. Here, we present a case of a 25years old female who came to the medicine OPD, with the chief complaints of intense itching over face and forehead, which was later on progressed to quadriparesis after 3 weeks. This case highlights neuropathic pruritus as an under-recognised early feature of neuromyelitis optica

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

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