On the robustness of standalone referring expression generation algorithms using RDF data

Ponencia presentada en el 2nd International Workshop on Natural Language Generation and the Semantic Web. Edimburgo, Escocia, 6 de septiembre de 2016.Fil: Duboué, Pablo Ariel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Domínguez, Martín Ariel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Estrella, Paula Susana. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.A sub-task of Natural Language Generation (NLG) is the generation of referring expressions (REG). REG algorithms are expected to select attributes that unambiguously identify an entity with respect to a set of distractors. In previous work we have defined a methodology to evaluate REG algorithms using real life examples. In the present work, we evaluate REG algorithms using a dataset that contains alterations in the properties of referring entities. We found that naturally occurring ontological re-engineering can have a devastating impact in the performance of REG algorithms, with some more robust in the presence of these changes than others. The ultimate goal of this work is observing the behavior and estimating the performance of a series of REG algorithms as the entities in the data set evolve over time.http://www.aclweb.org/anthology/W16-3500acceptedVersionFil: Duboué, Pablo Ariel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Domínguez, Martín Ariel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Estrella, Paula Susana. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Otras Ciencias de la Computación e Informació

Profiling students’ self-regulation with learning analytics: a proof of concept

The ability to regulate one's own learning processes is a key factor in educational scenarios. Self-regulation skills notably affect students' ef cacy when studying and academic performance, for better orworse. However, neither students or instructors generally have proper understanding of what self-regulated learning is, the impact that it has or how to assess it. This paper has the purpose of showing how learning analytics can be used in order to generate simple metrics related to several areas of students' selfregulation, in the context of a rst-year university course. These metrics are based on data obtained from a learning management system, complemented by more speci c assessment-related data and direct answers to self-regulated learning questionnaires. As the end result, simple self-regulation pro les are obtained for each student, which can be used to identify strengths and weaknesses and, potentially, help struggling students to improve their learning habits.Xunta de Galicia | Ref. ED431B 2020/3

Monitoring students’ self-regulation as a basis for an early warning system

Among the elements that determine a student’s academic success, their ability to regulate their own learning processes is an important, yet typically underrated factor. It is possible for students to improve their self-regulated learning skills, even at university levels. However, they are often unaware of their own behavior. Moreover, instructors are usually not prepared to assess students’ self-regulation. This paper presents a learning analytics solution which focuses on rating selfregulation skills, separated in several different categories, using activity and performance data from a LMS, as well as self-reported student data via questionnaires. It is implemented as an early warning system, offering the possibility of detecting students whose poor SRL profile puts them at risk of academic underperformance. As of the date of this writing, this is still a work in progress, and is being tested in the context of a first year college engineering course

Predictors and early warning systems in higher education: a systematic literature review

The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. Nowadays, these algorithms are widely used by entrepreneurs and researchers alike, having practical applications in a broad variety of contexts, such as in finance, marketing or healthcare. One of such contexts is the educational field, where the development and implementation of learning technologies led to the birth and popularization of computerbased and blended learning. Consequently, student-related data has become easier to collect. This Research Full Paper presents a literature review on predictive algorithms applied to higher education contexts, with special attention to early warning systems (EWS): tools that are typically used to analyze future risks such as a student failing or dropping a course, and that are able to send alerts to instructors or students themselves before these events can happen. Results of using predictors and EWS in real academic scenarios are also highlighted

Supporting intensive continuous assessment with BeA in a flipped classroom experience

This paper presents the changes performed in a university course to adopt European Higher Education Area principles taking advantage of new technologies and educational approaches. Particularly, a Flipped Classroom model that also involves an Intensive Continuous Assessment approach is adopted, moving the presentation of theoretical contents to videos that can be watched outside of the classroom and using the classroom face-to-face time to provide explanations, problem solving and to perform assessment activities every week. A main part of innovation in the experience comes from the use of an online tool (BeA - Blended e-Assessment) that facilitates the assessment and reviewing of paper-based exams. This tool supports teachers in assessment tasks, that can be performed in a faster, simpler, more transparent and less error-prone way. The paper shows the results of an experience involving a control group and an experimentation group, in which this new approach and tool have been applied. The results obtained demonstrate the effectiveness of both proposals. In conjunction, the paper describes how a traditional university course based on lectures can be successfully adapted to a more innovative approach based on the principles of active learning and accountability thanks to the use of our blended e-Assessment tool.Xunta de Galicia | Ref. ED431B 2017/67Xunta de Galicia | Ref. ED431D 2017/12Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-80515-

Exploring the synergies between gamification and data collection in higher education

In recent years, gamification techniques have been gaining popularity in all kind of educational scenarios, helping students improve their learning process by fostering engagement and attention. Implementing gamification aspects in a course can also provide an opportunity to gather student data that would not have been available otherwise. This paper describes a data gathering process in the context of a university course, as a work-in-progress. Among these data there is information regarding the participation of students in quizzes presented as games in the classroom. These quizzes combined questions covering course con-tents, as well as some regarding self-regulated learning habits. The main advantage observed was a high student participation in the quizzes. As a result, this gamification approach proved to be a more effective way to gather student data compared to other methods applied in previous academic years, which often failed due to many students ignoring optional activities.Xunta de Galicia | Ref. ED431B 2020/3

El ASPO en primera persona: relatos de estudiantes universitarixs viviendo la cuarentena en el nordeste argentino

En este libro se responde a la pregunta ¿cómo viven los estudiantes universitarios la cuarentena en el nordeste argentino?. Recopila relatos de 51 estudiantes que se realizaron en el marco del desarrollo de la materia Antropología Social de la carrera de Ciencias de la Educación de la Facultad de Humanidades de la Universidad Nacional del Nordeste. En los escritos cada estudiante cuenta en primera persona sus experiencias en la vivencia del aislamiento social,preventivo y obligatorio que se dispuso en el país por la pandemia por Covid-19.Las vivencias son muy diversas y exponen el modo en que el aislamiento alteró la vida del estudiante señalando tanto las dificultades como las oportunidades y los desafíos surgidos en medio de la inédita experiencia de vivir en cuarentena.Fil: Gandulfo, Carolina. Universidad Nacional del Nordeste. Facultad de Humanidades; ArgentinaFil: Alegre, Tamara Daiana. Universidad Nacional del Nordeste. Facultad de Humanidades; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: Domínguez, Martín Ariel. Universidad Nacional del Nordeste. Facultad de Humanidades; Argentin

P♤ : A process algebra for modeling prioritized stochastic timed systems (extended abstract)

We present P♤, a Stochastic Process Algebra (SPA) that allows for the modeling of timed systems with priorities and urgency. We de ne the semantics of P♤ in terms of Prioritized Stochastic Automata (PSA), an extension of automata with clock events, priorities and probabilistic symbolic transitions. PSAs are symbolic objects that have a concrete semantics on Probabilistic Timed Transition Systems (PTTS). Therefore, P♤ has semantics in two steps in terms of PTTS. We also de ne several operators directly on PTTS. They include parallel composition and a prioritizing operator. We show that this operators applied to PTTS commute (modulo probabilistic bisimulation) with their relatives in P♤ .Eje: Teoría (TEOR)Red de Universidades con Carreras en Informática (RedUNCI