Modeling language learning using specialized Elo ratings

Automatic assessment of the proficiency levels of the learner is a critical part of Intelligent Tutoring Systems. We present methods for assessment in the context of language learning. We use a specialized Elo formula used in conjunction with educational data mining. We simultaneously obtain ratings for the proficiency of the learners and for the difficulty of the linguistic concepts that the learners are trying to master. From the same data we also learn a graph structure representing a domain model capturing the relations among the concepts. This application of Elo provides ratings for learners and concepts which correlate well with subjective proficiency levels of the learners and difficulty levels of the concepts

Landscape of international event-based biosurveillance

Event-based biosurveillance is a scientific discipline in which diverse sources of data, many of which are available from the Internet, are characterized prospectively to provide information on infectious disease events. Biosurveillance complements traditional public health surveillance to provide both early warning of infectious disease events and situational awareness. The Global Health Security Action Group of the Global Health Security Initiative is developing a biosurveillance capability that integrates and leverages component systems from member nations. This work discusses these biosurveillance systems and identifies needed future studies

Contending with Spiritual Reductionism: Demons, Shame, and Dividualising Experiences Among Evangelical Christians with Mental Distress

The belief that mental distress is caused by demons, sin, or generational curses is commonplace among many evangelical Christian communities. These beliefs may have positive or negative effects for individuals and groups. Phenomenological descriptions of these experiences and the subjective meanings associated with them, however, remain somewhat neglected in the literature. The current study employed semi-structured interviews with eight evangelical Christians in order to idiographically explore their experiences of mental distress in relation to their faith and wider communities. Through an interpretative phenomenological analysis, two superordinate themes were constructed: negative spiritualisation and negotiating the dialectic between faith and the lived experience of mental distress. Participants variously experienced a climate of negative spiritualisation, whereby their mental distress was demonised and dismissed, and they were further discouraged from seeking help in secular institutions and environments. Participants often considered such dismissals of their mental distress as unhelpful and stigmatising and experienced heightened feelings of shame and suffering as a result. Such discouragement also contributed to the process of othering and relational disconnection. Alongside a rejection of church teachings, which exclusively spiritualised psychological distress, participants negotiated a nuanced personal synthesis of faith, theology, and distress, which assumed a localised and idiographic significance. This synthesis included advocating for the uptake of aetiological accounts, which contextualised mental distress in terms of the whole person and resisted de-politicised, dichotomised, and individualistic narratives. Results are discussed in relation to a broad range of literature in the field, while further research suggestions are provided.N/

Integration of computer-aided language learning into formal university-level L2 instruction

This paper presents our experience from pilot studies оn integration of intelligent learning and tutoring tools into official curricula for foreign/second-language (L2) learning. We report specifically on initial studies with learners of Russian as a second language at major universities in Italy and in Finland. An important challenge in both of these educational situations is the heterogeneous nature of the student contingent, including the presence of a sizable proportion of ‘heritage’ learners. Furthermore, the groups are often very large, which motivates the integration of an ICALL system. We describe the first integration attempt, an analysis of the emerging aspects and problems, and the design of a new experiment, which is on-going and takes into account the lessons learned. To the best of our knowledge, this is the first report on large-scale ICALL studies involving substantial numbers of ‘high-stakes’ learners of Russian at the intermediate-to-advanced levels – i.e., learners beyond the elementary level

Modeling language learning using specialized Elo ratings

Automatic assessment of the proficiency levels of the learner is a critical part of Intelligent Tutoring Systems. We present methods for assessment in the context of language learning. We use a specialized Elo formula used in conjunction with educational data mining. We simultaneously obtain ratings for the proficiency of the learners and for the difficulty of the linguistic concepts that the learners are trying to master. From the same data we also learn a graph structure representing a domain model capturing the relations among the concepts. This application of Elo provides ratings for learners and concepts which correlate well with subjective proficiency levels of the learners and difficulty levels of the concepts

Comparing domain-specific and domain-general BERT variants for inferred real-world knowledge through rare grammatical features in Serbian

Transfer learning is one of the prevailing approaches towards training language-specific BERT models. However, some languages have uncommon features that may prove to be challenging to more domain-general models but not domain-specific models. Comparing the performance of BERTić, a Bosnian-Croatian-Montenegrin-Serbian model, and Multilingual BERT on a Named-Entity Recognition (NER) task and Masked Language Modelling (MLM) task based around a rare phenomenon of indeclinable female foreign names in Serbian reveals how the different training approaches impacts their performance. Multilingual BERT is shown to perform better than BERTić in the NER task, but BERTić greatly exceeds in the MLM task. Thus, there are applications both for domain-general training and domain-specific training depending on the tasks at hand

Comparing domain-specific and domain-general BERT variants for inferred real-world knowledge through rare grammatical features in Serbian

Transfer learning is one of the prevailing approaches towards training language-specific BERT models. However, some languages have uncommon features that may prove to be challenging to more domain-general models but not domain-specific models. Comparing the performance of BERTić, a Bosnian-Croatian-Montenegrin-Serbian model, and Multilingual BERT on a Named-Entity Recognition (NER) task and Masked Language Modelling (MLM) task based around a rare phenomenon of indeclinable female foreign names in Serbian reveals how the different training approaches impacts their performance. Multilingual BERT is shown to perform better than BERTić in the NER task, but BERTić greatly exceeds in the MLM task. Thus, there are applications both for domain-general training and domain-specific training depending on the tasks at hand

Automated vocabulary discovery for geo-parsing online epidemic intelligence

Background Automated surveillance of the Internet provides a timely and sensitive method for alerting on global emerging infectious disease threats. HealthMap is part of a new generation of online systems designed to monitor and visualize, on a real-time basis, disease outbreak alerts as reported by online news media and public health sources. HealthMap is of specific interest for national and international public health organizations and international travelers. A particular task that makes such a surveillance useful is the automated discovery of the geographic references contained in the retrieved outbreak alerts. This task is sometimes referred to as "geo-parsing". A typical approach to geo-parsing would demand an expensive training corpus of alerts manually tagged by a human. Results Given that human readers perform this kind of task by using both their lexical and contextual knowledge, we developed an approach which relies on a relatively small expert-built gazetteer, thus limiting the need of human input, but focuses on learning the context in which geographic references appear. We show in a set of experiments, that this approach exhibits a substantial capacity to discover geographic locations outside of its initial lexicon. Conclusion The results of this analysis provide a framework for future automated global surveillance efforts that reduce manual input and improve timeliness of reporting.Google.orgNational Library of Medicine and the National Institutes of Health (grant G08LM009776-01A2