Conditions and the effects of an intelligent tutoring system usage for Russian high-stakes exam in English

The aim of the proposed study was to dwell on the field of intelligent tutoring systems as applied to high-stakes exam settings in foreign languages. The main research hypothesis of this paper was the following: Does the study attempt frequency within the suggested intelligent tutoring system affect the overall students’ learning performance in preparation for the Speaking part of the Russian high-stakes exam in the English language? Addressing this research hypothesis also resulted in acquiring understanding on key stakeholders’ perception of preparation for the Russian high-stakes exam in English. Research literature was thoroughly analyzed and the suggested intelligent system was described in detail. Data was collected through a computer-based automated procedure with further randomization and sampling. As a result of the study, three cohorts of users of the intelligent tutoring system were defined. Each cohort maintained a positive study dynamics experienced through the use of the intelligent tutoring system. Also, continuous aspiration for implementing online self-training environments was identified within the majority of a foreign language teachers’ community. The framework developed for the research can be used in future research as a foundation for investigating self-regulated learning environments created for the Speaking part preparation of high-stakes exam in foreign languages

Dynamic Estimation of Rater Reliability using Multi-Armed Bandits

One of the critical success factors for supervised machine learning is the quality of target values, or predictions, associated with training instances. Predictions can be discrete labels (such as a binary variable specifying whether a blog post is positive or negative) or continuous ratings (for instance, how boring a video is on a 10-point scale). In some areas, predictions are readily available, while in others, the eort of human workers has to be involved. For instance, in the task of emotion recognition from speech, a large corpus of speech recordings is usually available, and humans denote which emotions are present in which recordings

Using Crowdsourcing for Labelling Emotional Speech Assets

The success of supervised learning approaches for the classification of emotion in speech depends highly on the quality of the training data. The manual annotation of emotion speech assets is the primary way of gathering training data for emotional speech recognition. This position paper proposes the use of crowdsourcing for the rating of emotion speech assets. Recent developments in learning from crowdsourcing offer opportunities to determine accurate ratings for assets which have been annotated by large numbers of non-expert individuals. The challenges involved include identifying good annotators, determining consensus ratings and learning the bias of annotators

Estimating the scale of stone axe production: A case study from Onega Lake, Russian Karelia

The industry of metatuff axes and adzes on the western coast of Onega Lake (Eneolithic period, ca. 3500 – 1500 cal. BC) allows assuming some sort of craft specialization. Excavations of a workshop site Fofanovo XIII, conducted in 2010-2011, provided an extremely large assemblage of artefacts (over 350000 finds from just 30 m2, mostly production debitage). An attempt to estimate the output of production within the excavated area is based on experimental data from a series of replication experiments. Mass-analysis with the aid of image recognition software was used to obtain raw data from flakes from excavations and experiments. Statistical evaluation assures that the experimental results can be used as a basement for calculations. According to the proposed estimation, some 500 – 1000 tools could have been produced here, and this can be qualified as an evidence of “mass-production”

Context Cues For Classification Of Competitive And Collaborative Overlaps

Being able to respond appropriately to users’ overlaps should be seen as one of the core competencies of incremental dialogue systems. At the same time identifying whether an interlocutor wants to support or grab the turn is a task which comes naturally to humans, but has not yet been implemented in such systems. Motivated by this we first investigate whether prosodic characteristics of speech in the vicinity of overlaps are significantly different from prosodic characteristics in the vicinity of non-overlapping speech. We then test the suitability of different context sizes, both preceding and following but excluding features of the overlap, for the automatic classification of collaborative and competitive overlaps. We also test whether the fusion of preceding and succeeding contexts improves the classification. Preliminary results indicate that the optimal context for classification of overlap lies at 0.2 seconds preceding the overlap and up to 0.3 seconds following it. We demonstrate that we are able to classify collaborative and competitive overlap with a median accuracy of 63%