Data Mining of Digital Footprint during Assessment Grading for Intelligent Decision Making during Learning Process

В работе рассматриваются проблемы моделирования предметной области, генерации контрольно-измерительных материалов, а также оценки и интерпретации ответов на них методами интеллектуального анализа. Для решения этих проблем предложено ответы обучаемого, рассматриваемые как образовательный цифровой след, дополнить информацией, позволяющей делать выводы как об уровне развития компетентностей, так и о затруднениях учащегося и их причинах. Оценку уровня развития индивидуальных и групповых компетентностей предложено осуществлять через экспертные оценки и автоматическую проверку гипотез. Для генерации заданий и выявления причин затруднений учащихся предлагается использовать онтологические модели предметных областей на уровне «понимания» таксономии Блума. Рассмотрены свойства таких моделей. Приведены примеры применения этих подходов к принятию решений в образовательном процессе различных дисциплин. В результате были сформулированы требования к перспективному модельному обеспечению интеллектуальных обучающих системThe paper describes the problems of generation and modeling of learning assessments, their grading, and analyzing the process of their completion by learners using data mining methods. The authors propose to enhance the digital footprint of learner’s interactions with e-assessment systems with the information allowing determining demonstrated level competence development and the causes of learners’ mistakes and hypo. The learner’s level of competence development can be evaluated using expert estimates and the Certainty Factor. Generating assessments and determining the causes of learner’s mistakes can be done using ontological models of subject domains, built on the comprehension level of Bloom’s taxonomy. The paper explores the properties of such models. Examples of applying the proposed approaches to intelligent decision making during the learning process of different subject domains are shown. This leads to formulating the requirements for subject domain models for intelligent tutoring system

Polymorphisms associated with resistance to infectious diseases in different breeds of pigs of the Belgorod Region of Russia

Using the PCR-RFLP method, we genotyped pigs for the genes GBP1 (mutation c. [10A>G; 11A>G]), FUT1 (mutation G307A) and MUC4(A243G) associated with resistance to infectious diseases. A total of 188 boars of four breeds (Duroc, Large White, Landrace and Yorkshire) from the Belgorod Region of Russian Federation were studied. The highest frequency of favorable genotype AG for the polymorphism GBP1E2, which is resistant to pig respiratory syndrome, was characterized by the Duroc breed (0.38). The highest frequency of Eischerichia coli F18-resistant AA genotype was observed in the Landrace population (0.19). And in terms of resistance to E. coli with fibrium type F4 (K 88), the highest frequency of favorable genotype GG is characterized by the breed Large White (0,23). In general, it can be noted that genetic resistance in the studied boars is observed predominantly only to one infectious disease

Direct imaging of isofrequency contours of guided modes in extremely anisotropic all-dielectric metasurface

In this work, we investigate an all-dielectric metasurface based on the silicon on insulator platform for manipulating of guided modes in the visible and near-infrared spectral ranges. We use the Fourier modal method to demonstrate numerically, that guided modes excited in the metasurface support both hyperbolic-like and elliptic dispersion regimes. We implement a back focal plane microscope combined with a high refractive index solid immersion lens to directly image the isofrequency contours of the guided modes (surface waves). Reconstruction of dispersion unambiguously reveals the transition between different dispersion regimes