The Relationship between Students’ Social Competence, Emotional Intelligence and their Academic Achievement at Nazarbayev Intellectual School of Aktobe

In the modern world the tendency to increase students’ academic achievement can be easily observed and Kazakhstan is not an exception. After getting independence in 1991 the country strives for entering the world arena in all the spheres including education. In Nazarbayev Intellectual Schools high academic achievement is one of the priorities for students. To reach this aim students and teachers are provided with all the necessary facilities, new technologies and gadgets, and are expected to develop academically and professionally. Students spend the whole day at school, even devoting their time after school to additional lessons, while teachers should conduct research and participate in conferences in addition to their teaching. All these efforts are aimed at increasing students’ academic achievement and ensuring high position of Kazakhstan in the world educational rankings. However, not all the aims regarding academic success have been reached, leading to concerns about the key factors which influence students’ academic achievement. To investigate the problem quantitative correlational design study was employed. All the data was collected in one educational organization which is Nazarbayev Intellectual School of Aktobe. A sample of 152 participants was selected by using non-probabilistic purposive maximum variation sampling procedures. Strengths and Difficulties Questionnaire (SDQ) and Trait Emotional Intelligence Questionnaire (TEIQue) were used to collect data. For data analysis, descriptive and inferential statistics, as well as hierarchical regressions were conducted in the Statistical Package for the Social Sciences (SPSS). The results provided information on the level of students’ social competence and emotional intelligence. The study also focused on how high, average, and lower performing NIS students compare in terms of social competence and emotional intelligence. The influence of gender, age and medium of instruction on the relationship between academic achievement and students’ social competence and emotional intelligence was studied as well. В современном мире наблюдается тенденция повышать академическую успеваемость учащихся, и Казахстан в этом случае не исключение. После обретения независимости в 1991 году страна стремится к вступлению на мировую арену во всех сферах, включая образование. В Назарбаев Интеллектуальных школах высокая академическая успеваемость является одним из приоритетов для учащихся. Для достижения данной цели учащимся и учителям предоставляется все необходимое оборудование, новые технологии и приспособления; также требуется, чтобы они постоянно развивались академически и профессионально. Учащиеся весь день проводят в школе, посвящая время после уроков дополнительным занятиям, в то время как учителя помимо преподавания проводят исследования и участвуют в конференциях. Все эти усилия направлены на повышение академической успеваемости учащихся и обеспечению Казахстану высокой позиции в мировых рейтингах. Однако не все цели касательно академического успеха были достигнуты, что привело к особому интересу к ключевым факторам, влияющим на академическую успеваемость. Для исследования данной проблемы был использован количественный корреляционный подход. Все данные были собраны в одной образовательной организации – Назарбаев Интеллектуальной школе г. Актобе. С помощью детерминированной целевой выборки с максимальной вариацией были отобраны 152 участника исследования. Для сбора данных были использованы опросник «Сильные стороны и трудности» и опросник «Эмоциональный Интеллект». Для анализа данных в пакете для статистических данных (SPSS) были проведены анализы описательной и дедуктивной статистики, а также иерархическое моделирование регрессий. Результаты показали уровень социальной компетенции и эмоционального интеллекта учащихся. Исследование также фокусировалось на сравнении уровня социальной компетенции и эмоционального интеллекта учащихся с высокой, средней и более низкой успеваемостью. Влияние пола, возраста и языка обучения на взаимоотношения между академической успеваемостью, социальной компетенцией и эмоциональным интеллектом учащихся также было изучено

Many Colors of Assessment: Participation Matters

The problem of non-attendance and lack of student engagement in class is a widely recognized issue in educational circles around the world, including Kazakhstan. One of the reasons is the neglect of class participation in the current assessment models. This policy brief outlines the significance of class participation, considers the relation between class participation and improvement of academic performance, reviews the existing assessment practices, and argues for the inclusion of class participation as one of the aspects of assessment system in Kazakhstani organizations for secondary education

Handling limited datasets with neural networks in medical applications : a small-data approach

Motivation: Single-centre studies in medical domain are often characterised by limited samples due to the complexity and high costs of patient data collection. Machine learning methods for regression modelling of small datasets (less than 10 observations per predictor variable) remain scarce. Our work bridges this gap by developing a novel framework for application of artificial neural networks (NNs) for regression tasks involving small medical datasets. Methods: In order to address the sporadic fluctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work. The approach was compared to the state-of-the-art ensemble NNs; the effect of dataset size on NN performance was also investigated. Results: The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85 MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3%, outperforming an ensemble NN model by 11%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples). Conclusion: The significance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes

Subclass analysis of donor HLA-specific IgG in antibody-incompatible renal transplantation reveals a significant association of IgG4 with rejection and graft failure

Donor HLA-specific antibodies (DSAs) can cause rejection and graft loss after renal transplantation, but their levels measured by the current assays are not fully predictive of outcomes. We investigated whether IgG subclasses of DSA were associated with early rejection and graft failure. DSA levels were determined pretreatment, at the day of peak pan-IgG level and at 30 days post-transplantation in eighty HLA antibody-incompatible kidney transplant recipients using a modified microbead assay. Pretreatment IgG4 levels were predictive of acute antibody-mediated rejection (P = 0.003) in the first 30 days post-transplant. Pre-treatment presence of IgG4 DSA (P = 0.008) and day 30 IgG3 DSA (P = 0.03) was associated with poor graft survival. Multivariate regression analysis showed that in addition to pan-IgG levels, total IgG4 levels were an independent risk factor for early rejection when measured pretreatment, and the presence of pretreatment IgG4 DSA was also an independent risk factor for graft failure. Pretreatment IgG4 DSA levels correlated independently with higher risk of early rejection episodes and medium-term death-censored graft survival. Thus, pretreatment IgG4 DSA may be used as a biomarker to predict and risk stratify cases with higher levels of pan-IgG DSA in HLA antibody-incompatible transplantation. Further investigations are needed to confirm our results

Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation

Clinical datasets are commonly limited in size, thus restraining applications of Machine Learning (ML)techniques for predictive modelling in clinical research and organ transplantation. We explored thepotential of Decision Tree (DT) and Random Forest (RF) classification models, in the context of smalldataset of 80 samples, for outcome prediction in high-risk kidney transplantation. The DT and RF modelsidentified the key risk factors associated with acute rejection: the levels of the donor specific IgG anti-bodies, the levels of IgG4 subclass and the number of human leucocyte antigen mismatches betweenthe donor and recipient. Furthermore, the DT model determined dangerous levels of donor-specific IgGsubclass antibodies, thus demonstrating the potential of discovering new properties in the data whentraditional statistical tools are unable to capture them. The DT and RF classifiers developed in this workpredicted early transplant rejection with accuracy of 85%, thus offering an accurate decision supporttool for doctors tasked with predicting outcomes of kidney transplantation in advance of the clinicalintervention

Machine learning of LWR spent nuclear fuel assembly decay heat measurements

Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adopted to train machine learning (ML) models. The measured data is available for fuel assemblies irradiated in commercial reactors operated in the United States and Sweden. The data comes from calorimetric measurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuel assemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively. Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii) to generate and use synthetic data, as large dataset which has similar statistical characteristics as the original dataset. Three ML models are developed based on Gaussian process (GP), support vector ma-chines (SVM) and neural networks (NN), with four inputs including the fuel assembly averaged enrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. The outcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heat from the four inputs (ii) generation and application of synthetic data which improves the performance of the ML models (iii) uncertainty analysis of the ML models and their predictions. (c) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Neural networks for analysis of trabecular bone in osteoarthritis

This study investigated the correlation of age in male and female specimens with physico-mechanical properties of trabecular bone including compressive strength, bone volume fraction, structural model index, trabecular thickness factor, level of inter-connectivity and pore morphology. An artificial neural network was designed to analyse 35 available samples in order to account for complex inter-dependencies of the key parameters in multi-dimensional space. Trained by using Levenberg-Marquardt back propagation algorithm, the network achieved regression factor of 0·96 by optimisation and showed that age correlates strongly with the physical properties of the bone affected by severe osteoarthritis. In addition, the compressive strength was found to be the most important factor for predicting the bone aging. Within the limitations of the input data set, the model developed provides a reliable predictive tool to tissue engineering applications

Artificial neural networks in hard tissue engineering : another look at age-dependence of trabecular bone properties in osteoarthritis

Artificial Neural Network (ANN) model has been developed to correlate age of severely osteoarthritic male and female specimens with key mechanical and structural characteristics of their trabecular bone. The complex interdependency between age, gender, compressive strength, porosity, morphology and level of interconnectivity was analysed in multi-dimensional space using a two-layer feedforward ANN. Trained by Levenberg-Marquardt back propagation algorithm, the ANN achieved regression factor of R = 96.3% between the predicted and target age when optimised for the experimental dataset. Results indicate a strong correlation of the 5-dimensional vector of physical properties of the bone with the age of the specimens. The inverse problem of estimating compressive strength as the key bone fracture risk was also investigated. The outcomes yield correlation between predicted and target compressive strength with the regression factor of R = 97.4%. Within the limitations of the input data set, the ANNs provide robust predictive models for hard tissue engineering decision support