Вибір онлайн-інструментів для створення математичних тестів

The article considers online tools for creating tests, which should be used when teaching mathematics in both higher education and general secondary education. Among the variety of online means of creating tests by the method of expert evaluation, three were identified, which allow conducting various tests both in the classroom and remotely, which are free and do not require special conditions for their use and which work on smartphones. The advantages and disadvantages of three online tools for creating tests Kahoot!, Quizizz, Classtime are analyzed, and a comparative description of the selected tools is given. Criteria for the selection of such tools were identified – functional-didactic and organizational. The following indicators belong to the functional-didactic: the presence of different types of questions, including open-ended; use of formulas, both in questions and in answers; use of pictures, both in questions and in answers; no restrictions on the length of questions and answers; instant receipt of results by the teacher, their evaluation and analysis; instant receipt of results by the respondent; to the organizational: the availability of a free version; no need to install the program; ease of use – characterizes the convenience and clarity of the interface for creating tests and their use; possibility of testing in online and offline mode; time limits, both for a single question and the whole test; random order of questions/answer options; instant demonstration of the correct answer to the respondent. With the help of expert evaluation, it was found that according to these criteria, Quizizz is the most appropriate for testing.У статті розглядаються онлайн -інструменти для створення тестів, які слід використовувати при викладанні математики як у вищій школі, так і в загальній середній. Серед різноманіття онлайн -засобів створення тестів методом експертного оцінювання було визначено три, які дозволяють проводити різні тести як у класі, так і дистанційно, які є безкоштовними і не вимагають особливих умов їх використання та які працюють на смартфонах. Проаналізовано переваги та недоліки трьох онлайн -інструментів для створення тестів Kahoot !, Quizizz, Classtime та наведено порівняльний опис вибраних інструментів. Визначено критерії відбору таких засобів-функціонально-дидактичний та організаційний. До функціонально-дидактичних належать такі показники: наявність різних типів питань, у тому числі відкритих; використання формул, як у питаннях, так і у відповідях; використання малюнків як у питаннях, так і у відповідях; відсутність обмежень щодо тривалості запитань та відповідей; миттєве отримання результатів вчителем, їх оцінка та аналіз; миттєве отримання результатів респондентом; організаційним: наявність безкоштовної версії; немає необхідності встановлювати програму; простота використання - характеризує зручність і чіткість інтерфейсу для створення тестів та їх використання; можливість тестування в режимі онлайн та офлайн; часові межі, як для окремого питання, так і для всього тесту; випадковий порядок запитань/варіантів відповідей; миттєва демонстрація респонденту правильної відповіді. За допомогою експертної оцінки було встановлено, що за цими критеріями Quizizz є найбільш підходящим для тестування

Determination of the informational content of symptoms in the dynamic processes of assessing the patient’s condition in e-health

The study is devoted to substantiating the tactics of choosing the signs of the patient's condition for diagnostic decision-making on corrective medical intervention in mobile medicine. The aim of the research: to study a creation of a methodology for determining the integral informativeness of the patient's symptoms during remote monitoring of his condition. Materials and methods: this article is based on search results in PubMed, Scopus, MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, Cochrane Library, UK NHS HTA articles published between January 1991 and January 2021 and containing the search terms “information technology”, “Mobile medicine”, “digital pathology” and “deep learning”, as well as the results of the authors' own research. The authors independently extracted data on concealment of distribution, consistency of distribution, blindness, completeness of follow-up, and interventions. Results: concluded that to determine the Informativeness of symptoms in mobile monitoring of patients, it is possible to use risk indicators of predicted conditions as a universal method. Given that the Informativeness of the patient's condition changes constantly, for online diagnosis of conditions during remote monitoring of the patient it is recommended to use the function of informative symptoms from time to time and use a set of approaches to assess the Informativeness of patient symptoms. It is proposed to use the strategy of diagnosis and treatment using probabilistic algorithms based on the values of the risk of complications of the pathological process, as well as the formulas of Kulbach and Shannon to determine individual trends in the pathological patient process. Conclusion: there was proposed to use risk indicators of predicted conditions as a universal method for determining the informational content of symptoms in mobile monitoring of patients

Estimating lifetime effects of child development for economic evaluation: An exploration of methods and their application to a population screen for postnatal depression

Background: Early health interventions affecting child development can subsequently influence lifetime health and economic outcomes. These lifetime effects may be excluded from economic evaluation as empirical evidence covering the required time horizon is rarely available. One example is screening for postnatal depression where current guidelines do not account for lifetime effects despite evidence of a detrimental association between maternal depression and child development. Aims: To develop a methodological approach to estimate lifetime effects for economic evaluation and determine their influence on an evaluation assessing the cost-effectiveness of postnatal depression screening. Methods: Lifetime effects are estimated by linking results from two empirical studies. Firstly, growth curve models establish the effects of postnatal depression on development measures for children aged 3-11 using data from the Millennium Cohort Study. Secondly, child development measures are entered as explanatory variables in linear regression models predicting effects on lifetime health and economic outcomes using data from the 1970 British Cohort Study. An economic evaluation is conducted for scenarios which exclude/include lifetime effects to determine their influence on cost-effectiveness results. Findings: Postnatal depression was detrimentally associated with children’s cognitive and socioemotional development up to age 11. Detrimental changes in cognitive and socioemotional development were negatively associated with lifetime outcomes. Postnatal depression exposure was predicted to reduce children’s lifetime Quality Adjusted Life Years, increase healthcare and crime costs, and generate fewer monetary returns in education and employment. Cost-effectiveness results changed when including lifetime effects, leading to the recommendation of a screening strategy which treats a greater proportion of depressed mothers. Conclusions: Lifetime effects can influence cost-effectiveness results and their exclusion risks providing a partial analysis. This research demonstrates methods to estimate and include lifetime effects in economic evaluation. Similar approaches could be applied elsewhere to provide additional evidence for economic evaluation of other childhood interventions

Predictive Analysis of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study of Feature Selection Methods

The utilization of data mining techniques for the prompt prediction of academic success has gained significant importance in the current era. There is an increasing interest in utilizing these methodologies to forecast the academic performance of students, thereby facilitating educators to intervene and furnish suitable assistance when required. The purpose of this study was to determine the optimal methods for feature engineering and selection in the context of regression and classification tasks. This study compared the Boruta algorithm and Lasso regression for regression, and Recursive Feature Elimination (RFE) and Random Forest Importance (RFI) for classification. According to the findings, Gradient Boost for the regression part of this study had the least Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) of 12.93 and 18.28, respectively, in the case of the Boruta selection method. In contrast, RFI was found to be the superior classification method, yielding an accuracy rate of 78% in the classification part. This research emphasized the significance of employing appropriate feature engineering and selection methodologies to enhance the efficacy of machine learning algorithms. Using a diverse set of machine learning techniques, this study analyzed the OULA dataset, focusing on both feature engineering and selection. Our approach was to systematically compare the performance of different models, leading to insights about the most effective strategies for predicting student success

Learning Disabilities

Learning disabilities are a heterogeneous group of disorders characterized by failure to acquire, retrieve, and use information competently. These disorders have a multifactorial aetiology and are most common and severe in children, especially when comorbid with other chronic health conditions. This book provides current and comprehensive information about learning disorders, including information on neurobiology, assessment, clinical features, and treatment. Chapters cover such topics as historical research and hypotheses of learning disorders, neuropsychological assessment and counselling, characteristics of specific disorders such as autism and ADHD, evidence-based treatment strategies and assistive technologies, and much more

Investigating the Efficacy of Algorithmic Student Modelling in Predicting Students at Risk of Failing in the Early Stages of Tertiary Education: Case study of experience based on first year students at an Institute of Technology in Ireland.

The application of data analytics to educational settings is an emerging and growing research area. Much of the published works to-date are based on ever-increasing volumes of log data that are systematically gathered in virtual learning environments as part of module delivery. This thesis took a unique approach to modelling academic performance; it is a first study to model indicators of students at risk of failing in first year of tertiary education, based on data gathered prior to commencement of first year, facilitating early engagement with at-risk students. The study was conducted over three years, in 2010 through 2012, and was based on a sample student population (n=1,207) aged between 18 and 60 from a range of academic disciplines. Data was extracted from both student enrolment data maintained by college administration, and an online, self-reporting, learner profiling tool developed specifically for this study. The profiling tool was administered during induction sessions for students enrolling into the first year of study. Twenty-four factors relating to prior academic performance, personality, motivation, self-regulation, learning approaches, learner modality, age and gender were considered. Eight classification algorithms were evaluated. Cross validation model accuracies based on all participants were compared with models trained on the 2010 and 2011 student cohorts, and tested on the 2012 student cohort. Best cross validation model accuracies were a Support Vector Machine (82%) and Neural Network (75%). The k-Nearest Neighbour model, which has received little attention in educational data mining studies, achieved highest model accuracy when applied to the 2012 student cohort (72%). The performance was similar to its cross validation model accuracy (72%). Model accuracies for other algorithms applied to the 2012 student cohort also compared favourably; for example Ensembles (71%), Support Vector Machine (70%) and a Decision Tree (70%). Models of subgroups by age and by academic discipline achieved higher accuracy than models of all participants, however, a larger sample size is needed to confirm results. Progressive sampling showed a sample size \u3e 900 was required to achieve convergence of model accuracy. Results showed that factors most predictive of academic performance in first year of study at tertiary education included age, prior academic performance and self-efficacy. Kinaesthetic modality was also indicative of students at risk of failing, a factor that has not been cited previously as a significant predictor of academic performance. Models reported in this study show that learner profiling completed prior to commencement of first year of study yielded informative and generalisable results that identified students at risk of failing. Additionally, model accuracies were comparable to models reported elsewhere that included data collected from student activity in semester one, confirming the validity of early student profiling

Results of the Examination of Primary School Students By Means of Speech Therapy Screening

The number of senior preschoolers with speech development below the norm on the eve of school education is growing steadily annually. Updating the educational process organization, aiming at improving the efficiency and quality of primary school students education and, consequently, growing requirements for the level of their knowledge and skills, on the one hand, and the increase of the number of children with speech impairment, on the other, necessitate propedeutics of impairment and the development of special diagnostic tools. In this paper, we set an objective to analyze the reasons for which younger primary school students have difficulties in the process of written speech formation. The research is aimed at studying the level of the formation of basic processes and functions essential for literacy and writing skills acquisition. 240 primary school students were engaged in a pilot experiment aimed at assessing the efficiency of speech therapy screening. The traditional screening methods have always been the observation, conversation, but one of its modern methods is testing. The analysis of the results of the younger primary school students' frontal examination enables to state that in students from 1st to 4th grades there prevailed average level of formation of basic prerequisites and skills essential for writing skills teaching. According to the results of the study, we can affirm that the study of the degree of the formation of processes and functions essential for literacy and writing skills acquisition by means of speech therapy screening is an essential prerequisite of a comprehensive correction of the speech development of younger primary school students with dysgraphia, as it facilitates early identification of children from "risk groups" and those who have impairments of basic writing skills formation