Reviewerly/exHarmony huggingface dataset:
Detecting Careless Cases in Practice Tests

In this paper, we present a novel method for detecting careless responses in a low-stakes practice exam using machine learning models. Rather than classifying test-taker responses as careless based on model fit statistics or knowledge of truth, we built a model to predict significant changes in test scores between a practice test and an official test based on attributes of practice test items. We extracted features from practice test items using hypotheses about how careless test takers respond to items and cross-validated model performance to optimize out-of-sample predictions and reduce heteroscedasticity when predicting the closest official test. All analyses use data from the practice and official versions of the Duolingo English Test. We discuss the implications of using a machine learning model for predicting careless cases as compared with alternative, popular methods.
Ant Colony Based Approach for Finding Cost-Optimal Path

We consider a variational problem of finding the cost-optimal path on the surface of the terrain. The path minimizes an integral cost functional. Our main goal is to obtain an approximate solution as a piecewise linear function via ant colony algorithm. We adapt the algorithm for this purpose and propose various modifications. We also provide numerical examples and comparison of the introduced approaches.
Parenting Patterns and Study Habits on Mathematics Learning Outcomes of Fifth-Grade Elementary School Students

Parents play an important role in the child's learning process. The choice of parenting style applied at home also has an important role in the student learning process. This study aims to analyze the effect of parenting style and study habits on the mathematics learning outcomes of fifth-grade elementary school students. This research is ex-post-facto research. The sample in this study amounted to 124 students. Determination of the sample in this study using a random sampling technique. The method used in this study is a questionnaire. The analysis used in this study is simple regression and multiple regression. The results showed no effect between authoritarian parenting styles on student learning outcomes at 4.4%. There is an influence between parenting parents in the democratic category on student learning outcomes at 5.4%. There is no influence between the parenting style of permissive parents and student learning outcomes of 3.3%. There is an influence between study habits and student learning outcomes of 5.7%. Based on these results, it can be concluded that the parenting style of parents in the authoritarian, democratic, and permissive categories and students' learning habits significantly affect the results of learning mathematics for fifth-grade elementary school students.
The Think Pair Share Cooperative Learning Model Assisted by Children's Story Text Improves Speaking Skills

The learning model the teacher uses needs to be more diverse and more able to accommodate the development of speaking skills. It causes students' speaking skills not to develop optimally. This study aims to analyze the effect of the think pair share cooperative learning model assisted by children's story texts on speaking skills in fourth-grade elementary school students. This study used a quasi-experimental design with a non-equivalent control group design. The population in the study were all fourth-grade elementary school students consisting of 11 fourth-grade students with 340 students. The data collection method uses the non-test method in the form of an observation sheet. The technique used is descriptive statistical analysis and inferential statistics. The results of testing the hypothesis showed a significant difference between students taught with the cooperative model of the think pair share type assisted by children's story text and students taught with conventional learning models. The cooperative model of the think pair share type influences the speaking skills of fourth-grade elementary school students. It was concluded that the cooperative model of the think pair share type can improve speaking skills in fourth-grade elementary school students.
Ludo Word Game Assisted Visualization Auditory Kinesthetic (VAK) Learning Model on Student Science Learning Outcomes in Elementary Schools

The teacher's success in the learning process can be seen from the student learning results. Learning is said to be successful, also seen from how the teacher packages learning. However, teachers are still having difficulties using appropriate learning models in learning. This study aimed to analyze the effect of the Visualization Auditory Kinesthetic (VAK) learning model assisted by the Ludo Word Game. This type of research is a quasi-experimental study with a non-equivalent pretest-posttest control group design. The study population consisted of 45 students, with a random sampling technique to determine the sample. The sample of this research is the experimental class of 23 students and the control class of 22 students. The method used in collecting data is a test. The data collection instrument uses test questions. Data analysis techniques using inferential statistical analysis. The results of the study using the t-test obtained data, namely tcount of 19.893 while for ttable of 2.016, if seen from this, it can be said that tcount is greater than ttable, so there is a significant influence using the Visualization Auditory Kinesthetic (VAK) learning model assisted by Ludo Word Game on science learning outcomes for class V SD. It was concluded that the Ludo Word Game assisted Visualization Auditory Kinesthetic (VAK) learning model improved student learning outcomes. The implication of this research is using the Ludo Word Game-assisted Visualization Auditory Kinesthetic (VAK) learning model to increase students' enthusiasm for learning.
Continual Learning Assisted Dynamic Modeling for Industrial Multimode Quality Prediction

Conventional dynamic modeling approaches for multimode processes with dynamics can only establish a local model for each mode and have difficulty increasing their prediction range due to distribution discrepancies. When a new mode emerges, the re-trained prediction model inevitably forgets important information from the historical modes, resulting in performance degradation. To address this limitation, we propose a continual learning-based dynamic modeling method called continual echo state network (CESN) for multimode processes. The method uses elastic weight consolidation (EWC) to alleviate catastrophic forgetting of the historical modes. Finally, we present the experimental results of the three-phase flow process to demonstrate the effectiveness of the proposed CESN method.
L2-LiteSeg: A Real-Time Semantic Segmentation Method for End-to-End Autonomous Driving

The vehicle-borne optical imaging sensor system cannot accurately and quickly receive information about the driving environment, which may lead to the Autonomous Driving System (ADS) being unable to make accurate and rapid decisions, thus bringing potential safety hazards to autonomous driving. In order to solve the above problems, we propose a real-time semantic segmentation method for end-to-end autonomous driving. This method improves the practicability and reliability of autonomous driving technology by improving the real-time and accuracy of the semantic segmentation method. First, in order to solve the problem that existing semantic segmentation methods cannot meet the requirements of vehicle-borne embedded terminals for real-time and segmentation accuracy of semantic segmentation methods due to complex network structure, difficulties in extracting useful features in complex autonomous driving environments, high training hardware configuration, we propose a real-time semantic segmentation network model L2-LiteSeg, it solves the problems of the low extraction efficiency of feature and parameter redundancy by optimizing the configuration of Unified Attention Fusion Module (UAFM) modules and the network structure of the PP-LiteSeg model. On semantic segmentation datasets for autonomous driving and CamVid, L2-LiteSeg only uses 0.82M parameters, the semantic segmentation precision reaches 73.1%, 67.7% mIoU, and 67.7% mIoU respectively, and the reasoning speed reaches 73.68 Frames Per Second (FPS), 85.16 FPS, and 73.68 FPS respectively. This shows that the L2-LiteSeg method has achieved a good balance in the three indicators of parameter, accuracy, and reasoning speed and can meet the real-time and accuracy requirements of semantic segmentation by the vehicle-borne embedded terminals.
A Class of New Optimal Ternary Cyclic Codes over F&amp;lt;sub&amp;gt;3&amp;lt;sup&amp;gt;&amp;lt;i&amp;gt;m&amp;lt;/i&amp;gt;&amp;lt;/sup&amp;gt;&amp;lt;/sub&amp;gt; with Minimum Distance 4

As a branch of applied mathematics, coding theory plays an important role. Among them, cyclic codes have attracted much attention because of their good algebraic structure and easy analysis performance. In this paper, we will study one class of cyclic codes over F3. Given the length and dimension, we show that it is optimal by proving its minimum distance is equal to 4, according to the Sphere Packing bound.
Towards a Transparent and an Environmental-Friendly Approach for Short Text Topic Detection: A Comparison of Methods for Performance, Transparency, and Carbon Footprint

Online social media platforms have contributed significantly t o t he d issemination o f u ser-generated information.Many studies have proposed various techniques to analyze publicly available short texts to automatically extract topics.The majority of these works have mainly focused on the competitive performance of the proposed approaches.In this paper, our main focus is on how to tackle this problem by incorporating two other important qualities: Transparency and Carbon Footprint.These two pillars are cornerstones to fulfill the emerging international demands and to adhere to the new regulations, such as "Right to Explanation" and "Green AI".Based on these three qualities, this paper compares the most prominent algorithms in this field ( specifically within the category of unsupervised-retrospective learning), such as: Latent Dirichlet Allocation, Non-Negative Matrix Factorization, and K-Means, as well as two most recent approaches, such as: BERTopic and Contextual Analysis.By using two different datasets, the methods were evaluated for Performance.On average, the results show that BERTopic is the best-performing approach overall in terms of Performance.However, Contextual Analysis achieves the best Performance in one of the two datasets used.When considering the three qualities together, the results demonstrate the effectiveness and the benefits of the Contextual Analysis method towards a more transparent and greener approach for the topic detection task.
Global Cognition and Modelling Tasks in Arts and Education

The creation and use of models is the basis for cognition and action. The main challenge for education is to prepare for real action, using different possibilities to create action models (AM) and their components (templates) so as to build real action. The methodology used in the study is based on a critical analysis of different models of consciousness using the principle of complementarity. The informative theory of emotion as well as its generalisations and applications in decision making have been applied, with particular attention to educational tasks. The design and operation of consciousness and artificial intelligence (AI) is based on the creation, storage, and use of AM. Natural and artificial neural networks use the datasets needed for training. The task of education is to organise the most efficient use of available information to prepare for tasks whose content is not known in advance. As the uncertainty and novelty of tasks are rapidly increasing, preparations for the evaluation of information should be used with increasing generalisations. The role of art in education is therefore rapidly growing. Similar problems arise in the formation of weak AI (machine learning), which become more significant on the road to strong AI (super intelligence) or general AI (AGI). The results from the study support the complex development of arts, education, and AI. An emotion classification system has been developed to facilitate the understanding of various evaluation problems. The results obtained could be significant for ensuring the stability of AI. Proposals have been made for building a common world understanding and meaning of life through art within the educational system.
An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems

In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed; in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.
Secondary School Level Creative Tasks for Studying Poetry

The present study endeavors to analyze the challenges encountered by readers when interpreting poetry, and the development of their creative potential. Voluminous decoding of poetry texts is possible in the learning process if creative process is offered, which in turn enables to look for new ways of reading and explore comprehension labyrinths. Literature studies at secondary school require the development of creative task taxonomy, which would assist the reader – student to explore their creative potential and develop it deliberately. Creativity is beneficial in any area and can enrich every aspect of one’s life. It is not an inherent gift but a mastered ability to see the world, interact with it and respond accordingly. The main components of creativity can be categorised in two groups: cognitive (divergent thinking, general knowledge and thinking skills) and personality (focusing on the task and determination to complete it, motivation, tolerance towards the unknown) components. The prerequisites for creativity are connected with the educator’s attitude and expectations, as well as the ability to create a suitable environment, situations, learning tasks, because without them neither creative thinking nor action can be manifested. The educator is the initiator as well as the facilitator of the creative process, thinking and action. Based on the study of poetry of A. Čaks at secondary school, the article introduces the requirements and criteria for the development of creative tasks. The aim of the research is, based on the study of A. Čaks’ poetry in secondary school, to identify the conditions and criteria for developing creative tasks. The study was conducted using qualitative content analysis of students’ works and a case study as the research methods. The results of the case study indicate that when the teacher creates an appropriate environment and atmosphere, students are offered various creative tasks, and students can choose them according to their abilities, students willingly engage in the performance of various creative tasks. The developed methodological framework for introducing A. Čaks’ poetry is original and unprecedented in the history of literature methodology. The developed methodology can be used for the study of the personality and creative work of a specific poet (A. Čaks) as well as adapted and adjusted for the study of other writers. The ideas can be used by both practising and future literature teachers. The developed methodological framework and its results have been presented to prospective educators in the course “Methods of Teaching Latvian Literature,” educators in various continuing education courses, and at the scientific conference of the University of Latvia. Educators have highly appreciated the developed materials.
Illustrations of Expressions of Emotions in Children’s Books

Visual culture is an integral part of modern life. Books with the engaging visual design still attract children’s attention and are at the center of examples of their experiences and interactions. In children’s books, the image is the central part of perception and the accompanying text plays a secondary role. Visual perception influences and shapes children’s views about what the world around them is like. The book’s illustrations are often the primary criteria when choosing a book for a child, which also creates interest in the written content. By analyzing different illustrations, it is possible to distinguish several main features that are characteristic of depicting a certain emotion and how the characters feel. When looking at any illustration, the character’s facial expression is one of the primary objects looked at to determine how the character is feeling. Appropriate body posture creates the effect of movement alone or helps create the mood of an illustration by complementing the emotional background provided by facial expressions. A true representation of emotions in an illustration not only creates an enjoyable overall image but also clearly shows children how each emotion manifests itself and helps them understand themselves and in the people around them. The aim of the research is to investigate the role of children’s books in their psycho-emotional development and the techniques of depicting emotions in book illustrations. Research methods: theorethical – research of literature and Internet resources; case study. Research results – the importance of children’s book illustrations in children’s psycho-emotional development is substantiated, and the types of representation of various emotional expressions are determined. The case study confirms that not in all cases artists have paid attention to facial mimicry, which is the most active part of a person for the expression of emotions.
The Case of Redesigning the Curricula of Preschool Education Study Programme

According to society 5.0. and the Six pillars of the Recovery and Resilience Facility EU plan, Slovenia developed National Strategy for Higher Education to 2030, that encourage the digital transition of higher education institutions (HEI). Therefore, from the basic definition of digital competences, we define digital comeptencies of teachers of HEIs as the ability to use digital tools purposefully in the learning process in order to (1) provide a quality learning experience, (2) empower the use of digital technologies in teaching and learning and thus foster the development of digital competencies of students and (3) create opportunities for their use in authentic learning situations (Redecker &amp; Punie, 2017). Based on DigCompEdu model – educators present a role models for the use of digital technologies, encouraging safe and critical use of digital technologies by their students (Redecker &amp; Punie, 2017). Starting from the assumption that the quality of teaching requires appropriate structural, and procedural conditions (Elassy, 2015; Moss, 2016), we evaluated the current curriculum and reflect on the meaningful use of digital tools for planning, learning and teaching. On this bases we developed a model for curriculum redesign with the integration of digital technology. The model includes (i) a systematic analysis of the digital competences of higher education teachers and students; (ii) implementation of targeted training in the use of digital tools within the framework of field specific instructional design; (iii) the implementation, retuning and evaluation of the redesigned curricula of the Preschool Education study programme.
Factors influencing Digital Competence of Higher Education Students: a Scoping Review

In today’s society, digital competence is becoming increasingly relevant and important for functioning personally and professionally. Twenty-first-century students need competencies to adapt to a new type of individual information and individual-knowledge relationship, therefore, the education system should contemplate new ways for students’ development in this so-called information and knowledge society. The purpose of the present scoping review is to provide a comprehensive overview of relevant research regarding factors influencing the digital competence of higher education students. Arksey and O’Malley’s five-stage framework underpins the scoping review. Four databases were used to conduct the scoping literature review, including the EBSCOhost, Science Direct, Web of Science and Scopus. The inclusion criteria were peer-reviewed publications written in English from 2016–2022. Initially, 270 articles were found; the full texts of 67 articles were assessed; finally, 23 articles that met the inclusion criteria were included in the present research. This paper reports on three main categories: (1) characteristics of digital competence influencing factors, (2) digital competence measurement instrument, and (3) key findings. The study has identified the influencing factors of digital competence among university and college students, including socio-economic background, motivational belief strategies, attitudes towards ICTs, prior training on ICTs, digital citizenship skills, learning strategies, academic performance, study specialization, form and level. The study provides insights to improve students’ digital competence education in universities. Future research should focus on interviewing students and educators to understand their perspectives on factors influencing digital competence.
FUNCTIONAL MATHEMATICAL LITERACY IS A RELIABLE INDICATOR OF YOUTH'S EDUCATIONAL TRAJECTORY

Мақалада қазіргі таңда білім беруде жастардың білімдік траекториясын анықтауда фунционалды сауаттылықты қалыптастыру мен дамытудың өзекті мәселе екендігі негізделіп, көп жылғы теориялық және практикалық ізденіс негізінде пән мұғалімдерін, соның ішінде математика пәні мұғалімдерінің жоғары білімнен кейінгі дайындықтары мен жоғары білім беру сатысында дайындаудың әдістемелік мәселелерінің маңыздылығы қарастырылған. Білім беру сапасын және кеңкөлемді білімдік қолданысты сәйкесінше зерттейтін TIMSS пен PISA бағдарламаларын зерделеу негізінде және осы бағдарламаларға қатысқан Қазақстандық білім алушылардың жетістіктеріне салыстырмалы талдау жасау негізінде бағдарламалардың ұқсастықтары мен айырмашылықтарын нақтылайтын ұғымдық терминдерге түсініктеме берілген, аталған екі бағдарламалардың салыстырмалы ерекшеліктерінің сипатталмалары келтірілген. Атап айтқанда,бағдарламалардың әртүрлі бағыттылығы, бағдарламалық тестерді құру форматының принциптері, математикадан бөлініп алынған салалар бойынша ғана емес, сонымен бірге тексерілетін танымдық процестер бойынша да ерекшеленетіні, фундаментальдық идеялар мен құзреттілік деңгейлері бойынша дабағалау критерийлері қарастырылған. Сонымен қатар болашақ математика мұғалімдерін дайындауда арнайы пәннің алатын орны негізделген. Бағдарламаны жүзеге асыруда күтілетін нәтижелер мен алынған нәтижелер беріліп, жалпы қорытынды келтірілген..