Reviewerly/exHarmony huggingface dataset:
Qualitative Content Analysis for Critical Thinking and Skill Development: A Case of Chemistry Curriculum

Authors:
raw_author_name: Muhammad Jamil

Abstract: In the era of globalization and knowledge, critical thinking development is a necessary skill. The research focuses on analyzing the Chemistry curriculum for grades IX-X to explore its emphasis on fostering critical thinking skills among students being an essential skill for the twenty-first century. It aims to develop problem-solvers, independent thinkers, and decision makers capable of applying their knowledge to real-life challenges. The curriculum advocates for interactive, learner-centered, participative, inquiry-based, practical, problem-solving, and analytical skills. It prioritizes the measurement of students' capacity for critical judgment, focusing on analysis and synthesis. NVivo 12 software was used with a qualitative content analysis approach to analyze the document. The findings of the study revealed a significant focus on the development of critical thinking to make them problem solvers, independent thinkers, and decision-makers to apply to the challenges of real life. As regards the standards and benchmarks, the curriculum is designed accordingly for the promotion of in-depth knowledge and higher-order thinking skills. Furthermore, the curriculum advocates for interactive, learner-centered, participative, inquiry-based, practical, problem-solving, and analytical skills. The assessment guidelines prioritize the measurement of student's capacity for critical judgment, focusing on analysis and synthesis. The study concludes by providing recommendations for teacher training, resource allocation, collaboration among stakeholders, regular curriculum updates, further research, and fostering a culture of critical thinking within educational institutions.
Probabilistic prediction of “irrational” decisions from semantic composition of contexts

Authors:
raw_author_name: Ilya A. Surov

Abstract: Difficulties in prognosis of human behavior are due to the complexity of our cognition, routinely breaking the boundaries of classical rationality. The paper solves this problem for the simplest kind of such “irrational” behavior in which a single binary decision is made in three related contexts. Subjective meanings of these contexts relative to the basis decision alternative are represented by three qubit states, borrowed from quantum theory. These states are bound together by linear superpositions, which encode semantic composition of the contexts in the subject’s mind. The resulting theory supplements classical probabilistic model with nonlinear interference factor, accounting for the “irrational”, emotionally-semantic side of intelligence. This model is built for different realizations of two classic experiments used to study behavioral irrationality: the prisoner’s dilemma and the two-stage gambling task. In 24 such realizations, the interference phase is shown to fall in a narrow range of values, encoding regularities of semantic composition of contexts. Extrapolation of this regularity to novel experiments allows using the model in prognostic mode. This possibility is tested on the task of probabilistic prediction of target decision based on the same probability in two other contexts. For the prisoner’s dilemma and the two-stage gambling task the such prognosis has relative errors of 9 and 11% respectively. The proposed approach allows for putting other quantum models of cognition and decision to predictive and interpretable use, whereas its principles also apply to modeling of decisions with larger sets of contexts and behavioral options. By formalizing a novel type of semantic regularities behind “irrational” thinking, models of the present type open prospects for empowering the existing means for socio-economical analytics and prediction.
Directions for modifying the artificial bee colony algorithm to optimize control parameters for complex systems

Authors:
raw_author_name: Olga V. Bulygina
raw_author_name: Nikolay S. Kulyasov

Abstract: In recent years, bioinspired algorithms based on the use of a population approach and a probabilistic search strategy have become especially popular among researchers involved in multidimensional and multicriteria optimization. Such algorithms are based on the principles of cooperative behavior of a decentralized self-organizing colony of living organisms (bees, ants, birds, etc.) to achieve certain goals (for example, to meet nutritional needs). However, their practical application encounters a number of difficulties leading to a decrease in convergence. This article discusses the possibility of modifying the artificial bee colony algorithm by using a hybridization strategy with various data mining methods. One of these difficulties is the lack of a reasonable approach to determining initial search positions. As a solution, it is proposed to divide the population into clusters, the centers of which will be the initial positions. The need for interaction between individuals makes it advisable to use fuzzy clustering, which allows the formation of intersecting clusters. Another difficulty is associated with the choice of “free” parameters, for which the authors have not developed recommendations for choosing their optimal values. To solve this problem, it is proposed to use the idea of coevolution, which consists in the parallel launch of several interacting subpopulations, for each of which different “settings” are applied. The proposed algorithm is applicable to multidimensional optimization tasks, in which it is necessary to find such a combination of different types of elements belonging to some “large” population that will ensure the achievement of the maximum effect under given restrictions. Examples of such tasks are determining the species and quantitative composition of plants to form the terrestrial ecosystem of a carbon farm or mass recruiting, which consists of selecting a large number of personnel for the same positions.
Development of a web-based Multimedia Learning Management System (MlMS) in High School Physics Learning

Authors:
raw_author_name: Suwardi Annas
raw_author_name: Syukur Saud

Abstract: Purpose: This research aims to investigate: (1) students' needs for a web-based Multimedia Learning Management System (MLMS) in high school physics learning, (2) designing a prototype model for developing a web-based Multimedia Learning Management System (MLMS) in high school physics learning, (3 ) the validity of the web-based Multimedia Learning Management System (MLMS) development model in high school physics learning, (4) measuring the practicality of the web-based Multimedia Learning Management System (MLMS) development model in high school physics learning, and (5) the effectiveness of the Multimedia Learning Management System development model (MLMS) web-based in high school physics learning.&#x0D; Research methodology: The research method applied was research and development using the one-group pre-test and post-test design (ADDIE) model. The object of this research was high school students, with samples taken from four schools (three private schools and one state school) in South Tangerang.&#x0D; Results: The assessment results for static electricity material at schools I and II were in the medium category, with gain value of 0.64 and 0.63, respectively, while at school III, they were in the high category with a gain value of 0.70. Statistical significance (school I, school II, and school III) in Static Electricity shows t &lt; 0.05, while in dynamic electricity material, schools I and II are in the high category, with the same gain value of 0.7 and a significant level t-test results t &lt; 0.05.&#x0D; Implementation: The material expert validation results showed a validity level of 90.91% (very good), whereas the multimedia expert validation results reached 83.75% (very good). Professional physics teachers gave positive responses, with a material and multimedia acceptance rate of 84.82% (very good), whereas student responses reached 79.98% (very good). Effectiveness based on improving student learning outcomes was measured by providing pre-tests, treatments, and post-tests (One Group Pre-test, Post-test Design) using the N-gain value.
PENGARUH PEMBELAJARAN BLENDED LEARNING TERHADAP KEMANDIRIAN BELAJAR SISWA KELAS X SMK NEGERI 2 BONE

Authors:
raw_author_name: None Selpi
raw_author_name: None Suhardiman
raw_author_name: None Ahmad Nurul Ihsan B

Abstract: This research aims to determine the influence of Blended Learning on student self-directed learning in Grade X of SMK Negeri 2 Bone. The research was conducted from July to August 2023. This study is a quantitative descriptive research. The research population consisted of 304 students in Grade X of DKV at SMK Negeri 2 Bone, with a sample of 55 students. Data collection in the research process was carried out using questionnaires and documentation. Based on the research results, it is found that the respondents' perceptions of Blended Learning in relation to student self-directed learning amounted to 75%. This indicates that the respondents' opinions fall within the "agreeable," "good," or "likable" category because 75% falls within the range of 60% - 79.99%. Furthermore, the additional results of the t-test show that the calculated t-value is 0.625, which is smaller than the tabulated t-value of 2.006, and the significance value is 0.535, which is greater than 0.05. This suggests that the null hypothesis (Ho) is accepted, and the alternative hypothesis (H1) is rejected. Therefore, it can be concluded that Blended Learning has a non-significant influence on student self-directed learning.
Modelling of Flood Hazard Early Warning Group Decision Support System

Authors:
raw_author_name: Arief A. Soebroto
raw_author_name: Lily M. Limantara
raw_author_name: Ery Suhartanto
raw_author_name: Moh. Sholichin

Abstract: Early warning of flood hazards needs to be carried out comprehensively to avoid a higher risk of disaster. Every decision on early warning of a flood hazard is carried out in part by one party, namely the government or water resource managers. This research aims to provide a collaborative decision-making model for early warning of flood hazards through a Group Decision Support System Model (GDSS), especially in Indonesia. The novelty of this research is that the GDSS model involves more than one decision-maker and multi-criteria decision-making for early warning of flood hazards in the downstream Kali Sadar River, Mojokerto Regency, East Java Province, Indonesia. The GDSS model was developed using a hybrid method, namely the Analytical Network Process (ANP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). There was more than one decision result; voting was carried out using the BORDA method to produce the decision. The test results of GDSS were obtained using a Spearman rank correlation coefficient of 0.8425 and matrix confusion, an accuracy value of 86.7%, a precision value of 86.7%, a recall value of 86.7%, and an f-measure of 86.7%. Based on the test results, good results were obtained from the GDSS model. Doi: 10.28991/CEJ-2024-010-02-018 Full Text: PDF
An Improved MAP-Elites Algorithm via Rotational Invariant Operator in Differential Evolution for Continuous Optimization

Authors:
raw_author_name: Tae Jong Choi

Abstract: In this paper, we propose a new approach that enhances the continuous optimization performance of the MAP-Elites algorithm. The existing self-referencing MAP-Elites algorithm employed the “DE/rand/1/bin” operator from the differential evolution algorithm, which, due to its lack of rotational invariance, led to a degradation in optimization performance when there were high correlations among variables. The proposed algorithm replaces the “DE/rand/1/bin” operator with the “DE/current-to-rand/1” operator. This operator, possessing rotational invariance, ensures robust performance even in cases where there are high correlations among variables. Experimental results confirm that the proposed algorithm performs better than the comparison algorithms.
Artificial Interpretation: An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning

Authors:
raw_author_name: Vincent Gaffney

Abstract: The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated [1]. In specific areas, notably remote sensing, data sets have increased in extent and resolution to the point that manual interpretation is problematic and availability of skilled interpreters to undertake such work is limited. Interpretation of geophysical data sets associated with prehistoric submerged landscapes is particularly challenging. Following the last glacial, sea levels rose 120 metres globally, and vast, habitable landscapes were lost to the sea. These landscapes were inaccessible until extensive remote sensing data sets were provided by the offshore energy sector. In this paper, we provide results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c.188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7,0000 BP, along with the cultural heritage it contained [2]. As part of this project, machine learning tools were applied to detect and interpret features of archaeological significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for further, more complex leveraging of AI interpretation for the study of submarine palaeolandscapes. Image: Adobe Firefly Generative AI using keywords - marsh landscape deep underwater, auroch skull half-buried in peat
Latent Profile Analysis of Middle School Students’ Career Maturity and Academic Achievement, and the Effect of Relevant Factors

Authors:
raw_author_name: Bo Keum Choi

Abstract: Objectives The purposes of this study were to classify the latent profiles of Korean middle school students’ career maturity and academic achievement and also to explore the influence of relevant factors on the identified latent profiles.&#x0D; Methods For this purpose, 6,618 responses of the fifth band of the elementary 5 panel (9th grade) of Korea Education Longitudinal Study (KELS) were analyzed. The study analyzed the influence of variables such as personal characteristics, family environment, and school experience in predicting the latent profiles of middle school students' career maturity and academic achievement levels.&#x0D; Results The latent profiles of South Korean middle school students' career maturity and academic achievement were classified into five categories: lower career-lower academic group, upper group, middle group, lower group, and upper career-lower academic group. The forms of these profiles were found to be distinct. The verification of influencing factors revealed that gender, creativity, self-management, parental support, interaction with parents, attitude towards lessons, teachers' pressure for achievement, teacher relationships, and peer relationships all had significant impacts.&#x0D; Conclusions Based on these results, contemplation on the direction of middle school education in South Korea was discussed, along with academic understanding of the relationship between middle school students' career maturity and academic achievement. Additionally, implications for various support measures were also deliberated.&#x0D;
The Interaction Effects of Professional Learning Community and Principal Leadership on Teacher Practices

Authors:
raw_author_name: Hyun Ji Kim
raw_author_name: Dongsun Kim

Abstract: Objectives This study analyzes the TALIS 2018 data representative of Korean elementary and middle school teachers and analyzes the effect of professional learning communities on teachers' teaching practices and the interaction effect with principal leadership.&#x0D; Methods In order to estimate the effect of a professional learning community on teachers' teaching practice, this study used a propensity score matching method and a multilevel model to control self-selection errors that occur when analyzing observational data and to examine the hierarchical nature of the analyzed data. Supplemented.&#x0D; Results As a result of the analysis, it was confirmed that the professional learning community had a significant impact on elementary and middle school teachers' student cognitive activation class practice. In addition, in the case of middle schools, it was found that when the principal provided direct instructional guidance to teachers participating in the professional learning community, the teacher's class practice of activating student cognition increased.&#x0D; Conclusions Based on this, this study discusses the need for professional learning community policies to be continuously implemented.&#x0D;
LLM- Based Personalized Recommendations in Health

Authors:
raw_author_name: Boris A. Galitsky

Abstract: Today, LLMs are not good at personalization providing recommendation. They advise physicians and financial advisors to ask professionals in respective fields for help, even having user information available. Answering questions of software professionals, LLM needs to deliver in-depth answers with codes or algorithms, whereas for professionals in other fields would need definitions and main concepts. The intent of this chapter is to make LLM answer tailored to the needs of users, taking into account available information about them. To do that, we need to generalize available information about a person like her health record, maintaining the privacy of this person. We rely on meta-learning techniques to design a LLM prompt to produce a personalization prompt to obtain a suitable relevant information. Such &amp;ldquo;meta-prompt&amp;rdquo; is produced by generalization operation applied to available documents for the user. These documents need to be de-identified so that they are sufficient for personalization on one hand and will maintain user privacy on the other hand. The second neuro-symbolic technique to support personalization is abductive reasoning, acting in parallel to LLM fine-tuning. Traditional recommendation and personalization techniques as well as modern, deep learning &amp;ndash; based are presented, and the comparison is drawn to the proposed approach. We also share the evaluation and comparative analyses of these approaches. We consider an example for how to build personalization LLM systems coming from Langchain platform. We will explore how to construct chains to form a personalization profile for a user and apply it to user search and recommendation requests.
Development of artificial intelligence education program based on visualizing public data for cultivating knowledge-information processing and creative thinking competencies

Authors:
raw_author_name: Kwang-Ho Kim
raw_author_name: Hyun-Seok Yoo

Abstract: Objectives The purposes of this study are to develop an artificial intelligence utilization education program based on visualizing public data and using it for problem solving to cultivate knowledge-information processing and creative thinking competencies for middle school students, and to analyze its effectiveness in cultivating each of these competencies.&#x0D; Methods This study was conducted applying PDIE model of which stages contain preparation, development, implementation, and evaluation. and developed an eight-session education program for middle school students to visualize public data to find problems, explore ideas to solve them, and realize their ideas through making model using artificial intelligence. In this study, two classes of third grade middle school students were organized as a single group after convenience sampling to conduct pre- and post-tests. The collected data were analyzed using paired sample t-tests, and after among the open-ended responses, the education program was modified and supplied and supplemented.&#x0D; Results As a result of applying the education program of this study to a middle school, a substantial enhancement was found in both knowledge-information processing and creative thinking competencies, and learning satisfaction was at a good level.&#x0D; Conclusions It is significant that the artificial intelligence utilization education program based on visualizing public data is closely related to learners' lives, and the program has proven effective in cultivating knowledge-information processing and creative thinking capabilities of middle school students by applying the education program into the technological problem-solving process based on competency of the subject matter of technology.&#x0D;
A Study on the Perception of Multimedia Utilization Education in the Reflection Journal of Preliminary Early Childhood Teachers

Authors:
raw_author_name: Heejung Oh

Abstract: Objectives In this study, we tried to analyze the perception of multimedia utilization education in reflection journals of preliminary early childhood teachers.&#x0D; Methods This study utilized qualitative research methods. Specifically, 63 first-year students at S University, a three-year university located in Gyeonggi-do, were repeatedly read through reflective journals written after taking the ‘Multimedia Utilization Education’ course and field texts written by instructors to find stories that fit the purpose of the study. It was qualitatively analyzed by time, category, and large and small meaning units, and the researcher reorganized it to clearly reveal its meaning.&#x0D; Results As a result of the study, the final derived topics are as follows. First, understanding multimedia that feels close and distant. Second, recognizing and developing various aspects. Third, positively promoting work and learning as an early childhood teacher. Fourth, building up an identity as an early childhood teacher. And fifth, taking a step toward better multimedia utilization education.&#x0D; Conclusions This study is expected to be used as basic data to provide preliminary early childhood teachers' understanding of multimedia-based education, support directions for the actual educational content composition of the subject, and learner-centered educational guidelines.&#x0D;