Concept Discovery Research Articles

Pengembangan Aplikasi Media Pembelajaran Wordwall bagi Siswa Kelas V pada Mata Pelajaran PAI SDN Banjardowo 1 Jombang

A This study aims to describe the feasibility of wordwall learning media applications for grade V students in Islamic religious education subjects and to describe student responses to wordwall learning media applications for grade V students at SDN Banjardowo 1 Jombang. Product development that will be applied at SD Negeri Banjardowo 1 Jombang is in the form of wordwall learning media based on the problems that exist in the school. In this research, the development model uses ADDIE, which consists of analysis, design, development, implementation, and evaluation. This development research aims to test the feasibility of learning media, evaluate better learning outcomes, and determine student reactions to the developed application-based learning media. Based on the feasibility of wordwall application learning media from the results of the validator getting an average score of 82% and the results of material expert validation with an average score of 83% included in the very good category. At the student response stage based on the trial to 33 students, the average score was obtained to be 87 with a very good category. The results of student responses can be seen from 5 indicators in the closed questionnaire, namely: (1) students' opinions on learning based on contextual problems score percentage 81% positive category; (2) the ease of students in understanding the subject matter through a contextual approach score percentage 90% very positive category; (3) student motivation in learning that leads to concept discovery score percentage 92% very positive category; (4) student interest in learning based on real experience score percentage 91% very positive category; (5) enthusiasm for student learning with a contextual approachscore percentage 80% positive category. So it can be concluded that the wordwall application learning media is feasible to use and apply as a teaching media.

Discovery III: Missions to the Outer Planets Using Inertial Confinement Fusion Propulsion

In the future, space missions to the outer planets within our solar system that carry a human crew may be possible. This work revisits a NASA crewed spacecraft design concept Discovery II, which proposed to use a spherical torus magnetic confinement fusion reactor. The revised spacecraft Discovery III is modeled using a numerical spacecraft propulsion and mission profile code called HeliosX written in Fortran 95, but using an inertial confinement fusion engine for the same 172-ton payload mass, matching mass flow rates and exhaust velocities, achieving a mission time of 118 days for Jupiter and 212 days for Saturn. Both the inertial confinement fusion (ICF) and magnetic confinement fusion vehicles utilize expellant propellant for mass flow rate augmentation by the thermalization of injected hydrogen and thermonuclear fuel in the exhaust. Calculations are performed and the models are assessed for deviation from optimality. It is concluded that, while the ICF version is more massive, less mature technologically, and the worst performer in terms of jet efficiency, preliminary results indicate a lower fuel requirement, which may imply a technological advantage.

Penerapan Model Pembelajaran Discovery Learning untuk Meningkatkan Efikasi Diri Guru dan Mutu Pembelajaran PAI di Kecamatan Leuwiliang

This study aims to analyze the implementation of the Discovery Learning model and its influence on teacher self-efficacy and the quality of Islamic Religious Education (PAI) learning in Kecamatan Leuwiliang, Bogor Regency. This research uses a qualitative approach with a descriptive method, involving 27 PAI teachers from 9 clusters as key informants. Data were collected through observation, interviews, and documentation studies.The results show that 30% of the teachers have successfully implemented Discovery Learning, demonstrating high self-efficacy and improving learning quality by actively engaging students in the concept discovery process. However, 52% of teachers are aware of the model but do not implement it optimally, leading to varying self-efficacy and learning quality. The remaining 18% of teachers neither know about nor apply the model, continuing with conventional teaching methods, which yield unsatisfactory results.Challenges in implementing Discovery Learning include limited training, inadequate facilities, and insufficient time for lesson preparation. Nevertheless, teachers who received intensive training were able to overcome these obstacles, improving their self-confidence and learning quality. This study concludes that a good understanding and application of Discovery Learning positively impacts PAI teacher self-efficacy and learning quality. Therefore, more efforts are needed to enhance teacher training and mentoring to optimize the implementation of Discovery Learning across elementary schools in Kecamatan Leuwiliang.

Navigating the landscape of concept-supported XAI: Challenges, innovations, and future directions

This comprehensive review of concept-supported interpretation methods in Explainable Artificial Intelligence (XAI) navigates the multifaceted landscape. As machine learning models become more complex, there is a greater need for interpretation methods that deconstruct their decision-making processes. Traditional interpretation techniques frequently emphasise lower-level attributes, resulting in a schism between complex algorithms and human cognition. To bridge this gap, our research focuses on concept-supported XAI, a new line of research in XAI that emphasises higher-level attributes or 'concepts' that are more aligned with end-user understanding and needs. We provide a thorough examination of over twenty-five seminal works, highlighting their respective strengths and weaknesses. A comprehensive list of available concept datasets, as opposed to training datasets, is presented, along with a discussion of sufficiency metrics and the importance of robust evaluation methods. In addition, we identify six key factors that influence the efficacy of concept-supported interpretation: network architecture, network settings, training protocols, concept datasets, the presence of confounding attributes, and standardised evaluation methodology. We also investigate the robustness of these concept-supported methods, emphasising their potential to significantly advance the field by addressing issues like misgeneralization, information overload, trustworthiness, effective human-AI communication, and ethical concerns. The paper concludes with an exploration of open challenges such as the development of automatic concept discovery methods, strategies for expert-AI integration, optimising primary and concept model settings, managing confounding attributes, and designing efficient evaluation processes.

Scientific Reasoning Skills and Scientific Attitudes of Students in Learning Physics Using Guided Inquiry Model with Vee Map

This study aims to examine the effect of guided inquiry learning model with vee map on scientific reasoning skills and to examine the effect of guided inquiry learning model with vee map on scientific attitude of high school students on temperature and heat material. This type of research uses true-experiment with pretest-posttest control group design. The sample determination used cluster random sampling method. The samples of this study were XI MIPA 1 class as the control class and XI MIPA 2 as the experimental class. The research instruments used were pre-test and post-test questions using Lawson Classroom Test Scientific Reasoning indicators and scientific attitude questionnaires. Data analysis techniques using SPSS 26. The results showed that there was a significant effect of guided inquiry learning model with vee map on scientific reasoning skills with a significance of 0.000 and there was a significant effect of guided inquiry learning model with vee map on the scientific attitude of students with a significance of 0.000. Therefore it can be concluded that inquiry learning model accompanied by vee map can encourage students to be active in concept discovery and improve students' scientific reasoning skills.

The Effect Guided Discovery-Based Thermochemistry Module on Critical Thinking Skills of High School and Vocational Students

Critical thinking is a vital skill for success in higher education and the workforce. However, it is often inadequately emphasized in high school curricula, resulting in students lacking proficiency. Guided discovery-based modules provide a promising approach to enhancing critical thinking by fostering independent exploration and concept discovery. This study aimed to assess the effectiveness of a developed module in improving thermodynamics-related critical thinking skills among 10th-grade students in Boyolali Regency, Indonesia. Five schools were randomly selected, with two classes from each forming control and experimental groups. The experimental group utilized the developed module, while the control group used traditional textbooks. Validated by experts, a test was administered to evaluate students' critical thinking skills, and statistical analysis revealed a significant difference between the experimental and control groups. The developed module demonstrated superior efficacy in enhancing critical thinking skills compared to traditional textbooks. Variations in mean scores among the schools suggest the need for further investigation across diverse educational contexts. The study's outcomes have implications for teaching and learning in Indonesia and other countries where critical thinking is insufficiently emphasized in high school curricula. Ethical considerations were upheld, ensuring participant rights and confidentiality. Future research can explore the module's effectiveness in improving critical thinking across different subjects and educational settings

Feature subset selection in data-stream environments using asymmetric hidden Markov models and novelty detection

With the increase of computational power and memory capacity, it is possible to record and analyse lots of features of different nature in real time or in a data stream manner. Nonetheless, in many applications, not all variables may be relevant to explain the nature of the data. Feature subset selection strategies are therefore required to extract the relevant features to understand the data generating process. Much work has been performed in the area of supervised classification problems regarding feature selection, whereas for unsupervised environments, scarce studies have been conducted. In current feature subset selection methodologies for unsupervised data streams, the set of relevant variables are reevaluated or forgotten whenever a new instance or chunk of data arrives: this approach can be computationally expensive or unnecessary. Therefore, in this article, we provide a method to perform feature subset selection in unsupervised data streams. An embedded feature subset selection methodology based on asymmetric hidden Markov models and novel concept discovery strategies is used. This methodology can be used to detect novel concepts in data, and update the set of relevant features when a drift in the data stream is detected. Thus, the relevant variables are updated only when needed. To make this possible, we provide a model for batch and online problems, that is capable of dynamically determining the relevant variables to address feature selection in data streams. Additionally, the model provides domain insights using context-specific Bayesian networks which can be helpful to understand the dynamic process. To validate the proposed methodology, synthetic and real data from ball-bearings are used. Additionally, the proposed methodology is compared with other state of the art methodologies. The proposed method can be used to update the relevant features when needed and is more stable than its competitors.

Discovery and recognition of formula concepts using machine learning

Citation-based Information Retrieval (IR) methods for scientific documents have proven effective for IR applications, such as Plagiarism Detection or Literature Recommender Systems in academic disciplines that use many references. In science, technology, engineering, and mathematics, researchers often employ mathematical concepts through formula notation to refer to prior knowledge. Our long-term goal is to generalize citation-based IR methods and apply this generalized method to both classical references and mathematical concepts. In this paper, we suggest how mathematical formulas could be cited and define a Formula Concept Retrieval task with two subtasks: Formula Concept Discovery (FCD) and Formula Concept Recognition (FCR). While FCD aims at the definition and exploration of a ‘Formula Concept’ that names bundled equivalent representations of a formula, FCR is designed to match a given formula to a prior assigned unique mathematical concept identifier. We present machine learning-based approaches to address the FCD and FCR tasks. We then evaluate these approaches on a standardized test collection (NTCIR arXiv dataset). Our FCD approach yields a precision of 68% for retrieving equivalent representations of frequent formulas and a recall of 72% for extracting the formula name from the surrounding text. FCD and FCR enable the citation of formulas within mathematical documents and facilitate semantic search and question answering, as well as document similarity assessments for plagiarism detection or recommender systems.

RD-FCA: A resilient distributed framework for formal concept analysis

Formal Concept Analysis (FCA) is a data analysis technique with applications in data mining, artificial intelligence, software engineering, etc. The algorithms for FCA are computationally expensive, and their recursion tree is highly irregular and dynamic in nature. Several distributed FCA algorithms have been proposed to exploit parallelism within and across machines. However, none of the distributed approaches are able to recover from failures in the system. We propose RD-FCA, the first resilient distributed framework for FCA that uses novel load-balancing strategy, handles fail-stop failures and provides at-least-once semantics for concept discovery. Our asynchronous snapshot mechanism with incremental updates reduces the snapshot overhead and minimizes recalculation of concepts during recovery. RD-FCA also supports dynamic addition of workers to accelerate performance. Compared to MapReduce based approaches, RD-FCA performs an order of magnitude faster. We show through extensive evaluation that RD-FCA recovers efficiently from single, multiple, independent and cascading failures.

Analisis Pendekatan Saintifik Dalam Pembelajaran Tematik di Kelas IV MIS Al-Ikhlasiah Medan Sunggal

Research entitled "Analysis of Application of Scientific Approach to Science Learning in Grade 4 SD/MIS AL-IKHLASIAH". This study aims to determine how the achievement of scientific application that has been applied in the school. The condition of students learning is not yet fully implementing scientific steps such as observing, asking, trying, associating, and communicating in accordance with the demands of the 2013 curriculum. Lack of practicum in learning, because it only focuses on books that have been provided by the school, students only read and do not have an understanding of the meaning followed by the teacher asking questions. Impact on students' lack of ability in understanding the concept of the material. This condition must be solved because it has an impact on the students lack of ability in understanding the concept of the material. In solving problems, one of the actions that can be taken is to apply a scientific approach to learning that is appropriate and can help students be able to learn in a fun, meaningful, active way, be directly involved in concept discovery independently and can increase students' curiosity about learning materials.
 Keywords: Analysis, Scientific Approach, Thematic Approac.

The Domain Analysis Clinic

Domain analysis is a primary approach to the representation of shared ontological bases in knowledge organization. The growth of domain analysis as a paradigm is demonstrated over a period of two decades. The Domain Analysis Clinic, or DAC is a core methodology generated for focused concept discovery combining meta-analytical theoretical research with the formation of domain-specific knowledge organization systems. The emergence of the DAC as a tool for meta-analysis and discourse analysis has demonstrated the efficacy of focused concept formation for taxonomic representation as well as for theoretical discovery in KO. The evidence presented here strongly directly suggests the continued honing and maturity of domain analysis as a paradigm in KO.

PENERAPAN MODEL PEMBELAJARAN EXPERIENTIAL LEARNING PADA MATA PELAJARAN OTOMATISASI TATA KELOLA SARANA DAN PRASARANA KELAS XI SMK NEGERI 1 PANGKALANBARU

One of the objectives of the Office Governance Automation skill competency is to create a workforce who is able to apply their knowledge and skills in the field of office administration automation. Learning in productive subjects is not only conveying concepts, but more emphasis on solving problems that arise in everyday life, practicing their own concept discovery efforts through handling and treating material that is really real. That way students will be more active and easy to understand in the learning process, because the learning model is chosen properly and involves the experiences that students have. With the application of the Experiential Learning learning model in the subject of Automated Management of Facilities and Infrastructure, students become more enthusiastic and enthusiastic in participating in practical learning. One of them is because they have experienced and have their own experiences in their daily lives. Not only in the subject of Automated Management of Facilities and Infrastructure, but also in other subjects. By having their own experience, students become better prepared in solving cases or problems that occur in their daily lives. Students' critical, creative and innovative thinking skills can also be developed in responding to various problems that may occur to them later. In addition, the skills of students are also improved in this case the application of the use of office equipment or supplies (office supplies). With increased skills in the use of office equipment or supplies, of course, skills assessment indicators can be met.

MENINGKATKAN AKTIVITAS BELAJAR MATEMATIKA MELALUI MODEL DISCOVERY LEARNING DENGAN THINK-PAIR-SHARE PADA SISWA

Mathematics is one of the subjects that plays a very important role in the development of science and technology. Therefore, the government is always trying to make the quality of mathematics education better from time to time. After conducting classroom action research, based on the results of observations and reflections, it can be concluded that: The Discovery Learning Learning Model with Think-Pair-Share can improve students' mathematics learning activities. Improved class students' mathematics learning activities by applying this learning model from cycle I to cycle II. In the Think-Pair-Share implementation, students are given a sheet containing a summary of the material and questions regarding the material to be taught. Question sheets are arranged in the form of Student Activity Sheets (LKS). LKS are given after the process of observing and the subject matter is arranged systematically from easy questions to difficult questions. Students are given the opportunity to answer the question as best they can within the allotted time limit. The concept discovery process is carried out in groups (pairs), students have individual and group responsibilities and have the same opportunity to succeed. Students who have higher mathematical abilities can help students (partners) with lower abilities in learning mathematics.Keywords: Improve, Learning Activities, Discovery Learning Model, Think-Pair-Share.

Publications related to virtual laboratories during the covid 19 pandemic: A bibliometric review and analysis

During the COVID-19 pandemic, virtual laboratories are instrumental in learning physics concepts. A virtual laboratory is one media that can be used as a solution to carry out concept discovery activities through simulation using computer software. This article was created to explain the trend of publications related to virtual laboratories during the COVID-19 pandemic for 2020-2021 through bibliometric analysis of the literature. The papers analyzed were obtained using the Publish or Perish (PoP) software on the Google Scholar database. The following publication trend expresses in the form of graphic visualization, which is analyzed using VOSviewer software. Overall, articles with the keyword virtual laboratory published in the late 2020-2021 period focus on its relationship with Covid 19 and evaluation. This focus of research shows that during the past year, the focus of research is how to evaluate learning through virtual laboratories.

THE APPLICATION OF MUJA’S DISCOVERY LEARNING MODEL TO IMPROVE STUDENT SCIENTIFIC LITERACY SKILLS IN CLASS 9B AT THE 7TH STATE JUNIOR HIGH SCHOOL MUARO JAMBI

This study aims to improve students' scientific literacy skills in grade 9B at the 7th State Junior High School Muaro Jambi by learning Discovery Learning Muja. This classroom action research was conducted in two cycles. The research instruments were learning observation sheets, student observation sheets, scientific literacy tests, attitude questionnaires. The indicator of the success of students' scientific literacy was that at least 50% of respondents have achieved scientific literacy in the good category. The application of Muja's Discovery Learning on plant and animal breeding materials in grade 9B at the 7th State Junior High School Muaro Jambi can improve scientific literacy. 53% of students have reached the good category. Muja's Discovery Learning can be applied to the topics/materials that can be related to the local potential of the Muaro Jambi area. The concept discovery process will be more meaningful if it is carried out through investigations/experiments that emphasize student activities that support the development of scientific literacy.

Concept Discovery for The Interpretation of Landscape Scenicness

In this paper, we study how to extract visual concepts to understand landscape scenicness. Using visual feature representations from a Convolutional Neural Network (CNN), we learn a number of Concept Activation Vectors (CAV) aligned with semantic concepts from ancillary datasets. These concepts represent objects, attributes or scene categories that describe outdoor images. We then use these CAVs to study their impact on the (crowdsourced) perception of beauty of landscapes in the United Kingdom. Finally, we deploy a technique to explore new concepts beyond those initially available in the ancillary dataset: Using a semi-supervised manifold alignment technique, we align the CNN image representation to a large set of word embeddings, therefore giving access to entire dictionaries of concepts. This allows us to obtain a list of new concept candidates to improve our understanding of the elements that contribute the most to the perception of scenicness. We do this without the need for any additional data by leveraging the commonalities in the visual and word vector spaces. Our results suggest that new and potentially useful concepts can be discovered by leveraging neighbourhood structures in the word vector spaces.

BELAJAR DENGAN DIALOG (Deep Dialog dan Critical Thiking Sebagai Model Penerapan Pendidikan Karakter)

The caregivers of the Church Soya, in carrying out the sagat learning process, were accustomed to using monotonous lecture methods, the use of methods, strategies and learning media rarely seen in the learning process. This study aims to find out what the learning process is with the dialogue on Church Formal Education (PFG) for GPM Soya's. The data of this study were analyzed using qualitative research methods with data collection techniques are observation, interviews and literature and data analysis techniques are data reduction, display data and draw conclusions and verification. By using Deep Dialog and Critical Thiking Theory with dialogue methods in the learning process with results indicators are Silence, community building, concept discovery activities, reflection and evaluation.The results found in this study were that caregivers found it very difficult to use the method of dialogue with students, so the teaching and learning process carried out was very monotonous by using the lecture method. Children's creativity and caregivers are not honed and have no development. In-depth dialogue with children's goals and caregivers are open, honest, respectful of others and independent learning has not occurred in the learning process. These findings are analyzed by means of learning with silence in establishing internal and external dialogue, building a class community for democratic dialogue, finding concepts for independence in dialogue, reflection as an educational and evaluation dialogue as a means of achieving learning with dialogue.Keyword: Church Formal Dialogue and Education (PFG)

Fuzzy-Based Concept Learning Method: Exploiting Data With Fuzzy Conceptual Clustering.

Concepts have been adopted in concept-cognitive learning (CCL) and conceptual clustering for concept classification and concept discovery. However, the standard CCL algorithms are incapable of tackling continuous data directly, and some standard conceptual clustering methods mainly focus on the attribute information, ignoring the object information that is also important to improve clustering analysis and concept classification ability. Therefore, in this article, we present a novel concept learning method, called the fuzzy-based concept learning model (FCLM), to address these two issues by exploiting concept hierarchical relations in concept lattices. More specifically, we first show some new related notions for FCLM based on a regular fuzzy formal decision context; among these notions, the object-oriented and attribute-oriented fuzzy concept similarities are used to achieve the concept similarity measure in concept lattices. Moreover, a novel fuzzy concept learning framework is designed, and its corresponding learning algorithms are developed. Finally, we conduct some experiments on various real-world datasets to demonstrate that the proposed method can achieve the state-of-the-art classification performance among similarity-based learning methods. In addition, we further verify the effectiveness of our method in concept discovery on the MNIST dataset.

Overcoming Language Priors in VQA via Decomposed Linguistic Representations

Most existing Visual Question Answering (VQA) models overly rely on language priors between questions and answers. In this paper, we present a novel method of language attention-based VQA that learns decomposed linguistic representations of questions and utilizes the representations to infer answers for overcoming language priors. We introduce a modular language attention mechanism to parse a question into three phrase representations: type representation, object representation, and concept representation. We use the type representation to identify the question type and the possible answer set (yes/no or specific concepts such as colors or numbers), and the object representation to focus on the relevant region of an image. The concept representation is verified with the attended region to infer the final answer. The proposed method decouples the language-based concept discovery and vision-based concept verification in the process of answer inference to prevent language priors from dominating the answering process. Experiments on the VQA-CP dataset demonstrate the effectiveness of our method.

Concept Discovery through Information Extraction in Restaurant Domain

Concept identification is a crucial step in understanding and building a knowledge base for any particular domain. However, it is not a simple task in very large domains such as restaurants and hotel. In this paper, a novel approach of identifying a concept hierarchy and classifying unseen words into identified concepts related to restaurant domain is presented. Sorting, identifying, classifying of domain-related words manually is tedious and therefore, the proposed process is automated to a great extent. Word embedding, hierarchical clustering, classification algorithms are effectively used to obtain concepts related to the restaurant domain. Further, this approach can also be extended to create a semi-automatic ontology on restaurant domain.