Model Of Concept Learning Research Articles

Peningkatan Hasil Belajar IPS Menggunakan Model Pembelajaran Konsep Pada Siswa

Social Sciences (IPS) education has an important role in forming students' understanding of society and the surrounding environment. However, many students have difficulty understanding the concepts taught. Therefore, this research aims to improve social studies learning outcomes by implementing a more interactive and interesting concept learning model.This research was conducted in an elementary school in Jakarta involving two different classes. One class uses conventional learning methods, while the other class applies a concept learning model. Learning outcome measurements were carried out before and after implementing this method.The concept learning model used in this research includes teaching strategies that prioritize in-depth understanding through group discussions, use of visual media, and practical activities. Students are invited to actively participate and relate social studies concepts to their daily experiences.The research results show a significant increase in student learning outcomes using the concept learning model compared to conventional methods. The average student final exam score increased by 20%, and the level of student engagement in the learning process also showed positive progress.

Undergraduate nursing student's attitudes to learning during clinical practice in different semesters when using a conceptual learning model grounded in a caritative caring perspective - A cross-sectional study.

To describe undergraduate nursing students' attitudes to learning during clinical practice in different semesters when using the conceptual learning model, Model for Improvements in Learning Outcomes (MILO) grounded in a caritative caring perspective. With the intention to support interlinking between theory and praxis and offer understanding and structure to facilitate learning, MILO, theoretically grounded in hermeneutics and a caritative caring perspective based on ethical values, was implemented. MILO consists of four contextual concepts (peer learning, co-clinical teachers, student-centred and student-active supervision) and four intrapersonal concepts (nursing, a reflective approach, a critical approach, quality and safety). A descriptive comparative quantitative study design was applied at a Swedish university, 3 hospitals and 13 municipalities in one county. Cross-sectional data collected via a questionnaire developed to assess attitudes to learning related to MILO's contextual and intrapersonal concepts and their applications were used. 209 students in semester 3, 4 and 6 participated in 6 different clinical practice courses. In comparison, intrapersonal concepts, that is, the student's own characteristics and abilities were viewed to be of greater value for learning than contextual, that is, organisational-related concepts in all semesters. Understanding the needs of others and reflective learning were rated to be of major importance. Students in semester 3 valued the use of the applications the highest. To be supervised in pairs was rated the lowest in semester 6. Some of the concepts and their applications were to great extent not applied. In all semesters, fundamentals in caritative caring and characteristics and abilities related to the individual student were rated to be of greater importance for learning than environmental support. Providing students opportunities to develop independency seems essential. Use of a learning model such as MILO is dependent on a bearing of a caritative caring culture and a shared understanding between all involved in student learning during clinical practice.

A proposition to teach engineering projects using Problem-based Learning (PBL) concepts

This article shows the design of a mechanical engineering project using problem-based learning concepts. The work shows the complexity of the teaching-learning process of a complex problem such as developing, simulating, and testing a prototype of a piezoresistive pressure sensor. This type of problem is typical in theoretical-practical classes involving different concepts learned during an undergraduate course. The cognitive structure may or may not be influenced by the presentation of ideas and methods for organizing the content and executing the desired project. Using a pressure sensor project made it possible to develop the concepts of learning models by applying the idea of project-based learning (PBL). The main results obtained were the product design topology, operating concepts of a sensor device, computer-aided design projects, analysis of simulation results and assembly, and mechanical prototyping concepts, involving different materials for machining and product encapsulation.

Model Concept Learning Sebagai Penunjang Keaktifan Pada Aktivitas Pembelajaran Siswa

This study aims to analyze the application of the concept learning model in increasing student learning activeness in PAI learning. This research is quantitative, with a Time-Series type experimental design (time-series). The results of the analysis of this study indicate that the application of the concept learning model in PAI learning can increase the activeness of student learning, as seen from the more students who experience an increase in learning activeness after the application of the concept learning model as many as 29 students out of 30 students. The advantages of the application process are: 1) Students are more motivated to learn; 2) Students find it easier to understand the material; (3) The level of students' understanding of teaching materials is getting better. The constraints and drawbacks are; 1) requires quite a lot of time; 2) The teacher must be more intensive because the teacher is no longer the one providing the information but working together with the students to manage the class to find the basic concepts of the material to be studied; 3) In learning activities will tend to be dominated by active students only.

Probabilistic Learning and Psychological Similarity.

The notions of psychological similarity and probabilistic learning are key posits in cognitive, computational, and developmental psychology and in machine learning. However, their explanatory relationship is rarely made explicit within and across these research fields. This opinionated review critically evaluates how these notions can mutually inform each other within computational cognitive science. Using probabilistic models of concept learning as a case study, I argue that two notions of psychological similarity offer important normative constraints to guide modelers' interpretations of representational primitives. In particular, the two notions furnish probabilistic models of cognition with meaningful interpretations of what the associated subjective probabilities in the model represent and how they attach to experiences from which the agent learns. Similarity representations thereby provide probabilistic models with cognitive, as opposed to purely mathematical, content.

An innovative conceptual model for education and training on hybrid warfare

Abstract This study aims to present an innovative conceptual model of learning, teaching, and training that appears to be applicable to the field of hybrid warfare. It is based on integrating three critical elements such as reference curriculum, teaching methodology, and online support capabilities. By using qualitative research through correlating critical analysis of relevant sources, logical scheme, and cognitive task analysis, each descriptive element is analysed in order to provide an eloquent image of the proposed conceptual model. Also, to gain real feedback on the proposed model as well as on its constitutive elements, the quantitative research using statistical data grouping is performed. The analysis and interpretation of the collected data demonstrate not only a high interest in the designed conceptual model, but also a positive impact at the level of each descriptive element on the academic staff and students from the military and civilian university environment.

Rationale for a Conceptual Model of Teaching English for Special Purposes

Teachers’ professional and personal qualities and the latest information and communication technologies combined with student-centered teaching methods are the prerequisites for effective English language teaching. The study's objective is to design a conceptual model of active English language learning to be implemented in the course of professional training for technical specialists in academic settings. The conceptual model for teaching English for special purposes has been designed based on the descriptive method, scientific literature data analysis, and theoretical modelling technique. The method of expert evaluations was used to identify professionally significant research areas. Specific features of intensive learning technologies, which can be effectively combined in the learning process, have been identified. A modern interpretation of active learning in English as a generalized conceptual model of learning is presented. The established combination of intensive teaching methods and technologies can be used to teach English for Special Purposes at technical universities.

Pengaruh model pembelajaran pencapaian konsep ( concept attainment) terhadap hasil belajar siswa pada materi larutan elektrolit dan non elektrolit

This study aims to determine the learning outcomes of students of class X MIPA SMA N 1 Lintongnihuta who were taught using the concept attainment learning model and who were taught using conventional learning on electrolyte and non-electrolyte solution materials and to find out the significant influence of the application of the learning model concept attainment to improve student learning outcomes on electrolyte and non-electrolyte solutions. The population in this study were students of class X MIPA SMA N 1 Lintongnihuta, totaling 6 classes. The research sample was class X MIPA 3 as an experimental class which was taught using a concept attainment learning model and class X MIPA 5 as a control class which was taught using conventional learning. This study used a test instrument of 17 multiple choice questions. The results showed that the average experimental class student learning outcomes before and after the application of the concept attainment learning model were 44,58 and 78,83 respectively, and increased by 61%. The average learning outcomes of control class students before and after conventional learning were carried out were 42,33 and 70,52 respectively, and increased by 48%. Hypothesis testing was carried out with a one-party t test with the results of research on the concept attainment learning model, the results obtained were tcount(3.542) > ttable(1.996), then H0 was rejected and Ha was accepted, which means there is a significant effect on the use of the learning model on concept attainment on student learning outcomes on electrolyte and non-electrolyte solutions.

Recreational Game Socialization Using PowerPoint as a Practical Media

This activity is a form of training for teachers and lecturers in Banda Aceh City, where the use of Microsoft Powerpoint can be adapted to make a simple game. We chose games Hidden Picture Games, Matching Game, Telepathy Game, Top Five quiz, Mystery Box, and Lucky Box. The implementation method consists of three stages, namely introduction, practice, and demonstration. The results of this activity have become a concept and collaborative learning model through recreational game media that can be used at universities in Indonesia in making service activities in utilizing Microsoft PowerPoint practical media to create innovations and develop an educational game as well as improve the ability of teachers and lecturers to utilize instructional Media.

Measuring Intrinsic Traits of Children at Zoos

Zoos are in a unique position to affect the development of youth in ways that are consistent with cultivating pro-environmental behaviors. The purpose of this study was to examine three intrinsic traits of youth important to the goals of conservation education: prior knowledge about animals, interest in animals, and value for animals. The framework of this study is the conceptual model of learning that posits that any changes that occur during a single visit to an informal learning institution (such as a zoo) will be affected and informed by the intrinsic traits of the individual and various external factors. This study explored three intrinsic traits predicted to be important for informal learning. The research design followed a constructivist design meant to capture measurements of prior knowledge, interest, and value from the participants’ own perspectives. This study describes these three traits in a sample of 37 youth between the ages of 7 and 14, largely zoo members, attending the zoo with their parents. In particular, I describe unique methods of measuring these three traits including a drawing activity meant to assess the children’s construction of knowledge about animals, and questionnaires meant to assess the youth’s interest and value. The data analysis in this paper provides descriptions of the three traits as they are constructed among the youth participants in order to inform future correlations between intrinsic traits and in-zoo behavior. The results of the study indicated that youth organize their knowledge about animals around ecological and morphological concepts and that this forms the basis for their interest in and value for animals.

Supervisors’ experiences of undergraduate nursing students’ learning in clinical practice when applying caring and learning as parallel processes in a caritative caring conceptual learning model (Part 2)

The Model for Improvements in Learning Outcomes (MILO) is theoretically grounded and designed to intertwine didactics, pathophysiology and medicine with specific concepts important for learning. The aim was to describe supervisors' experiences of undergraduate nursing students' learning during clinical practice when using MILO. A qualitative and explorative design was used. Seventeen supervisors, thirteen women and four men from different departments at three hospitals in southern Sweden participated. After using the model, data were collected through four focus group interviews with open unstructured interview questions and analysed using inductive latent content analysis. Twelve subcategories, four generic subcategories and one main category emerged. The students developed a questioning approach and were more reflective, open and compliant. Twosomes enhanced learning. Specific documents generated structure and feelings of participation. The supervisors felt that taking the students' pre-understanding into account and a caring approach in the learning environment were valuable for enhanced learning. The students established a caring relationship with the patients and the patients’ perspective became emphasized. Using MILO, intertwining between the natural and the professional became possible; enhanced learning in nursing skills together with a more caritative caring approach towards the patient was revealed. The need of compassion is discussed.

Development of a Global Health Learning Progression (GHELP) Model.

There has been a steady increase in global health experiential opportunities offered within healthcare professional training programs and with this, a need to describe the process for learning. This article describes a model to contextualize global health learning for students who complete international advanced pharmacy practice experiences (APPEs). Students from University of North Carolina at Chapel Hill, Purdue University, and the University of Colorado completed a post-APPE survey which included open-ended questions about knowledge, skills, and attitudes one week after completing an international APPE. Students were also invited to participate in a focus group. All 81 students who participated in an international APPE completed the open-ended survey questions and 22 students participated in a focus group discussion. Qualitative data from both the survey and focus groups were coded in a two-cycle open coding process. Code mapping and analytic memo writing were analyzed to derive to a conceptual learning model. The Global Health Experience Learning Progression (GHELP) model was derived to describe the process of student learning while on global health experiences. This progression model has three constructs and incorporates learning from external and internal influences. The model describes how students can advance from cultural awareness to cultural sensitivity and describes how student pharmacists who participate in international experiential education develop global health knowledge, skills, and attitudes.

Driver Attribute Filling for Genes in Interaction Network via Modularity Subspace-Based Concept Learning from Small Samples

The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. In consideration of gene interaction network as a complex network, previous studies have made efforts on the driver attribute filling of genes via network properties of nodes and network propagation of mutations. However, there are still obstacles from problems of small size of cancer samples and the existence of drivers without property of network neighbours, limiting the discovery of cancer driver genes. To address these obstacles, we propose an efficient modularity subspace based concept learning model. Our model can overcome the curse of dimensionality due to small samples via dimension reduction in the task of attribute concept learning and explore the features of genes through modularity subspace beyond the network neighbours. The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. Generally, our model shows a promising prospect in the application of interaction network analysis of tumorigenesis.

CAESAR: concept augmentation based semantic representation for cross-modal retrieval

With the increasing amount of multimedia data, cross-modal retrieval has attracted more attentions in the area of multimedia and computer vision. To bridge the semantic gap between multi-modal data and improve the performance of retrieval, we propose an effective concept augmentation based method, named CAESAR, which is an end-to-end framework including cross-modal correlation learning and concept augmentation based semantic mapping learning. To enhance the representation and correlation learning, a novel multi-modal CNNs based CCA model is developed, which is to capture high-level semantic information during the cross-modal feature learning, and then capture maximal nonlinear correlation. In addition, to learn the semantic relationships between multi-modal samples, a concept learning model named CaeNet is proposed, which is realized by word2vec and LDA to capture the closer relations between texts and abstract concepts. Reenforce by the abstract concept information, cross-modal semantic mappings are learnt with a semantic alignment strategy. We conduct comprehensive experiments on four benchmark multimedia datasets. The results show that our method has great performance for cross-modal retrieval.

Dynamic Three-Way Concept Learning Model Based on PS-Rough Set

The PS-rough set adds the dynamic characteristics of the elements moving in and moving out of the set, and introduces it into the three-way concept learning, and establishes the dynamic three-way concept learning model based on the PS-rough set. Firstly, the related definitions of dynamic three-way concept learning are introduced, and the calculation method of conditional probability function is given. Secondly, the related dynamic characteristics of the dynamic three-way concept learning model are proposed and proved, and the dynamic three-way concept learning algorithm is given, and the validity of the algorithm are verified by an example. Finally, the dynamic three-way concept learning model solves the dynamic change problem of the sample subset, reduces the computation of the probability of the condition, and gives a simple three-way concept division method.

Grounding judgement in context: A conceptual learning model of clinical reasoning.

Contemporary research on clinical reasoning focuses on cognitive problem-solving processes. However, the decisive role that clinical context plays in clinical reasoning is often overlooked. We explored how novice learners make sense of the patient encounter in the clinical situation. In particular, we examined medical students' own judgements concerning diagnostic and management decisions and how the clinical context impacts on this. We aimed to produce a conceptual model of how students learn clinical reasoning in the clinical environment. We used grounded theory methodology to develop a conceptual learning model. A total of 23 medical students in their third academic year were recruited. Qualitative data were gathered from semi-structured interviews, participant observations and field interviews, during clinical clerkships. Learners participating in the clinical environment experienced tensions, called 'Disjunctions.' These disjunctions emerged in the context of the student-patient encounter and in particular in situations where an element from the interaction with the patient was perceived as being inconsistent with existing frames of reference. We categorised the sources of disjunctions into four subcategories: (a) observing the manifestations of clinical signs in reality; (b) fitting the symptoms to a diagnosis; (c) considering management decisions, and (d) communicating a medical decision to the patient. Disjunctions involved an affective component and were associated with feelings of uncertainty. These tensions provoked reactions from the learners, leading them to reassess and modify held assumptions in order to accommodate the encountered inconsistent elements. This facilitated changes in judgement. When making a judgement, participants learned to take into consideration situational elements. Students experience disjunctions in the clinical environment as they encounter situations that challenge their frames of reference. These disjunctions carry significant learning potential. This study can contribute to knowledge concerning the role of the patient encounter in advancing clinical reasoning by transforming problematic habits of the mind.

An Active Inference Approach to Modeling Structure Learning: Concept Learning as an Example Case.

Within computational neuroscience, the algorithmic and neural basis of structure learning remains poorly understood. Concept learning is one primary example, which requires both a type of internal model expansion process (adding novel hidden states that explain new observations), and a model reduction process (merging different states into one underlying cause and thus reducing model complexity via meta-learning). Although various algorithmic models of concept learning have been proposed within machine learning and cognitive science, many are limited to various degrees by an inability to generalize, the need for very large amounts of training data, and/or insufficiently established biological plausibility. Using concept learning as an example case, we introduce a novel approach for modeling structure learning—and specifically state-space expansion and reduction—within the active inference framework and its accompanying neural process theory. Our aim is to demonstrate its potential to facilitate a novel line of active inference research in this area. The approach we lay out is based on the idea that a generative model can be equipped with extra (hidden state or cause) “slots” that can be engaged when an agent learns about novel concepts. This can be combined with a Bayesian model reduction process, in which any concept learning—associated with these slots—can be reset in favor of a simpler model with higher model evidence. We use simulations to illustrate this model's ability to add new concepts to its state space (with relatively few observations) and increase the granularity of the concepts it currently possesses. We also simulate the predicted neural basis of these processes. We further show that it can accomplish a simple form of “one-shot” generalization to new stimuli. Although deliberately simple, these simulation results highlight ways in which active inference could offer useful resources in developing neurocomputational models of structure learning. They provide a template for how future active inference research could apply this approach to real-world structure learning problems and assess the added utility it may offer.

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.

Developing investigation group learning model based on Marzano instructional framework to promote students’ higher order thinking skill

Higher order thinking skill is one of the skills that must be possessed by 21st century students. The purposes of this study are to develop a conceptual model of investigation group learning model based on Marzano’s instructional framework and to analyze the design and validation of the learning model. The approach used in this study was Research and Development (R&D). Then, observations, questionnaire, interviews, and Focus Group Discussion (FGD) were used as the data collection techniques. The results of validity test showed that the investigation group learning model based on Marzano’s instructional framework is valid and relevant to be applied in social studies learning at junior high school level to improve students’ higher order thinking skills. In addition to obtaining a conceptual learning model, this development also created learning instruments, namely syllabus, lesson plan, teaching materials, observation sheet of critical and creative thinking skills, and test of critical and creative thinking skills that can be used in social studies learning at junior high school level especially on the material of Plurality. Considering that the purpose of education in general is to improve students’ skills, so the investigation group learning model based on Marzano’s instructional framework can present effective learning criteria.

Local Sensitive Dual Concept Factorization for Unsupervised Feature Selection

In this paper, we present a novel Local Sensitive Dual Concept Learning (LSDCL) method for the task of unsupervised feature selection. We first reconstruct the original data matrix by the proposed dual concept learning model, which inherits the merit of co-clustering based dual learning mechanism for more interpretable and compact data reconstruction. We then adopt the local sensitive loss function, which emphasizes more on most similar pairs with small errors to better characterize the local structure of data. In this way, our method can select features with better clustering results by more compact data reconstruction and more faithful local structure preserving. An iterative algorithm with convergence guarantee is also developed to find the optimal solution. We fully investigate the performance improvement by the newly developed terms, individually and simultaneously. Extensive experiments on benchmark datasets further show that LSDCL outperforms many state-of-the-art unsupervised feature selection algorithms.