Concept Of Learning Research Articles

Learning About Lunar Phases: Exploring Taiwanese University Students’ Conceptions of and Approaches to Learning by Holographic Projection

Extant literature has preliminarily confirmed the potential benefits of holographic projections for educational purposes. Yet, students’ experiences of learning by holographic projection have rarely been addressed. The main purpose of this study was therefore to explore students’ conceptions of and approaches to learning by holographic projection. A holographic projection system was devised to assist students in learning the concept of lunar phases. This study also investigated the relationships between students’ conceptions and approaches, and determined if there were any variations based on students’ learning gains. Phenomenographic analysis was adopted to analyze 30 university students from Taiwan, and a multiple-choice conceptual test was conducted to assess their learning achievements. The main findings indicated that students with conceptions of learning by holographic projection as increasing the presence of a scene or mapping 2D images to 3D structures tended to adopt surface approaches, such as only observing the changes presented in the holographic projection, repetitively operating the system functions without intended purposes, or simply following the instruction guidelines to meet the minimal requirements. However, this study unexpectedly found that students who utilized surface approaches had significantly higher learning gains on the conceptual test than their counterparts did.

Two-way concept-cognitive learning method: a perspective from progressive learning of fuzzy skills

ABSTRACT Concept-cognitive learning and two-way concept-cognitive learning have been widely used to learn concepts from given cues, and fruitful results have been obtained. However, there are still some limitations: (i) The existing two-way concept-cognitive learning methods based on competences focused on dichotomous skills, but they did not consider the proficiency of skills in the concept-cognitive learning process and ignored the dynamic learning of skills. (ii) The existing necessary and sufficient information granules based on competences cannot be learned directly from any information granule. (iii) Most of the other existing two-way concept-cognitive learning models in fuzzy formal context have been studied through an antitone Galois connection, but they cannot directly deal with an isotone Galois connection between items and fuzzy skills. To overcome these issues, we propose a novel two-way concept-cognitive learning model based on fuzzy skills to learn fuzzy skill-based cognitive concepts (i.e. necessary and sufficient fuzzy skill-based information granules) from a perspective of skill proficiency. And we optimize the two-way concept-cognitive learning model to learn directly fuzzy skill-based cognitive concepts from any information granule and save time for learning concepts. Furthermore, a progressive learning mechanism is explored to deal with dynamic data. The experimental results of 12 UCI data sets show that the proposed concept-cognitive learning models in this paper are feasible and effective.

Application of Adaptive Machine Learning in Non-Stationary Environments

In the current data-driven era, the application of adaptive machine learning in non-stationary environments has become particularly important. This paper explores the basic concepts of adaptive machine learning and its applications in financial market forecasting and industrial equipment monitoring, demonstrating its superior performance in high-noise, dynamic environments. The research results indicate that adaptive machine learning models significantly improve prediction accuracy, response speed, and robustness through strategies such as online learning and incremental learning. In financial market forecasting, the mean squared error (MSE) was reduced to 0.015, and the fault detection accuracy in industrial equipment monitoring increased to 95%. The paper also proposes future research directions, including multi-source data fusion, anomaly detection, computational efficiency enhancement, and model interpretability. Adaptive machine learning technology not only enriches the theoretical framework of machine learning but also provides new solutions for practical applications in various fields, paving the way for further development of intelligent systems.

Teaching and learning for sustainable futures: Reclaiming, collaboration and empathy

Introducing community engagement to my annual class of Visual Art honours students has become increasingly challenging. There is a healthy resistance to the perception of ‘cultural tourism’, and students are sensitive to the idea of entering a community as voyeurs with the perception of privileged “saviours”. I offer a particular example of an emerging annual exchange with the African Reclaimers Organisation (ARO), who have partnered with the University of Johannesburg (UJ) in a range of projects that can add value to upcycling of collected and sorted waste products. The pervasive news of corruption, violence, and rolling blackouts undermines well-being and increasingly perpetuates despair, apathy, or distraction. In this climate, a question of response in an educational environment is pertinent. How does one teach and learn about sustainable futures? How can educators foster empathy, agency, and activism among students? This article draws on ecology activist and scholar Dylan McGarry’s expanded concept of transgressive social learning using skills transfer through an apprenticeship, which is helpful in framing a model of teaching and learning based on social exchange. Including selected student reflections, the paper considers methods and practices that model ways to develop various forms of personal and social agency through exploring empathetic encounters.

Efektivitas Model Pictorial Riddle Berbantuan Aplikasi Qreatif terhadap Pemahaman Konsep Dan Aktivitas Belajar Siswa Kelas III SDN Wonosari 01

Problems in learning IPAS in primary schools often arise, especially in the use of monotonous teaching methods and media that do not support active participation of students so that they can cause misconceptions in students. These problems need to be overcome by using the right learning model and media. This study aims to examine the effectiveness of Pictorial Riddle learning model with Qreatif application on students' concept understanding and learning activities. The type of research used is quantitative with a Quasi Experimental design of the Non-equivalent Control Group Design type. The study population was 51 third grade students of SDN Wonosari 01. The sample was obtained through saturated sampling technique. The experimental class used Pictorial Riddle model and Qreatif media, while the control class used conventional model and Canva PPT. Data were collected through test and non-test techniques. Data analysis techniques included normality test, homogeneity test, hypothesis test (t-test), and N-gain test. The t-test results showed a significant difference in concept understanding between the experimental and control classes with a 2-tailed sig value of less than 0.05 (0.001 < 0.05) and a t-count value of 7.340 > t-table 2.009. The number of students who completed the KKM in the experimental class was greater than the control class (88%> 46%) this indicates a difference in understanding the concepts of the control and experimental classes. The mean N-Gain value of the experimental class is higher than the control class (65.60% > 42.05%). Analysis of student activeness scores showed that the experimental class was higher than the control class (observation: 84% > 66%; questionnaire: 81% > 66%). In conclusion, the Pictorial Riddle learning model with Qreatif application is effective in improving students' concept understanding and learning activities.

Mathematics Worked Examples with Tracing Gesture Makes Learning Last

The immediate measurement of tracing effect in the previous studies have shown that the use of tracing gesture in the visuospatial-based worked examples instruction may enhance the learning of mathematical concepts, specifically the geometrical principles. The present study further explored whether the use of tracing gesture in the visuospatial-based worked examples during the acquisition phase results in better memory retention on the learning over time. To investigate the robustness of tracing effect, a 4-weeks interval delayed post-test was conducted after the participants (mean age = 12.37, SD = 0.27) were asked to study worked examples on angles relationships involving parallel lines either without tracing instruction or with tracing instruction. The results showed that the tracing group (N = 30) outperformed the non-tracing group (N = 30) in the delayed post-test and reported lower levels of test difficulty, suggesting that tracing gesture may help to retain the knowledge gained during instruction and consolidated the learning during the intervening period.

The Tale of Designing a Clinical-Cases Manual for Rotations and Mixed Methods Analysis of Students' Participatory Experience in Co-Creation.

The post-pandemic era ignited the concepts of virtual learning, enhancing a strong need for a specific clinical case manual of commonly encountered scenarios in internal medicine. In this article, we describe the process for creating a clinical cases manual which can be followed for any other clinical science. Further, we report the participatory experience of students in the co-creation of manual. The hand-written notes of the teacher during the rotation of internal medicine were improvised, and created into a digital version. The editorial team which included mainly student volunteers participated in digitization and reviewing the content, diagrams and flowcharts. We describe the process of designing the clinical-cases manual for rotations in internal medicine, which includes handwritten notes, review of the notes, formation of editorial team, digitization of text and artwork, editing and book release. It can be replicated for any other clinical sciences in rotations. In addition, the online survey with both quantitative and qualitative type of questions was used to assess the students' participatory experiences in co-creating manual. The clinical-cases manual for rotations in internal medicine was released and received well. The online survey responses revealed that the participating students benefited in terms of professional and personal development. There was an inter-item statistical difference implying that all the participants were agreeing or strongly agreeing to survey questions. All participants agreed on the usefulness of the manual. The main themes identified through qualitative analysis were technical skills acquisition, lifelong learning and teaching, self-assessment, discipline, time management, teamwork and communication skills. The creation of a clinical case manual for rotation, specific to the local needs can be done through a systematic process, that can be enriched by involving students. Encouraging the participation of students in co-creation is an important academic exercise that contributes to professional and personal development.

Motif-Based Contrastive Learning for Community Detection.

Community detection has become a prominent task in complex network analysis. However, most of the existing methods for community detection only focus on the lower order structure at the level of individual nodes and edges and ignore the higher order connectivity patterns that characterize the fundamental building blocks within the network. In recent years, researchers have shown interest in motifs and their role in network analysis. However, most of the existing higher order approaches are based on shallow methods, failing to capture the intricate nonlinear relationships between nodes. In order to better fuse higher order and lower order structural information, a novel deep learning framework called motif-based contrastive learning for community detection (MotifCC) is proposed. First, a higher order network is constructed based on motifs. Subnetworks are then obtained by removing isolated nodes, addressing the fragmentation issue in the higher order network. Next, the concept of contrastive learning is applied to effectively fuse various kinds of information from nodes, edges, and higher order and lower order structures. This aims to maximize the similarity of corresponding node information, while distinguishing different nodes and different communities. Finally, based on the community structure of subnetworks, the community labels of all nodes are obtained by using the idea of label propagation. Extensive experiments on real-world datasets validate the effectiveness of MotifCC.

Food Recommendation System for a Healthy Liver Using Machine Learning

Nowadays, the work routine, the problems we face, make us not pay proper attention to healthy eating. As a result, people get sick. We have created through mathematical knowledge (statistics, probability, linear algebra, geometry), combining the concepts of Machine Learning (Content Based Filtering, TD IDF Vectorizer Algorithm, Conditional Independence, Count Vectorization) with Python language, a Recommendation System for all people suffering from liver. Liver is one of the most important organs of our body, because it makes 500 essential functions in the organism. But we have to be aware about the types of food that we use. Also, we must be very careful with our lifestyle and diet on foods. The aim of this article is to recommend the most appropriate foods for a healthy liver. This article will help the p

The Impact of Mobile Seamless Learning Strategies and Digital Literacy on Students' Concept Mastery in Islamic Education Courses

This study aims to investigate the impact of the Mobile Seamless Learning Strategy and digital literacy on students' concept mastery in Islamic Education. The growing development of science, along with social, cultural, and economic growth, demonstrates the positive direction of technology and information development in Indonesia. However, the disconnection between formal and non-formal education has created a lack of coherence among educators, making it difficult for students to master learning concepts. Limited classroom hours necessitate alternative solutions, such as the Mobile Seamless Learning Strategy, which bridges in-school and out-of-school education through digital media. Digital literacy is also considered a critical factor in this process. The research method employed is a literature study, gathering relevant data from books, journals, dictionaries, magazines, and other sources without the need for field research. The findings suggest that this approach can significantly enhance students' understanding of learning material, contributing to the achievement of educational goals.

A Critical Exploration of the Educational Value of Concepts in In-Depth Learning : Focusing on Deleuze’s Pedagogy of a Concept

A Critical Exploration of the Educational Value of Concepts in In-Depth Learning : Focusing on Deleuze’s Pedagogy of a Concept

Spatio-temporal physics-informed neural networks to solve boundary value problems for classical and gradient-enhanced continua

Recent advances have prominently highlighted physics informed neural networks (PINNs) as an efficient methodology for solving partial differential equations (PDEs). The present paper proposes a proof of concept exploring the use of PINNs as an alternative to finite element (FE) solvers in both classical and gradient-enhanced solid mechanics. To this end, spatio-temporal PINNs are designed to represent continuous solutions of boundary value problems within spatio-temporal space. These PINNs directly incorporate the equilibrium and constitutive equations in their differential and rate forms, bypassing the requirement for incremental implementation. This simplifies application of PINNs to solve complex mechanical problems, particularly those involved in the context of gradient-enhanced continua. Moreover, traditional meshing is no longer required as it is replaced by a point cloud, making it possible to overcome meshing drawbacks. The results of this investigation prove the effectiveness of the proposed methodology, especially with regards to non-monotonic loading conditions and irreversible plastic deformation. Compared to classical FE approaches, the proposed spatio-temporal PINNs are more readily applied to complex problems, which are tackled in their raw form. This is especially true for gradient-enhanced continuum problems, where there is no need to introduce additional degrees of freedom as in classical FE approaches. However, PINNs training generally requires more computation time, a challenge that can be mitigated by employing the concept of transfer learning as shown in this paper. This concept, which is very useful when performing parametric studies, involves applying knowledge grained from solving one problem to another different but related one. The use of PINNs as mechanical solvers is shown to be highly promising in the forthcoming era, where advancements in GPU technology can further enhance their performance in terms of computation time.

Development of Student Worksheets Based on Problem Based Learning (PBL) Assisted by PhET Simulations to Improve Mastery of High School Physics Concepts

This development research aims to produce a product in the form of a Student Workshees (known LKPD) based on a problem-based learning model that is feasible, practical and effective for improving students' mastery of concepts. This research is included in Research and Development (R&D) with a 4D model, which consists of defining, designing, developing, and disseminating. The product developed is Newton's Law LKPD supported by other independent curriculum tools: Learning Outcomes, Learning Goal Flow, Teaching Modules, and concept mastery test instruments. Data collection techniques use validation sheets, learning implementation observation sheets, student response questionnaires, and test instruments. The validity of the product was assessed by three expert validators, who were physics lecturers, and three practitioner validators, who were physics subject teachers. Their evaluations were performed using a Likert scale. Practicality is obtained from the teacher's observation sheet and student response questionnaires regarding learning. Effectiveness was obtained from the results of limited product trials in the form of an increase in pretest and posttest scores, which were analyzed using the n-gain test. The results of the validity of the product developed have a percentage, namely LKPD 92.12%; Learning Achievement 92.08%; Flow of Learning Objectives 92.60%; Teaching modules 89.78% and test instruments 91.15% with very valid and reliable categories above 75%, so the product can be said to be suitable for use. The results of the product's practicality were in the form of an implementation sheet by the teacher. Student responses of 92.08% and 82.10% were written in the convenient category. The results of the n-gain test for students' concept mastery were 0.74 in the medium category and included in the very effective criteria. Thus, the LKPD, Learning Outcomes, Learning Goal Flow, Teaching Modules and test instruments produced are feasible, practical and effective in improving students' mastery of concepts.

Special Issue on Informatics Education: Teaching Data Science through an Interactive, Hands-On Workshop with Clinically-Relevant Case Studies.

In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians. Addressing the limited exposure of healthcare providers to leverage and critique data science methods, this interactive workshop aims to provide clinicians with foundational knowledge in data science, enabling them to contribute effectively to teams focused on improving care quality. The workshop focuses on meaningful topics for clinicians, such as model performance evaluation and introduces machine learning through hands-on exercises using free, interactive python notebooks. Clinical case studies on sepsis recognition and opioid overdose death provide relatable contexts for applying data science concepts. Positive feedback from over 300 participants across various settings highlights the workshop's effectiveness in making complex topics accessible to clinicians. Our approach prioritizes engaging content delivery and practical application over extensive programming instruction, aligning with adult learning principles. This initiative underscores the importance of equipping clinicians with data science knowledge to navigate today's data-driven healthcare landscape, offering a template for integrating data science education into healthcare informatics programs or continuing professional development.

Exploring fundamental engineering course instructors' test usage beliefs and behaviors: A multicase study

AbstractBackgroundTests are commonly and heavily used in fundamental engineering courses (FECs) to assess student learning of concepts. With existing literature presenting mixed benefits and disadvantages of testing to students' motivation to learn and documenting widely alternative assessments, the lack of questioning of heavy and common test usage must be addressed to diversify classroom assessment and promote intentionality in test usage.Purpose/HypothesisThis study begins to address the lack of questioning by exploring and uncovering test usage beliefs and behaviors of seven FEC instructors from two engineering departments in a land‐grant, public, Research 1 universityDesign/MethodGrounded in the Situated Expectancy–Value Theory (SEVT), we conducted a multicase study. Data include two interviews, course syllabi, and sample tests provided by the participants, and public documents from the institution and departments. We conducted a priori and emergent coding and thematic analysis to identify the beliefs and behaviors before developing individual case summaries for cross‐case analysis to identify groupings.ResultsThree test usage groups emerged: enthusiastic, default, and questioning. All test usage groups featured tests heavily in their FECs, resulting in varying alignment between these participants' test usage beliefs and behaviors.ConclusionsOur findings reveal the various factors that can shape FEC instructors' test usage beliefs and behaviors, and the complexity in terms of alignment. This work lays important implications, including laying the foundations for future scholarship on testing in engineering education research and leveraging findings to begin efforts in diversifying assessment approaches and promoting intentional test usage in FECs.

Psychomatics-A Multidisciplinary Framework for Understanding Artificial Minds.

Although large language models (LLMs) and other artificial intelligence systems demonstrate cognitive skills similar to humans, such as concept learning and language acquisition, the way they process information fundamentally differs from biological cognition. To better understand these differences, this article introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science. It aims to delve deeper into the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs. To achieve this goal, Psychomatics will rely on a comparative methodology, starting from a theory-driven research question-is the process of language development and use different in humans and LLMs?-drawing parallels between LLMs and biological systems. Our analysis shows how LLMs can map and manipulate complex linguistic patterns in their training data. Moreover, LLMs can follow Grice's Cooperative principle to provide relevant and informative responses. However, human cognition draws from multiple sources of meaning, including experiential, emotional, and imaginative facets, which transcend mere language processing and are rooted in our social and developmental trajectories. Moreover, current LLMs lack physical embodiment, reducing their ability to make sense of the intricate interplay between perception, action, and cognition that shapes human understanding and expression. Ultimately, Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence, both artificial and biological. Moreover, by drawing parallels between LLMs and human cognitive processes, Psychomatics can inform the development of more robust and human-like artificial intelligence systems.

Using systems modeling to facilitate undergraduate physiology student learning and retention of difficult concepts.

Physiology concepts, such as acid-base balance, may be difficult for students to understand. Systems modeling, a cognitive tool, allows students to visualize their mental model of the problem space to enhance learning and retention. Methods: We performed a within-subjects three-period randomized control comparison of systems modeling versus written discussion activities in an undergraduate asynchronous online Anatomy and Physiology II course. Participants (n=108) were randomized to groups with differing treatment orders across three course units: endocrine, immune, and acid-base balance. Participants demonstrated content understanding either through constructing systems modeling diagrams (M) or written discussion posts (W) in a MWM, MMW, or WMM sequence. Results: For each of three units, student performance was assessed on six standardized multiple-choice questions embedded within a 45-question exam. The same six questions per unit, eighteen questions total, was again assessed on the 75-question final exam. The groups demonstrated no significant difference in performance in the endocrine unit exam (mean difference, MD=-0.036). However, the modeling group outperformed the writing group in the immune unit exam (MD=0.209) and widened the gap in the acid-base balance unit exam (MD=0.243). On the final exam, performance was again higher for the modeling group on acid-base balance content as mean difference increased to 0.306 despite the final exam content for acid-base balance being significantly more difficult compared to other units (modeling: F(2)=29.882, p<.001; writing: F(2)=25.450, p<.001). Conclusions: These results provide initial evidence that participation in systems modeling activities enhance student learning of difficult physiology content as evidenced by improved multiple-choice question performance.

Imagining on The New Development Concept of Life-Long Learning Towards Carbon Action Plan

Imagining on The New Development Concept of Life-Long Learning Towards Carbon Action Plan

A conceptual learning analysis of paired after action and intra action reviews for health emergencies.

Processes of self-reflection and the learning they allow are crucial before, during, and after acute emergencies, including infectious disease outbreaks. Tools-such as Action Reviews-offer World Health Organization (WHO) member states a platform to enhance learning. We sought to better understand the value of these tools and how they may be further refined and better used. We searched the publicly available WHO Strategic Partnership for Health Security website for paired reports of Action Reviews, that is, reports with a comparable follow-up report. We complemented the paired action reviews, with a literature search, including the gray literature. The paired action reviews were analyzed using the "Learning Health Systems" framework. We identified three paired action reviews: Lassa Fever After Action Reviews (AARs) in Nigeria (2017 and 2018), COVID-19 Intra-Action Reviews (IARs) in Botswana (2020 and 2021), and COVID-19 IARs in South Sudan (2020 and 2021). Action Reviews allowed for surfacing relevant knowledge and, by engaging the right (in different contexts) actors, asking "are we doing things right?" (single loop learning) was evident in all the reports. Single loop learning is often embedded within examples of double loop learning ("are we doing the right things?"), providing a more transformative basis for policy change. Triple loop learning ("are we learning right"?) was evident in AARs, and less in IARs. The range of participants involved, the level of concentrated focus on specific issues, the duration available for follow through, and the pressures on the health system to respond influenced the type (i.e., loop) and the effectiveness of learning. Action Reviews, by design, surface knowledge. With favorable contextual conditions, this knowledge can then be applied and lead to corrective and innovative actions to improve health system performance, and in exceptional cases, continuous learning.

Good results from sensor data: Performance of machine learning algorithms for regression problems in chemical sensors

Accurately predicting unseen data, instead of mere memorization of training examples, is a critical goal of machine learning. This generalization is particularly important in the field of chemical sensors, where the ability to accurately predict the chemical properties or concentration levels of unknown samples is crucial. The paper presents a comprehensive yet accessible introduction to various machine learning concepts, highlighting the importance of model interpretability and generalization in ensuring reliable and accurate results in this context. Nonlinear sensor array data are utilized to introduce key concepts (e.g., bias-variance tradeoff) and techniques (linear models, partial least squares regression, support vector machines, k-nearest neighbors, decision trees, ensemble methods, automated machine learning, symbolic regression, and artificial neural networks), providing a solid foundation to make informed decisions when selecting machine learning techniques for sensor-specific regression applications. The results clearly indicate a number of conclusions. First, overparameterized deep feedforward neural networks show great accuracy and generalization when trained on a sufficiently large dataset. Second, symbolic regression models proved to be more accurate than deep feedforward neural networks and classical machine learning techniques on smaller datasets. Third, the performance of various machine learning models was dataset-dependent, showing the importance of comparative studies to determine the most suitable approach. It is clear that the optimal model cannot be known a priori. This paper aims to provide a starting point for investigations on the performance of different machine learning techniques in chemical sensor applications.