Concept Of Learning Research Articles

Evaluation of Future Faculty Perspectives on Teaching and Learning of Electrochemistry & Thermochemistry: A Microstudy Approach

Education revolves around teaching and learning with instruction being at the center. This study seeks to investigate the whole trend of the perspectives of future faculty on the teaching and learning of critical chemical concepts “Electrochemistry” and “Thermochemistry”. The objectives are to study and extrapolate the trend and probe their applications to industry and other concepts in chemistry. Questionnaires were administered on graduate students including graduate assistants across universities in three continents: North America, Asia, and Africa. Result shows that the concepts require practical demonstration, interwoven and have applications to energy and power industries. It also reveals that learners’ motivation must be aroused prior to learning the concepts, including that learners’ attitudes is most vital in the classroom settings.

Unsupervised machine learning highlights the challenges of subtyping disorders of gut-brain interaction.

Unsupervised machine learning describes a collection of powerful techniques that seek to identify hidden patterns in unlabeled data. These techniques can be broadly categorized into dimension reduction, which transforms and combines the original set of measurements to simplify data, and cluster analysis, which seeks to group subjects based on some measure of similarity. Unsupervised machine learning can be used to explore alternative subtyping of disorders of gut-brain interaction (DGBI) compared to the existing gastrointestinal symptom-based definitions of Rome IV. This present review aims to familiarize the reader with fundamental concepts of unsupervised machine learning using accessible definitions and provide a critical summary of their application to the evaluation of DGBI subtyping. By considering the overlap between Rome IV clinical definitions and identified clusters, along with clinical and physiological insights, this paper speculates on the possible implications for DGBI. Also considered are algorithmic developments in the unsupervised machine learning community that may help leverage increasingly available omics data to explore biologically informed definitions. Unsupervised machine learning challenges the modern subtyping of DGBI and, with the necessary clinical validation, has the potential to enhance future iterations of the Rome criteria to identify more homogeneous, diagnosable, and treatable patient populations.

Viewpoint independent face recognition via extrapolation in paper wasps.

Visual recognition of three-dimensional signals, like faces, is challenging because the signals appear different from different viewpoints. A flexible, but cognitively challenging solution is viewpoint independent recognition, where receivers identify signals from novel viewing angles. Here, we use same/different concept learning to test viewpoint independent face recognition in Polistes fuscatus, a wasp that uses facial patterns to individually identify conspecifics. We find that wasps use extrapolation to identify novel views of conspecific faces. For example, wasps identify a pair of pictures of the same wasp as the 'same', even if the pictures are taken from different views (e.g. one face 0° rotation, one face 60° rotation). This result is notable because it provides the first evidence of view invariant recognition via extrapolation in an invertebrate. The results suggest that viewpoint independent recognition via extrapolation may be a widespread strategy to facilitate individual face recognition.

Machine learning validated compact circularly polarized dual port-MIMO antenna with polarization diversity for ku-band applications

A novel dual Port C-Shape MIMO broadband printed antenna with wide axial ratio bandwidth (ARBW) is modelled and built for Ku band Applications. The unit cell of the proposed antenna has a compact size of 25 × 26 × 0.8 mm3 engraved on RT duriod 5880 material (ε r = 2.2, thickness of 0.8 mm). The proposed model consists of C shaped radiator, plus symbol structure and a modified ground plane. The machine learning concept is used to investigate the dimensional dependency of the plus sign on the CP behaviour. The suggested antenna shows impedance bandwidth (−10 dB) and 3-dB ARBW of 6 GHz (12 GHz to 18 GHz) which is covering the whole Ku band. The diversity performance parameters and antenna parametric investigation is also carried out to justify the MIMO identity of the proposed design. The simulated and measured counterparts are found in harmony. The strength of the proposed antenna is its planar structure, wide ARBW, polarization diversity, and wide operating Ku bandwidth.

Comparison of teaching and learning concepts network for Mendelian inheritance

ABSTRACT In order to improve science education and learning, analysis of the actual field of science education is necessary. The purpose of this study is to investigate the difference between taught curriculum and learned curriculum that appears in the actual field of science education from a conceptual aspect. For this, the concepts presented by the teacher in class and the concept network learned by the students were visualised using Semantic Network Analysis. The learned concept network was analyzed immediately after class and at a follow-up (4 weeks later) to determine the effect of memory. As a result of the study, the concepts of taught curriculum and learned curriculum were similar, but the network structures were different. In other words, there were differences in the connections between concepts. In the taught curriculum, concepts were structured without being divided so that subtopics could be clarified, and as a result, the learned curriculum was also structured without being able to distinguish subtopics. In order to reduce the difference between taught curriculum and learned curriculum, there is a need to connect and teach concepts using intermediate concepts or connections concepts between concepts presented in the intended curriculum. Additionally, students’ prior knowledge influenced the learned curriculum. Therefore, in future research, there is a need to analyze how various factors that affect prior knowledge like shadow education or learner characteristics affect the learned curriculum.

IMPLEMENTASI AUGMENTED REALITY (AR) DI PERGURUAN TINGGI MENGGUNAKAN UJI-T BERPASANGAN

Today, higher education faces growing challenges in integrating technology to enhance unique and innovative student learning experiences. One technology that has been in the spotlight in recent years and is very widely used today is Augmented Reality (AR). Considering that currently, there are still many students who have difficulty understanding learning concepts well, have difficulty applying the material studied in everyday life, it is hoped that the use of Augmented Reality (AR) in higher education can be a solution. Augmented Reality (AR) can change the way students view learning as well as its extensive use which can also be felt by all elements of the academic community at universities as tertiary institutions. Thus, this research aims to find out how much influence the use of Augmented Reality (AR) has in universities, namely lecturers, students and universities. The method used in this research is paired t-test. From the research results, it is known that the application of Augmented Reality (AR) has a significant effect in higher education with an effectiveness value of 70% which is included in the interpretation category of effectiveness as quite effective

Decoding the disciplines: creating mutual understanding in multiprofessional education

Decoding the disciplines as a pedagogical approach to overcome threshold concepts in student learning. The method focuses on a process of making discipline-specific thinking and acting more accessible to students in order to enhance their learning process. Students of any discipline have difficulties with discipline-specific contents, methods or ways of thinking or acting, the so-called bottlenecks. In addition, there are bottlenecks specific to each discipline, such as topics related to scientific work. Findings from a Scholarship of Teaching and Learning approach in a multi-professional group of students in an online seminar session during the corona pandemic are presented. A decoding process on how to find a theoretical approach to one’s research question was conducted. The implementation of the decoding method took place either in face-to-face decoding expert interviews or in a writing process using predetermined guiding questions. Students’ reflections on the use of the method were analyzed using normalization process theory to identify facilitating and inhibiting aspects for the face-to-face and written implementation of the method in interprofessional learning groups. The results show that students could experience a role change or a new role when the decoding topic changes. With the interview method in particular, students can take on the role of experts or novices depending on the topic and thus actively participate. Whether a role change happens or not always depends on the dynamics and the communicative exchange of the group. Decoding takes a lot of time, especially for reflective writing.

Design and Optimization of Multicomponent Electrolytes for Lithium‐Sulfur Battery: A Machine Learning Concept and Outlook

AbstractLithium‐sulfur batteries (LSBs) have attracted increasing attention in the past decades due to their great potential to the next‐generation high‐energy‐density storage systems. As important as electrodes, electrolytes that could strongly determine battery performance via component regulation have left big difficulties in clarifying their complex interactions caused by multicomponent as well as the intricate formation mechanism of passivation layers at the electrolyte‐electrode interfaces. Fortunately, machine learning (ML), which is time‐saving and highly efficient, has played an irreplaceable role in accelerating discovery, design, and optimization of novel electrochemical energy storage materials. In this concept, we summarize the complex issues present in multicomponent electrolytes and focus on optimization of electrolyte formulations based on ML in order to provide an outlook on the recent development and perspectives on LSBs from the viewpoint of high‐performance electrolytes.

Prediction of segmental motor outcomes in traumatic spinal cord injury: Advances beyond sum scores

Background and objectivesNeurological and functional recovery after traumatic spinal cord injury (SCI) is highly challenged by the level of the lesion and the high heterogeneity in severity (different degrees of in/complete SCI) and spinal cord syndromes (hemi-, ant-, central-, and posterior cord). So far outcome predictions in clinical trials are limited in targeting sum motor scores of the upper (UEMS) and lower limb (LEMS) while neglecting that the distribution of motor function is essential for functional outcomes. The development of data-driven prediction models of detailed segmental motor recovery for all spinal segments from the level of lesion towards the lowest motor segments will improve the design of rehabilitation programs and the sensitivity of clinical trials. MethodsThis study used acute-phase International Standards for Neurological Classification of SCI exams to forecast 6-month recovery of segmental motor scores as the primary evaluation endpoint. Secondary endpoints included severity grade improvement, independent walking, and self-care ability. Different similarity metrics were explored for k-nearest neighbor (kNN) matching within 1267 patients from the European Multicenter Study about Spinal Cord Injury before validation in 411 patients from the Sygen trial. The kNN performance was compared to linear and logistic regression models. ResultsWe obtained a population-wide root-mean-squared error (RMSE) in motor score sequence of 0.76(0.14, 2.77) and competitive functional score predictions (AUCwalker = 0.92, AUCself-carer = 0.83) for the kNN algorithm, improving beyond the linear regression task (RMSElinear = 0.98(0.22, 2.57)). The validation cohort showed comparable results (RMSE = 0.75(0.13, 2.57), AUCwalker = 0.92). We deploy the final historic control model as a web tool for easy user interaction (https://hicsci.ethz.ch/). DiscussionOur approach is the first to provide predictions across all motor segments independent of the level and severity of SCI. We provide a machine learning concept that is highly interpretable, i.e. the prediction formation process is transparent, that has been validated across European and American data sets, and provides reliable and validated algorithms to incorporate external control data to increase sensitivity and feasibility of multinational clinical trials.

MedNER: A Service-Oriented Framework for Chinese Medical Named-Entity Recognition with Real-World Application

Named-entity recognition (NER) is a crucial task in natural language processing, especially for extracting meaningful information from unstructured text data. In the healthcare domain, accurate NER can significantly enhance patient care by enabling efficient extraction and analysis of clinical information. This paper presents MedNER, a novel service-oriented framework designed specifically for medical NER in Chinese medical texts. MedNER leverages advanced deep learning techniques and domain-specific linguistic resources to achieve good performance in identifying diabetes-related entities such as symptoms, tests, and drugs. The framework integrates seamlessly with real-world healthcare systems, offering scalable and efficient solutions for processing large volumes of clinical data. This paper provides an in-depth discussion on the architecture and implementation of MedNER, featuring the concept of Deep Learning as a Service (DLaaS). A prototype has encapsulated BiLSTM-CRF and BERT-BiLSTM-CRF models into the core service, demonstrating its flexibility, usability, and effectiveness in addressing the unique challenges of Chinese medical text processing.

FOREIGN LANGUAGE LEARNERS’ PERCEPTIONS OF THEIR ONLINE ROLES IN EMERGENCY REMOTE TEACHING

Nearly all facets of life, including language instruction, have been impacted by the COVID-19 epidemic and the devastating earthquake in February 2023. Consequently, remote education, also known as e-learning or virtual learning, has taken over as the standard offering in numerous contexts. In this regard, remote teaching practices have been preferred in many higher education institutions. However, gaining a comprehensive understanding of the evolving educational practices during this period requires an awareness of how pupils respond to this change. In the course of the emergency remote teaching, language learners' conceptions of instruction and learning were examined using a metaphor analysis approach. For this purpose, a semi-structured form utilizing the Turkish university students' metaphorical concepts regarding their roles in online learning, along with an open-ended questionnaire where participants shared their background information, was employed. Data collected from 56 foreign language learners from different departments at a state university in Turkey was analysed using elicited metaphor analysis. The results showed that foreign language learners used a variety of metaphors, which were categorized as passive recipient, independent learner, flexible person, ineffective consumer, lazy individual, and lost guide, suggesting both favourable and unfavourable dispositions toward emergency remote teaching. In light of the research results, several recommendations and implications were provided.

Preparing for the Transition to Academic Practice: A Graduate Medical Education Elective.

Background Residents and fellows as educators (RFAE) programs typically focus on clinical teaching skills in single departments, which may not be sustainable for those with limited trainees or faculty. Objective To determine the feasibility and value of a 2-week interdepartmental RFAE elective for advanced teaching skill development and transition to practice as clinician educators. Methods Facilitated discussion, simulation, and critiqued peer presentations developed participants' skills in teaching, curriculum design, professional development, and scholarship. Assessments in this prospective intervention included 2 self-reported surveys addressing: (1) teaching process and motivation (Conceptions of Learning and Teaching [COLT]), and (2) skills and attitudes. We administered both surveys at baseline, immediate-post, and 3-month-post elective with data compared across time points using Kruskal-Wallis tests. Program evaluation comprised daily open-ended surveys on engagement and an end-of-course feedback survey. Results There were 79 participants from 2019 to 2023. Survey response rates were 84.8% (67 of 79) at baseline, 58.2% (46 of 79) immediate-post, and 51.9% (41 of 79) 3-month-post. Most participants were residents (89.9%, 71 of 79), female (60.8%, 48 of 79), from pediatrics and/or medicine departments (77.2%, 61 of 79), and in their final year of training (77.2%, 61 of 79). COLT factor orientation to professional practice scores increased in the immediate-post (3.3) compared to baseline (2.5) surveys (P=.008). Teaching skills attitudes scores increased for all questions in 3-month-post compared to baseline surveys. In open-ended questions, participants emphasized the importance of professional development sessions in guiding their careers toward medical education. Conclusions This interdepartmental elective was feasible, favorably received, and sustained over time, with observed changes in participants' teaching skills attitudes.

Exploring How Generating Metaphor Via Insight Versus Analysis Affects Metaphor Quality and Learning Outcomes.

Metaphor generation is both a creative act and a means of learning. When learning a new concept, people often create a metaphor to connect the new concept to existing knowledge. Does the manner in which people generate a metaphor, via sudden insight (Aha! moment) or deliberate analysis, influence the quality of generation and subsequent learning outcomes? According to some research, deliberate processing enhances knowledge retention; hence, generation via analysis likely leads to better concept learning. However, other research has shown that solutions generated via insight are better remembered. In the current study, participants were presented with science concepts and descriptions, then generated metaphors for the concepts. They also indicated how they generated each metaphor and rated their metaphor for novelty and aptness. We assessed participants' learning outcomes with a memory test and evaluated the creative quality of the metaphors based on self- and crowd-sourced ratings. Consistent with the deliberate processing benefit, participants became more familiar with the target science concept if they previously generated a metaphor for the concept via analysis compared to via insight. We also found that metaphors generated via analysis did not differ from metaphors generated via insight in quality (aptness or novelty) nor in how well they were remembered. However, participants' self-evaluations of metaphors generated via insight showed more agreement with independent raters, suggesting the role of insight in modulating the creative ideation process. These preliminary findings have implications for understanding the nature of insight during idea generation and its impact on learning.

Guest Editorial Special Issue on Conceptual Learning of Mathematics-Intensive Concepts in Engineering

Guest Editorial Special Issue on Conceptual Learning of Mathematics-Intensive Concepts in Engineering

Artificial intelligence and headache.

In this narrative review, we introduce key artificial intelligence (AI) and machine learning (ML) concepts, aimed at headache clinicians and researchers. Thereafter, we thoroughly review the use of AI in headache, based on a comprehensive literature search across PubMed, Embase and IEEExplore. Finally, we discuss limitations, as well as ethical and political perspectives. We identified six main research topics. First, natural language processing can be used to effectively extract and systematize unstructured headache research data, such as from electronic health records. Second, the most common application of ML is for classification of headache disorders, typically based on clinical record data, or neuroimaging data, with accuracies ranging from around 60% to well over 90%. Third, ML is used for prediction of headache disease trajectories. Fourth, ML shows promise in forecasting of headaches using self-reported data such as triggers and premonitory symptoms, data from wearable sensors and external data. Fifth and sixth, ML can be used for prediction of treatment responses and inference of treatment effects, respectively, aiming to optimize and individualize headache management. The potential uses of AI and ML in headache are broad, but, at present, many studies suffer from poor reporting and lack out-of-sample evaluation, and most models are not validated in a clinical setting.

(Dis)connection between curriculum, pedagogy and learners’ lived experience in Nepal’s secondary schools: an environmental (in)justice perspective

This paper offers a novel analysis of how Nepal is delivering its commitment to secondary education provision that is advancing environmental sustainability, tracing a trajectory that begins with national policies relating to environmental sustainability and incorporating the national curriculum framework, textbooks, pedagogies used in classrooms, and learner experiences and anticipated actions. We consider Nepal’s education about and for environmental sustainability in the context of theories of environmental justice, and question if and how secondary provision might promote the behavioural change that Nepal recognises is vital for environmental sustainability. Qualitative data were generated through policy analysis, critical content analysis of secondary-level curriculum and textbooks, classroom observations, semi-structured interviews with 15 teachers and 4 headteachers, and a range of in-person activities with 24 students in purposively selected four community secondary schools in the three diverse locations across Nepal. The results illuminate pronounced disconnections across modalities that indicate incoherence and unresolved debates in the underlying narrative of what environmental sustainability is and the role of education in addressing it. Our findings suggest that learners’ ideas, opinions, thinking and experiences should be encouraged and celebrated in the classroom to aid learners in translating conceptual learning into practical, sustainable behaviours, as well as to contribute to environmental justice. The findings appeal to the concerned stakeholders for their consideration of future policy and programme development that promotes environmental justice through education and establishes a connection between classroom learning and students’ lived experiences through a participatory approach, collaboration, and critical and creative thinking.

Predicting intraocular lens tilt using a machine learning concept.

To use a combination of partial least squares regression and a machine learning approach to predict intraocular lens (IOL) tilt using preoperative biometry data. Kepler University Clinic Linz, Linz, Austria. Prospective single-center study. Optical coherence tomography, autorefraction, and subjective refraction were performed at baseline and 8 weeks after cataract surgery. In analysis I, only 1 eye per patient was included and a tilt prediction model was generated. In analysis II, a pairwise comparison between right and left eyes was performed. In analysis I, 50 eyes of 50 patients were analyzed. Difference in amount, orientation, and vector from preoperative to postoperative lens tilt was -0.13 degrees, 2.14 degrees, and 1.20 degrees, respectively. A high predictive power (variable importance for projection [VIP]) for postoperative tilt prediction was found for preoperative tilt (VIP = 2.2), pupil decentration (VIP = 1.5), lens thickness (VIP = 1.1), axial eye length (VIP = 0.9), and preoperative lens decentration (VIP = 0.8). These variables were applied to a machine learning algorithm resulting in an out of bag score of 0.92 degrees. In analysis II, 76 eyes of 38 patients were included. The difference of preoperative to postoperative IOL tilt of right and left eyes of the same individual was statistically relevant. Postoperative IOL tilt showed excellent predictability using preoperative biometry data and a combination of partial least squares regression and a machine learning algorithm. Preoperative lens tilt, pupil decentration, lens thickness, axial eye length, and preoperative lens decentration were found to be the most relevant parameters for this prediction model.

Variational data encoding and correlations in quantum-enhanced machine learning

Abstract Leveraging the extraordinary phenomena of quantum superposition and quantum correlation, quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers. This paper tackles two pivotal challenges in the realm of quantum computing: firstly, the development of an effective encoding protocol for translating classical data into quantum states, a critical step for any quantum computation. Different encoding strategies can significantly influence quantum computer performance. Secondly, we address the need to counteract the inevitable noise that can hinder quantum acceleration. Our primary contribution is the introduction of a novel variational data encoding method, grounded in quantum regression algorithm models. By adapting the learning concept from machine learning, we render data encoding a learnable process. This allowed us to study the role of quantum correlation in data encoding. Through numerical simulations of various regression tasks, we demonstrate the efficacy of our variational data encoding, particularly post-learning from instructional data. Moreover, we delve into the role of quantum correlation in enhancing task performance, especially in noisy environments. Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference, thus advancing the frontier of quantum computing.

Predictive analytics for financial compliance: Machine learning concepts for fraudulent transaction identification

Predictive analytics has emerged as a pivotal tool in financial compliance, offering sophisticated methods for identifying fraudulent transactions through the application of machine learning (ML) concepts. As financial institutions grapple with increasingly complex fraud schemes and stringent regulatory requirements, the integration of predictive analytics with ML provides a proactive approach to fraud detection and prevention. Machine learning algorithms excel in analyzing vast datasets, identifying hidden patterns, and making real-time predictions. In the realm of financial compliance, supervised learning models such as logistic regression, decision trees, and random forests are commonly used to classify transactions as legitimate or fraudulent. These models are trained on historical transaction data, learning to recognize the subtle indicators of fraud by identifying correlations between various features and fraudulent outcomes. This allows for high-accuracy predictions on new, unseen data. Unsupervised learning techniques, such as clustering and anomaly detection, are equally critical in predictive analytics for financial compliance. These methods do not require labeled data and are adept at uncovering novel fraud patterns by detecting outliers and irregularities that deviate from normal transactional behavior. Anomaly detection algorithms, including k-means clustering and isolation forests, can identify transactions that exhibit unusual characteristics, flagging them for further investigation. The integration of predictive analytics with real-time data processing capabilities enhances the agility of fraud detection systems. Streaming analytics and real-time scoring enable the continuous monitoring of transactions, ensuring that suspicious activities are identified and addressed promptly. This real-time aspect is crucial for minimizing the impact of fraudulent transactions and ensuring compliance with regulatory standards. Despite the advancements, implementing predictive analytics for financial compliance involves challenges such as ensuring data quality, addressing privacy concerns, and maintaining model transparency. Financial institutions must navigate these challenges by employing robust data governance practices, leveraging secure data processing techniques, and adopting explainable AI models that provide insights into their decision-making processes. In conclusion, predictive analytics, powered by machine learning concepts, offers a robust framework for identifying fraudulent transactions and enhancing financial compliance. By leveraging advanced ML algorithms and real-time data processing, financial institutions can proactively detect and prevent fraud, thereby safeguarding their operations and ensuring adherence to regulatory mandates. This approach not only mitigates financial losses but also strengthens the overall integrity and trustworthiness of the financial system.

Teaching Practice of Middle School English Reading under Deep Learning

With the continuous promotion of quality education, deep learning has become one of the core concepts in the development of middle school education. In traditional English reading teaching, the implementation of teaching work can no longer meet students’ current English learning requirements. Based on this, the author will focus on the deep learning method in this article, analyze the current situation of middle school English reading teaching and explore the development path of middle school English reading teaching under the concept of deep learning, providing reference and assistance for the innovation and reform of middle school English teaching.