Search

Background: Traditionally, direct medical competences are taught in medical studies, whereas leadership and quality management competences are hardly taught, although graduates are already confronted with management tasks at the beginning of their clinical work. With the upcoming amendment of the Medical Licensing Regulations, this topic area will probably be addressed and must be adequately taught by the faculties. The learning objectives in the area of quality management listed in the current working version of the German National Catalogue of Learning Objectives in Medicine (NKLM) 2.0 have so far been formulated in rather general terms and need to be concretized. Aim: To develop a competence-based learning objectives catalog for the topic area of quality management in medical studies as a structured framework recommendation for the design of faculty teaching-learning programs and as a suggestion for further development of the NKLM. Methods: The competence-based learning objectives catalog was developed by an eight-member working group "Quality Management in Education, Training and Continuing Education" of the Gesellschaft für Qualitätsmanagement in der Gesundheitsversorgung e.V. (GQMG) within the framework of a critical synthesis of central publications. The members of the project group have many years of project experience in quality management in health care as well as in university didactics. Results: Six basic competence goals as well as 10 specific competence goals could be formulated and consented upon. These are each flanked by a list of essential basic concepts and examples. These focus on quality improvements, including patient safety and treatment success against the background of a physician leadership role in an interprofessional context. Discussion: A competency-based set of learning objectives has been compiled that encompasses the necessary concepts and basic knowledge of quality management required for those entering the profession to understand and actively participate in quality management after completing medical school. To the authors' knowledge, no comparable learning objectives catalog is currently available for medical studies, even internationally. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Concept formation requires animals to learn and use abstract rules that transcend the characteristics of specific stimuli. Abstract concepts are often associated with high levels of cognitive sophistication, so there has been much interest in which species can form and use concepts. A key abstract concept is that of sameness and difference, where stimuli are classified as either the same as or different than an original stimulus. Here, we used a simultaneous two-item same-different task to test whether paper wasps (Polistes fuscatus) can learn and apply a same-different concept. We trained wasps by simultaneously presenting pairs of same or different stimuli (e.g. colours). Then, we tested whether wasps could apply the concept to new stimuli of the same type (e.g. new colours) and to new stimulus types (e.g. odours). We show that wasps learned a general concept of sameness or difference and applied it to new samples and types of stimuli. Notably, wasps were able to transfer the learned rules to new stimuli in a different sensory modality. Therefore, P. fuscatus can classify stimuli based on their relationships and apply abstract concepts to novel stimulus types. These results indicate that abstract concept learning may be more widespread than previously thought. [ABSTRACT FROM AUTHOR]

The article offers information on the effectiveness of the paper plane investigation method in teaching students the concepts of forces, motion, and interaction. Topics include the Next Generation Science Standard (NGSS) 3-PS2 Motion and Stability: Forces and Interaction, the benefits of the 5E instructional model, and the importance of bridging Common Core State Standards (CCSS) for Literacy and Mathematical Practices and science concepts.

The rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learning resource recommendation services should be more personalised and have more choices. In this paper, we propose a joint recommendation algorithm for knowledge concepts and learning partners based on improved MMoE (Multi-gate Mixture-of-Experts). Firstly, the heterogeneous information network (HIN) is constructed based on the MOOC platform and appropriate meta-paths are selected in order to extract the human–computer interaction information and student–student interaction information generated during the learners' online learning processes more completely. Secondly, the temporal behavioural characteristics of students are obtained based on their learning paths as well as their knowledge of conceptual characteristics, and LSTM (Long Short-Term Memory) is used to mine students' current learning interests. Finally, the gating network in MMoE is changed into an attention mechanism network, and for different tasks, multiple attention mechanism networks are used to fuse the learner's human–computer interaction information, student–student interaction information, and interest characteristics to generate learner representations that are more in line with the respective task and to complete the tasks of knowledge conception and learning partner recommendation. Experiments on publicly available MOOC datasets show that the method proposed in this paper provides more accurate and varied personalization services to online learners compared to the latest proposed methods. [ABSTRACT FROM AUTHOR]

Human knowledge learning is a natural acquisition process of contextualization and dynamic adaptation, and classroom teaching is neither the only nor the best way to learn. The current rapid development of "cloud" resource technology and the concept of ubiquitous learning, which is characterized by personalization anytime and anywhere, has challenged the absolute teaching function of teachers and the neat and uniform teaching methods of textbooks and classrooms. Ubiquitous learning is a historical return to human learning, and teachers need to transform their roles in this change, to improve their adaptability and to become the organic driving force to realize the contemporary information aggregation structure. Based on this understanding, this paper begins with an analysis of the intelligent form of current "cloud" resources and explores the conceptual understanding and transformation pathways of the ecological context of ubiquitous learning and the corresponding role of teachers. In this paper, we design a system consisting of a teacher, a student, and an administrator, which can publish English test papers, create and manage a personal library of test papers, monitor students' practice status, and review students' performance. Storage management can be automated and intelligent, and all storage resources are integrated together, and customers see a single storage space. It improves storage efficiency, solves the waste of storage space through virtualization technology, can automatically redistribute data, and improves the utilization of storage space, while having load balancing and fault redundancy functions. Cloud storage can achieve scale effect and elastic expansion, reduce operating costs, and avoid wasting resources. Through the cloud platform, teachers can answer questions and assign homework for students online. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents important findings that were observed as interconnected key elements of high-quality co-teaching of a physics course for pre-service science teachers while the collaborating faculty experienced a pedagogical convergence. This pedagogical convergence is akin to the conceptual convergence that Roschelle (1992) has described as being a central mechanism in conceptual change learning. Four elements of that pedagogical convergence are identified and discussed: two found within Roschelle's framework and two recognized by the faculty members through their reflection on the course. This pedagogical convergence had a significant positive influence on the design and, more importantly, the implementation of the co-taught physics course, resulting in a productive and impactful collaboration between the two faculty members. The paper includes perspectives from both faculty members on each element of the pedagogical convergence and students as well as on the teaching context and critical aspects of practice. The ideas in the paper should provide college faculty members meaningful directions as to how co-teaching could be implemented across a range of science disciplines and the framework can serve as a foundation for creating a co-taught college science course. [ABSTRACT FROM AUTHOR]

This paper provides a critical examination of the domain of artificial intelligence (AI) in education, with a focus on the expectations and practical implications accompanying its integration into teaching. The expectations have been propelled by two interconnected concepts: (1) the potential for AI to automate pedagogical processes, replacing teachers in certain scenarios; and (2) the notion that teachers’ insights can be augmented through AI-based analysis. Drawing on two ethnographic studies in Swedish primary and secondary schools, this paper explores the enactments of pupils, teachers and two AI-based educational technologies. The aim is to demonstrate how automation and augmentation can emerge in teachers’ practice. Utilizing inspiration from a relational epistemological problematisation of socio-technical phenomena, the paper demonstrates how rather than automation and augmentation, AI in education is an act of symmation in which automation and augmentation is co-produced by the technology and teachers’ different hidden work, in this paper conceptualised as adaptations, experimentations, compensations and confirmations. The paper suggests that the study of symmation in relation to the teaching profession can be productive in further exploring the yet limited understanding of AI in educational practice. [ABSTRACT FROM AUTHOR]

This paper exponents the concept of virtual learning as an alternative mode of learning within the context of Nigerian University System (NUS). The paper extrapolates the beginning of higher education in Nigeria with a view to situating the developments that have taken place from the post-independence to date. The suitability or otherwise of the in-person mode in the context of the current reality (virtual learning) is critically examined with a view to identifying the challenges. The paper, while making use of some secondary data, clinically hazards the challenges militating against the thriving of Electronic Learning in the NUS arising from the challenge of corona virus in the year 2020. Gleaning from lack of Information and Communication Technology (ICT), poor attitude and policy deficit challenges, the paper paper makes some recommendations which could impact on policy development in the NUS. The paper, therefore, strongly advocates the need to make NUS at par with what obtains in global virtual conclave. [ABSTRACT FROM AUTHOR]

For an academic field to remain vital, it is necessary to reflect upon its conceptual foundations and learn from adjacent disciplines. On that note, aesthetics can provide significant insights into advertising. While aesthetic elements have received attention in advertising research, no systematic attempts have been made to provide a comprehensive overview of this literature. The purpose of this conceptual article is to bring different branches of aesthetic theory into advertising studies. Marketers have historically focused on product functionality and often appeal to consumers through explicit messages. This article fills an important gap by delineating and advocating the conceptual foundation of advertising aesthetics as a research area worthy of further attention. In short, we understand advertising aesthetics as the artlike properties of and sensory responses to ads. A review of 309 papers provides the thematic outline of the study. Seven key themes are identified: (1) the nature of advertising aesthetics, (2) advertising creativity, (3) textuality, (4) social aesthetics, (5) cross-cultural differences, (6) the role of media in creating aesthetic opportunities, and (7) aesthetic categories beyond the beautiful and sublime. These themes offer a novel way of exploring contemporary issues in advertising. The article concludes with suggestions for future research. [ABSTRACT FROM AUTHOR]

This paper presents a novel exploration into the realm of online piano teaching, specifically through the integration of microbial systems, drawing upon cross-disciplinary insights from economics, biotechnology, and technology management. Our objective is to revolutionize the traditional piano teaching model by constructing an online teaching platform underpinned by micro service architecture. This platform leverages the evolutionary learning principles of bacterial communication, modelled on meta cellular automata, to simulate intricate user interactions and engagements on the platform, thereby fostering an innovative approach to piano education. This study's core involves applying microbial communication systems to enhance the online piano teaching platform's interactive and adaptive learning environment. This approach represents a groundbreaking step in educational technology and illustrates the potential of biotechnological concepts in reshaping online learning experiences. The platform's development and operational efficiency are critically analyzed from an economic and technology management perspective. Performance testing indicates robust functionality under high user loads, with an exception rate of 0% and an average response time of approximately 4 seconds even with 300 concurrent threads. This demonstrates the platform's scalability and reliability, key economic viability and management factors. Furthermore, this interdisciplinary approach offers a fresh perspective on utilizing biotechnological insights in digital education platforms. The successful integration and performance of the platform underscore its potential as a pioneering model in online music education, opening new avenues for pedagogical innovation and exploration. It signifies a step forward in blending advanced biotechnological concepts with digital teaching tools, providing valuable directions for future research and development in piano teaching methodologies. [ABSTRACT FROM AUTHOR]

In many applications, artificial neural networks are best trained for a task by following a curriculum, in which simpler concepts are learned before more complex ones. This curriculum can be hand-crafted by the engineer or optimised like other hyperparameters, by evaluating many curricula. However, this is computationally intensive and the hyperparameters are unlikely to generalise to new datasets. An attractive alternative, demonstrated in influential prior works, is that the network could choose its own curriculum by monitoring its learning. This would be particularly beneficial for continual learning, in which the network must learn from an environment that is changing over time, relevant both to practical applications and in the modelling of human development. In this paper we test the generality of this approach using a proof-of-principle model, training a network on two sequential tasks under static and continual conditions, and investigating both the benefits of a curriculum and the handicap induced by continuous learning. Additionally, we test a variety of prior task-switching metrics, and find that in some cases even in this simple scenario the a network is often unable to choose the optimal curriculum, as the benefits are sometimes only apparent with hindsight, at the end of training. We discuss the implications of the results for network engineering and models of human development. [ABSTRACT FROM AUTHOR]

Copyright of Teaching Innovations / Inovacije u Nastavi is the property of University of Belgrade, Faculty of Teacher Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Internationalization in higher education is essential, and although active learning methodologies are increasing and allow students to develop transversal skills, most still have a very local scope. In this context, the Collaborative Online International Learning (COIL) methodology is an interesting approach to benefit the students' development. It consists of an online program that involves creating multicultural teams to develop a specific learning project. Although this methodology is expanding, its use in physiology is still scarce. This paper aims to show an example of applying COIL methodology in physiology topics to enhance higher-education students' innovation and business skills. Our example project developed a sports-assessment service concept focused on physiology and biomechanics assessments. The program involved teams from Brazil, Germany, and Spain, comprising undergraduate and master students. Over 7 weeks, these teams, mentored by professors and researchers, engaged in workshops covering COIL methodology, business model design, executive summary planning, economic analyses, and communication techniques. Key outcomes included learning new concepts, developing soft skills, building confidence in innovative solution proposals, and experiencing diverse cultures. Challenges faced were language barriers, scheduling, task complexity, and logistical issues. This experience confirms the effectiveness of incorporating programs using COIL methodology into educational curriculums. Doing so exposes physiology students to innovation, entrepreneurship, and business creation while strengthening their professional connections and opening up postgraduation opportunities. NEW & NOTEWORTHY: Although the Collaborative Online International Learning (COIL) methodology is expanding, its use in physiology is still scarce. Our example COIL project of 7 weeks developed a sports-assessment service concept focused on physiology and biomechanics assessments. The program involved teams from Brazil, Germany, and Spain, comprising undergraduate and master's students. Students perceived extracurricular activities in this format as beneficial. Coaches also expressed positive views about such initiatives, noting benefits for students and their development. [ABSTRACT FROM AUTHOR]

A challenge to many real-world data streams is imbalance with concept drift, which is one of the most critical tasks in anomaly detection. Learning nonstationary data streams for anomaly detection has been well studied in recent years. However, most of the researches assume that the class of data streams is relatively balanced. Only a few approaches tackle the joint issue of imbalance and concept drift. To overcome this joint issue, we propose an ensemble learning method with generative adversarial network-based sampling and consistency check (EGSCC) in this paper. First, we design a comprehensive anomaly detection framework that includes an oversampling module by generative adversarial network, an ensemble classifier, and a consistency check module. Next, we introduce double encoders into GAN to better capture the distribution characteristics of imbalanced data for oversampling. Then, we apply the stacking ensemble learning to deal with concept drift. Four base classifiers of SVM, KNN, DT and RF are used in the first layer, and LR is used as meta classifier in second layer. Last but not least, we take consistency check of the incremental instance and check set to determine whether it is anormal by statistical learning, instead of threshold-based method. And the validation set is dynamic updated according to the consistency check result. Finally, three artificial data sets obtained from Massive Online Analysis platform and two real data sets are used to verify the performance of the proposed method from four aspects: detection performance, parameter sensitivity, algorithm cost and anti-noise ability. Experimental results show that the proposed method has significant advantages in anomaly detection of imbalanced data streams with concept drift. [ABSTRACT FROM AUTHOR]

External program review is often considered the "gold standard" approach to evaluating academic programs in higher education. Despite the importance of program review, skepticism persists regarding its ability to meaningfully engage faculty and have sustaining impact on program improvement. This paper builds on the academic Program Review Learning Community conceptual model and presents a process for applying a project management facilitation technique that leverages a course structure. The Program Review Course allows for multiple programs to be reviewed concurrently as part of an interdisciplinary cohort. Within the cohort, faculty teams gain access to templates, information sessions, workshops, and discussion forums, with opportunities for engagement both within and across multiple disciplines. Using learning modules, this approach is designed to create efficiency and collaboration in delivering program review tools and customized supports. Utilizing a course structure for program review fits well with a new way to think about program review because successful learning communities are often embedded within institutional structures. Use of this approach is expected to increase clarity and consistency, improve administrative feasibility, provide guidance for those new to program review, promote collegiality within and across programs, and involve team members in the creation of action planning. The authors propose that a course-based approach for conducting program review, with teams of disciplinary faculty joining an interdisciplinary cohort and supported by quality assurance practitioners and educational developers is a novel way to provide structure and academic development while meeting legislative requirements. [ABSTRACT FROM AUTHOR]

Intelligent technologies permeate all aspects of contemporary society and urban life. It is essential to educate the workforce of smart cities in order to effectively meet emerging technological demands. In this paper, we present an e-course that focuses on discrete competencies associated with different smart city roles. Initially, we present the conceptual learning framework for the development of the competency-based learning course and we define the objectives of the research. The paper continues with a discussion of the model's application steps and an examination of the course's skills, competencies, and roles. The acquired knowledge was measured using pre- and post-course tests, and questionnaires were used to investigate the relevance and quality of the learning material and the learning acquisition of the participants. Evaluation results showed that the course was relevant to the concept of smart cities, useful for their work duties, while participation in the course resulted in increased overall competency in all three smart city job profiles. [ABSTRACT FROM AUTHOR]

In general, tags are used to interpret the content of music, while the music itself expresses emotion. The emotional information conveyed by the same music is described by a large number of emotion tags in various ways. This paper proposes and establishes an algorithm for music retrieval based on emotional tags. By modelling user emotional tags and music, a bipartite graph with emotional tags and music as nodes is first created. The tags and semantic similarity between the music are then calculated using the T_SimRank algorithm, and the popularity of the music is calculated using the T_PageRank algorithm. Finally, the two methods are combined using the concept of ranking learning to produce the final ranking of the music. Experiments demonstrate that the method suggested in this paper can better satisfy user retrieval needs than conventional cosine similarity and tag co-occurrence-based similarity methods and that the fusion of multiple methods is preferable to a single method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Entrepreneurship, Management & Innovation is the property of Wyzsza Szkola Biznesu-National Louis University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To help students choose the knowledge concepts that meet their needs so that they can learn courses in a more personalized way, thus improving the effectiveness of online learning, this paper proposes a knowledge concept recommendation model based on tensor decomposition and transformer reordering. Firstly, the student tensor, knowledge concept tensor, and interaction tensor are created based on the heterogeneous data of the online learning platform are fused and simplified as an integrated tensor; secondly, we perform multi-dimensional comprehensive analysis on the integrated tensor with tensor-based high-order singular value decomposition to obtain the student personalized feature matrix and the initial recommendation sequence of knowledge concepts, and then obtain the latent embedding matrix of knowledge concepts via Transformer that combine initial recommendation sequence of knowledge concepts and knowledge concept learning sequential information; finally, the final Top-N knowledge concept recommendation list is generated by fusing the latent embedding matrix of knowledge concepts and the students' personalized feature matrix. Experiments on two real datasets show that the model recommendation performance of this paper is better compared to the baseline model. [ABSTRACT FROM AUTHOR]

The teaching characteristic of colleges and universities is determined by the teaching quality of teachers, but it is difficult to calculate with a linear mathematical explanation how to judge the teaching quality of teachers. In most colleges and universities, expert evaluations, supervision groups, peers listening to lectures in the classroom, and students' after-class evaluations are still used to determine the teaching ability of professors. It is undeniable that these judgment methods have certain practicality. Especially for new teachers, experts and peers can find problems in time and help new teachers quickly correct them, and they can also know how to better practice the "student-centered" teaching concept from the feedback of students after class. However, these judgment standards also have their limitations. For example, the classroom quality judgment standards for experts and peers are mostly set by some administrative departments according to social requirements, leadership requirements, and subjective cognition. Various institutions should have different weights for the same criterion, and if they all employ the same criteria, it will easily lead to imprecise conclusions. In addition, the expert group and students are also subjective and incomplete in the stage of teacher evaluation. Based on different nonquantitative factors, the evaluation standards for teachers in colleges and universities also need the scientific theoretical basis. Therefore, many scientists are considering whether to use a more intelligent and reasonable way to judge the quality of teaching. Nowadays, with the popularization of artificial intelligence (AI), the use of neural networks (NNs) is becoming more and more comprehensive. According to the characteristics of NNs, this paper uses the GA NNs algorithm to signify that this algorithm can be effectively used in the evaluation of teachers' teaching quality. In this paper, a large number of experimental results show that the use of GA NNs can be used to evaluate the quality of teaching, and the evaluation results can be obtained quantitatively in this way. For the assessment of teaching quality, quantitative results are also more powerful in teaching evaluation. [ABSTRACT FROM AUTHOR]

Learning from multiclass imbalanced data streams with concept drift and variable class imbalance ratios under a limited label budget presents new challenges in the field of data mining. To address these challenges, this paper proposes an adaptive active learning method for multiclass imbalanced data streams with concept drift (AdaAL-MID). Firstly, a dynamic label budget strategy under concept drift scenarios is introduced, which allocates label budgets reasonably at different stages of the data stream to effectively handle concept drift. Secondly, an uncertainty-based label request strategy using a dual-margin dynamic threshold matrix is designed to enhance learning opportunities for minority class instances and those that are challenging to classify, and combined with a random strategy, it can estimate the current class imbalance distribution by accessing only a limited number of instance labels. Finally, an instance-adaptive sampling strategy is proposed, which comprehensively considers the imbalance ratio and classification difficulty of instances, and combined with a weighted ensemble strategy, improves the classification performance of the ensemble classifier in imbalanced data streams. Extensive experiments and analyses demonstrate that AdaAL-MID can handle various complex concept drifts and adapt to changes in class imbalance ratios, and it outperforms several state-of-the-art active learning algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Revista EIA is the property of Escuela de Ingenieria de Antioquia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Brain computer interfaces (BCI), which establish a direct interaction between the brain and the external device bypassing peripheral nerves, is one of the hot research areas. How to effectively convert brain intentions into instructions for controlling external devices in real-time remains a key issue that needs to be addressed in brain computer interfaces. The Riemannian geometry-based methods have achieved competitive results in decoding EEG signals. However, current Riemannian classifiers tend to overlook changes in data distribution, resulting in degenerated classification performance in cross-session and/or cross subject scenarios. Methods: This paper proposes a brain signal decoding method based on Riemannian transfer learning, fully considering the drift of the data distribution. Two Riemannian transfer learning methods based log-Euclidean metric are developed, such that historical data (source domain) can be used to aid the training of the Riemannian decoder for the current task, or data from other subjects can be used to boost the training of the decoder for the target subject. Results: The proposed methods were verified on BCI competition III, IIIa, and IV 2a datasets. Compared with the baseline that without transfer learning, the proposed algorithm demonstrates superior classification performance. In contrast to the Riemann transfer learning method based on the affine invariant Riemannian metric, the proposed method obtained comparable classification performance, but is much more computationally efficient. Discussion: With the help of proposed transfer learningmethod, the Riemannian classifier obtained competitive performance to existingmethods in the literature. More importantly, the transfer learning process is unsupervised and time-efficient, possessing potential for online learning scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Revista Internacional de Educación para la Justicia Social is the property of Universidad Autonoma de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Monitoring tool wear is key for the optimization of manufacturing processes. To achieve this, machine learning (ML) has provided mechanisms that work adequately on setups that measure the cutting force of a tool through the use of force sensors. However, given the increased focus on sustainability, i.e., in the context of reducing complexity, time and energy consumption required to train ML algorithms on large datasets dictate the use of smaller samples for training. Herein, the concepts of active learning (AL) and transfer learning (TL) are simultaneously studied concerning their ability to meet the aforementioned objective. A method is presented which utilizes AL for training ML models with less data and then it utilizes TL to further reduce the need for training data when ML models are transferred from one industrial case to another. The method is tested and verified upon an industrially relevant scenario to estimate the tool wear during the turning process of two manufacturing companies. The results indicated that through the application of the AL and TL methodologies, in both companies, it was possible to achieve high accuracy during the training of the final model (1 and 0.93 for manufacturing companies B and A, respectively). Additionally, reproducibility of the results has been tested to strengthen the outcomes of this study, resulting in a small standard deviation of 0.031 in the performance metrics used to evaluate the models. Thus, the novelty presented in this paper is the presentation of a straightforward approach to apply AL and TL in the context of tool wear classification to reduce the dependency on large amounts of high-quality data. The results show that the synergetic combination of AL with TL can reduce the need for data required for training ML models for tool wear prediction. [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Behavioral Analysis / Revista Mexicana de Análisis de la Conducta is the property of Sociedad Mexicana de Analisis de la Conducta and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we are interested in the study of the use of the Electronics Workbench (EWB) simulator in the study of amplitude modulation and demodulation. We conducted a study with 50 Moroccan students in the second year of the scientific baccalaureate option physical sciences at the high school Abdellah Laroui of the city of Fez. Using the pre-test and post-test methodology with an experimental group (25 students) and a control group (25 students). The results of the two groups are analyzed using the student t-test. The results obtained in the post-test show that the average of the test administered to both groups, control (MG-Con) and experimental (MG-Exp), found a non-significant difference (MG-Exp - MG-Con = 15.36 - 15.08 = 0.28 > 0). In addition, using interviews, we asked students and teachers to give their opinions on questions about the use of the Electronics Workbench (EWB) simulator in learning. The results obtained show that the laboratory experiments cannot replace the simulator (EWB) which can be used as an alternative pedagogical means to help students understand the concepts in electricity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Inquiry Based Activities (JIBA) / Araştırma Temelli Etkinlik Dergisi (ATED) is the property of Journal of Inquiry Based Activities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Convolutional neural networks often suffer from catastrophic forgetting. Although the memory replay (MR) method is an effective continual learning method for mitigating catastrophic forgetting, it typically involves high training costs. This paper proposes a novel strategy for improving the training efficacy of MR. Inspired by the learning style of humans, who first learn easier concepts and then gradually learn harder ones, we hypothesize that the learning order based on the data difficulty may be beneficial for MR. Accordingly, this paper introduces a novel continual learning paradigm, namely continual learning easy to hard (CLeToh), to optimize the training of MR. CLeToh first measures the data difficulty and arranges the data from easy to hard. Then, CLeToh trains the model with easier samples and integrates harder samples until all the data are trained gradually. Our approach increases the convergence speed, stabilizes the training process, and overcomes the problem of catastrophic forgetting of MR. [ABSTRACT FROM AUTHOR]

This paper presents a reflective analysis of a novel approach to Problem-Based Learning (PBL) to teach abstract concepts in a large-class setting, specifically tailored for a third-year required undergraduate course, "Introduction to Geotechnical Engineering." The primary objective is to enhance student engagement and learning outcomes by employing forensic case studies-based learning, also known as murder mysteries. This unique adaptation of PBL offers a fresh perspective on teaching abstract concepts by introducing real-world engineering failures relevant to the topic. Students then identify potential reasons for failure, rank them, and cooperatively explore them. By progressing from the known to the unknown, students develop a comprehensive understanding of the fundamental principles they later encounter. by progressing from the known to the unknown This approach overcomes the limitations of traditional teaching methods that introduce abstract concepts before presenting real-world examples. The murder mysteries capture students' attention and interest, allowing them to experience the process of doing real-world engineering. Consequently, the course rating improved significantly, achieving the highest score in the last twenty years - 4.9 out of 5.0, well above the average course rating of 3.8 during the same period. The paper delves into the background, methodology, challenges, and reflections on implementing and evaluating this engaging and effective PBL adaptation in a large-class setting for teaching abstract concepts in engineering. [ABSTRACT FROM AUTHOR]

This paper provides an active learning strategy with 34 interactive Excel spreadsheets incorporating 8 core calculative principles for use in management accounting courses. Central to this "interactive learning strategy" is how the dynamic formula functionality of Excel enables highly complex calculative exercises to be parsimoniously structured in templates with color-coded instant feedback. Careful design and selection of solution cells focus learning on core concepts of calculative principles. The strategy involves three forms of interactive learning: students interact with the exercises via the instant feedback functionality, students can interact personally with the instructor, and students can collaboratively interact with other students. With the exercises, the learning strategy is easily implementable into in-person and online classes as complements to traditional lecture segments and textbook exercises. It offers a more engaging and more productive learning experience as perceived by students compared to traditional "pen and paper" exercises (for online classes too). Data Availability: Data are available upon request. [ABSTRACT FROM AUTHOR]

Since the moral education acts an important role in daily life, the construction of moral education curriculum will have important consequences on the final effect of moral education work through the management of curriculum objectives, curriculum structure, curriculum content, curriculum implementation, and curriculum evaluation. The development and application of big data bring new direction for the enhancement and progress. This work gives the definition of big data in the teaching of moral education in universities. Secondly, the application status of big data in moral education teaching in colleges is studied. According to the existing research, the popularization of big data technology has completely changed the teaching concept of moral education in colleges and universities. Based on the second part, abundant theoretical research has been achieved and successfully applied in the teaching of moral education. Finally, this paper designs targeted big data strategies and analyzes the differences of different big data technologies in the effects of moral education to improve the effects of moral education in modern colleges and universities. The research of this paper has both theoretical and practical significance. [ABSTRACT FROM AUTHOR]