Knowledge Learning Research Articles

Q vs A proximities in construed modes of learning: Excitement and performance of university students in China’s Northeast

This study explores how Q-proximity and A-proximity affect the student's learning excitement and performance, and how they differ in these effects. Using the Construal Level Theory (CLT), a questionnaire was designed with multiple models (in which the student owns the Q, A, both or neither). The study then factored Qs and As to obtain two respective proximities and link them to excitement and performance scores. Based on 590 useful questionnaire responses, the study identified the following findings, 1. with structural equation modelling (SEM) both Q and A proximities encouraged student learning excitement, although the A excitement was greater than the Q excitement and 2. the Q-proximity decreases performance whilst the A-proximity increases performance. Additionally, the study used multiple analyses of individual learning modes concerning excitement and performance. The results of the multiple regression analysis appear to be consistent with the research of Malik (Current Psychology 43:545–564, 2024), and the inclusion of AI (Artificial Intelligence) learning modes. The main conclusion of the study is that Chinese learners prefer answers to the questions in their learning, teaching and application of knowledge.

The background and experience of neuroscience teachers in Australian universities: A cross-sectional survey.

Neuroscience is an academic discipline taught within a broad range of university degrees and programs. The expertise and experience of neuroscience teaching staff contribute to the student's learning experience and knowledge acquisition. We undertook a survey to characterize the neuroscience teaching workforce and practices in Australian universities, and to investigate access to and deficiencies in neuroscience teaching resources and training. Two hundred neuroscience teaching staff completed our anonymous online survey. The responses indicated that neuroscience is primarily being delivered by highly qualified (86% with doctoral degrees, 27% with formal qualifications in tertiary teaching), research-active (45% were recently primary supervisors of research students) teaching academics with secure employment (77% in full-time continuing positions). There were more females (61.5%) than males (38.5%) in the sample and most respondents taught neuroscience to students enrolled in a range of degrees/programs. Most survey respondents could access an anatomy laboratory for teaching (87%) but access to specialised online resources, such as augmented reality presentations, customised game-based learning approaches, and/or online brain atlases, was limited. Most survey respondents reported they would benefit from increased access to neuroscience teaching resources and/or peer-tested teaching materials (80%), an informal network of Australian neuroscience teaching peers (64%), and/or training workshops on neuroscience teaching (59%). Approximately half of survey respondents supported the creation of national guidelines for neuroscience teaching curricula. The survey results identify specific gaps in teacher training and resources and inform the development of strategies to support tertiary teachers of neuroscience and student learning.

Enhancing few-shot lifelong learning through fusion of cross-domain knowledge

Humans can continually solve new problems with a few examples and enhance their learned knowledge by incorporating new ones. Few-shot lifelong learning (FSLL) has been presented to mimic human learning ability. However, they overlook the significance of cross-domain knowledge and little effort has been made to investigate it. In this paper, we explore the effects of cross-domain knowledge in FSLL and propose a new framework to enhance the model’s ability by fusing cross-domain knowledge into the learning process. Moreover, we investigate the impact of both debiased and non-debiased models in the FSLL context for the first time. Compared with previous works, our setting presents a unique challenge: the model should continually learn new knowledge from cross-domain few-shot data and update its existing knowledge by fusing new knowledge throughout its lifelong learning process. To address this challenge, the proposed framework focuses on learning and updating while migrating the well-known issues of forgetting and overfitting. The framework comprises three key components designed for learning cross-domain knowledge: the Debiased Base Learning strategy, Knowledge Acquisition, and Knowledge Update. The superiority of the framework is validated on mini-ImageNet, CIFAR-100, OfficeHome, and Meta-Dataset. Experiments show that the proposed framework exhibits the capability to perform in cross-domain situations and also achieves state-of-the-art performance in the non-cross-domain situation.

Documenting research in simulation science to enhance understanding for reusability.

One goal of Open Science is to promote reusability, which requires understanding and documentation. Reusability spans a spectrum from the straightforward reuse of existing materials to the extraction and adaptation of specific elements, depending on the maturity of the reused research and the research context. Beyond knowledge, understanding is crucial for enabling reusability. Simply reading an article is often insufficient; thus, publishing the underlying data and software is recommended. While the FAIR principles (Findable, Accessible, Interoperable, Reusable) facilitate the discovery and legal reuse of data and software through metadata, they fall short of promoting comprehensive understanding. By applying insights from the epistemology of simulation to computational mechanics research cases, we tested our hypothesis that the simulation process involves critical components beyond software and data that are essential to understand for reuse. Our findings indicate that reusability in these cases predominantly involves adjusting existing methodologies-a combination of different process steps from an epistemological perspective. Therefore, it is imperative to document not only learned knowledge but also the decisions regarding adjustments, assumptions and applications that concern the entire simulation process. Documentation is vital for understanding and herby enabling true reusability in scientific research, aligning with philosophical considerations of transparency and the nature of scientific knowledge.

Knowledge, Attitude, And Oral Hygiene Practices Among Law Students In Chengalpatu District: A Cross‑Sectional Study

Introduction: poor oral health can have a profound effect on the quality of life. The experience of pain, endurance of dental abscesses, problems with eating and chewing, embarrassment about the shape of teeth or about missing, discoloured or damaged teeth can adversely affect people's daily lives and wellbeing. The purpose of this study was to evaluate the oral hygiene and dental health knowledge of government law college, chengalpatu district, law students. Materials and methods: thirty questions about fundamental knowledge, mindset, and practice were given to one hundred and fifty law students at the government law college in chengalpatu district, tamilandu india. Microsoft excel was used to tabulate the data, and the statistical package for the social sciences programme was used for statistical analysis. Results: of the participants in this study, only 39.4% were aware that regular dental check-ups were necessary. 8.5% of participants made two annual visits to the dental clinic andof the individuals, 57% had never been to a dental facility. Dental health education is important, according to 65.5% of participants, and it may be important, according to 21.8%. The results indicated a statistically significant association between the participant's year of law degree and the responses, as determined by inferential statistics using the pearson chi-square test. Conclusion: the study's findings indicate that law students' understanding of oral health care and proper oral hygiene is adequate. But the learned knowledge needs to be applied in day-to-day activities. This can be accomplished by altering their perspective on maintaining good dental hygiene

Revisit English Learner Autonomy Among Chinese Non-English Major Students During the COVID-19 Lockdown

As the primary and immediate learning context, schools are underrepresented in learner autonomy studies. Scholars’ concerns over the intricate nature of schools lead to inadequate attention on the medium of learner autonomy development. To fill this research gap, a convergent mixed method design included a self-developed questionnaire, and four semi-structured interviews were employed to examine the non-English major sophomores’ learner autonomy during the COVID-19 pandemic. The triangulation of quantitative and qualitative evidence yielded that between a public and a private university, there was a statistically significant difference in English as a foreign language (EFL)s’ motivation for autonomous learning during the COVID-19 lockdown. But other than the degree of motivation, no difference was captured regarding EFLs’ belief and knowledge of autonomous English learning, as well as their metacognitive knowledge. Overall, EFLs were confident about their capacity to do autonomous English learning but engaged in a few systematical autonomous English learning during the COVID-19 pandemic. Based on the results, discussions over Chinese EFLs’ learner autonomy and possible explanations for the motivation differences are included. Pedagogy implications and limitations are elaborated on at the end. Plain Language Summary Using a mixed method design, this study reveals the English as a foreign language (EFL) learner autonomy and the role of schools in differentiating it, during the COVID-19 pandemic. The triangulation of quantitative and qualitative data yielded that despite mainfesting confidence in their capacity for autonomous learning, Chinese EFL learners did not engage in active, systematic autonomous language learning during the lockdowns. The motivation for autonomous English learning differed between universities, with learners from the less prestigious private University Qiu demonstrating more motivation than EFLs from the top public University Nan. The study contributes to understanding EFL learner autonomy during a challenging time of school lockdowns and their motivation issues. It highlights the discrepancy between learners’ self-reported autonomy and engagement in independent language learning. Additionally, it challenges the assumption that learners from prestigious universities would exhibit higher motivation for autonomous learning, showing that motivation can vary depending on the university context.

Research on Knowledge Discovery and Sharing in AIGC Virtual Teaching and Research Room Empowered by Big Data Analysis and Natural Language Processing Algorithms

This paper introduces a pioneering framework named Deep Reinforcement Learning based AI-Generated Content for Virtual Teaching (DRL-AIGC-VR), which aims to transform the landscape of online education and research. At the heart of this system is the integration of Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP), making it exceptionally suited for the dynamic and evolving environment of virtual teaching and research rooms. The uniqueness of DRLAIGC-VR lies in its adaptive content curation and presentation capabilities, achieved through a combination of Deep Q-Networks (DQN) with attention mechanisms. This innovative approach allows the system to personalize learning experiences by tailoring them to individual student performance and engagement levels. Simultaneously, it focuses on presenting the most pertinent information, thereby streamlining and optimizing the learning process. One of the most significant features of this system is its ability to handle and analyze large-scale educational data, a vital aspect in today’s big data-driven world. This capability ensures that DRL-AIGCVR offers a highly interactive, responsive, and efficient learning environment, addressing the varied requirements of students and researchers. The implementation of DRL-AIGC-VR in virtual educational settings has shown remarkable improvements in several key areas, including learning outcomes, student engagement, and knowledge retention. These enhancements are indicative of the substantial progress that the framework brings to the domain of virtual education, positioning it as a leading solution in the realm of AI-driven learning platforms. Overall, DRL-AIGC-VR represents a significant step forward in harnessing the power of AI to enrich and elevate the educational experience in virtual settings, paving the way for more advanced, personalized, and effective online learning and research methodologies.

Real-time identification of precursors in commercial aviation using multiple-instance learning

This research pioneers the application of precursor concepts to preemptively identify and prevent aviation safety incidents using Machine Learning (ML). Airlines and governing organizations, such as the Federal Aviation Administration (FAA) in the United States, have been trying to prevent safety incidents during routine operations. However, this task is challenging due to the lack of timestep-wise event annotation in flights and the complexity involved in the timely identification of incidents prior to their occurrence. To address these issues, we propose a real-time precursor identification methodology combining Multiple-Instance Learning (MIL) and feature-based Knowledge Distillation (KD) learning. Our two-stage approach, involving deep MIL for labeling and a KD-based model for real-time warnings, demonstrates state-of-the-art performance and a time delay of 2.99ms using a dataset of 23,549 real flights. Further experiments using t-distributed Stochastic Neighbor Embedding (t-SNE) and occlusion method confirm our model’s transparency, enabling the generation of reliable quantitative precursor scores and facilitating reasoning about the causes of safety incidents at the parameter level. Additionally, statistical analysis of precursors reveals varying evolution times for different safety events, which indicates that pilots have at least 8 s to react after receiving a warning. In conclusion, our research provides a theoretical foundation and technical support for the next generation of online risk warning systems, enhancing flight safety and offering a pathway towards more intelligent and secure flight operations.

Halal and Muslim-Minority Countries: A Systematic Literature Review

Muslim-minority countries have emerged among the largest exporters of halal products for the Muslim market and received significant numbers of inbound Muslim travellers. The global nature of the halal industry value chain calls for a thorough understanding about halal, particularly in the context of Muslim-minority countries. To gain this understanding, a systematic literature review was conducted to explore the themes of halal research in Muslim-minority countries. A total of 22 empirical research articles were analysed based on a review protocol adapted from ROSES. The articles were sourced from the Scopus and Web of Science, with Google Scholar as a supplementary database. The study generated five main themes that synthesise the scholarly knowledge about halal in the context of Muslim-minority countries at the consumer, organization, and institution levels of analysis. The findings illustrate the lack of understanding about halal among the industry players and consumers, as well as the lack of central governance and policymaking within the halal industry in Muslim-minority countries. Based on the findings, the study provides future direction for research and practice.

Many-to-many: Domain adaptation for water quality prediction

Predicting water quality is crucial for sustainable water management. To mitigate data scarcity for specific water quality targets, domain adaptation methods have been employed, adjusting a model to perform in a related domain and leveraging learned knowledge to bridge domain differences. However, these methods often fall short by overfitting certain domain-specific patterns, overlooking consistent water quality patterns in multi-water domains. Despite regional variations, these Consistent patterns show fundamental commonalities and can be observed across monitoring sites, stemming from their widespread and interconnected nature. Addressing these limitations, we introduce the Many-to-Many Domain Adaptation framework (M2M) for prediction to bridge the gap between multi-source domains and multi-target domains, aligning shared insights with the distinct profiles of individual monitoring sites while considering their geographical interconnections. M2M adeptly addresses the formidable challenge of concurrently deciphering and integrating multifaceted patterns across an array of source and target domains, while also navigating the intricate regional heterogeneity intrinsic to the water quality of different sites. The M2M includes a domain pattern fusion module for consistent pattern extraction and numerical scale maintenance from source domains, a domain pattern sharing module for sharing pattern extraction from target domains, and an M2M learning method to ensure the training of these modules. Extensive experiments conducted on 120 diverse monitoring stations demonstrate that M2M markedly enhances the accuracy of water quality predictions using various time series encoders. Code available at https://anonymous.4open.science/r/M2M.

Efficient Multitask Reinforcement Learning Without Performance Loss.

We propose an iterative sparse Bayesian policy optimization (ISBPO) scheme as an efficient multitask reinforcement learning (RL) method for industrial control applications that require both high performance and cost-effective implementation. Under continual learning scenarios in which multiple control tasks are sequentially learned, the proposed ISBPO scheme preserves the previously learned knowledge without performance loss (PL), enables efficient resource use, and improves the sample efficiency of learning new tasks. Specifically, the proposed ISBPO scheme continually adds new tasks to a single policy neural network while completely preserving the control performance of previously learned tasks through an iterative pruning method. To create a free-weight space for adding new tasks, each task is learned through a pruning-aware policy optimization method called the sparse Bayesian policy optimization (SBPO), which ensures efficient allocation of limited policy network resources for multiple tasks. Furthermore, the weights allocated to the previous tasks are shared and reused in new task learning, thereby improving sample efficiency and the performance of new task learning. Simulations and practical experiments demonstrate that the proposed ISBPO scheme is highly suitable for sequentially learning multiple tasks in terms of performance conservation, efficient resource use, and sample efficiency.

Combining replay and LoRA for continual learning in natural language understanding

Large language models have significantly improved dialogue systems through enhanced capabilities in understanding queries and generating responses. Despite these enhancements, task-oriented dialogue systems—which power many intelligent assistants—face challenges when adapting to new domains and applications. This challenge arises from a phenomenon known as catastrophic forgetting, where models forget previously acquired knowledge when learning new tasks. This paper addresses this issue through continual learning techniques to preserve previously learned knowledge while seamlessly integrating new tasks and domains. We propose Experience Replay Informative-Low Rank Adaptation or ERI-LoRA, a hybrid continual learning method for natural language understanding in dialogue systems that effectively combines the replay-based methods with parameter-efficient techniques. Our experiments on intent detection and slot-filling tasks demonstrate that ERI-LoRA significantly outperforms competitive baselines in continual learning. The results of our catastrophic forgetting experiments demonstrate that ERI-LoRA maintains robust memory stability in the model, demonstrating its effectiveness in mitigating these effects.

From Theory to Practice: The Effects of Community of Practice Implementation on Out-of-School Youth in the Philippines

Communities of Practice (CoP) aims to provide services and skills to partner communities while also establishing links to communicate learned knowledge to broader groups of people. This study identified and analyzed the impact of communities of practice in Barangay Nagtenga, Sta. Cruz, Ilocos Sur. It used a mixed quantitative and qualitative study approach to evaluate the level of satisfaction of the 18 CoP participants as well as their CoP learning and experience. The data demonstrated high levels of CoP satisfaction, notably with resource speakers and the quality of service provided. Participants shared their learning experiences during the CoP, focusing on themes such as knowledge acquisition, skill development, learning transfer to practice, and entrepreneurial management. This study emphasizes the importance of CoPs in empowering marginalized groups and promoting personal and professional development. Recommendations include maintaining and growing CoP initiatives, as well as conducting frequent evaluations to ensure continuous improvement.

How motivations and costs affect financial knowledge learning? Evidence from a large-scale FinTech experiment in China

Utilizing the launch of mobile wealth management services in China in 2013 as a shock to financial education, we analyze a panel of 16,440 households through a difference-in-differences strategy to examine how learning motivations and costs affect financial knowledge learning. Results show that using mobile services encourages risky asset holdings and enhances the self-discipline to learn, which, coupled with low-cost and personalized learning tools provided through the services, increases financial knowledge. We contribute by utilizing a large-scale experiment beyond the classroom to show the role of learning motivations and costs in financial knowledge learning, therefore help clear the controversy on the effectiveness of financial education.

Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem

Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem

A User Study on Explainable Online Reinforcement Learning for Adaptive Systems

Online reinforcement learning (RL) is increasingly used for realizing adaptive systems in the presence of design time uncertainty because Online RL can leverage data only available at run time. With Deep RL gaining interest, the learned knowledge is no longer represented explicitly but hidden in the parameterization of the underlying artificial neural network. For a human, it thus becomes practically impossible to understand the decision-making of Deep RL, which makes it difficult for (1) software engineers to perform debugging, (2) system providers to comply with relevant legal frameworks, and (3) system users to build trust. The explainable RL technique XRL-DINE, introduced in earlier work, provides insights into why certain decisions were made at important time steps. Here, we perform an empirical user study concerning XRL-DINE involving 73 software engineers split into treatment and control groups. The treatment group is given access to XRL-DINE, while the control group is not. We analyze (1) the participants’ performance in answering concrete questions related to the decision-making of Deep RL, (2) the participants’ self-assessed confidence in giving the right answers, (3) the perceived usefulness and ease of use of XRL-DINE, and (4) the concrete usage of the XRL-DINE dashboard.

The efficacy of simulation-based learning versus non-simulation-based learning in endocrinology education: a systematic review and meta-analysis

BackgroundSimulation-Based Learning (SBL) is increasingly adopted in medical education across various specialties, employing realistic simulations to significantly enhance learning experiences. However, a comprehensive evaluation of its effectiveness specifically in endocrinology has not yet been conducted. The study aims to systematically review and meta-analyze the impact of SBL versus Non-Simulation-Based Learning (NSBL) on knowledge acquisition, skills, satisfaction, and interest in learning among endocrinology trainees.MethodsThis systematic review and meta-analysis adhered to the PRISMA guidelines, searching PubMed, Web of Science, Embase, Cochrane library, China National Knowledge Infrastructure (CNKI), Wanfang Data, Weipu, and Chinese Biomedical Database (CBM) until March 2024. We included randomized controlled trials comparing SBL to NSBL in endocrinology education. The quality evaluation relied on the Cochrane risk-of-bias assessment tool. The main results included evaluations from both theoretical and practical assessments. Additional measures consisted of assessing satisfaction and interest in learning.ResultsWe identified 22 studies suitable for systematic review and 21 for meta-analysis, involving a total of 2517 participants. SBL greatly enhanced theoretical knowledge [standardized mean difference (SMD) = 1.00, 95% confidence interval (CI): 0.68–1.32, P < 0.00001, I2 = 89%] and practical skills (SMD = 1.56, 95% CI: 1.11–2.01, P < 0.00001, I2 = 93%) compared to NSBL. Additionally, SBL was associated with higher satisfaction and greater interest in learning. No significant publication bias was detected, and sensitivity analysis confirmed the stability of these findings.ConclusionsSBL significantly enhances knowledge, skills, satisfaction, and interest in learning within endocrinology education compared to NSBL. These findings support the integration of high-quality SBL into endocrinology curricula to improve educational outcomes. Future research should explore the lasting effects of SBL on knowledge retention and clinical practice, as well as to evaluate its cost-effectiveness and compatibility with various educational tools in diverse settings.

Validation of the Teachers AI-TPACK Scale for the Indian Educational Setting

Educators work in extremely dynamic and complex classroom environments where they must continuously alter and update their understanding. Specifically, possession of rich, organised, and integrated knowledge from various domains including knowledge on subject-matter, knowledge of students' thinking and learning, and, increasingly, knowledge of technology is essential. By integrating these three aspects, Mishra and Koehler developed the Technological Pedagogical and Content Knowledge (TPACK) framework in 2006 which offers a thorough and comprehensive method for incorporating technology into the education setting. On the parallel, use of information technology has rapidly increased in the field of education, especially with the introduction of Artificial Intelligence (AI). Thus, the Technological Pedagogical Content Knowledge (TPACK) framework needed to be updated to reflect the growing incorporation of AI into educational standards. Hence, investigator Ning and colleagues in the year 2024 built a framework for incorporating AI into TPACK and developed a robust scale titled Teachers AI-TPACK Scale that measures the teachers competencies in incorporating AI into their teaching environment. The objective of this work was to test the validity of the scale in the Indian educational setting. With a sample size of 660 teaching faculties in universities and colleges across India, this study followed the routine stages such as construct validity analysis in the form of Exploratory Factor Analysis using SPSS V27, followed by Confirmatory Factor Analysis in AMOS software. The original scale with 39 items across seven dimensions were retained throughout the validation process and resulted in a high reliability score of 0.907. This provides compelling evidence for the validity and reliability of the teachers AI-TPACK scale in measuring Indian educators' knowledge and skills at the juncture of AI with pedagogy, technology and content. This is currently the only scale available to measure this construct in India.

Machine Translation-Friendly Language for Improved Machine Translation Output in Academic Writing

Academic writing aims to disseminate scholarly knowledge to a target audience in concise language. During this process, translators sometimes translate academic texts into foreign languages, so that the output of scholarly research can be read by speakers of different languages. In such cases, translators are to translate the source text into the target language in a clear and comprehensible language. This paper discusses how the authors of scholarly texts can structure their writing properly, so that they can help translators compose clear and concise translation output. Although the knowledge and skills of the translator are a key consideration in ensuring the quality of translated texts, the source text itself is highly influential in the quality of the output. Furthermore, although they might partially differ from one language to another, academic writing has some well-established conventions. Therefore, translators might experience some problems when they translate scholarly texts. This study aims to pinpoint such issues and to offer viable strategies that could be used for solving them before the translation process. Commonly encountered problems that impair the quality of the translation output are as follows: wordiness, ambiguous expressions, failing to consider the discourse structure of the target language while organising the source text, using conjunctions carelessly, using the passive voice unnecessarily, using indirect language to express ideas, using synonymous words sequentially, typos, and other similar problems. Though experience in academic translation can help solve some of these issues, it might not be possible to produce good translation output if the source text is poorly written. Success in academic translation can be achieved through collaboration between the author of an academic text and its translator. For this reason, it is necessary to raise the awareness of writers and translators on this issue.

Enhancing Education and Training for Professionals and Formal Carers: Implementing Microlearning

Abstract Background Current technological, economic and social changes trigger the need for new concepts and strategies to support lifelong learning. Education, including work-based learning, is in need of transformation, requiring renewal and innovative ways of relating appropriately to the way we live, work and learn today (Downes, 2005; Kuhlmann &amp; Sauter 2008; Chisholm 2005). Staff shortages and heavier workloads has reduced the availability of health care staff to attend in-depth training courses. This, coupled with the recent moves away from traditional face-to-face learning to e-learning, we had to re-consider teaching theories and teaching methods. Methods In this poster I will present how we utilised microlearning to improve staff knowledge about dementia and nutrition. Microlearning is a method of delivering small, bite-sized pieces of information that can be easily consumed and retained by learners. It offers customised learning experiences that cater to the unique needs and preferences of each learner and provides an engaging and interactive learning experience through the use of multimedia, quizzes, and games. Results Microlearning facilitates self-directed lifelong learning, as short activities can be easily integrated into everyday activities. Small learning steps with small pieces of information can be used for learning in between and on-demand. In this way microlearning enables individuals to stay up to date in today’s knowledge society and offers a viable supplement to more time-consuming and formalized modes of learning, such as classroom courses or web-based trainings. Additionally, this poster will describe how we have applied microlearning as an emerging pedagogy that enables learners and clinicians to engage in short, focused, asynchronous and just-in-time learning. Conclusion Microlearning as an educational strategy has demonstrated a positive effect on the knowledge and confidence of health professions learners in performing procedures, retaining knowledge, studying, and engaging in collaborative learning.