Model Of Learning System Research Articles

Has the smart classroom revolutionized general education in Chinese universities? A comparative analysis of its influence path with that of traditional multimedia classrooms

ABSTRACT Although general education in China undertakes an important mission to balance the overspecialization of higher education and cultivate wise citizens, its realization is facing challenges. The emergence of the smart classroom (SC) has introduced new concepts to transform general education. However, the impact of SCs on student approaches to learning (SAL) and learning outcomes and the differences from the traditional multimedia classroom (TMC) have not yet been explored. Therefore, this study proposes a 3P learning system model to examine the relationships between physical instructional environments, teacher instruction, SAL, and the development of generic skills. A study survey was completed by 596 students from three Chinese universities, and the results indicated that the cultivation of generic skills was better in SCs than in TMCs. This primarily stemmed from the enhanced student engagement resulting from the multifaceted teaching functions present in SCs. In addition, several pieces of evidence suggested that the impact of general education teacher instruction was not as significant as anticipated in SCs. These findings provide valuable direction for reforming general education and improving teacher instruction.

Determination of soluble solids content in loquat using near-infrared spectroscopy coupled with broad learning system and hybrid wavelength selection strategy

Soluble solids content (SSC) serves as a crucial metric in assessing loquat quality. To achieve fast, non-destructive, and accurate detection of SSC, the combination of near-infrared (NIR) spectroscopy and broad learning system (BLS) model was developed in this study. Meanwhile, the influence of six spectral preprocessing methods and six wavelength selection algorithms on SSC prediction was explored, and the performance of the BLS model was compared with the widely used partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) models. The results showed that Savitzky-Golay smoothing combined with standard normal transformation (SG-SNV) offered the optimal pretreatment, and the hybrid method, namely interval variable iterative space shrinking analysis and successive projections algorithm (iVISSA-SPA), emerged as the most effective wavelength selection method. The BLS model outperformed both PLSR and LS-SVM models. Specifically, the BLS model based on the iVISSA-SPA method achieved the optimal SSC prediction results, with RP2 = 0.8646, RMSEP = 0.5104, and RPD = 2.7481. Therefore, NIR spectroscopy coupled with the BLS model and hybrid wavelength selection strategy could rapidly, non-destructively and accurately detect the SSC of loquat, providing a viable alternative for SSC prediction in fruit.

A data-driven LSTMSCBLS model for soft sensor of industrial process

Abstract In the chemical industry, data-driven soft sensor modeling plays a crucial role in efficiently monitoring product quality and status. Industrial data in these applications typically exhibit significant temporal characteristics, meaning that current information is influenced by data from previous periods. Effectively extracting and utilizing these temporal features is essential for achieving accurate soft sensor modeling in complex chemical scenarios. To address this challenge, this study proposes a data-driven Broad Learning System (BLS) model, which combines Long Short-Term Memory (LSTM) networks with an adaptive algorithm known as the Stochastic Configuration Algorithm (SC), referred to as LSTMSCBLS. The model operates in two stages: temporal feature extraction and final prediction. In the temporal feature extraction stage, the integration of the LSTM network with a feature attention mechanism allows for efficient extraction of temporal features from high-dimensional time-series data. In the final prediction stage, the SC is integrated into the BLS, effectively mitigating issues related to node space redundancy and the determination of the number of nodes. The effectiveness and superiority of the proposed model are demonstrated through two industrial case studies involving a debutanizer column and a sulfur recovery unit.

Construction of Practical Teaching Mode of Law Course Based on Multi-Mode and Low-Resource Language Learning

China's legal profession is a new profession emerging in the process of China's rule of law. Its development and refinement are increasing in accordance with the rapid social, economic and political development. However, as a lawyer, people need to learn real kung fu. For complex and changeable legal practice problems, people should use the simplest and most effective way to solve them. Therefore, it is necessary to study the curriculum of law specialty to achieve its purpose. It is also necessary to reform the traditional education methods, introduce modern scientific and technological means, and improve their effectiveness, so as to construct a practical teaching mode of Professional Courses in Law (PCL). For practical legal courses, it can be conducted through multi-mode low-resource language learning method. This paper aimed to study how to study the ability of students to obtain information, process information and analyze and process information based on intelligent multi-image feature fusion. Based on the investigation of teaching objectives and teachers' teaching requirements, and the analysis and arrangement of the investigation results, a multi-task learning system model based on image intelligent features was established. In this experiment, 494 valid questionnaires were used to analyze the current situation of PCL practical teaching. Among them, 10.32% and 65.59% of the students were very satisfied with the results of PCL practical teaching, and 18.02% and 59.31% of the students liked PCL practical teaching very much. In the survey on the influencing factors of PCL practical teaching activities, 61.34% of the students thought that the students did not know enough and were not enthusiastic enough. On the whole, the students are satisfied with the practical teaching arrangement of PCL, but from the actual situation, the effect of practical teaching is not very good, and the expected goal has not been achieved.

Using artificial intelligence to predict mortality in AKI patients: a systematic review/meta-analysis.

Acute kidney injury (AKI) is associated with increased morbidity/mortality. With artificial intelligence (AI), more dynamic models for mortality prediction in AKI patients have been developed using machine learning (ML) algorithms. The performance of various ML models was reviewed in terms of their ability to predict in-hospital mortality for AKI patients. A literature search was conducted through PubMed, Embase and Web of Science databases. Included studies contained variables regarding the efficacy of the AI model [the AUC, accuracy, sensitivity, specificity, negative predictive value and positive predictive value]. Only original studies that consisted of cross-sectional studies, prospective and retrospective studies were included, while reviews and self-reported outcomes were excluded. There was no restriction on time and geographic location. Eight studies with 37 032 AKI patients were included, with a mean age of 65.3 years. The in-hospital mortality was 18.0% in the derivation and 15.8% in the validation cohorts. The pooled [95% confidence interval (CI)] AUC was observed to be highest for the broad learning system (BLS) model [0.852 (0.820-0.883)] and elastic net final (ENF) model [0.852 (0.813-0.891)], and lowest for proposed clinical model (PCM) [0.765 (0.716-0.814)]. The pooled (95% CI) AUC of BLS and ENF did not differ significantly from other models except PCM [Delong's test P=.022]. PCM exhibited the highest negative predictive value, which supports this model's use as a possible rule-out tool. Our results show that BLS and ENF models are equally effective as other ML models in predicting in-hospital mortality, with variability across all models. Additional studies are needed.

Multi-task Information Enhancement Recommendation model for educational Self-Directed Learning System

In Self-Directed Learning Systems (SDLS), suggesting specific exercises to students after viewing educational videos is crucial, which promotes the integration of learning resources and improves learning efficiency. Given that exercises and educational videos are distinct learning resources, suggesting exercises centered on educational videos containing knowledge concepts is referred to as cross-type recommendations. Traditional cross-type recommendation methods usually separate data representation and recommendation tasks, which hampers effective knowledge transfer and information fusion. Furthermore, educational resources frequently feature an irregular distribution of information, posing a significant challenge to existing models in extracting crucial information. Besides, due to the inadequate data, the cross-type recommendation system needs help in developing a robust model. To address these challenges, we propose a Multi-task Information Enhancement Recommendation (MIER) Model in this paper. This model provides a comprehensive recommendation capability by integrating resource representation and merging recommendations. Multi-task learning improves the capacity to integrate information and transfer knowledge. Then, we propose an Information Enhancement Encoder (IEE) module that utilizes the attention mechanism to enhance the model’s ability to extract essential information from educational videos and exercises. Finally, to address the problem of inadequate data, we design a fusion structure that incorporates the concept knowledge graph as expert information into the model. To evaluate the effectiveness of the MIER, we compare it with other models on both predicting concepts and recommending exercises task. The corresponding results demonstrate that the MIER outperforms all the other models. We comprehensively evaluated the MIER model’s performance across a range of datasets varying in labeled data quantity. The findings demonstrate that the model consistently achieves outstanding performance and exhibits robustness, even under conditions of limited label availability.

The Effect of Using Marzano Model and Renzulli Learning System on Developing Critical Thinking of 10th graders in Jordan in Writing Composition of English

This study investigated the effect of using Marzano model and Renzulli learning system on developing critical thinking of tenth grade students in writing English composition in Jordan. The researchers designed a writing pre-test and post-test to test the students’ skills in writing composition, where its validity and reliability were verified. This study followed quasi-experimental research design methodology. The sample of the study consisted of (233) tenth grade students in Al-Mazar Directorate of Education during the first semester 2020/2021 including (120) male and (113) female students. The findings indicated that there were statistically significant differences at (α= ≤ 0.05) between the three groups, the (Renzulli, Marzano, and Conventional), in favor of Renzulli learning system. Furthermore, there were no statistically significant differences at (α= ≤ 0.05) that could be attributed to the variable of gender (female and male). Several recommendations were suggested as conducting further research to investigate the effect of applying Renzulli learning system and Marzano Model with different variables and with other language skills.

Application of wireless sensor network based on improved genetic algorithm in English blended learning

Adaptive genetic algorithm is one of the classic algorithms to solve multi-objective optimization problems. In order to make the convergence set to the Pareto boundary more uniform, this article improves how to compare multiple distances in the algorithm, introduces the concept of comparing the difference between single distances, and uses the adaptive and mutation of crossover operators and the minimum distance filtering mechanism. An improved NSGA-II adaptive algorithm is proposed. Among other things, in the machine learning system model, the environment refers to an external source of information, which provides research-related information. This study proved that learning requires systematic organization, similar to short-term memory in information processing models. Find the external environment, then use the methods of thinking analysis, integration, comparison and induction to obtain knowledge, and store the knowledge in a scientific way. With the rapid development of knowledge in modern society, on the basis of the development of English text analysis technology and network technology, the reform of China's education system has reached a new level. Reforming modern teaching methods is an important part of education reform. More and more educators are considering analyzing English texts as one of the important tools to reform teaching methods and improve teaching effects. Among other things, based on the B/S model development technology, the teaching system is researched using production theories and methods, and the teaching system is actually being tested, developed and applied. By comparing the effects of computer network teaching methods and traditional teaching methods, the feasibility and effectiveness of the network teaching system are determined.

What can we learn about integration of novel words into semantic memory from automatic semantic priming?

ABSTRACT According to the Complementary Learning Systems model of word learning, only integrated novel words can interact with familiar words during lexical selection. The pre-registered study reported here is the first to examine behavioural and electrophysiological markers of integration in a task that relies primarily on automatic semantic processing. 71 young adults learned novel names for two sets of novel concepts, one set on each of two consecutive days. On Day 2, learning was followed by a continuous primed lexical decision task with EEG recording. In the N400 window, novel names trained immediately before testing differed from both familiar and untrained novel words, and, in the time window between 500–800 ms post onset, they also differed from novel names that had undergone a 24-hour consolidation, for which a small behavioural priming effect was observed. We develop an account that attributes the observed effects to processes rooted in episodic, rather than semantic, memory.

A complementary learning systems model of how sleep moderates retrieval practice effects.

While many theories assume that sleep is critical in stabilizing and strengthening memories, our recent behavioral study (Liu & Ranganath, 2021, Psychonomic Bulletin & Review,28[6], 2035-2044) suggests that sleep does not simply stabilize memories. Instead, it plays a more complex role, integrating information across two temporally distinct learning episodes. In the current study, we simulated the results of Liu and Ranganath (2021) using our biologically plausible computational model, TEACH, developed based on the complementary learning systems (CLS) framework. Our model suggests that when memories are activated during sleep, the reduced influence of temporal context establishes connections across temporally separated events through mutual training between the hippocampus and neocortex. In addition to providing a compelling mechanistic explanation for the selective effect of sleep, this model offers new examples of the diverse ways in which the cortex and hippocampus can interact during learning.

Dynamic adaptive graph convolutional transformer with broad learning system for multi-dimensional chaotic time series prediction

Chaotic time series data is extensively applied in financial stocks, climate monitoring, and sea clutter, in which data fusion from various sources and multi-sensor information make accurate predictions of chaotic time series challenging under complex nonlinear conditions. Previous works focus on designing different model frameworks to capture the temporal dependence and extract richer nonlinear features to improve the accuracy of univariate chaotic time series prediction, which ignores the spatial dependence of multivariable. However, in this paper, we argue that spatial correlation among multiple variables is essential to improve the prediction accuracy of chaotic time series. To fill the gap, we innovatively propose a Dynamic Adaptive Graph Convolutional Transformer with a Broad Learning System (DAGCT-BLS), a GCN and Transformer-based model utilizing multivariate spatial dependence for multi-dimensional chaotic time series forecasting. In DAGCT-BLS, the multivariate chaotic time series are reconstructed into the phase space, and the reconstructed data are rapidly feature-extracted using a cascade network BLS with frozen weights to maximize the retention of chaotic properties and nonlinear relationships. Then, the Dynamic Adaptive Graph Convolutional Network (DAGCN) is proposed to capture the spatial correlation among the multiple variables. Finally, improved multi-head attention of the Transformer Encoder is used to capture the temporal dependence of the phase point sequence. Experiments of our proposed model on three datasets (Lorenz, Rossler, and Sea clutter) show that DAGCT-BLS can achieve the best prediction performance and have strong interpretability, and multivariate-based joint modeling of chaotic time series helps to improve the prediction performance.

Reliable federated learning based on dual-reputation reverse auction mechanism in Internet of Things

Federated learning, a promising distributed machine learning paradigm, has been used in various Internet of Things (IoT) environments to solve isolated data island issues and protect data privacy. However, since the central server in federated learning cannot detect the local training process of the client, it is vulnerable to adversarial attacks against its security and privacy by malicious clients during the training process. To address this problem, this work proposes a federated learning system model based on dual-reputation reverse auction in IoT. Specifically, due to resource constraints, not all clients participate in the federated learning process, so we propose the reputation-bid ratio based greedy half-splitting algorithm to select some clients to participate in the federated learning, which can guarantee that each client has the chance to be selected while selecting as many honest and high-quality clients as possible. Then, we propose an adaptive dropout aggregation method based on a training quality score, which can effectively defend against malicious workers’ attacks. After the completion of federated learning, we put forward a subjective evaluation incentive mechanism based on a second reputation to realize the fair incentive. Furthermore, we store and manage reputations through blockchain in our model to ensure their availability. Theoretical analysis deduces the complexity and security of our proposed model. Finally, simulation results indicate that our proposed model can achieve the highest accuracy across all test datasets compared to the baseline. Particularly, on the CIFAR10 dataset, the accuracy of our proposed model surpasses the baseline by 5% to 30%. In responding to sudden attacks initiated by normally participating workers, our model exhibits the fastest reaction time, with accuracy surpassing the baseline by 8% to 40%.

LIFE CYCLE MANAGEMENT MODELS OF COMPONENT EDUCATIONAL RESOURCES OF THE COMPANY

Learning management systems are characterized by the fact that the scenarios of their operation are related to various aspects of educational activity, which in turn requires costs for the creation of professional training programs, electronic educational resources and their exploitation. These problems confirm the relevance of research aimed at developing models of life cycles for electronic educational resources, programs and qualification levels of specialists, as well as at creating effective tools for automated management of the learning process. The purpose of this study is to optimize the life cycles of electronic educational resources, training programs, employer requirements and increase efficiency in managing the continuous process of training specialists. Analytical studies of life cycle models of electronic educational content, learning and educational activity management systems, content management systems were carried out in the work in order to select and develop tools for automated design and filling of educational resources, as well as synthesis of the life cycle management system of its main components. Models of the life cycles of the main components of the educational environment have been developed, which allow for their coordination and optimization for the system of automated management of training processes and professional development. Distinctive features of the models are the comprehensive synchronization of the life cycles of all components with the assessment of the effectiveness of this process, as well as adjustment to the changing requirements of the external environment. A model for synchronizing the life cycles of the components of the educational environment, including electronic educational content and a system for managing learning and professional development processes, has been developed. A feature of the model is a comprehensive assessment of the effectiveness of the synchronization process at all its stages for the automated selection and/or synthesis of personalized training programs and electronic resources in accordance with the changing requirements of standards and employers.

A Study on the Utilization of Polyphonic Folk Songs in College Choral Teaching in the Context of Big Data

Abstract Introducing polyphonic folk songs into the choral teaching classroom in colleges and universities not only helps to carry forward the excellent traditional culture but also promotes the students’ emotional education and physical and mental health development. This paper analyzes the status quo and dilemma of the use of polyphonic folk songs in college choral teaching and puts forward the model of music choral intelligent tutoring system (ITS) and the method of spectral tone recognition as countermeasures. A collaborative filtering algorithm is used to construct the learning resource recommendation algorithm in the music chorus intelligent guided learning system model, which is based on the ITS system structure. The YIN algorithm is used to extract the audio, and the pitch template matching is accomplished with the help of the improved DTW algorithm to complete the choral ability assessment. An improved normal harmonic algorithm has been proposed to preprocess the audio signal and achieve spectral tone recognition for multi-part folk songs in the fundamental frequency extraction stage of spectral tone recognition. In a comprehensive university in Sichuan Province, China, the teaching practice of polyphonic folk song chorus was carried out. The students’ scores in the dimensions of learning interest and learning confidence increased by 0.976 and 0.698 on average after the experiment. The p-values were all less than 0.05, showing significant differences.

A new approach to a wicked problem: development of a cross-sector community-centered learning network to tackle childhood food inequity.

Food insecurity is a complex societal problem that disproportionately impacts households with children and those led by minoritized populations, with negative impacts on health across the life course. System to Achieve Food Equity adapted the learning systems model, used to address similarly complex problems, to tackle food insecurity at a neighborhood level. SAFE, born out of the COVID-19 pandemic, leverages a family-centered, community-based, cross-sector network fundamentally aimed at changing the food system so that all children in Cincinnati have the food they need to thrive. Through the following principles, Community-Led Network, Co-Production with Community, Equitable Sustainability, Learning to Learn Together, Distributing Leadership and Power, and Shared Data and Governance, SAFE has grown to over 300 individuals and 100 organizations, funded 9 novel interventions, distributed over 270,000 meals, and created a collaborative of motivated like-minded stakeholders. Future work includes improved data collection and sharing, support for increased stakeholder engagement and greater distribution of leadership and power, advocacy for policy change, refining measurement tools of network maturity for community settings, and collaboration with other efforts that contribute to food security indirectly.

Mutual dimensionless improved bearing fault diagnosis based on Bp-increment broad learning system in computer vision

Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial for ensuring normal machinery operation. However, the nonlinear and non-stationary vibration signals generated by machinery in harsh environments pose significant challenges in distinguishing fault signals from normal ones. Although several fault diagnosis methods based on mutual dimensionless indicators (MDI) have been proposed, they often fail to achieve effective and accurate health monitoring. Hence, this paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model based on the combined synergistic of two modules, to address the existing challenges. Firstly, a new mutual dimensionless indicator (VMDI) with high sensitivity and low overlap is refactored. Secondly, leveraging the advantages of incremental learning algorithms, a novel Broad Learning System (BLS) model for quickly identifying different fault types is constructed. Finally, the proposed method is validated using multiple datasets and verified through a comparative analysis with a published method based on dimensionless indicators (DI). The results demonstrate the effectiveness of the proposed method in fault diagnosis.

Construction of an innovative model of English online teaching based on a multimedia English learning system model

Abstract This paper explores a multimedia learning system suitable for students’ independent English learning through the current development of assisted English learning systems, university-level English teaching requirements, and learner characteristics. Based on the model of the proposed multimedia English learning system, in the endpoint detection stage of speech, short-time excess rate and short-time energy are combined to process the endpoint detection of speech. In the final speech comparison stage, the dynamic time planning algorithm DTW is analyzed and improved before the similarity recognition of speech. We also complete the requirement analysis of the English-speaking learning system by combining the relevant theories and applying the speech signal processing technology and template matching technology to realize a multimedia-assisted learning system suitable for college students to conduct independent English learning. Through the English-speaking test of two classes, the control class using traditional online teaching had many failures reaching 25%, while the experimental class using the English-assisted learning platform passed all of them. 80 points or more were achieved by only 5% of the control class, while 20% of the experimental class. The average score of the control class was 63.4, while the experimental class was 72.1, and the experimental class was 8.7 points higher than the control class. Therefore, teaching English-assisted learning platforms can improve students’ English-speaking abilities.

Entrepreneurship Education: Encouraging Entrepreneurial Intentions for Equality Education Students in Semarang

This study aimed to determine the factors that influence students' entrepreneurial intentions and the influence of entrepreneurship education on students' intention to become entrepreneurs. This research study used a mixed methods approach. The collection of research data was carried out with the help of a questionnaire (for quantitative data) and through group discussion forums (for qualitative data). Respondents in the study were 100 students who attended equality education in Semarang. This shows found there is a difference between the levels of interest of students who take part in entrepreneurship learning and those who do not. Family background and income have positive effects on students' intentions to become entrepreneurs. Students with entrepreneurial family backgrounds tend to be more interested in becoming entrepreneurs than students who do not have an entrepreneurial family background. In addition, students' perspectives on entrepreneurship positively affected their entrepreneurial intentions. Significance: Students' perspectives on entrepreneurship are related to their motivation to start new businesses that would be easily accepted by the social environment. All respondents agreed that entrepreneurial intentions could be improved through the role of entrepreneurship education during inequality education. To ensure the education is more targeted and all entrepreneurship education goals are achieved, it should have more effective curricula, methods, and learning system models. Cooperation between all elements incorporated in the entrepreneurship education system is indispensable for generating successful young entrepreneurs through entrepreneurship education.

Python Automated Tool to Detect Over Speeding of A Vehicle Through ANPR Technology

Abstract: Today, every country has serious issues with traffic management and car ownership identification. Car owners who violate traffic laws and drive too fast may be difficult to spot. It could be challenging for traffic officers to collect the license plate because of the speed of the moving car, making it impossible to identify and penalize such offenders. The creation of an automated system for number plate identification is crucial as a remedy for this problem. A web application that can identify license plates and display them will be created. To achieve the desired outcomes, EasyOCR and OpenCV will be combined with machine learning system models (such K-means or CNN). We'll make use of the YOLO dataset.

A Theoretical Framework for a Mathematical Cognitive Model for Adaptive Learning Systems.

The emergence of artificial intelligence has made adaptive learning possible, but building an adaptive system requires a comprehensive understanding of students' cognition. The cognitive model provides a crucial theoretical framework to explore students' cognitive attributes, making it vital for learning assessment and adaptive learning. This study investigates 52 experts, including primary and secondary school teachers, mathematics education experts, and graduate students, based on the 16 cognitive attributes in the TIMSS 2015 assessment framework. Through an analysis of their attribute questionnaires, the Interpretive structural modeling (ISM) method is used to construct a five-level mathematical cognitive model. The model is then revised through oral reports and expert interviews, resulting in a final cognitive model ranging from "memorize" to "justify". The cognitive model describes the relationship between different attributes in detail, enabling the development of adaptive systems and aiding in the diagnosis of students' cognitive development and learning paths in mathematics.