Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System

Purpose In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Design/methodology/approach In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Findings In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Originality/value In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

Cross-cultural learning competence, a critical skill in our globally interconnected world, is advanced through the application of the Backpropagation (BP) neural network algorithm. This innovative approach involves leveraging neural network techniques to model and enhance individuals' abilities to navigate and understand diverse cultural contexts. The BP neural network algorithm facilitates personalized learning experiences by adapting to individuals' cultural backgrounds and preferences. This research explores a comprehensive approach for assessing and enhancing cross-cultural learning competence among Chinese college students, integrating the Word Embedding Multilingual Model with the Back Propagation Neural Network (WEMM-BPNN) algorithm. Recognizing the importance of global competencies in higher education, our study focuses on leveraging advanced neural network techniques to evaluate and elevate students' cross-cultural learning abilities. The WEMM-BPNN model combines the power of word embedding and multilingual considerations, tailoring the learning experience to individual cultural backgrounds. Through a meticulous analysis of cross-cultural data and linguistic patterns, the algorithm refines its recommendations for personalized learning strategies. The research aims not only to assess the current state of cross-cultural learning competence but also to provide targeted interventions to enhance students' intercultural understanding and adaptability. By merging linguistic models with neural network algorithms, this study offers a pioneering approach to cultivating cross-cultural competencies, contributing valuable insights to the ongoing discourse on globalized education.

Learning style is an important factor that accounts for the individual student learning in any learning environment. Every student has a different learning style and different ways to percept, process, retain and understand new information. In this paper, a new approach is proposed to classify students learning style automatically and dynamically depending on their learning behavior in a learning management system (LMS). There have been several approaches proposed for automatic learning style detection. One of the widely accepted and frequently used classification techniques is the decision tree classifier. The decision tree classifier mainly depends on the construction of strong decision rules which are required to identify learning styles accurately. The lack of strong decision rules would lead to the misclassification of individual students learning style. Hence, this paper mainly focuses on the construction of strong decision rules to strengthen the existing decision tree classifier to accurately and precisely classify students learning style thereby improving the classification accuracy. The proposed approach has experimented with an average of 300 students enrolled for the online courses in Moodle LMS. Initially, the students' behavior are extracted from the web log files of LMS and then preprocessed to build decision tree classifier using strong decision rules based on the three learning dimensions of standard Felder Silverman learning style model (FSLSM). The evaluation result is obtained using the inference engine with forward reasoning searches of the rules until the correct learning style is determined. Based on the result obtained, the prediction of learning style is done for the new students automatically and accurately using the significant rules built in the decision tree classifier. The experimental result proved that the processing dimension shows variance in classification whereas perception and input dimension shows less variance with an average accuracy of 87%.

Forecasting and alert of atmospheric bioaerosol concentration profile based on adaptive genetic algorithm back propagation neural network, atmospheric parameter and fluorescence lidar

Pavement cracks are an important hidden danger for driving safety. Rapid and accurate detection of road crack types and effective treatment can greatly reduce the incidence of traffic accidents, which has important practical significance. Based on the advantages of scale invariant feature transform (SIFT) and back-propagation neural network (BP) algorithm, this paper proposes a method of road crack detection and recognition for four types of cracks, such as cracks, longitudinal cracks, transverse cracks and massive cracks.Firstly, the crack image is denoised and enhanced; Then scale invariant feature transform method is used to extract the feature point information of the route crack image; Finally, Back propagation neural network is used for training and recognition. The results show that this method can quickly and accurately identify the crack type, in which the accuracy of training set is 98.79%, and the accuracy of recognition set is 86.47%. The results show that the combination of scale invariant feature transform and back propagation neural network algorithm can effectively improve the efficiency of pavement crack type detection.

Adaptive e-learning systems are considered one of the interesting research areas in technology-based learning strategies. The main goal of these systems is to offer learns a personal and a unique learning experience based on their preferences, needs, educational background, learning style, etc. The objective of this research is to identify the learning style of the learner. The identification is based on using web Log Mining data which contain learning behavior of the learner, and then the learning styles are mapped to Felder-Silverman Learning Style Model categories using Fuzzy C means Algorithm. The learning style can be changed over a period of time therefore the system has to adapt to the changes. For this, an Artificial Neural Network Algorithm is used to predict the learning style of a learner.

Various types of learners can be observed in today’s e-learning environments. Learning Analytics can offer insights on a student’s actions and behaviour. We can correlate this data with the identification of learning styles like in the Felder-Silverman Learning Styles Model (FSLSM), and use it for user modelling. Supported by visualizations on learning styles and learning behaviour students and instructors can reflect the learning process. Based on our previously published conceptual model for linking learning analytics and learning styles in e-learning environments, we present an Integrated Learning Styles Analytics system (ILSA), which supports identification of learning styles as well as analysis and visualization of activity data in the Moodle LMS. ILSA consists of a questionnaire for learning style identification derived from FSLSM and offers immediate results to its participants. Next, by utilizing various data sources in Moodle, e.g. log data or the grade book, we are able to correlate a user’s activity with their learning styles. This work offers details on the finalized concept as well as its implementation. By providing insights on data sources in Moodle and presenting various visualizations, this work allows teachers to reuse the system in their e-learning courses.

Relational model of compositions of enamel coatings and sintering temperature based on back propagation neural network algorithm

To constantly enhance the accuracy of wind power prediction and furthermore reduce the uncertainty of power grid dispatching, this study proposes an improved back propagation (BP) neural network algorithm. The original prediction method of BP neural network algorithm has been improved, and the traditional minimum square error (SE) perform function is abandoned. Maximum correntropy criteria (MCC) algorithm which is more conducive to deal with non‐Gaussian error and big noise is introduced, and a new perform function is created. Through the analysis of examples, the feasibility of MCC algorithm is verified. Comparing to the traditional mean SE (MSE) perform function, MCC perform function could drop the limit error of prediction, reduce root MSE and increase the correlation between forecasting power and real power. The most important is that the prediction accuracy is enhanced.

Automatic detection of learning styles for an e-learning system

Introduction: Personal learning has become very difficult for effective e-learning systems, which enables the delivery of tailor-made educational content to different students. This article presents an advanced recommendation system that uses Felder-Silverman Learning Style Model (FSLSM) with a hybrid approach that uses both learning-based and course assessment-based recommendations. FSLSM groups users in distinct groups such as active-reflective, sensing-intuitive and visual verbal, allowing the system to identify user preferences through interaction data. The proposed system uses web scraping to gather extensive course details, including multimedia content and user feedback. Learning styles are analysed based on their performance results in the questionnaire. These styles are mapped to the FSLSM model, while course assessments, based on collaborative filtering, improve the recommendation process. By merging these two approaches, the system ensures that the recommended course corresponds to both the user's preferred learning style and the collective quality assessments from other students. Evaluation results show the system's efficiency in increasing the student's satisfaction and commitment compared to traditional recommendation models. This hybrid frame not only provides a dynamic and adaptive learning experience, but also offers a scalable solution for future e-learning platforms.

This study seeks to improve urban supply chain management and collaborative governance in the context of public health emergencies (PHEs) by integrating fuzzy theory with the Back Propagation Neural Network (BPNN) algorithm. By combining these two approaches, an early warning mechanism for supply chain risks during PHEs is developed. The study employs Matlab software to simulate supply chain risks, incorporating fuzzy inference techniques with the adaptive data modeling capabilities of neural networks for both training and testing. The results demonstrate that the proposed model effectively identifies factors contributing to supply chain deterioration, with a warning error as low as 0.001, significantly enhancing the accuracy and timeliness of demand forecasting. The BPNN algorithm, through its self-learning and adaptive features, facilitates dynamic optimization and precise scheduling across various stages of the supply chain. This capability is particularly valuable in addressing challenges associated with sudden demand spikes and resource allocation. As a result, the mechanism is able to accurately and promptly identify adverse trends in the supply chain, thereby enhancing the efficiency and flexibility of urban emergency responses, mitigating risks, and offering both theoretical and practical contributions to urban collaborative governance.

Gamification has become a powerful tool for enhancing student engagement in educational settings However, the effectiveness of a gamified system depends on its alignment with individual student characteristics. This has led to the rise of adaptive gamification, where game elements are tailored to meet the unique needs of each learner. This research aims to bridge the gap between adaptive gamification and student-centered learning by designing a personalized gamification platform based on the Felder-Silverman Learning Style Model (FSLSM) and Bartle's Four Player Types. However, traditional questionnaires can be overwhelming and potentially affect student engagement. This study aims to reduce lengthy versions of these questionnaires that retain diagnostic power while being more manageable for students, with the help of factor analysis and comparative analysis, FSLSM and Bartle’s player type questionnaires were reduced. Then a questionnaire is developed with reduced version of FSLSM and Bartle’s player type with some more questions to capture learners’ preferences for gamification element, themes, and suggestions for improving the gamified platform for education. This developed questionnaire has been floated among college students. Out of 218 valid responses, fuzzy c-means clustering revealed four distinct clusters, with two major clusters showing strong preferences for specific gamification themes. These themes were integrated into the platform to create a more engaging and personalized experience. By reducing and validating the FSLSM and Bartle’s questionnaires, this research offers a streamlined approach for educators to implement personalized gamification strategies in the classroom. The findings provide insights for developing effective, student-centered adaptive gamification systems in education.

In order to reduce the error of automatic frequency control of transmission machinery under different disturbances, an automatic frequency control system of transmission machinery based on back-propagation (BP) neural network algorithm is designed. The gradient descent method and Newton method are combined to optimize the BP neural network. Aiming at the periodicity and trend of the actual mechanical vibration frequency time series, the first order backward difference is processed by the difference method, and the autoregressive sequence is derived to obtain the control model. The experimental results show that the system can realize the automatic control of transmission mechanical frequency with small error under different types of interference, and the control effect is ideal.

Learning styles vary according to the individual and this diversity is fundamental in terms of teaching as curricula must respond adaptively to the various learning styles of pupils. This study conducts an analysis of an Arabic form of the Index of Learning Styles (ILS), a 44-item questionnaire designed to determine learning styles using the Felder-Silverman learning style model. This study focuses on the interpretation of data derived from the Arabic form of the Index of Learning Styles (ILS) to establish correlations between the learning styles of 1024 female students drawn from two specific departments at the King Abdul- Aziz University in Saudi Arabia. The findings, generated by Multiple Correspondence Analysis and cross-validated by correlation analysis, demonstrate a definite link between certain learning styles from opposing dimensions that are considered to be contradictory within the same dimension of learning. The validity and reliability of the Arabic scale was established and compared to the examples reported in the literature. Findings show comparable reliability and factor analysis supports the interdependencies between dimensions and perhaps the constructs they intend to assess. The results of this paper have implications for the design of e- learning tools, materials and sessions in order to adapt to the relationships between learning styles and have a positive impact on the learners themselves and their learning experience