International Journal of Continuing Engineering Education and Life-Long Learning (16 papers in press)

Special Issue on: OA Adaptive E-Learning Technologies and Experiences

• Open Access

  Construction of intelligent student management evaluation information system based on clustering algorithm ( Free Full-text Access )

  by Hongyuan Ma, Yin Chen, Yujia Song 
  Abstract: For a long time, the management evaluation information work of students has relied on the subjective evaluation of people. Such evaluation is not highly professional and scientific, and the evaluation experience is not easy to be inherited and preserved. This paper selected the K-means clustering algorithm to build an intelligent learning management evaluation information system, because it can better reflect the actual needs and solve the application requirements according to the actual needs. This paper uses the grades of 20 college students as data samples and applies the K-means clustering algorithm to the intelligent student management and evaluation information system. The management and processing mechanism of students in colleges and universities had been improved to improve the speed of data analysis, so that the outstanding rate of students had increased by about 20% compared with the original. This could help the decision-making of colleges and universities to be more professional. Through mobile communication intelligent decision support system, the construction of the student management evaluation information system was realised.
  Keywords: k-means clustering algorithm; information evaluation system; performance analysis; clustering algorithm; student performance.
  DOI: 10.1504/IJCEELL.2025.10074391
   
  

Special Issue on: OA Smart and Continuing Education and Life-Long Learning

• Open Access

  Creation and application of education and management intelligence software based on artificial intelligence strategy ( Free Full-text Access )

  by Luyun Gao, Jie Xiao 
  Abstract: With the rapid development of AI, intelligent technology is in a rapid development stage an in-depth discussion on the testing methods, testing standards, and applications of AI software can help ensure the ultimate business purpose of software applications. In the continuous deepening of teaching reform, the introduction of AI technology into teaching is a major measure to improve the level of teaching and education management. With the development of technology, a variety of new technologies have been widely used in the development of information technology, making all kinds of software in the process of rapid development, also undergoing rapid changes. Through the detailed analysis of the application of this technology in education and management, the technical upgrading of the corresponding software could be effectively realised. Based on the background of AI, this paper analysed the characteristics and applications of AI and studied the structural model of software components.
  Keywords: artificial intelligence; intelligent software; education and management; probabilistic programming.
  DOI: 10.1504/IJCEELL.2025.10074389
   
  

• Open Access

  Multi-objective construction of English online autonomous learning based on mobile intelligent information system ( Free Full-text Access )

  by Juan Liu 
  Abstract: With the spread of English around the world, peoples demands on English are increasing day by day. For Chinese people, English is not their mother tongue, so English learning would become very difficult. There are two reasons for this difficulty, one is the subjective initiative of English learning, the other is the lack of understanding of English learning methods. This paper started with English learning methods and focuses on the current situation of English autonomous learning. On this basis, this paper used mobile intelligent information system to find and improve some problems in English teaching. Through the performance test of the English autonomous learning system, it was found that when the number of concurrent users reaches 1,000, the network utilisation rate of the system increased from 5% to 55%, which reduced the use of the entire system by 50%. On the basis of 1,000 terminals, the average memory utilisation rate and CPU utilisation rate reached the highest value, 45% and 57% respectively. Therefore, the application of English online autonomous learning is the most important topic at present.
  Keywords: English web-based self-directed learning; mobile intelligent information systems; self-directed learning indicators; artistic intelligence; multimedia technology; multi-objective optimisation.
  DOI: 10.1504/IJCEELL.2025.10074390
   
  

Special Issue on: Smart and Continuing Education and Life Long Learning Part 2

• Open Access

  Evaluation method of Chinese grammar interactive teaching based on sentiment classification ( Free Full-text Access )

  by Xue Wang, Guozhang Li 
  Abstract: To better cultivate students’ interest in learning Chinese grammar and master effective learning strategies, the interactive teaching method of Chinese grammar is widely used in the classroom. However, most students cannot be completely objective and fair when evaluating Chinese language interactive teaching, and cannot guarantee the objectivity and rationality of evaluation information. There are still many problems in the evaluation work. These problems also impact the original purpose of Chinese language teaching evaluation work, ultimately affecting the teaching efficiency and quality between teachers and students. This paper applied sentiment classification technology to the evaluation of Chinese grammar interactive teaching, and conducted a brief research on the evaluation methods of Chinese grammar interactive teaching. It was proposed that the application of emotion classification technology in the current teaching evaluation system of colleges and universities would improve the evaluation quality of Chinese grammar interactive teaching by about 21.9%. Furthermore, it provided some impetus to the interaction between teachers and students, teaching quality, and teaching strategies of Chinese grammar.
  Keywords: teaching evaluation; emotional classification; interactive teaching; Chinese grammar.
  DOI: 10.1504/IJCEELL.2025.10074282
   
  

Special Issue on: Smart and Continuing Education and Life Long Learning Part Two

• Open Access

  Innovation of college English listening and speaking teaching model based on multimedia technology ( Free Full-text Access )

  by Zhiguo Feng, Ciren Deji 
  Abstract: With the improvement of the modern education system, multimedia technology is widely embedded in the classroom teaching of various subjects. Classroom teaching based on multimedia technology conveys relevant knowledge information to students through the use of text, images, sounds, and other media, reasonably selects modern learning media according to students’ actual learning conditions, and adequately combines it with traditional classrooms, which can significantly enrich classroom teaching and help students understand and remember knowledge, and improve students’ quality. At present, in teaching speaking and listening in English classes in colleges, the application of mobile information technology is relatively poor, resulting in the inability to make breakthroughs in the quality of English classroom teaching. To address this issue, this paper applied multimedia technology to the exploration of the innovation of college English listening and speaking education mode, and constructed a teaching environment under multimedia technology through big data technology. According to the trial’s results, employing the multimedia technology-based teaching mode for college English speaking and listening could enhance instruction by 9.72% compared to the conventional mode. This allowed colleges and universities to offer higher-quality college English courses and enhance students’ English language proficiency.
  Keywords: English listening and speaking education; multimedia technology; big data; university English.
  DOI: 10.1504/IJCEELL.2025.10074019
   
  

• Open Access

  How intelligent semi-supervised learning illuminates influencing factors in college students employment psychology ( Free Full-text Access )

  by Yi Zhou 
  Abstract: This work proposed an analysis model of the influencing factors of college students’ employment psychology combined with intelligent semi supervised learning technology. The analysis effect of the influencing factors of college students’ employment psychology is further improved, helping college students correct their mindset and better cope with social employment. In addition, it introduced the class-aware contrastive learning module and the label-guided iterative self-incremental learning module, which help the model fully explore the potential features of unlabelled data and effectively solve the problem of insufficient labelled data on the psychological factors of college students’ employment. It indicated that the higher the mental health literacy of graduates, the higher their psychological resilience level. Therefore, when providing employment guidance, schools need to carry out the work in stages and groups, cultivate students’ psychological resilience and positive coping styles in the face of setbacks, and enhance graduates’ confidence in their own career development.
  Keywords: semi-supervised learning; college students; employment psychology; influencing factors.
  DOI: 10.1504/IJCEELL.2025.10074083
   
  

Special Issue on: Smart Education in the Digital Society

• Personalized recommendation method for online learning resources in blended learning based on knowledge graph  
  by Quanzhong Yang, Yu Zhang, Xiaoli Li, Feifei Shen, Xiaoyin Wang, Ruili Zhang, Yongwei Chen 
  Abstract: To improve the hit rate of personalised learning resource recommendations, this paper proposes a knowledge graph-based personalised recommendation method for online learning resources in blended learning. First, user browsing data on online learning resources is collected through web crawlers to construct a knowledge graph. Then, the self-organising map network and K-means clustering algorithm are used to cluster the learning resource content, while graph neural networks calculate the similarity between users and resources. Finally, the users rating for recommended resources is predicted, and a time-weighted strategy is introduced to dynamically adjust the ratings, completing the recommendation based on the rating results. Experimental results show that this method consistently maintains a hit rate above 95%, achieves a minimum normalised discounted cumulative gain of 0.92, and reaches a maximum entropy value of 0.75.
  Keywords: blended learning; online learning resources; resource recommendation; knowledge graph; self-organising mapping network; K-clustering algorithm.
  DOI: 10.1504/IJCEELL.2025.10072393
   
  
• Predicting the learning effectiveness of higher mathematics based on learning behaviour analysis  
  by Fang Huang 
  Abstract: A prediction method of higher mathematics learning results based on learning behaviour analysis is proposed aiming at the problems of large mean square error and low PL value in the prediction of higher mathematics learning results. First of all, the data of students’ learning behaviour is mined and students’ files are constructed; secondly, the dimensionality of learning behaviour data is reduced through the application of the time series t-SNE dimensionality reduction technique, and collaborative filtering algorithm is used to extract students’ learning behaviour characteristics. Then, the sample set of learning behaviour characteristics is constructed, and fuzzy clustering algorithm is used for clustering. Finally, the minimum loss function is established by using the random gradient descent method to predict the learning performance based on the federated learning algorithm. The experimental findings indicate that the PL value predicted by this approach consistently stays above 90%, and the model achieves a minimum mean square error (MSE) of 0.05, demonstrating high prediction accuracy and robust performance.
  Keywords: learning behaviour; learning effectiveness; student profiles; fuzzy clustering; federated learning.
  DOI: 10.1504/IJCEELL.2025.10073537
   
  
• An evaluation of the impact of multimedia online live teaching on students' academic performance  
  by Hua Liu 
  Abstract: To address the limitations of low recall, precision, and evaluation accuracy in conventional evaluation approaches, a new method for evaluating the impact of multimedia online live teaching on students’ academic performance is proposed. The random forest algorithm is used to screen the influencing factors, and principal component analysis is adopted to reduce the dimensionality of the influencing factor data. The dimensionality-reduced data is input into the optimised BP neural network for iterative training to obtain relevant evaluation results. Experimental outcomes indicate that the multimedia online live teaching in accordance with the proposed approach can attain a maximum recall rate of 98.15%, a maximum precision rate of 98.74%, and an evaluation accuracy rate varying from 90.2% to 96.8% when it comes to the factors affecting students’ academic performance.
  Keywords: multimedia; online live teaching; student; academic performance; impact evaluation; random forest algorithm; principal component analysis; PCA: GA optimised BP neural network.
  DOI: 10.1504/IJCEELL.2025.10073906
   
  
• Influencing factors of learning behavior intention under the background of college English curriculum reform  
  by Shu Wang 
  Abstract: This paper analyses the influencing factors of learning behaviour intention under the background of college English curriculum reform with UTAUT model. This paper constructs an analytical framework that includes four core elements and corresponding regulatory variables: performance expectation, effort expectation, social influence and contributing factors, designs a targeted questionnaire, and proposes and tests a series of hypotheses. Through the reliability test and regression analysis of the collected data, this study examines the factors influencing learning intention under the background of English curriculum reform is completed. Results demonstrate that this method achieves over 90% reliability and coverage in analysing learning intention factors, and the analysis effect is good.
  Keywords: English curriculum reform; learning behaviour intention; UTAUT model; regression analysis.
  DOI: 10.1504/IJCEELL.2025.10073687
   
  
• An analysis of the enhancement effect of student classroom learning participation in immersive virtual learning environment  
  by Kuo Xu 
  Abstract: In order to explore the effectiveness of immersive virtual learning environments (IVLE) and their impact on students classroom learning participation, an analysis of the effect of enhancing students classroom learning participation in IVLE is conducted. First, the characteristics of IVLE are examined. Subsequently, the AlphaPose pose estimation algorithm is adopted to accurately analyse the characteristics of student classroom participation, and a multi-level classification model for classroom participation based on improved support vector machine and ant lion algorithm optimisation is constructed. Finally, strategies to effectively enhance students classroom learning participation in IVLE are proposed, including optimising environment design, providing personalised learning support, establishing incentive mechanisms, and strengthening teacher guidance and support. The experimental results demonstrate that the participation accuracy of the proposed method is maintained above 90%, and the average hourly frequency of students distracted behaviour is decreased to five occurrences.
  Keywords: immersive virtual learning environment; IVLE; student classroom learning; participation rate; improve effectiveness.
  DOI: 10.1504/IJCEELL.2025.10073688
   
  
• A quality evaluation method for Chinese online teaching content based on deep learning  
  by Yanling Xu 
  Abstract: To refine the precision and efficiency of evaluating online educational content quality, a sophisticated Chinese online teaching content evaluation technique harnessing deep learning has been devised. The methodology commences with the construction of an evaluation architecture that integrates a hierarchical indicator system. Following this, the Euclidean distance method is applied to scrutinise the correlation within Chinese online teaching materials, while feature quantification decomposition is adopted to isolate key characteristics from the content, thereby facilitating the extraction of crucial educational information. Conclusively, the isolated Chinese online teaching materials are utilised as inputs, with the resultant quality evaluation serving as outputs, and a deep learning-based model is developed using deep belief networks to perform a nuanced evaluation of the quality of Chinese online teaching materials. Experimental findings indicate that the method, achieves a markedly reduced maximum RMSE error of just 0.28, and substantially decreases the evaluation timeframe.
  Keywords: deep learning; Chinese online teaching; content of courses; quality evaluation.
  DOI: 10.1504/IJCEELL.2025.10073689
   
  
• A fuzzy evaluation of information resource management performance in higher education from the perspective of knowledge management  
  by Zhimin Zhao, Pei Niu 
  Abstract: In order to help universities better manage information resources and improve the quality of teaching and research, this paper conducts a fuzzy evaluation of the performance of educational information resource management in universities from the perspective of knowledge management. Firstly, analyze the knowledge transformation mode and design a performance evaluation index system; Secondly, Cronbach's Alpha was used to calculate the reliability of the indicators. Then, calculate the weight of the indicators and multiply the weight vector with the fuzzy relationship matrix to obtain the fuzzy evaluation result. The results show that the comprehensive evaluation values of educational information resource management performance in the three universities are 83.2691, 78.9212, and 82.3145, respectively, with evaluation levels of "excellent", "good", and "good", which is consistent with the evaluation of them by various sectors of society. This provides a reference for the development of educational information resource management in local universities.
  Keywords: Cronbach's alpha; fuzzy relation matrix; weight vector; fuzzy evaluation.
  DOI: 10.1504/IJCEELL.2025.10074086
   
  
• Evaluation system for the effectiveness of college English curriculum teaching reform based on DPSIR model  
  by Wei Lin 
  Abstract: In order to improve the effectiveness of college English curriculum teaching reform and ensure the scientific and objective nature of teaching evaluation, this paper proposes an evaluation system for the effectiveness of college English curriculum teaching reform based on the Driving Force Pressure State Impact Response (DPSIR) model. Principles for selecting evaluation indicators for the effectiveness of college English course teaching reform, constructing a preliminary evaluation system, and conducting reasonable assignment and quantitative analysis of expert questionnaires. Introduce the DPSIR model to classify indicators, determine indicator weights based on these criteria layers, use the TOPSIS model to calculate closeness, obtain the reform effectiveness rating standard, and evaluate the reform effectiveness indicators based on this. The experimental results show that the utilization efficiency of English teaching resources in this method is 99.8%, the highest evaluation accuracy can reach 99.5%, and the evaluation time varies from 1.5s to 6.2s, indicating that this method can improve the effectiveness evaluation of college English course teaching reform.
  Keywords: driving force pressure state impact response; DPSIR model;TOPSIS model; CIPP evaluation model; weight assignment.
  DOI: 10.1504/IJCEELL.2025.10074088
   
  
• Interactive physical education teaching and learning environment based on immersive VR Immersive VR  
  by Xiaoai Gao 
  Abstract: This study explores the development of an interactive physical education teaching and learning environment utilising immersive virtual reality (VR) technology. Firstly, the characteristics of an interactive physical education teaching environment under immersive VR are analysed. Secondly, by integrating the mathematical models of the high-pass acceleration channel, tilt coordination channel, and high-speed angular velocity channel, high-intensity acceleration, body tilt, and angular velocity changes are accurately simulated. Finally, with the support of immersive VR technology, real-time interaction between students and virtual scenes is achieved through layout calculation, lighting simulation, motion sensing interaction capture, pose tracking, collision detection, and viewpoint transformation. The test results show that the method proposed in this article can significantly increase the student interaction frequency, reaching up to 17 times per hour, and exhibits a significant advantage in the utilisation rate of teaching equipment.
  Keywords: immersive VR; interactive learning; physical education teaching;learning environment.
  DOI: 10.1504/IJCEELL.2025.10074089
   
  
• A method for detecting students concentration in online Chinese course learning based on deep learning  
  by Hang Liu 
  Abstract: There is a problem of poor detection effect in detecting student concentration during online Chinese course learning. Therefore, a deep learning-based method for detecting student concentration in online Chinese courses is designed in this paper. Firstly, the facial expression feature attributes of Chinese online learning students are determined, and the Gabor + LBP method is used to extract their facial expression features. Then, the AlphaPose algorithm is used to obtain the joint coordinates of Chinese online learning students and extract specific action detail features. Finally, convolutional blocks are introduced for student focus feature vector classification, and a deep learning-based focus detection model is constructed to output the detection results. The experimental results show that this proposed approach improves the accuracy of identifying students’ attentiveness in remote Mandarin learning. This method helps to more accurately grasp students’ learning status, thus providing strong support for optimising online Chinese course teaching strategies and improving teaching quality.
  Keywords: deep learning; Chinese courses; online learning; student focus; testing; spatial attention; fully connected layer.
  DOI: 10.1504/IJCEELL.2025.10074091