International Journal of Continuing Engineering Education and Life-Long Learning (13 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
   
  

• Open Access

  Analysis of factors influencing student learning experience in the blended online-offline smart education model ( Free Full-text Access )

  by Yuan Fang, Juan Hu 
  Abstract: Research on the influencing factors of students' learning experience in the blended online-offline smart education model is significant for optimising the allocation of educational resources and improving teaching effectiveness. In this study, first, 14 key factors were identified across five dimensions, including course environment, platform, and course design. Through expert inquiry, logical connections between factors were analysed, and an adjacency matrix was established. Next, the reachable matrix was calculated to reveal the hierarchical structure, defining the reachable set and antecedent set to extract factors hierarchically. Finally, an interpretive structural model was constructed to demonstrate the hierarchy and interactions among factors. Test results indicate that the proposed method maintains over 95% accuracy in analysing learning experience factors, with a factor weight stability index ranging between 0.97 and 0.99 and a fluctuation amplitude of only 0.02.
  Keywords: blended online and offline; smart education model; learning experience; analysis of influencing factors.
  DOI: 10.1504/IJCEELL.2025.10074576
   
  

• Open Access

  Study on fuzzy comprehensive evaluation of English teaching quality based on artificial neural network ( Free Full-text Access )

  by Lingyan Mao, Yuting Yan, Aihua Shen 
  Abstract: In order to improve the coverage and accuracy of evaluation indicators, a fuzzy comprehensive evaluation method for English teaching quality based on artificial neural networks is proposed. Firstly, association rules and rough set techniques are used to collect and analyse English teaching data, and a fuzzy comprehensive evaluation index system for English teaching quality is constructed. Secondly, a multi-layer fuzzy comprehensive evaluation model is constructed, and sample data of English teaching quality is obtained by the expert scoring method. The data are used for subsequent artificial neural network learning and prediction. Finally, a fuzzy comprehensive evaluation model for English teaching quality is constructed using BP neural network. By training and learning the internal data association pattern of the network, English teaching quality evaluation is achieved. The experimental results show that the indicator coverage of the proposed method remains stable between 94.6% and 95.4%, and the highest evaluation accuracy reaches 98%.
  Keywords: artificial neural network; English teaching quality; fuzzy comprehensive evaluation; evaluation index system.
  DOI: 10.1504/IJCEELL.2025.10074954
   
  

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

• Open Access

  Design of distance English translation teaching system based on digital multimedia intelligent equipment ( Free Full-text Access )

  by Fali Liang 
  Abstract: With the development of science and technology and network technology, the application of artificial intelligence technology, and the advent of the internet era, computer multimedia-assisted foreign language teaching technology has been continuously innovated and improved. First, suitable digital multimedia equipment, including large database software, mobile multimedia intelligent servers, and equipment that supports voice recognition and machine translation, are selected. The system realises interaction and resource sharing between students and teachers through computer networks and database management functions. At the same time, multi-language recognition technology, voice synchronisation translation equipment, and multimedia teaching software are used to support cross-border translation and remote video conferencing interfaces, realising flexible and traceable remote teaching. The results of this experiment show that in the post-test scores of the experimental group (Group M) and the control group (Group N), the average score of group M is 9.708, and the average score of group N is 8.74.
  Keywords: English translation teaching; digital multimedia; distance teaching; intelligent assistance; teaching and training.
  DOI: 10.1504/IJCEELL.2025.10074868
   
  

Regular Issues

• Fuzzy Evaluation of Digital Teaching Quality of Theoretical Courses in Application-oriented Universities under AI Empowerment  
  by Jian Xie, Dan Chu 
  Abstract: In order to overcome the problems of low precision of indicators, high error rate in weight calculation, and low evaluation accuracy in traditional evaluation methods, a fuzzy evaluation method of digital teaching quality of theoretical courses in application oriented universities by AI empowerment is proposed. Using factor analysis to screen fuzzy evaluation indicators for teaching quality, building a fuzzy evaluation indicator system for teaching quality by AI empowerment, and using random forest algorithm to calculate indicator weights. The fuzzy evaluation results of the digital teaching quality of applied theoretical courses in universities are obtained by combining indicator weights and fuzzy comprehensive evaluation methods. The experimental results show that the minimum precision of the teaching quality evaluation index of the proposed method is 96.17%, the minimum error rate of the evaluation index weight calculation is 3.15%, the quality evaluation accuracy is above 93.3%, and the evaluation effect is good
  Keywords: AI empowerment; Application oriented universities; Theoretical courses; Digitization; Teaching quality; Fuzzy evaluation; Random forest algorithm; Fuzzy comprehensive evaluation.
  
  

Special Issue on: Smart Education in the Digital Society

• 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
   
  
• Study on resource allocation of English multimedia network teaching for digital education reform  
  by Fan Yang, Luping Zhang 
  Abstract: This study developed an innovative method for allocating English multimedia network teaching resources to overcome limitations in traditional approaches, including poor validity, low matching accuracy, and process inefficiency. The proposed solution involved three key phases. First, teaching resources were collected and processed using Kalman filtering for noise reduction; subsequently, comprehensive feature extraction was performed through multi-task width learning; finally, user preferences were calculated via an interest model while resource matching was optimised using graph planning algorithms. The allocation model incorporated Pearson correlation coefficients to enhance precision. Experimental results demonstrated exceptional performance metrics: allocation validity approaching 100%, absolute matching error consistently below 0.2%, and allocation efficiency reaching 99%. These outcomes confirmed the methods significant improvement over conventional techniques, particularly in digital education reform. The systems robust performance stemmed from its integrated approach combining advanced filtering, comprehensive feature analysis, and intelligent matching algorithms, establishing a new performance benchmark for educational resource allocation systems in both accuracy and operational efficiency.
  Keywords: Kalman filter algorithm; allocation of teaching resources; width learning; user interest model; K-means algorithm.
  DOI: 10.1504/IJCEELL.2025.10074516
   
  
• Fuzzy comprehensive evaluation of MOOC English teaching quality based on improved entropy method  
  by Yifan Liang 
  Abstract: In the practical application of MOOC English teaching quality evaluation, the entropy method exhibits high sensitivity to the degree of data dispersion. Once the sample data presents a local concentration trend or is disturbed by extreme values, it will cause an imbalance in the allocation of indicator weights, ultimately undermining the evaluation accuracy. Therefore, a research on fuzzy comprehensive evaluation of MOOC English teaching quality based on improved entropy method is proposed. In this method, an evaluation index system is constructed for MOOC English teaching quality, and grey relational analysis is conducted to screen indicators. Then indicator data is collected, and outlier cleaning is performed to ensure data quality. Subsequently, with processed indicator data as input, the entropy method is used to determine indicator weights, and fuzzy comprehensive evaluation method is applied to improve indicator weights. Based on the determined indicator weights, the fuzzy comprehensive evaluation of MOOC English teaching quality is completed. The results show that this method can effectively determine key evaluation indicators, with a high sensitivity coefficient of 1.97, the highest evaluation accuracy of 0.98, and the highest evaluation time of 3.792 seconds, demonstrating a good evaluation performance.
  Keywords: MOOC English teaching; teaching quality evaluation; grey correlation analysis; entropy method; fuzzy comprehensive evaluation method.
  DOI: 10.1504/IJCEELL.2026.10074951
   
  
• The characteristics of MOOC learners online learning behaviour and learning performance evaluation  
  by Shan Li 
  Abstract: To accurately analyse and evaluate the online learning situation of MOOC learners, this article focuses on MOOC learners and proposes innovative evaluation methods. First, using techniques such as association feature extraction and adaptive mining, the online learning behaviour data of MOOC learners are collected. Second, by analysing in detail the behavioural data of course access, video learning, homework submission, and daily grades, the external performance characteristics of MOOC learners online learning behaviour can be quantitatively analysed. Finally, a multi-level fuzzy comprehensive evaluation method is used to construct a learning performance evaluation system, which quantitatively evaluates learning performance by setting evaluation factor sets, weight allocation, and comment sets. The test results show that the proposed method exhibits the highest feature analysis accuracy of 98% and performance evaluation accuracy of 95%.
  Keywords: MOOC learners; online learning behaviour; characteristic analysis; learning performance evaluation.
  DOI: 10.1504/IJCEELL.2026.10074952
   
  
• Evaluation of classroom teaching effectiveness empowered by AI teachers based on bat algorithm random forest classification  
  by Qingmei Lu, Junli Li, Yuan Gao 
  Abstract: The large amount and complex types of data empowered by AI teachers in classroom teaching result in evaluation outcomes that fail to accurately reflect teaching effectiveness. To address this, research is conducted on evaluating the effectiveness of AI teacher-empowered classroom teaching based on bat algorithm-optimised random forest classification. First, an evaluation index system for AI teacher-empowered classroom teaching effectiveness is constructed, covering multiple dimensions. Second, variance inflation factor and principal component analysis are employed to screen indicators, ensuring efficiency. Finally, a bat algorithm-optimised random forest classification model processes complex data to build an accurate and highly generalisable evaluation model for teaching effectiveness. Results indicate that the method achieves a mean square error below 0.2, a Spearman rank correlation coefficient exceeding 0.95, and an evaluation time of up to 3.75 seconds.
  Keywords: AI teacher empowering classroom; teaching effectiveness evaluation; random forest classification algorithm; bat algorithm.
  DOI: 10.1504/IJCEELL.2026.10074953