International Journal of Continuing Engineering Education and Life-Long Learning (18 papers in press)

Special Issue on: Smart and Continuing Education and Life Long Learning PART ONE

• Open Access

  Research on interactive audio-visual-oral teaching in college English ( Free Full-text Access )

  by Haixia Yin, Liming Wang 
  Abstract: With the advancement of information and communication technologies and globalization, English has become essential for accessing global information and promoting socio-economic development. This highlights the importance of effective English education in colleges. An interactive audio-visual-oral teaching mode (TM) is proposed to enhance students’ language skills through active participation. Supported by virtual reality (VR) and artificial intelligence (AI), this approach creates immersive 3D learning environments, improving students’ interest and self-directed learning. VR enriches audiovisual teaching with realistic sensory experiences, while AI personalizes instruction. Together, they offer innovative tools for modern college English education, fostering both theoretical understanding and practical communication skills.
  Keywords: English interactive teaching; audiovisual classroom; virtual reality; artificial intelligence.
  DOI: 10.1504/IJCEELL.2025.10072638
   
  

• Open Access

  Multimedia-based evaluation on the intelligent distance education mode of college English ( Free Full-text Access )

  by Jia Song, Zhiguo Feng 
  Abstract: As society develops, Distance Education (DE) has become increasingly important in modern education. The implementation of the DE mode provides learning opportunities for more and more people. Applying modern Information Technology (IT) to DE can effectively change the current education mode, especially applicable to the opening and growth of DE. With the help of modern IT, English distance learning can solve the serious shortage of educational resources and provide a good infrastructure for the implementation of distance learning. Therefore, this paper analysed the characteristics and functions of English DE, studied the application and existing problems of multimedia network English DE, and put forward some corresponding optimisation strategies to solve the current issues. Through comparison, the new mode’ teaching content design effect was 9.1% higher than the traditional ones, and the quality of teaching was 10.8% better than the conventional model. In other words, intelligent teaching and network multimedia can promote the English DE and improve its implementation quality.
  Keywords: college English teaching; information-based intelligent distance education; network multimedia; teaching optimisation strategy.
  DOI: 10.1504/IJCEELL.2025.10072600
   
  

• Open Access

  Indicators and characteristics of career visualisation ability based on cloud computing ( Free Full-text Access )

  by Zhen Zhang, Guixin Luo, Min Zhang 
  Abstract: Professional core competitiveness is the content that all countries in the world pay attention to. Especially in the information age, with the rapid development of artificial intelligence, how to better adapt to the professional environment is an urgent problem that needs to be solved. At present, there are problems with the evaluation of occupational visualisation ability and feature construction, such as large data differences, difficulty in fusion, and difficulty in efficiently extracting and standardising various occupational data. Moreover, due to the lack of adaptability and flexibility in the adopted technical means, the ability to analyse and express data is limited. To solve this problem, this paper takes cloud computing as the main technology and constructs an index system of individual visualisation ability. It meets the requirements of modern personnel training and promotes social development base on meeting individual development needs. The index system includes five dimensions: learning development ability, work communication ability, organisational integration ability, career transformation ability, and emotional control ability. There are four reference variables under each dimension. The reliability analysis of the scale of this index system shows that the internal consistency reliability coefficient, retest reliability coefficient, and split-half reliability coefficient of other subscales and total scales are above 0.84, except for the three reliability coefficients of career transformation ability subscales ranging from 0.75 to 0.77.
  Keywords: cloud computing technology; professional ability improvement; visual indicators of vocational ability; professional ability characteristics.
  DOI: 10.1504/IJCEELL.2025.10072639
   
  

• Open Access

  Journalism and communication professional personnel training and precise teaching methods based on artificial intelligence ( Free Full-text Access )

  by Xuhui Zhu, Ziwen Wang 
  Abstract: With the rapid development of media technology, the role of journalism and communication education in higher institutions has become increasingly critical. This study investigates how artificial intelligence (AI) and big data technologies can enhance the accuracy and effectiveness of journalism and communication teaching. Focusing on four universities in Jiangsu Province, the research evaluates key factors influencing teaching quality and explores AI-driven precise teaching methods. Results show an average score of 79.55, indicating significant improvement in classroom instruction and talent training. The findings provide valuable references for optimising journalism education and offer new insights into integrating AI into teaching practices, ultimately supporting more efficient and targeted communication professional development.
  Keywords: big data; artificial intelligence; college journalism communication course; precise teaching method.
  DOI: 10.1504/IJCEELL.2025.10072601
   
  

• Open Access

  The interrelationship of behavioural, emotional, and cognitive classroom engagement in higher education: understanding the mechanisms of student success ( Free Full-text Access )

  by Jin Yan, Linxi Shi, Choon Keong Tan 
  Abstract: This study delves into the correlation between undergraduate student engagement and their academic achievements within the context of Chinese culture. It systematically explores the interrelationship of various aspects of student engagement, including teacher-student relationships, student-student relationships, as well as cognitive and behavioural dimensions. Additionally, it investigates the moderating role of gender in the relationship between student engagement and student academic achievement. The results provide the following insights: 1) overall student engagement, teacher-student relationship, cognitive engagement, and behavioural engagement exhibit significant direct effects on student academic achievement, while student-student relationships do not significantly impact student academic achievement; 2) among the various components of student engagement, behavioural engagement has the most substantial influence on student academic achievement, followed by the teacher-student relationship. Cognitive engagement has the least pronounced effect on student academic achievement; 3) the four main components of student engagement affect student academic achievement in a serial pattern: teacher-student/student-student relationship → behavioural engagement → cognitive engagement → student academic achievement; 4) the influence of teacher-student relationship/behavioural engagement on student academic achievement is more prominent in male students than in female students.
  Keywords: behavioural engagement; BE; cognitive engagement; CE; student-student relationship; SSR; teacher-student relationship; TSR; student engagement; SE; student academic achievement; SAA.
  DOI: 10.1504/IJCEELL.2025.10072789
   
  

• Open Access

  Human-computer interaction design of intelligent multimedia information technology in online sports teaching platform ( Free Full-text Access )

  by Wenyu Zhang, Dan Huang 
  Abstract: With the advancement of information technology, intelligent multimedia has become essential in online physical education. This study explores human-computer interaction design in sports online teaching platforms, focusing on how intelligent multimedia technology enhances teaching effectiveness. Through experiments assessing student motivation, academic performance, and satisfaction of both students and teachers, results show that intelligent multimedia significantly boosts student engagement and learning outcomes. The design improves interaction, overcomes limitations of traditional teaching, and increases student satisfaction by 7.9%. It also enhances teacher satisfaction, demonstrating that intelligent multimedia integration promotes effective online physical education and supports digital education development.
  Keywords: sports online teaching; intelligent multimedia; human-computer interaction; HCI; digital teaching.
  DOI: 10.1504/IJCEELL.2025.10072925
   
  

• Open Access

  Visual translation of English classroom teaching system based on mobile learning ( Free Full-text Access )

  by Zhiguo Feng, Xiaosa Wang 
  Abstract: China's education is undergoing a new transformation. The development of the times requires that the teaching method of English translation change from 'teacher-led' to 'student-led', and intuitive classroom teaching is particularly important. Based on 'mobile learning', this article proposes a new model of English translation teaching that is different from the traditional teaching method. By introducing 'mobile learning' to compare the traditional teaching model, this article proposes a new model of English translation classroom teaching that meets the requirements of the times to solve objective problems such as the single translation method in the English classroom teaching system. The comparison results show that compared with the traditional English translation teaching model, the new model based on mobile learning has increased student satisfaction by 25%, and in terms of student freedom, English scores have increased by 23%. The learning effect index has increased from 0.43 to 0.7, and the student time freedom score has increased from 2.0 to 3.6 (5-level scale). It also shows that the English translation teaching model based on mobile learning is more in line with the times and is of great significance to students in English translation teaching classrooms.
  Keywords: classroom teaching system; English translation; classroom teaching; mobile learning.
  DOI: 10.1504/IJCEELL.2025.10073008
   
  

• Open Access

  Application of intelligent sensing and digital teaching mode in physical education teaching in colleges and universities ( Free Full-text Access )

  by Min Wu, Xiaoling Song 
  Abstract: To address the limitations of traditional university physical education in time and space and its inability to meet individual student needs, this study proposes an intelligent sensing + digital teaching model. Centred on a smart sensor network, it collects real-time motion data via wearable devices, uses Kalman filtering for noise reduction, and applies CNNs for accurate movement recognition. K-means clustering analyses student profiles to generate personalised training programs. An interactive digital environment built on Moodle enables data visualisation, real-time feedback, and online guidance, forming a ‘perception-analysis-feedback’ closed-loop system. Experiments show improved data quality and recognition accuracy, with a 28.6% fitness improvement among low-level students, demonstrating the model’s effectiveness in promoting scientific, precise physical education teaching.
  Keywords: digital teaching; college physical education; personalised training; Kalman filter algorithm; convolutional neural networks.
  DOI: 10.1504/IJCEELL.2025.10073046
   
  

• Open Access

  Intelligent music teaching system and method based on human-computer interaction technology ( Free Full-text Access )

  by Wen Yang, Dongming Hu 
  Abstract: An intelligent music teaching system based on human-computer interaction is proposed to address limitations in traditional music education. The system includes three modules: video-supervised teaching, instrument selection, and course selection. These form a complete intelligent teaching framework. Experimental comparisons were made between traditional and intelligent methods across three key issues in music education. Feedback from six students showed improvements in learning efficiency, engagement, and personalized instruction when using the intelligent system. Data analysis confirms the system’s potential to enhance music education by offering more interactive and tailored experiences. This study demonstrates the effectiveness of integrating interactive technology into music pedagogy.
  Keywords: intelligent music teaching; human-centred computing; precision rate; cost of economy.
  DOI: 10.1504/IJCEELL.2025.10073128
   
  

• Open Access

  The impact of blockchain big data algorithm technology on the interactive intelligent teaching mode of Chinese language and literature courses ( Free Full-text Access )

  by Suqin Liu 
  Abstract: With the advancement of curriculum reform, the teaching objectives of Chinese language and literature courses have gradually shifted to cultivating students’ practical application abilities and personalised learning. Interactive teaching provides students with more opportunities for self-expression, which is conducive to their development. This paper studies the evaluation and scoring of live teaching and recorded teaching by students majoring in Chinese language and literature through questionnaire survey, and sets up a simple group and a live teaching model group under the interactive intelligent teaching mode. The questionnaire data were analysed using big data decision tree technology. The results showed that about 58.5% of the students were delighted with the live teaching and believed it was effective. Their learning attitude and results had been significantly improved. However, more students disagreed with the recorded teaching mode.
  Keywords: interactive intelligent teaching mode; hash algorithm; decision tree; SHA256 algorithm.
  DOI: 10.1504/IJCEELL.2026.10073055
   
  

• Open Access

  Artificial intelligence-based emotion recognition application of English teaching in smart learning ( Free Full-text Access )

  by Cheng Huang 
  Abstract: With the rise of the intelligent era, innovative learning has gained increasing attention, particularly regarding students’ needs. While English teaching has moved away from the ‘dumb English’ approach, focusing more on integrating listening, speaking, reading, and writing, many still view English as a subject rather than a language, affecting teaching effectiveness. Due to spatial and temporal limitations, emotional interaction between teachers and students is lacking. This study explores an AI-supported emotion recognition teaching model, integrating relevance, originality, and impact (ROI) theory with innovative English education. An echo state network was constructed, and the algorithm was optimised. Emotion classification and speech signal preprocessing were implemented. Experimental results show improvements in students’ performance in vocabulary (3.8%), listening (4.5%), reading (5.9%), and speaking (7.1%) compared to traditional methods, enhancing smart learning quality and classroom interaction.
  Keywords: artificial intelligence; smart learning; English teaching; emotion recognition.
  DOI: 10.1504/IJCEELL.2025.10073221
   
  

• Open Access

  Automatic speech recognition based on adaptive parameters technology in English MOOC teaching system ( Free Full-text Access )

  by Juan Qian 
  Abstract: With the rise of MOOCs and the popularity of the internet, more and more English learners are beginning to study independently online. In order to accomplish system platform adaptation, enhance system compatibility, and increase the scoring mechanism's accuracy and dependability, this study studies a scoring approach based on adaptive parameters (AP). The system acquires the formant information of the learner's pronunciation and the standard reference pronunciation following preprocessing, FFT transformation, formant extraction, and other procedures. It contains a separate scoring parameter creation module to create adaptive parameters before speech scoring. The learner pronounces multiple voices in the scoring parameter generating module, and the expert assigns a score based on the learner's pronouncing experience. The experimental class's pre-test and post-test scores are in agreement with the design sample t-test result. This study can increase students' motivation to learn and enhance their practical English skills.
  Keywords: internet of things; IoT; automatic speech recognition; ASR; English MOOC teaching; humidity sensor; performance evaluation.
  DOI: 10.1504/IJCEELL.2025.10073313
   
  

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.
  
  
• 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