International Journal of Continuing Engineering Education and Life-Long Learning (12 papers in press)

Regular Issues

• Semiotics in engineering education for enhancing communication awareness  
  by José Luis Díaz Palencia, Yanko Ordonez Ontiveros, Julian Roa Gonzalez 
  Abstract: The aim of this work is to explore the potential benefits of considering semiotics in engineering education to promote communication skills and cultural awareness among engineering students. We introduce semiotic-based activities in various engineering courses at the university level. The intention is to promote students’ awareness concerning the role of symbols in engineering, which lead to a better understanding of their cultural and communicative significance. The classroom activities introduced within this work highlight the need for clear, consistent, and universally understandable symbols in engineering practice to promote a universal community approach in the engineering profession. This paper is rooted in the dynamic nature of signs (as noted by Peirce, 1931) and the necessity for pre-service engineers to adapt to evolving symbols as they may appear in their future professions.
  Keywords: semiotics; engineering education; symbolic communication; cultural context; technical communication.
  DOI: 10.1504/IJCEELL.2025.10070058
   
  
• Based on the S-O-R model - how does interaction influence engineering students’ English learning continuance on online platform  
  by Wei Yin, Jiayi Li, Kehan Ji 
  Abstract: Students’ satisfaction and continuance intention are critical for the success of online courses. Extensive research has been conducted to explore the contributors of continuance intention, focusing on the student-related and technological elements. However, few attempts have been made to comprehensively explore the influence of online learning interaction on students’ continuance intention. Based on the stimulus-organism-response (S-O-R) model, this study proposed a model taking online interactions as the stimulus variable, learners’ satisfaction as the organism variable, and learners’ continuance intention as the response variable. Data on student engagement, satisfaction, and persistence in online learning were collected from a sample of 771 engineering students, ranging from freshmen to postgraduates. The data were analysed using structural equation modelling (SEM) to validate the proposed theoretical model. The results indicate that student-content interaction exerts the most significant influence on students’ continuance intention among the four types of online learning interactions, highlighting the importance of learning materials’ content quality. Meanwhile, the other three types of online interaction also have considerable influence coefficient. The findings provide some advice on improving e-learning interactions to promote engineering students’ online English learning satisfaction and continuance intention..
  Keywords: online interaction; engineering students; online English learning; satisfaction; learning continuance; S-O-R model.
  DOI: 10.1504/IJCEELL.2025.10070136
   
  

Special Issue on: Online Learning and Digital Education Opportunities and Challenges

• Anomaly detection of learners' online learning behaviour based on educational data mining  
  by Baoyu Huang 
  Abstract: To improve the accuracy and efficiency of anomaly detection in online learning behaviour of learners, a research on anomaly detection of online learning behaviour of learners based on data mining is proposed. Firstly, by utilising the monitoring and recording module of the online education platform, the online learning status images of learners are collected and processed for noise reduction, binarisation, and connectivity. Then, based on this, the degree of change in the centroid of the human body contour is introduced to extract features for online learning behaviour anomaly detection. Finally, the improved YOLOv3 model is applied to construct an online learning behaviour anomaly detection model for learners, achieving the function of behaviour anomaly detection. The experimental outcomes reveal that the proposed method attains a detection precision exceeding 94.7%, with a peak detection latency of 1.81 ms.
  Keywords: data mining; learners; online learning behaviour; anomaly detection.
  DOI: 10.1504/IJCEELL.2025.10068950
   
  
• Grey correlation analysis on influencing factors of online learning satisfaction of college students' Chinese course  
  by Yaping Zeng, Kun Yang, Yicheng Yang 
  Abstract: To optimise the effectiveness of online learning, a study was conducted on the influencing factors of satisfaction with Chinese language courses for college students. Firstly, the concept of satisfaction with online learning among college students is elaborated, and an evaluation index system is constructed by selecting indicators to complete the selection of influencing factors. Then, a corresponding survey questionnaire is designed to collect data, and a grey correlation model is constructed to complete the grey correlation analysis of influencing factors. The results showed that all selected factors were significantly correlated with online learning satisfaction, with the highest correlation between teacher-student interaction and learner interaction and learning satisfaction, reaching 0.975 and 0.941, respectively. Educators should focus on teacher-student interaction and learner interaction, and comprehensively consider factors such as self-management, learning motivation, teacher support, and curriculum design to provide students with a high-quality online learning environment.
  Keywords: college students' Chinese course; online learning satisfaction; influencing factors; grey relational analysis.
  DOI: 10.1504/IJCEELL.2025.10068951
   
  
• A prediction method for teaching effect of college English online course based on learner behaviour mining  
  by Meng Su 
  Abstract: Aiming at the problems of low mining accuracy, poor prediction accuracy and long time in traditional prediction methods, this paper proposes a prediction method of college English online course teaching effect based on learner behaviour mining. The k-means algorithm is used to cluster the learner behaviour data, and the fuzzy association rules are combined to realise the learner behaviour mining. The teaching effect prediction index system of college English online course is built, and the teaching effect prediction of college English online course is realised according to the calculation results of index weight and PSO-BP neural network. The results show that the maximum accuracy of the proposed method is 96.9%, the range of prediction accuracy is 95.7%~97.9%, and the maximum prediction time is 1.63 s.
  Keywords: learner behaviour mining; college English; online course; teaching effect prediction; fuzzy association rules; PSO-BP neural network.
  DOI: 10.1504/IJCEELL.2025.10068957
   
  
• A fuzzy comprehensive evaluation of blended teaching quality under the background of digital education development  
  by Yan Yang 
  Abstract: To address the challenges posed by traditional evaluation techniques, such as significant weight relative error, protracted computation durations, and compromised precision, a fuzzy comprehensive evaluation method of blended teaching quality under the background of digital education development is proposed. The fuzzy comprehensive evaluation index system of blended teaching quality under the background of digital education development is established by clarifying evaluation objectives and principles, combing key elements, designing preliminary evaluation indexes and perfecting the index system. The determination of the significance levels for evaluation metrics is conducted through a dynamic weighting approach integrated with the analytic hierarchy process, the overall blended teaching quality is assessed utilising a fusion of the fuzzy comprehensive evaluation technique. The experimental outcomes demonstrate that the index weight calculation attains a minimal relative error of 0.268, with a swift calculation time of 0.485 s at its minimum, accuracy range for this computation spans from 94% to 97%.
  Keywords: digital education development; blended teaching quality; fuzzy comprehensive evaluation; variable weight analytic hierarchy process; AHP; index weight.
  DOI: 10.1504/IJCEELL.2025.10068958
   
  
• Analysis of factors influencing mobile learning behaviour of English curriculum students based on UTAUT model  
  by Lingbo Han 
  Abstract: To improve the teaching effectiveness of English courses, the UTAUT model is introduced in this study to conduct an analysis of the influencing factors of mobile learning behaviour among English course students. Firstly, construct an analysis model for the influencing factors of English course students' mobile learning behaviour, and then use this model to explore the impact of six variables on students' mobile learning behaviour. The experimental results show that the correlation coefficients between variables such as behavioural intention, learning satisfaction, perceived usefulness, perceived entertainment, behavioural intention, and community influence are between 0.726 and 0.928. All show significant positive correlations, all of which can have a significant impact on students' mobile learning behaviour. Gender has certain differences in the influencing variables of student mobile learning behaviour. Finally, the female population is more affected by the variables.
  Keywords: UTAUT model; English courses; students; mobile learning behaviour; analysis of influencing factors.
  DOI: 10.1504/IJCEELL.2025.10068962
   
  
• The impact of MOOC and online live teaching on English learning performance of college students  
  by Qinghua Yang 
  Abstract: In order to improve the effectiveness and learning performance of English learning among college students, this study investigates the impact of MOOC and online live teaching on their English learning performance. Firstly, the limitations of traditional English learning models in universities were analysed. Secondly, based on the characteristics of MOOC and online live teaching, it is analysed that MOOC and online live teaching have the advantages of diverse learning resources, enhanced learning interactivity and participation, personalised teaching, flexibility and convenience. Finally, the impact of MOOC and online live teaching on the English learning performance of college students is elaborated from four aspects: improving learning effectiveness and motivation, meeting diverse needs of students, improving learning efficiency, and cultivating self-learning ability. The example analysis results show that the attendance rate, classroom participation, and English learning performance of the experimental group are higher than those of the control group.
  Keywords: MOOC; online live teaching; college students; English learning performance.
  DOI: 10.1504/IJCEELL.2025.10068959
   
  
• Personalised recommendation of mobile learning materials based on hierarchical hidden Markov models  
  by Zhaofeng Li, Ping Hu, Yuchuan Hu, Yan Zhang 
  Abstract: The research on personalised recommendation of mobile learning materials can meet learners' learning needs, thus further improving their learning efficiency and effectiveness. In order to solve the problems of low accuracy, low recall and low user satisfaction in traditional personalised recommendation methods for mobile learning materials, a personalised recommendation method of mobile learning materials based on hierarchical hidden Markov model is proposed. K-means clustering algorithm is used to mine the data of mobile learning platform, and user portraits of mobile learning platform are extracted according to the data mining results and user interest feature vectors. According to the user portrait and hierarchical hidden Markov model, the top-N recommendation list is generated to realise personalised recommendation of mobile learning materials. The experimental results show that the maximum accuracy of personalised recommendation of mobile learning materials is 97.3%, the maximum recall rate is 97.9%, and the average satisfaction rate is 98.1.
  Keywords: hierarchical hidden Markov model; mobile learning materials; personalised recommendation; K-means clustering algorithm; user portrait.
  DOI: 10.1504/IJCEELL.2025.10068960
   
  
• Hybrid teaching quality evaluation based on PSO-BP neural network  
  by Ying Zhou, Lei Zhang 
  Abstract: The existing methods for evaluating the quality of blended learning lack unified standards and evaluation systems, resulting in low comparability of evaluation results. Therefore, a hybrid teaching quality evaluation method based on PSO-BP neural network was studied. Firstly, the selection of quality evaluation indicators for blended learning should follow the principles of scientificity, comprehensiveness, and testability. Then, the analytic hierarchy process (AHP) was used to construct a mixed teaching quality evaluation index system. Finally, the obtained evaluation indicators will be used as inputs for the PSO-BP neural network, and through the learning and optimisation capabilities of the neural network, hybrid teaching quality evaluation will be achieved. The experimental results indicate that, the R² of the PSO-BP neural network model for evaluating the quality of blended learning is 0.985, indicating high fitting ability. The actual level is consistent with the evaluation level, with an evaluation error of less than 2%, indicating practicality.
  Keywords: PSO-BP neural network; analytic hierarchy process; AHP; evaluation of blended teaching quality.
  DOI: 10.1504/IJCEELL.2025.10068961
   
  
• Evaluation method of English game teaching classroom-learning effect based on ResNet algorithm  
  by Liangjie Li 
  Abstract: Due to the limitations of current methodologies in accurately reflecting the nuances of learning, leading to suboptimal assessment accuracy and efficiency, this study introduces a novel evaluation approach for the learning outcomes in English game-based classrooms, utilising the ResNet algorithm. This method comprehensively considers the academic performance variations of students pre- and post-instruction, establishing an evaluation index system. The selection of indices is informed by grey correlation analysis and multicollinearity analysis. Following this, relevant classroom data are gathered and normalised. Principal component analysis is employed to distil the salient features from the data. Once these numerical and visual data are inputted, the ResNet algorithm is trained to assess the learning impact of English game-based instruction. The findings indicate that the F1-score consistently remains above 0.95, the AUC value approaches 1, and the maximum evaluation duration is 16.837 seconds.
  Keywords: English game teaching; learning effect evaluation; ResNet; data processing.
  DOI: 10.1504/IJCEELL.2025.10068724
   
  
• Innovative thinking scale of electrical engineering students in MOOC learning at MBKM activities  
  by Jahril Nur Fauzan, Roer Eka Pawinanto, Budi Mulyanti 
  Abstract: The objectives of this research endeavour encompass three main aspects: 1) the creation of a novel measurement tool, referred to as the innovative thinking scale (ITS), which aims to evaluate variations in innovative thinking among individuals; 2) the evaluation of the effectiveness and reliability of the innovative thinking scale; 3) the investigation of the associations between different indicators of the innovative thinking scale. Using a quantitative research design, a two-stage study is undertaken to ascertain the validity and reliability of the ITS instrument. Content validity and construct validity are established through an intensive procedure involving a panel of experts in the field of telecommunication engineering and the application of Pearson correlation analysis. The sample for this study consisted of 43 students pursuing a degree in electrical engineering, with a specific focus on telecommunication engineering. These students are selected based on their participation in the freedom of learning independent campus.
  Keywords: innovative thinking scale; ITS; MBKM; MOOC.
  DOI: 10.1504/IJCEELL.2025.10068968