Forthcoming and Online First Articles

International Journal of Continuing Engineering Education and Life-Long Learning

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

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International Journal of Continuing Engineering Education and Life-Long Learning (46 papers in press)

Regular Issues

  • Application of fuzzy ahp in the evaluation of students’ cognitive ability   Order a copy of this article
    by Zhaohui Wei, Ziyan Luo, Peng Sang, Juan Du 
    Abstract: Cognitive theory holds that each individual’s cognitive ability is different. If teachers can adopt appropriate evaluation method, they will be able to accurately know each student’s cognitive ability and characteristics, which is conducive to personalised teaching implementation. Therefore, based on the revised Bloom cognitive theory, this paper establishes the evaluation index system of students’ cognitive ability by adopting the analytic hierarchy process (AHP) method, carries out fuzzy evaluation of students’ test scores with the fuzzy theory, works out the weight of each indicator of cognitive ability by expert scoring method, and finally obtains the comprehensive evaluation of students’ cognitive ability. It is proved that this method can be more objective and comprehensive in evaluation of the students’ cognitive ability, and it can reflect their achievement degree of each goal, show their strengths and weaknesses, offering some valuable information for students’ later study.
    Keywords: Bloom’s cognitive theory; fuzzy logic; analytic hierarchy process; AHP.
    DOI: 10.1504/IJCEELL.2023.10036490
     
  • Research on Effectiveness Model of Online Learning for College Students in Big Data Era   Order a copy of this article
    by Xiaojun Zhang, Xiaoji Yang 
    Abstract: In order to enhance the effectiveness of online learning of college students in the era of big data, this paper puts forward the research on the effectiveness model of online learning of college students. This paper establishes a fusion clustering model for the evaluation of online learning effect, and uses fuzzy fusion grouping method to analyse the panel data of online learning effect evaluation of college students combined with big data mining method, analyses the characteristics of online learning behaviour of college students and mining association rules. Linear programming model is used to optimise the scheduling of online learning resources for college students. The experimental results show that the design method has high accuracy and reliability in predicting the online learning effect of college students in the era of big data, and improves the convergence and optimisation ability of learning process.
    Keywords: learning analysis; big data era; online learning; effectiveness assessment.
    DOI: 10.1504/IJCEELL.2023.10036730
     
  • Formative assessment method of English language application ability based on consistency assessment   Order a copy of this article
    by Guanguan Zeng 
    Abstract: Due to the fact that learners’ mastery of English language is not taken into account in the current methods, resulting in higher error and lower credibility of the assessment results, a formative assessment method of English language application ability based on consistency assessment is proposed. According to the principles of formative assessment, the implementation conditions of formative assessment include clear assessment objectives, diversified assessment means, determination of assessment criteria and timely feedback of information. Specific assessment tasks are defined and theoretical model of autonomous learning is constructed to realise the assessment of English language application ability. Taking the students of two classes of automobile maintenance major in college was the test objects; the final assessment scheme is set. Experimental results show that the proposed method can effectively reduce the assessment error and improve the credibility of the assessment results.
    Keywords: consistency assessment; English language; application ability; formative; assessment.
    DOI: 10.1504/IJCEELL.2023.10036976
     
  • Research on the integration and optimization of MOOC teaching resources based on deep reinforcement learning   Order a copy of this article
    by Kun Jiao, Xin Han, Li Xu, Terry Gao 
    Abstract: There are some problems in the merging of English MOOC learning information, such as high packet loss rate and high repetition rate. This paper designs a new method of resource data merging with the help of deep reinforcement learning. Analyse the operation mode of English MOOC platform, and collect teaching resources for the initial application of MOOC under this platform. The pre-processing of the initial collection of resources is completed through translation, Chinese word segmentation, semantic annotation of word segmentation results and other steps. The features of English MOOC teaching resources are extracted, and the deep enhancement learning algorithm is adopted to optimise. Through comparative experiments, it is found that the packet loss rate of the combined learning information is only 0.28% and the integrated resource repetition rate is only 0.89%.
    Keywords: deep reinforcement learning; English teaching; MOOC teaching; integration of teaching resources; resource characteristics; feature weight value.
    DOI: 10.1504/IJCEELL.2023.10036977
     
  • Research on online evaluation method of MOOC teaching quality based on decision tree-based big data classification   Order a copy of this article
    by Jiefeng Wang, Humin Yang 
    Abstract: In order to improve the online evaluation ability to massive open online course (MOOC) teaching quality, an online evaluation method of MOOC teaching quality based on decision tree-based big data classification is proposed. First, a big data statistical analysis model is built to identify fuzzy degree parameters. Then, the quality index system is obtained to realise big data fusion and cluster analysis. It is concluded that this method has high accuracy in online evaluation. In this paper, the method shows that the accuracy as high as 0.996 when the number of iterations reaches 500. Its innovation lies in the analysis of the global optimal solution of online evaluation of distance MOOC teaching quality by using the big data decision tree model, which improves the information management of MOOC online evaluation.
    Keywords: big data statistical analysis; decision tree-based classification; MOOC teaching quality; online evaluation method; information fusion and clustering.
    DOI: 10.1504/IJCEELL.2023.10037599
     
  • Research on teaching quality evaluation model of distance education in colleges based on analytic hierarchy process   Order a copy of this article
    by Xiao-long Wen 
    Abstract: There are some problems in the existing teaching quality evaluation methods, such as low evaluation accuracy, puts forward a model of distance education teaching quality evaluation in colleges and universities by means of analytic hierarchy process. From this perspective in students, peers and supervisors, obtain the evaluation index of teaching quality and calculate its weight value, analysing the hierarchical process is introduced. In this model, the evaluation indexes in the hierarchy are sorted and the consistency is verified, and the teaching quality evaluation model of distance education in colleges based on AHP. Through the research of the proposed method, the conclusion is drawn that the error of distance education effect of imparting knowledge evaluation by using the proposed model is relatively low, and it is feasible to some extent.
    Keywords: distance education; effect of education; evaluation model; consistency verification.
    DOI: 10.1504/IJCEELL.2023.10037919
     
  • Evaluation model of College Students' online learning level difference based on support vector machine   Order a copy of this article
    by Kunzhe Liu, Xiqiang Ge, Jiefeng Wang 
    Abstract: There are some problems in the evaluation of online learning level differences among college students, such as low accuracy and long evaluation time, to propose a new research method. By extracting online learning behaviour characteristics, to determine behaviour evaluation indexes and evaluation standards, and by the depth of the residual neural network method to remove interference index, using the Lagrange multiplier method, the evaluation problem is transformed into the dual problem, and the nonlinear transformation, thus seeking the optimal classification plane, to obtain the optimal classification function, difference evaluation data accessed by the big data technology, through the normalised processing the data, obtain the final evaluation results, complete online learning evaluation level differences. Through comparison, the accuracy of this method is 97%, and the time cost of assessment is always less than 9.5 ms.
    Keywords: support vector machine; online learning; difference evaluation; behaviour characteristics.
    DOI: 10.1504/IJCEELL.2023.10039301
     
  • An online teaching process monitoring method of MOOC platform based on video recognition
    by Humin Yang, Jiefeng Wang 
    Abstract: In order to overcome the problem of low monitoring accuracy of traditional methods, a new online teaching process monitoring method based on video recognition for MOOC platform is proposed. The weight coefficient of online teaching process monitoring information of MOOC platform is calculated, and the monitoring information is reconstructed. On this basis, video recognition method is used to mine the online teaching process monitoring information of MOOC platform. Through the construction of monitoring model, the online teaching process monitoring of MOOC platform is realised. The experimental results show that the mean square error analysis results of the monitoring method based on video recognition verify that the effect of different gender students is different. The method has high correction coefficient, high monitoring accuracy and good application effect, which can effectively improve the effect of students’ online learning.
    Keywords: video recognition; MOOC platform; online teaching; process monitoring.
    DOI: 10.1504/IJCEELL.2024.10039621
     
  • Design of reservation model of teaching equipment in NC laboratory based on BS mode   Order a copy of this article
    by Haibo Liu, Xinbo Zhang, Qingzhong Gong 
    Abstract: In order to solve the job scheduling problem of CNC laboratory equipment concurrent reservation, the design method of CNC laboratory teaching equipment reservation model based on BS mode is proposed. By using BS mode, the equipment management structure of numerical control laboratory is designed hierarchically. Build the reservation time series model to realise the load transmission and information estimation in the process of reservation. According to the statistical results, the reservation fitness function is designed, and the reservation scheduling and reservation algorithm are designed. Based on this, the database security storage function and SMS processing function of the model are designed to realise the construction of teaching equipment reservation model in CNC laboratory. Experimental results show that: compared with the traditional equipment design model, the model designed in this paper has higher reservation success rate and better application performance.
    Keywords: BS mode; numerical control laboratory; teaching equipment reservation; statistical characteristic quantity; fitness function.
    DOI: 10.1504/IJCEELL.2023.10039781
     
  • A balanced allocation method of English MOOC teaching resources based on QoS constraints   Order a copy of this article
    by Xiaodong Yuan 
    Abstract: In order to overcome the problems of long allocation time and high bandwidth consumption in traditional resource allocation methods, this paper proposes a new balanced allocation method of English MOOC teaching resources based on QoS constraints. With the support of big data and cloud computing technology, MOOC English teaching resources are mined and collected, and the resource balanced allocation model is constructed by using MMPs algorithm. The resource balanced allocation of virtual machine is realised through the selection of QoS constraint parameters, physical resource mapping and QESA resource allocation system. On the basis of experiments, the proposed method has the advantages of balanced resource allocation and execution time.
    Keywords: QoS constraints; MOOC resources; MMPS algorithm; QESA system.
    DOI: 10.1504/IJCEELL.2023.10039782
     
  • Mobile learning determinants that influence Indian university students’ learning satisfaction during COVID-19   Order a copy of this article
    by Md Qamar, Mohd Ajmal, Abdullah Malik, Mohd. Ahmad, Juhi Yasmeen 
    Abstract: Due to the COVID-19 pandemic whole world went under strict lockdown, including educational institutes. This led to the quick reshaping of educational systems to provide uninterrupted education to the students. Preferably, both teachers and students switched from physical classrooms to online classrooms. This overnight change brought numerous challenges for a country like India. But the authors of this study see it as an opportunity and aim to explore mobile learning (m-learning) determinants that influence Indian university students’ learning needs during the COVID-19. For this, the data were gathered using a web-based questionnaire from 557 students of seven different universities (both public and private) in India. Next, the data were quantitatively analysed using reliability analysis, confirmatory factor analysis, and multiple regression analysis. The results show that out of three first-order m-learning variables, only two (system and service quality items) have a positive impact on students’ learning satisfaction in the Indian context. In the end, the implications of the study in the adoption of m-learning at different Indian universities have been discussed.
    Keywords: COVID-19 and online learning; m-learning determinants; students’ learning satisfaction; India.
    DOI: 10.1504/IJCEELL.2023.10040816
     
  • Automatic quantitative assessment of English writing proficiency based on multi feature fusion   Order a copy of this article
    by Fengtian Xu 
    Abstract: In the existing quantitative evaluation methods of English writing level, the accuracy of feature extraction is low and the error is high. An automatic quantitative evaluation method of English writing level based on multi-feature fusion is put forward. By using vector space model and Jekard similarity coefficient to determine the cosine similarity of English text, the features of English text are extracted by Manhattan distance. Through kernel function, the multi-feature fusion of English writing text is realised. The multivariate linear regression model is used to determine the feature weight of English text and to quantitatively process the feature data. The automatic quantitative evaluation model of English writing level is constructed to complete the automatic quantitative evaluation of English writing level. The experimental results show that the accuracy of the proposed automatic quantitative evaluation method is always higher than 90 and the minimum error of text feature extraction is about 2%.
    Keywords: multi-feature fusion; English writing; automatic assessment; corpus.
    DOI: 10.1504/IJCEELL.2023.10041093
     
  • Authentic learning environment for in-service trainings of cyber security: a qualitative study   Order a copy of this article
    by Mika Karjalainen, Anna-Liisa Ojala 
    Abstract: Today’s rapidly digitalising world has led to business processes becoming digitalised, which necessitates paying attention to the cybersecurity issues inherent in those digital processes. The multi-disciplinary nature of working life and the complexity of cybersecurity issues place demands on learning environments. The present study examined the requirements for optimal in-service training to ensure individual and organisational learning of the competencies that are crucial for dealing with cybersecurity incidents. Building on theories of authentic learning and qualitative research methods, the study identified three fundamental components and four elements of optimal in-service cybersecurity training. The research found that practicing organisational actions increased readiness and competence to act in the face of a real cybersecurity incident. A comprehensive cyber arena supports the implementation of optimal training and thus the efficiency of in-service training.
    Keywords: cyber security; cyber arena; cyber range; authentic learning environment; training; exercises; peadagogy; in-service training.
    DOI: 10.1504/IJCEELL.2023.10041126
     
  • Teaching Practice-oriented Computer Vision Courses in COVID-19 Pandemic   Order a copy of this article
    by Jing Tian 
    Abstract: This paper aims to present an online teaching pedagogic experience for the practice-oriented computer vision course during the COVID-19 pandemic. COVID-19 has been disruptive to the education system worldwide, particularly to the computer vision course that usually requires face-to-face lectures and project collaboration during the study. This paper addresses three fundamental questions in teaching computer vision courses: 1) how to design the course topic and adapt to the online teaching format?; 2) how to conduct hybrid project collaboration in a hybrid mode?; 3) how to conduct the course assessment efficiently online? More specifically, this paper presents the pedagogic experience, including learning objectives, course curriculum structure, teaching methodologies, as well as final holistic assessments. The presented approach is an effective way of teaching practical computer vision courses, as verified by feedback from students. These experiences can be insightful to other lecturers who need to design, develop and deliver similar courses in the post pandemic era.
    Keywords: engineering education; computer vision teaching; active learning.
    DOI: 10.1504/IJCEELL.2022.10041479
     
  • Recommended methods for teaching resources in public English MOOC based on data chunking   Order a copy of this article
    by Zhenhua Wei 
    Abstract: In order to overcome the problems of time-consuming and high recommendation error in traditional public English MOOC teaching resource recommendation methods, this paper proposes a new public English MOOC teaching resource recommendation method based on data partition. The data of public English MOOC teaching resources are collected, and hierarchical clustering algorithm is used to preprocess public English MOOC teaching resources to support the recommendation demand of mobile MOOC teaching resources. According to the preprocessing results, the data block algorithm is used to divide the resource data iteratively. Finally, we calculate the similarity of users’ resource use and preference, and construct the public English MOOC teaching resource recommendation model based on the index weight results. Comparative validation results show, in the conventional method, the proposed method recommended consuming less and less precision compared to the recommended.
    Keywords: data partition; public English; MOOC teaching resources; block partition.
    DOI: 10.1504/IJCEELL.2023.10045729
     
  • An Evaluation Method of English Online Learning Behavior Based on Feature Mining   Order a copy of this article
    by Chao Han 
    Abstract: In order to overcome the problems of low precision of feature retrieval and poor clustering effect of traditional online learning behaviour evaluation methods, this paper designed an English online learning behaviour evaluation method based on feature mining. Firstly, the unbiased estimation theory is used to quantitatively sample English online learning behaviour. After data preprocessing, the clustering centre is divided, and the data is allocated to different clusters through clustering processing, and the feature mining results are obtained through iteration. Then the evaluation index system is constructed, and the final evaluation results are obtained on the basis of the grey interval clustering treatment of the evaluation index. According to the test results, the retrieval accuracy of behaviour features of method of this paper is closer to one, and its clustering effect on different online learning behaviour features is good, which proves that it has achieved the design expectation.
    Keywords: online learning behaviour; data clustering; characteristics of the mining; evaluation indicators.
    DOI: 10.1504/IJCEELL.2023.10042512
     
  • Personalized recommendation method of college English online teaching resources based on hidden Markov model   Order a copy of this article
    by Qing Tian 
    Abstract: Aiming at the problem of the high comprehensive evaluation index of recall and precision in the traditional personalised recommendation methods of college English online teaching resources, a personalised recommendation method of college English online teaching resources based on hidden Markov model is proposed. Extract label features, student learning behaviour features, and time weight features, and pre-process the extracted college English online teaching resource data, build a recommendation model based on the pre-processing results, and use a hidden Markov model to process the hidden data to obtain the maximum likelihood estimates the parameter, and inputs the parameter into the recommendation model to obtain the optimal parameter, that is, the optimal recommendation result. The simulation results show that the comprehensive evaluation index values of precision and recall of the recommended results of the proposed method are within 0.9 and 0.5, which has a good recommendation effect and meets the needs of the development of network teaching.
    Keywords: hidden Markov model; university English; online teaching resources; personalised recommendation method.
    DOI: 10.1504/IJCEELL.2023.10042696
     
  • Interactive design method of English online learning interface based on visual perception   Order a copy of this article
    by Shenglan Wang 
    Abstract: Due to the problems of long interaction time, poor data recovery effect and poor satisfaction of the existing interactive design methods of English online learning interface, an interactive design method of English online learning interface based on visual perception is proposed. Based on the principle of visual perception, this paper optimises the interface structure, enhances the data interactive processing ability of this method by using the interactive interface engine technology, improves the data recovery rate by combining with the data weight design, and realises the interactive design requirements of English online learning interface. Experimental results: the interaction time of the design method is generally less than 1.5s, the data recovery rate is generally more than 80%, and the teaching satisfaction is more than 90%, which shows that the method has good interaction, high information recovery rate, high satisfaction and strong practicability in the practical application process.
    Keywords: visual perception; English online learning; interactive interface; engine technology.
    DOI: 10.1504/IJCEELL.2023.10044147
     
  • Research on College Students' Emotional Experience in Online Learning   Order a copy of this article
    by Lifeng Wang, Zi Ye, Shaotong Zhu 
    Abstract: Online education is the main form of pedagogy during the COVID-19 pandemic. The description and analysis of the emotional experience of online education for college students are of considerable significance. This paper surveyed and investigated the online learning experience of 1,147 college students during the pandemic. Moreover, their experience of offline and online learning was analysed. The results demonstrate that college students prefer face-to-face learning. Online learning produces more negative emotions, whereas face-to-face learning induces more positive emotions; academic situation, self-management, learning atmosphere, interpersonal relationship, self-expression, collective honour are the leading causes of students’ emotions.
    Keywords: face-to-face learning; online learning; emotional experience.
    DOI: 10.1504/IJCEELL.2023.10044197
     
  • EXPLORATION OF GENDER SPECIFIC AND LEARNING ENVIRONMENT SPECIFIC COMFORT LEVEL IN COLLABORATIVE LEARNING   Order a copy of this article
    by Anitha D. Dhakshina Moorthy, Purnima Ahirao, Ramaa AnanthaMurthy, Vrinda Ullas 
    Abstract: Collaborative learning creates interest and engagement in learning. The collaborative learning activities can be conducted in one of the three learning environments or modes: face-to-face, completely online and blended mode and with different gender compositions. It is essential to understand the comfort level of students when working in different gender compositions and different learning environments. Hence, this research study is carried out with different group compositions in three different learning environments of collaborative learning: online, blended and face-to-face. One hundred seventy students from four educational institutions in India participated in this study. A survey is administered to them on the various parameters of comfortable learning. From the results obtained, it is observed that online or blended learning results in an increased comfort level of students than face-to-face learning sessions and the gender composition affects the comfort level.
    Keywords: collaborative learning; learning environments; gender; comfort; team formation.
    DOI: 10.1504/IJCEELL.2023.10044388
     
  • The method of online classroom teaching quality evaluation based on deep data mining   Order a copy of this article
    by Jing Hou  
    Abstract: In order to overcome the problems of traditional online classroom teaching quality evaluation methods, such as low accuracy of quality evaluation and poor effect of classroom teaching quality improvement, this paper proposes an online classroom teaching quality evaluation method based on deep data mining. Fuzzy comprehensive evaluation method is used to quantify the evaluation index of online classroom teaching quality; The evaluation matrix is constructed to calculate the weight of classroom teaching quality evaluation index; The online classroom teaching quality evaluation indicators are classified by naive Bayes classification algorithm; With the help of deep data mining algorithm, this paper evaluates the post classification evaluation index, constructs the online classroom teaching quality evaluation model, and completes the online classroom teaching quality evaluation. The experimental results show that the accuracy of the proposed method is about 0.9, and it can effectively improve the quality of online classroom teaching.
    Keywords: Deep data mining; Classifier; Naive Bayes classification algorithm; Evaluation matrix.
    DOI: 10.1504/IJCEELL.2024.10046211
     
  • Corpus-driven recommendation algorithm for English online autonomous learning resources   Order a copy of this article
    by Chao Han 
    Abstract: In order to overcome the problems of the traditional recommendation algorithm of English online learning resources, such as low accuracy, poor convergence and low success rate of recommendation. This paper proposes a recommendation algorithm for online language autonomous learning resources driven by CORUS. Based on the learning vector quantisation (LQN) algorithm, the model of subject word generation based on vector corpus resources is established. In the parameter training of the classification model, the vector weight is normalised to complete the optimisation of the corpus resource classification LVQ subject model. According to the binary particle swarm optimisation algorithm, the personalised recommendation model of autonomous learning resources is implemented. Experimental results show that the proposed vector quantisation network algorithm has high convergence, and the recommended success rate is 99.5%. Therefore, the method proposed in this paper can effectively complete the recommendation of English online autonomous learning resources.
    Keywords: corpus; English learning; learning resources; recommendation algorithm; binary particle swarm.
    DOI: 10.1504/IJCEELL.2024.10046212
     
  • An English listening and speaking ability training system based on binary decision tree   Order a copy of this article
    by Yu Wang  
    Abstract: In order to solve the problems of low speech resolution accuracy and poor auxiliary training effect of traditional auxiliary training system, a design method of English listening and speaking ability auxiliary training system based on binary decision tree is proposed. The hardware design includes English listening and speaking data extraction module, storage module and training module. In terms of software, the binary decision tree algorithm is used to construct the binary decision tree training model of English listening and speaking training. Through the algorithm, the data information of English learners in the process of listening and speaking ability training is extracted and the training data is evaluated. The experimental results show that: this system has high accuracy in the discrimination and evaluation of English speech data, and low occupancy rate of system storage space, which is conducive to improving students’ English listening and speaking ability and English learning achievement.
    Keywords: binary decision tree; data extraction module; storage module; ability training module; effect evaluation.
    DOI: 10.1504/IJCEELL.2023.10045483
     
  • A personalized recommendation of mobile learning model based on content awareness   Order a copy of this article
    by Yuanyuan Luo 
    Abstract: In order to overcome the problems of traditional recommendation methods such as large error in recommendation results and long time-consuming process of recommendation results generation, the paper proposes a personalised recommendation method based on content-aware mobile learning mode. First, design the recommendation process architecture, which mainly includes a user demand analysis module, a user preference analysis module, and a mobile learning model resource library decision module. Then, use the energy function and insert the dataset to design the content perception process. Finally, according to the perceptual results, a user emotional topic model with a supervision mechanism is used to complete personalised recommendation. The experimental results show that the average absolute error value of the recommendation results obtained by the method in this paper is between 0.060.15, the maximum recommendation result generation process takes only 4.5s, and the clustering effect of different mobile learning modes is better.
    Keywords: mobile learning model; personalised recommendation; recommender process architecture; energy function; content awareness; affective thematic model.
    DOI: 10.1504/IJCEELL.2023.10045484
     
  • Effect of personal response systems on students' academic performance and perception in online teaching   Order a copy of this article
    by Hongjiang Wang, Zuokun Li, Xiaojin Wang 
    Abstract: To minimise the negative impact of the epidemic on education, online teaching was taking off all over the world. In order to improve the interactive effect of online teaching, some scholars have applied personal response systems (PRSs) during the online teaching process. However, there is few experimental studies to validate the effectiveness of PRSs, such as students performance and perceptions. In the paper, based on the quantitative and qualitative mixed analysis method, which is the best way to gain information of specific experience, 98 sophomores majoring in Applied Mathematics are sampled to test students academic performance and perception in online teaching using PRSs, e.g., UMU. Some conclusions can be drawn: 1) the experimental groups academic performance was higher than the control group; however, there was no significant difference between them; 2) majority of students have positive perceptions, such as learning motivation (LM), learning engagement (LE) and learning satisfaction (LS) towards PRSs. And there was no significant difference in gender perception. At last, the paper concluded the advantages, disadvantages and suggestions of PRSs used in online teaching. The limitation of research and the direction of future research are discussed.
    Keywords: personal response systems; PRSs; academic performance; perception; online teaching.
    DOI: 10.1504/IJCEELL.2023.10046526
     
  • Investigating the Effect of Online Teaching using a SPOC   Order a copy of this article
    by Hongtao Yu 
    Abstract: The outbreak of COVID-19 in 2019 brought about unprecedented changes to higher education. Following the deployment of the Education Ministry, Chinese higher learning institutions carried out practical activities in response to the classes suspended but learning continues appeal by making use of internet technologies. In order to understand the effect of online teaching, this research uses qualitative research methods to study online learning of students in a certain university, and selects four case classes with different learning abilities. After conducting a comparative analysis of final exam scores, student questionnaire surveys and interviews, analysis of the effectiveness of students online learning of the course, the results show that students online learning performance is positively correlated with the scores of the preceding courses, and the online learning duration is positively correlated. Online learning is more suitable for students with strong self-discipline and three suggestions are given for effective teaching in the future.
    Keywords: SPOC; online learning; empirical research.
    DOI: 10.1504/IJCEELL.2023.10046527
     
  • Multi-dimensional Dynamic Evaluation of MOOC English Mixed Teaching Based on BP Neural Network   Order a copy of this article
    by Mian Wang 
    Abstract: In order to overcome the problem of low accuracy in current evaluation methods of MOOC English mixed teaching, this paper proposes a multi-dimensional dynamic evaluation method based on BP neural network. By collecting evaluation data from teaching experts, teachers and students, the basic dataset of English teaching evaluation is constructed. The data from the evaluation basic dataset were taken as input samples, and the input samples were normalised. The input samples were input into the constructed BP neural network evaluation model, and the multi-dimensional dynamic evaluation results of MOOC mixed English teaching were output. Experimental results show that the evaluation accuracy of the proposed method is more than 90%, and the convergence can be achieved only for about 50 times, the convergence speed is faster and the evaluation time is shorter.
    Keywords: BP neural network; MOOC English teaching; multi-dimensional dynamic evaluation; Basic dataset.
    DOI: 10.1504/IJCEELL.2024.10047879
     
  • Comprehensive retrieval method of MOOC teaching resources based on eigenvalue extraction   Order a copy of this article
    by Jia Peng, Xikai Li 
    Abstract: In order to solve the problems of traditional MOOC teaching resources retrieval methods, such as low retrieval accuracy, poor retrieval recall and low retrieval efficiency, this paper proposes a comprehensive retrieval method of MOOC teaching resources based on eigenvalue extraction. The feature value of MOOC teaching resources is extracted by grey level co-occurrence matrix, and the similarity calculation of resource content features is realised by feature attribute annotation; the MOOC teaching resources search process is designed, and the comprehensive retrieval of MOOC teaching resources is realised by feature value extraction. The experimental results show that the retrieval accuracy of this method is low, the retrieval recall rate is as high as 95%, and the retrieval efficiency of teaching resources is effectively improved.
    Keywords: feature value extraction; similarity calculation; resource retrieval; MOOC teaching.
    DOI: 10.1504/IJCEELL.2023.10047880
     
  • Sustaining College Students’ Continuance Intention Toward Online Learning in the Post-COVID-19 Era   Order a copy of this article
    by Minghua He, Jialiang Qin, Rongfang Tang 
    Abstract: Since the outbreak of the COVID-19 epidemic, universities in China have integrated online teaching, and a total of 17.75 million college students across the country have participated in these virtual learning systems. The extensive growth in online education that we have witnessed is unprecedented in the history of higher education. Based on the technology acceptance model (TAM), this study investigates the impacts of perceived enjoyment, ease of use, and usefulness on Chinese college students’ continuance intention toward online learning. Our empirical findings indicated that the continuance intention of college students was affected by these perception factors and that innovativeness played a moderating role in the relationship between perceived enjoyment and continuance intention and perceived usefulness and continuance intention. These results provide theoretical and practical insights for universities and online education platforms that can be used to stimulate endogenous student motivation and sustain online learning behaviour in the post-COVID-19 era.
    Keywords: online learning; online education; college students; continuance intention; innovativeness; perceived enjoyment; COVID-19; technology acceptance model; TAM.
    DOI: 10.1504/IJCEELL.2024.10048378
     
  • Ontology-based Hierarchical Retrieval Model for Digital English Teaching Information   Order a copy of this article
    by Zhihong Xiao 
    Abstract: In order to overcome the low recall and precision of traditional English teaching information retrieval model, this paper designs a hierarchical retrieval model of digital English teaching information based on ontology. TF-IDF and simhash algorithm are used to judge the similarity of digital English teaching database documents and calculate the weight of English teaching information retrieval keywords. Using the relationship between different retrieval concepts to build a semantic network diagram, build a hierarchical retrieval model of digital English teaching information, adjust the retrieval results according to the user’s interest, and get more accurate retrieval results. The experimental results show that the recall rate of the model is more than 94%, the precision rate is more than 96%, and the average retrieval time is only 0.44s, which shows that the recall rate and precision rate of the design model are higher, and the retrieval time is shorter.
    Keywords: ontology; digital; English teaching information; hierarchical retrieval; TF-IDF; simhash algorithm.
    DOI: 10.1504/IJCEELL.2023.10049281
     
  • Design of online learning behavior feature mining method based on decision tree   Order a copy of this article
    by Xiaoyin Yang 
    Abstract: In order to solve the problems of traditional feature mining methods, such as low precision of feature extraction and high time cost of mining, this paper proposes an online learning behaviour feature mining method based on decision tree. SVM is used to obtain online learning behaviour data and heterogeneous support vector, and online learning behaviour feature data is extracted by transforming data form. Then, the behaviour feature data is preprocessed by the agglomerative hierarchical clustering method. Based on the analysis of the principle of decision tree, the root information gain maximisation data is obtained, and the online learning behaviour feature mining is realised by correcting the leaf node error. The experimental results show that the feature extraction accuracy of this method can reach 98%, and the mining time is always less than 2.5 s, which proves that it can meet the design expectations.
    Keywords: online learning behaviour; feature mining; SVM; mapping function; hierarchical agglomerative clustering; decision tree; error correction.
    DOI: 10.1504/IJCEELL.2023.10049282
     
  • English teaching information feedback system based on Internet of things   Order a copy of this article
    by Weijia Li 
    Abstract: In order to overcome the problems of low feedback accuracy and efficiency in traditional English teaching information feedback system, this paper proposes a new feedback system based on internet of things. The function requirements of the system are analysed and the overall architecture of the system is designed. In order to meet the needs of the system, the hardware part of the system designs the module of English teaching information collection, the load balance module of teaching information and the feedback module of the message board of English teaching information. In the software part of the system, the time series analysis technology in the internet of things is used to evaluate the feedback ability of English teaching information and calculate the output results. The experimental results show that the designed system has higher feedback precision and lower feedback accuracy, the highest feedback accuracy is 97%, and has stronger practical performance.
    Keywords: internet of things technology; English teaching information; feedback system; message board feedback module; time series.
    DOI: 10.1504/IJCEELL.2023.10049283
     
  • A teaching evaluation method of English online course based on conscious normal cloud model   Order a copy of this article
    by Mian Wang 
    Abstract: Because the traditional English online course teaching evaluation method does not consider the characteristics of cloud numbers, the evaluation weight is low, the result accuracy is not high, and the evaluation time is too long. Therefore, the paper proposes an English online course teaching evaluation method based on the self-conscious normal cloud model. First, analyse the cloud digital features of the conscious normal cloud, and obtain the cloud digital feature distribution of the course evaluation on the basis of determining the weight of the course teaching evaluation; secondly, determine the specific input value x and the expected value Ex of the English online course, and calculate The degree of certainty of course teaching evaluation is to construct a conscious normal comprehensive evaluation cloud model to realise the comprehensive evaluation of English online course teaching. The experimental results show that the proposed method can effectively improve the evaluation effect of English online course teaching. The accuracy of teaching evaluation can reach 98%, and the evaluation time of each index does not exceed 1 s. It can be seen that the proposed method has certain value and significance.
    Keywords: conscious normal cloud model; teaching evaluation weight; expected value; certainty; English online course.
    DOI: 10.1504/IJCEELL.2023.10049284
     
  • Automatic classification of multi source and multi granularity teaching resources based on random forest algorithm   Order a copy of this article
    by Dahui Li, Peng Qu, Tao Jin, Changchun Chen, Yunfei Bai 
    Abstract: In traditional teaching resource classification methods, the classification accuracy is low and the RDV value of classification convergence is high. Through fuzzy information mining and fusion clustering method, multi-source and multi-granularity teaching resource data is obtained; with the help of incremental orthogonal component analysis method, the dimension of multi-source and multi-granularity teaching resource data is reduced; the teaching resource data is brought into random forest, and the filtering error of teaching resource is determined according to the classification parameter nonlinear feature recognition results, and the multi-source and multi-granularity teaching resource classification is completed. The experimental results show that the highest classification accuracy is about 98%, and the lowest RDV is about 0.015.
    Keywords: random forest algorithm; multi-source; multi-granularity; teaching resources; automatic; automatic classification.
    DOI: 10.1504/IJCEELL.2023.10049285
     
  • Research on deep mining of MOOC multimodal resources based on improved Eclat algorithm   Order a copy of this article
    by Yu Cao, Shu-Wen Chen, Yu-Xi Wang 
    Abstract: In order to overcome the problems of low recall and precision in traditional MOOC multimodal resource mining methods, this paper proposes a new MOOC multimodal resource deep mining method based on improved Eclat algorithm. Based on cloud computing technology, according to MOOC resource pool structure, MOOC multi-modal knowledge map is constructed, and hash chain is used to analyse the attribute connection rules between knowledge maps. Based on the attribute connection rules, the improved Eclat algorithm is used to transform the captured modal information of resources, so as to design the MOOC multi-modal resource deep mining process and get the results of resource deep mining. The experimental results show that the recall and precision of this method are above 97%, the mining effect is better, and the mining time is always less than 0.7 s, the mining efficiency is higher, and the actual application effect is better.
    Keywords: modal mining; association rules; MOOC resources; Eclat algorithm.
    DOI: 10.1504/IJCEELL.2023.10049286
     
  • An anomaly detection of learning behavior data based on discrete Markov chain   Order a copy of this article
    by Dahui Li, Peng Qu, Tao Jin, Changchun Chen, Yunfei Bai 
    Abstract: In order to overcome the problems of large anomaly detection error and long detection time in traditional learning behaviour data anomaly detection methods, this paper proposes a learning behaviour data anomaly detection method based on discrete Markov chain. This method analyses the types of learning behaviour data, and determines the influencing factors of learning behaviour data. With the help of support vector machine, the data extraction range is determined, and the data redundancy is determined to complete the data pre-processing. This paper analyses the basic principle of discrete Markov chain, constructs the discrete Markov chain model, and completes the detection of abnormal learning behaviour data. The experimental results show that the maximum detection error of the proposed method is about 2%, and the detection time is always less than 2.5s.
    Keywords: discrete Markov chain; learning behaviour data; support vector machine; redundancy.
    DOI: 10.1504/IJCEELL.2023.10049287
     
  • Resource sharing system of College English education based on wireless sensor network   Order a copy of this article
    by Huanxia Deng 
    Abstract: In order to solve the problems of low efficiency and poor security of college English education resource sharing, a resource sharing system of college English education based on wireless sensor network is proposed. The overall function of the resource sharing system is designed. In the hardware design of the system, the hardware of the wireless sensor node is designed. The model of PIC18LF6680 microprocessor is selected, and the standard RS232 serial port is extended to MAX232 level converter. Digital sensors are designed to realise the high-speed transmission of shared resources. On this basis, the hierarchical structure of the system is designed. In the software part, Kalman filter is used to detect noise and measure noise. According to different network conditions, the delay of wireless network system is controlled. The results show that the maximum sharing efficiency of the system is 98%, and the energy consumption of the system is low.
    Keywords: wireless sensor network; WSN; college English; educational resources; sharing system.
    DOI: 10.1504/IJCEELL.2023.10049288
     
  • Study on teacher student interaction in flipped classroom of college oral English based on Mobile Learning   Order a copy of this article
    by Xin Wang 
    Abstract: In order to solve the problem of low analysis accuracy in the traditional method of interactive effect analysis of spoken English classroom in colleges and universities, a mobile learn-based method of interactive effect analysis of spoken English classroom in colleges and universities is proposed. By analysing the characteristics of teacher-student interaction in flipped classroom, a mobile learning APP was constructed. To obtain relevant information about the teacher-student interaction effect, an evaluation system of teacher-student interaction effect of flipped classroom in college English speaking based on mobile learning was established. Combined with fuzzy comprehensive evaluation method and multi-level gray evaluation method, the interactive effect of flipped classroom in college English speaking was analysed according to the evaluation results. The experimental results show that the accuracy of the proposed method is up to about 99%, and the analysis time is about 4 s.
    Keywords: unfixed software; college oral English; flipped class; second language knowledge exchange participants effect; evaluation model; fuzzy comprehensive evaluation; diversified gray evaluation.
    DOI: 10.1504/IJCEELL.2022.10049816
     
  • Optimization design of distance education resource recommendation system based on hierarchical linear model   Order a copy of this article
    by Lili Zhou 
    Abstract: In order to solve the problems of large recommendation error and low efficiency in existing distance education resource recommendation system, this paper proposes an optimisation design method of distance education resource recommendation system based on hierarchical linear model. The system is combined with hierarchical design, overall planning, design and realisation of design objectives, structure, logical structure, key technologies, etc. to realise the real-time data model updating of Hadoop distributed file system. The hierarchical linear model is used to construct the distance education resource recommendation model. According to the model, the optimisation objective function of distance education resource recommendation is set. The optimisation objective function is solved by the hybrid recommendation algorithm of content and collaborative filtering, and the optimisation design of distance education resource recommendation system is completed. The experimental results show that the proposed optimisation system has low recommendation error and high recommendation efficiency, and the minimum recommendation error is only 0.11.
    Keywords: hierarchical linear model; distance education resources; resource optimisation; recommendation system.
    DOI: 10.1504/IJCEELL.2022.10049817
     
  • Cloud storage system of teaching resources based on Internet of things   Order a copy of this article
    by Xinhui Zhang 
    Abstract: In order to overcome the problems of high resource occupancy rate, poor data storage security and slow resource access speed in traditional teaching resource storage system, a cloud storage system of teaching resources based on internet of things is proposed. The system consists of user access module, resource management module and resource cloud storage module. The user access module is responsible for the user registration and login functions, the resource management module uses the data similarity measurement method to merge the duplicate data of teaching resources, and the resource cloud storage module encrypts the teaching resources through the AES algorithm in the internet of things. The experimental results show that the resource occupancy rate of cloud storage of teaching resources is only 30%, the security of data encryption can reach 97%, and the shortest storage speed of teaching resources is about 2.2 min.
    Keywords: internet of things; teaching resources; cloud storage; data similarity measurement method; AES algorithm; parallel storage.
    DOI: 10.1504/IJCEELL.2022.10049818
     
  • MOOC English online course recommendation algorithm based on LDA user interest model   Order a copy of this article
    by Zhongping Yao 
    Abstract: In order to improve the efficiency and accuracy of course recommendation and improve user satisfaction, a MOOC English online course recommendation algorithm based on LDA user interest model is proposed. Wavelet transform method is used for data denoising to improve the accuracy of recommendation results; Using support vector machine to classify courses to improve the efficiency of course recommendation; LDA user interest model is established to describe the characteristics of students’ online learning behaviour. According to the characteristics of students’ interest and learning behaviour, the matching topics can be selected to realise English online course recommendation. The experimental results show that the highest accuracy of course recommendation of this method is 92%, and the student satisfaction basically reaches more than 90 points, which verifies the effectiveness of this method.
    Keywords: LDA user interest model; course recommendation; wavelet transform; support vector machine; SVM; data denoising.
    DOI: 10.1504/IJCEELL.2024.10050126
     
  • MOOC distance teaching effect evaluation method based on fuzzy entropy   Order a copy of this article
    by QingQin Chen 
    Abstract: In order to overcome the problems of low evaluation accuracy and long evaluation time in traditional evaluation methods, a MOOC distance teaching effect evaluation method based on fuzzy entropy is proposed. Firstly, mining MOOC distance learning data. Secondly, according to the needs of teaching effect evaluation, build the MOOC distance teaching effect evaluation index system. Finally, according to the principle of fuzzy entropy, the fuzzy entropy weight of the evaluation index is calculated, the fuzzy entropy weight is normalised, and the attribute matrix of the evaluation index is constructed. The ideal point and closeness degree are calculated according to the attribute matrix, and the effect of MOOC distance teaching is evaluated through the closeness degree. The experimental results show that compared with the traditional evaluation methods, this method greatly improves the evaluation accuracy on the basis of reducing the evaluation time, and the maximum evaluation accuracy is 97%.
    Keywords: fuzzy entropy; MOOC; distance learning; impact assessment.
    DOI: 10.1504/IJCEELL.2024.10050127
     
  • An optimization of higher education resources search method based on multi-state hierarchical model   Order a copy of this article
    by Ping Li  
    Abstract: In order to overcome the problems of low recall rate, precision rate and large search time consumption of traditional methods, optimisation of higher education resources search method based on multi-state hierarchical model is studied. This paper analyses the objective function of higher education resources search, sets the related constraint conditions, and selects the higher education resources search mode by ant colony algorithm. In order to further improve the search quality, the ant colony algorithm was improved by selecting ant colony species, determining communication subgroups, communication period and information exchange between subgroups, and the algorithm was used to optimise the search mode, that is, resource stratified search. A multi-state hierarchical model is built to search higher education resources. Experimental results show that the recall rate of this method is always above 93%, the precision rate is above 92%, and the average search time consumption is 0.66 s.
    Keywords: multi-state hierarchical model; higher education resources; search method optimisation; ant colony algorithm; heterogeneous multiple ant colony algorithm.
    DOI: 10.1504/IJCEELL.2024.10050128
     
  • Design of interactive platform for online audio-visual teaching of English based on augmented reality technology   Order a copy of this article
    by Ruijuan Han 
    Abstract: In order to overcome the problems of high teaching cost, low efficiency of implementation and low utilisation of teaching resources, this paper proposes an interactive platform of English audio-visual online teaching based on augmented reality technology. According to the characteristics of the current online audio-visual teaching class of English, the user needs are analysed. The platform hardware is divided into web server, teacher client and touch integrator based on MVC framework, the connection between teachers and students is realised through hardware design. The augmented reality technology is applied to the interactive application of online audio-visual teaching of English. Through AR technology, educational background is created, classroom teaching is strengthened, and make the task cooperation between teachers and students smoother. The experimental results show that the platform can reduce the teaching cost and improve the efficiency of the system and the utilisation of teaching resources.
    Keywords: augmented reality technology; interactive platform; MVC framework; web server.
    DOI: 10.1504/IJCEELL.2022.10050532
     
  • Multimedia-assisted oral English teaching system based on B/S architecture   Order a copy of this article
    by Yu Zhang 
    Abstract: The traditional system fails to consider the practical ability of students, which results in unsatisfactory teaching results. Therefore, this paper designs a new multimedia aided oral English teaching system based on B/S architecture. Firstly, B/S architecture is used to divide the system into browser side, middle layer and server side. Among them, the browser side (foreground) can realise the daily operation of the website; server side (background) combined with AI technology to achieve system functions. This system can create a more real learning environment for students, so that students can feel the Standard English pronunciation. The simulation results show that after the application of the system, students can receive the teaching content in as little as 10.30 min, and the teaching cost for six days is about 21,000 yuan. The scores of students’ oral English test are all higher than 90 points, which proves that the system has a good effect of oral English teaching.
    Keywords: B/S architecture; multimedia technology; page design; oral English teaching; teaching effect.
    DOI: 10.1504/IJCEELL.2022.10050533
     

Special Issue on: Technology and Innovation Management in Education

  • Study on abnormal behavior recognition of MOOC online English learning based on multidimensional data mining   Order a copy of this article
    by Fengxiang Zhang, Feifei Wang 
    Abstract: In order to overcome the problems of low recognition accuracy and long recognition time of traditional English learning abnormal behaviour recognition methods, this paper proposes MOOC online English learning abnormal behaviour recognition method based on multidimensional data mining. Firstly, set up the multi-dimensional association item set of MOOC online English learning behaviour, mine the learning behaviour data for correction. Secondly, students’ MOOC online English learning behaviour characteristics are extracted from students’ target contour and blinking behaviour characteristics. Then, taking this as the training sample subset, the individual member classifier is constructed by the mixed perturbation method to classify the learning behaviour. Finally, the abnormal behaviour identification of MOOC online English learning is completed. The experimental results show that the proposed method has high accuracy and short recognition time.
    Keywords: multidimensional data mining; MOOC online English learning; abnormal behaviour; mixed perturbation method; individual member classifier.
    DOI: 10.1504/IJCEELL.2022.10047472