International Journal of Continuing Engineering Education and Life-Long Learning (20 papers in press)

Regular Issues

• Study on quality evaluation of online and offline mixed teaching reform based on big data mining  
  by Guoxia Hu, Suntai Sun, Zhongxiao Sun 
  Abstract: In order to improve the accuracy of the reform quality research and shorten the overall research time, the reform quality research is carried out based on the big data mining technology. First, the local density information of the data is calculated and the required samples are mined. Secondly, the probabilistic undirected graph model is used to remove the noise in the mining samples and improve the accuracy of the sample data. Finally, the PCA algorithm in big data is used to calculate the contribution rate of the sample data, and the reform evaluation model is constructed. The test results of different indicators show that the accuracy rate of the research method is 92.6%, and the evaluation time is only 12.7 s, which can effectively improve the evaluation accuracy and shorten the evaluation time.
  Keywords: big data mining; online and offline mixed teaching; PCA algorithm; reform in education; quality assessment.
  DOI: 10.1504/IJCEELL.2024.10053206
   
  
• Online and offline hybrid teaching data mining based on decision tree classification  
  by Yu Cao, Shu-wen Chen, Hui-sheng Zhu 
  Abstract: In order to overcome the problems of large mining errors and low classification accuracy of traditional teaching data mining methods, a hybrid online and offline teaching data mining method based on decision tree classification is proposed. First of all, the online and offline mixed teaching data is obtained with the help of crawler technology. Secondly, data repair method is adopted to ensure data consistency, and duplicate data values are determined by distance value to complete data pre-processing. Finally, according to the construction of the decision tree, determine the root entropy and leaf entropy of the mixed teaching data, create the root node, attribute list and class list of the mixed teaching data, and complete the online and offline mixed teaching data mining. The experimental results show that the proposed method can effectively reduce the error of data mining, with the error coefficient not exceeding 0.2, and improve the classification accuracy.
  Keywords: decision tree classification; online and offline teaching; data mining: crawler technology; root entropy; leaf entropy.
  DOI: 10.1504/IJCEELL.2024.10053207
   
  
• An Evaluation method of online education reform effect based on fuzzy weight  
  by Hui Lv, Mingyang Gao, Tian Hong 
  Abstract: In order to overcome the problems of large calculation error and low evaluation accuracy of traditional online education reform effect evaluation methods, this paper proposes a new online education reform effect evaluation method based on fuzzy weight. First, the key influencing factors of online teaching reform effect evaluation are determined for different subjects. Then, the cluster algorithm is used to determine the cluster centre of the evaluation index data, and the construction and quantification of the online teaching reform effect evaluation index system are completed. Finally, the fuzzy weight is determined, and the online education reform effect evaluation algorithm is constructed by using the judgment matrix and the training evaluation index data set. The experimental results show that this method can reduce the calculation error of evaluation weight and improve the evaluation accuracy, and the evaluation accuracy is always kept above 90%.
  Keywords: fuzzy weight; online education reform; effect evaluation; clustering algorithm; product quantification model; judgement matrix.
  DOI: 10.1504/IJCEELL.2024.10053208
   
  
• Evaluation Method of Immersive Situation Interpretation Teaching Based on Natural Language Processing
  by Weihua Wang 
  Abstract: In order to overcome the problems of poor evaluation accuracy and low evaluation efficiency, this paper proposes an immersive situational interpretation teaching effect evaluation method based on natural language processing. Firstly, the evaluation system of interpretation teaching should be established; secondly, it constructs a set of factors for interpreting evaluation and determines the evaluation criteria for interpreting teaching difficulty; then, according to the natural language processing method, calculate the similarity of the evaluation standards at all levels; finally, a teaching effect evaluation function is constructed to evaluate the teaching effect of immersive situational interpretation. The experimental results show that the evaluation results of this method are closer to the students’ scores, the accuracy of the effect evaluation is improved by 8%, and the evaluation time is shortened by about 5 s, indicating that the accuracy of the evaluation results of this method is higher.
  Keywords: natural language processing; situational interpretation; teaching effect evaluation; objective weighting method; automatic word segmentation.
  
  
• Data mining method of English autonomous learning behavior based on decision tree
  by Juan Zhang, Pingyang Li, Xiaoli Sun 
  Abstract: Because the traditional data mining methods of English autonomous learning behaviour have the problems of low accuracy and long mining time, a decision tree-based data mining method of English autonomous learning behaviour is proposed. Firstly, the students’ learning behaviour data is collected, and then the collected behaviour data is classified by the decision tree method, and the data is divided into different types. Finally, according to the data classification results, the cart decision tree method is used to obtain the optimal split point of the decision tree through tree building and pruning operations, and the optimal binary tree is generated to realise the data mining of English autonomous learning behaviour. The experimental results show that the highest comprehensive coefficient of data mining of the research method in this paper is increased by 0.08 and 0.04 respectively, and the accuracy and efficiency of data mining are improved.
  Keywords: decision tree; learning behaviour; data mining; data acquisition platform; information gain; collaborative filtering; cart decision tree; prune.
  
  
• Research on quality evaluation of teaching reform based on Cauchy function
  by Dakai Li, Liu Yang 
  Abstract: In order to improve the recall of the evaluation results and the accuracy of the feature clustering of the teaching reform data, this paper designs a teaching reform quality evaluation method based on Cauchy function. Firstly, mining the characteristics of teaching reform evaluation data, and using TOPSIS analysis method to score the evaluation indicators, establish the evaluation indicator system. Secondly, a judgment matrix is constructed for the evaluation index system, and the weight of the index is determined according to the importance of the evaluation index. Finally, taking Cauchy function as membership function, the final evaluation result is obtained through fuzzy integration. The experimental results show that with the increase of the number of experiments, the precision of the evaluation results obtained by this method is always above 95%, and the maximum clustering accuracy of the teaching reform feature data can reach 97%.
  Keywords: Cauchy function; teaching reform; quality assessment; TOPSIS analysis method; index weight; membership function.
  
  
• Evaluation algorithm of online and offline mixed teaching quality based on multivariate statistical analysis
  by Shijuan Shen, Qingqing Shi, Xiaojing Bai 
  Abstract: In order to improve the accuracy of teaching quality evaluation and reduce the evaluation time, an online and offline mixed teaching quality evaluation algorithm based on multivariate statistical analysis is designed. Association rules are used to set the influencing factors to determine the rule conditions, and to determine the influencing factors of teaching quality. The weight of influencing factors is calculated, and Lagrange multiplier is introduced to determine the influencing factors. The influencing factors are grouped by factor analysis method, and the determined influencing factors with high correlation are classified by cluster analysis. The discriminant function criterion is constructed by discriminant analysis, and the discrimination and evaluation of different influencing factors are realised. The experimental results show that the highest evaluation accuracy of the method in this paper reaches 97%, indicating that it effectively improves the accuracy of the evaluation and reduces the evaluation time.
  Keywords: multivariate statistical analysis; online and offline mixed teaching; quality assessment; Lagrange multiplier; discriminant function.
  
  
• Cloud computing based method for optimal allocation of college network course education resources
  by HaiHua Huang, XinBin Yang 
  Abstract: To improve the classification accuracy of online course education resources and reduce the time consumption in the process of optimal allocation of resources, this paper proposes a method of optimal allocation of online course education resources in colleges and universities based on cloud computing. The cloud platform for the allocation of college online course education resources is built, and the data of college online course education resources are obtained by LDA topic function. The adaptation decision of college online course education resources is designed, and the optimal allocation of college online course education resources is realised according to the cloud computing method. The experimental results show that the proposed method takes less than 3.9 s to optimise the allocation of 1,200 G online course education resources, the classification accuracy can reach 99.0%, and the allocation efficiency is effectively improved, indicating that the application effect of this method is good.
  Keywords: Shannon formula; cloud computing; LDA topic function; allocation of educational resources; adaptation decisions.
  
  
• APPLICABILITY OF THE DESIGN THINKING PROCESS TO THE DEVELOPMENT OF CAPSTONE PROJECT PROPOSALS  
  by FERNANDO CEZAR LEANDRO SCRAMIM, Rui M. Lima, Hong Yuh Ching, Denise Rieg 
  Abstract: The purpose of this paper is to present an empirical study that explores how design thinking can be applied to develop ideas for capstone projects in undergraduate engineering programs. Action research was the research method used in this study. The collected data consisted of students’ project proposals, grades, classroom observations, and focus group findings. This study has two levels of implications: 1) for the practice of educators, as they can adjust the approach developed to help undergraduate engineering students find a potential idea for a viable and relevant capstone project; 2) for research, thereby adding to the ongoing discussion on exploring the design thinking process as a conceptual structure that provides a basis for dealing with difficult situations and solving complex problems in undergraduate courses.
  Keywords: design thinking process; capstone project; engineering education; scientific methodology course; project-based learning; PBL.
  DOI: 10.1504/IJCEELL.2024.10060500
   
  

Special Issue on: Future Intelligent Educational Environments Innovations Challenges and Applications

• University online teaching resource sharing open platform based on deep learning  
  by Liangxi Ding 
  Abstract: Aiming at the problems of high energy consumption, low classification accuracy of shared data and poor sharing effect of open network teaching resource sharing platform, an open network teaching resource sharing platform based on deep learning is designed. First, the application module, database module, and database retrieval function module are setup on the platform. Then, online teaching resources are classified in colleges and universities by using deep learning algorithms, and the characteristics of online teaching resources in colleges and universities are determined. Finally, an open platform for sharing online teaching resources in colleges and universities is built. The experimental results show that the platform designed in this paper has low energy consumption, which is always lower than 20 j, and the data classification accuracy of shared online teaching resources is always higher than 90%, which can effectively improve the sharing effect of online teaching resources in colleges and universities, and has good practical application performance.
  Keywords: deep learning; application module; database module; database retrieval function module; softmax regression.
  DOI: 10.1504/IJCEELL.2024.10051304
   
  
• A classification method of College Mathematics MOOC teaching resources based on machine learning  
  by Xuemei Shen 
  Abstract: In order to improve the classification effect and efficiency of College Mathematics MOOC teaching resources, a classification method of College Mathematics MOOC teaching resources based on machine learning is proposed. Firstly, the mean method is used to clean the sample data of College Mathematics MOOC teaching resources, and the maximum and minimum standardisation method is used to sample the data. Then the adaptive sliding window mutual information method is used to extract the characteristics of College Mathematics MOOC teaching resources. Finally, based on the two-step clustering algorithm, the MOOC teaching resources of College Mathematics are classified. The experimental results show that this method has a good classification effect on College Mathematics MOOC teaching resources. The classification recall rate remains above 93%, the classification accuracy remains above 92%, and the classification time is only 18.6 s. It can effectively improve the classification efficiency of College Mathematics MOOC teaching resources, and has good practical application performance.
  Keywords: College Mathematics; machine learning; MOOC teaching resources; two-step clustering algorithm; teaching resource classification.
  DOI: 10.1504/IJCEELL.2024.10061524
   
  
• An accurate recommendation method of English online teaching video resources based on firefly  
  by Fengxiang Zhang, Feifei Wang 
  Abstract: In order to solve the problem of poor recommendation effect of online English teaching resources, an accurate recommendation method of online English teaching video resources based on firefly was proposed. Firstly, the learning demand parameters of the recommendation method were optimised, the learning demand network model was constructed, the weight value of the demand was allocated by the attention mechanism, and the learning demand information was obtained by combining the dice activation function. Secondly, firefly algorithm is applied to set firefly brightness in combination with learning needs, and the target corresponding to top-N demand that attracts the nearest distance is taken as the recommended object. Finally, based on the fit algorithm, the resources of the main internal recommendation are screened to achieve accurate recommendation. The test results show that the probability of effective viewing of the video resources of the design method reaches 82.0%, the probability of repeated use is more than 75.92%, which increases by 10% and the score increases by more than ten points. Therefore, the method can effectively improve the recommendation effect of teaching video resources and academic performance.
  Keywords: firefly; instructional video resources; learning needs network model; pooling function; attention mechanism; dice activation function; attraction distance.
  DOI: 10.1504/IJCEELL.2024.10060199
   
  
• An online learning behaviour monitoring of students based on face recognition and feature extraction  
  by Dong-yuan Ge, Jian Li, Hai-ping Luo, Tuo Zhou, Wen-jiang Xiang, Xi-fan Yao 
  Abstract: In order to effectively improve the accuracy and efficiency of students' online learning behaviour monitoring, an online learning behaviour monitoring method based on face recognition and feature extraction is proposed. The relevant theories of face recognition technology and feature extraction methods are analysed, and the global features of students' online learning behaviour are collected by monitoring video images and using face recognition technology. Using the feature extraction method, the local features of students' online learning behaviour are extracted according to the grey value of video pixels. On this basis, it constructs the monitoring model of students' online learning behaviour to realise the monitoring of students' online learning behaviour. The experimental results show that the proposed method has good monitoring effect on students' online learning behaviour, and can effectively improve the accuracy and efficiency of students' online learning behaviour monitoring. The maximum monitoring accuracy of the proposed method is more than 97%.
  Keywords: face recognition technology; feature extraction method; online learning; behaviour monitoring.
  DOI: 10.1504/IJCEELL.2024.10051306
   
  
• An online teaching quality evaluation method based on deep belief network  
  by Chun Liang, Hai-lin Peng 
  Abstract: In order to improve the evaluation effect and accuracy of online teaching quality, an online teaching quality evaluation method based on deep belief network is proposed. We establish the evaluation index system of online teaching quality, collect the data related to teaching quality, teaching attitude, teaching content, teaching methods and teaching influence by using crawler technology, and extract the data characteristics of online teaching quality evaluation index. Combined with the data characteristics, the online teaching quality evaluation model is constructed by using the deep belief network, and the evaluation index data is input into the evaluation model to obtain the online teaching quality score. The experimental results show that the error rate of the proposed method is only 4.9%, and the average accuracy rate of online teaching quality evaluation is as high as 97.2%, which has the characteristics that the accuracy of online teaching quality evaluation is higher than the effect.
  Keywords: online teaching; deep belief network; DBN; teaching quality evaluation; restricted Boltzmann machine; RBM; evaluation index system.
  DOI: 10.1504/IJCEELL.2024.10052683
   
  
• A comprehensive evaluation of network teaching quality in colleges and universities based on entropy weight TOPSIS model  
  by QingQin Chen 
  Abstract: Because the traditional evaluation methods have the problems of low weight calculation accuracy and high evaluation error rate, a comprehensive evaluation method of network teaching quality in colleges and universities based on entropy weight TOPSIS model is proposed. Firstly, questionnaire survey, expert interview and statistical methods are used to obtain the data related to network teaching. Secondly, the evaluation index system is established according to relevant principles. Finally, the entropy weight method is used to determine the weight of the evaluation index, and combined with the calculation results of the weight of index, the TOPSIS model is used to get the relevant evaluation results. The experimental results show that this method can realise the evaluation of network teaching quality in colleges and universities. The average accuracy of evaluation index weight calculation is 95.9%, and the average evaluation error rate is 7.6%.
  Keywords: entropy weight method; TOPSIS model; teaching quality evaluation; questionnaire survey; expert interview; statistical method.
  DOI: 10.1504/IJCEELL.2024.10051305
   
  
• English teaching resource database retrieval based on adaptive differential evolution algorithm  
  by Mengzhang Liu 
  Abstract: In order to improve the retrieval recall rate of English teaching resource database, the retrieval accuracy of English teaching resource database needs improvement and the retrieval time of English teaching resource database needs to be shortened. Therefore, English teaching resource database retrieval method based on adaptive differential evolution algorithm is studied. Firstly, the semantic feature distribution of English teaching resource database is analysed by combining semantic information fusion and cluster analysis. Then, the spatial undersampling technology is used to process the distributed sampling and information fusion of the English teaching resource database. Finally, according to the fusion results, the adaptive differential evolution algorithm is used to retrieve the English teaching resource database. The experimental results show that the average recall rate of the proposed method is 90.7%, the retrieval accuracy of English teaching resource database is 97.7%, and the retrieval time is 22.7 ms.
  Keywords: adaptive differential evolution algorithm; cluster analysis; semantic information fusion; English teaching; resource database retrieval.
  DOI: 10.1504/IJCEELL.2024.10051372
   
  
• The user satisfaction evaluation of MOOC teaching platform based on multidimensional association rules  
  by Ying Jin 
  Abstract: To overcome the problems of low recall and precision and high error rate of satisfaction evaluation in traditional evaluation methods, a user satisfaction evaluation method of MOOC teaching platform based on multidimensional association rules is proposed. We use Boolean matrix and weight to improve apriori algorithm, and multidimensional association rules mining algorithm based on improved apriori algorithm to mine MOOC teaching platform data. The evaluation index system is constructed according to the data mined, and the evaluation index weight is calculated in combination with the weight factor, so as to build a satisfaction evaluation model based on second-order hidden Markov. The evaluation index data is input into the model, and the user satisfaction results of MOOC teaching platform are obtained. The simulation results show that the average recall rate is 96.9%, the average accuracy rate is 96.2%, and the evaluation error rate is always below 2.2%.
  Keywords: multidimensional association rules; MOOC teaching platform; user satisfaction assessment; improved apriori algorithm; second-order hidden Markov.
  DOI: 10.1504/IJCEELL.2024.10061521
   
  
• An online multimedia teaching quality evaluation method based on association rule mining  
  by Meng Qu 
  Abstract: In order to improve the accuracy and efficiency of online teaching quality evaluation, this paper proposes a new online multimedia teaching quality evaluation method based on association rule mining. Firstly, the association rule mining method is used to collect online multimedia teaching data, which is used as the sample data of teaching quality evaluation. Secondly, it comprehensively analyses the process and influencing factors of online multimedia teaching, and constructs the evaluation index system of online multimedia teaching quality. Finally, the analytic hierarchy process is used to calculate the weight and maximum characteristic root of the evaluation index, and the comprehensive scoring function of online multimedia teaching quality is constructed. The output score corresponds to different evaluation grades. The experimental results show that compared with the traditional evaluation methods, this method has higher evaluation accuracy and lower evaluation time, and the highest evaluation accuracy is close to 98%.
  Keywords: association rule mining; online multimedia teaching; teaching quality evaluation; comprehensive scoring function.
  DOI: 10.1504/IJCEELL.2024.10061523
   
  
• Study on personalised recommendation method of online education resources under the background of teaching reform  
  by Hanyu Zheng 
  Abstract: In order to achieve the research goal of solving the problems of accuracy, recall and low F1 value of traditional online education resources personalised recommendation methods, a new personalised recommendation method of online education resources under the background of teaching reform was designed. Using the BERT model to extract learner preference vectors and feature vectors of online educational resources, an improved discrete differential evolution algorithm is designed, which is used to recall and sort online educational resource sequences. Combined with the collaborative filtering algorithm to generate recommendation sequences, personalised recommendation results of online education resources are obtained. Simulation experiments show that the accuracy rate curve of this paper method relatively flat, accuracy rate is always above 93%, the maximum recall rate is 98%, and the F1 mean is 9.67, the recommended results are reliable.
  Keywords: teaching reform; online educational resources; personalised recommendation; BERT model; discrete differential evolution algorithm; collaborative filtering algorithm.
  DOI: 10.1504/IJCEELL.2024.10061520
   
  
• Study on online English teaching quality evaluation method based on action orientation  
  by Xiaojuan Liu 
  Abstract: In this paper, an action-oriented online English teaching quality evaluation method was designed. Firstly, three first-level indicators and 12 second-level indicators are selected based on the action-oriented theory to complete the determination of online English teaching quality evaluation indicators, and then the rough set theory is used to determine the weight of indicators. Finally, according to the positive correlation coefficient between different evaluation indicators, the primary score of online English teaching quality evaluation is obtained. According to the evaluation of English teaching quality by students, teachers and experts and managers, the participants' preference score is obtained, and the primary score and participants' preference score are combined to achieve a scientific evaluation of online English teaching quality. The results of comparative experiments show that the proposed method can control the evaluation delay time between 7.4 s and 12.2 s, the delay is very small, and the maximum instruction number per unit time can reach 3.97 million. Results show fast response, and high accuracy of evaluation, up to 92%. The practice shows that this method has good application performance and is worth popularising.
  Keywords: action-oriented theory; rough set theory; index weight; preference score; online English teaching; quality evaluation.
  DOI: 10.1504/IJCEELL.2024.10061522