International Journal of Continuing Engineering Education and Life-Long Learning (28 papers in press)

Regular Issues

• MOOC English online course recommendation algorithm based on LDA user interest model  
  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  
  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  
  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
   
  
• 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. Setup application module, database module and database retrieval function module in the platform, classify online teaching resources in colleges and universities by using deep learning algorithm, determine the characteristics of online teaching resources in colleges and universities, and build an open platform for sharing online teaching resources in colleges and universities. The experimental results show that the platform designed in this paper has low energy consumption, which is always lower than 20j, 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 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, 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
   
  
• An Online learning behavior 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. Analyse the relevant theories of face recognition technology and feature extraction methods, and collect the global features of students’ online learning behaviour 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 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, improve the retrieval accuracy of English teaching resource database and shorten the retrieval time of English teaching resource database, the 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
   
  
• 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. 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%, 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
   
  
• 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.
  
  
• 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
   
  
• A MOOC online teaching quality evaluation method based on fuzzy algorithm  
  by Yang Guo 
  Abstract: MOOC is a new method of online evaluation, which takes a long time to evaluate the quality of traditional teaching. For MOOC online teaching quality, an evaluation system is established under the principle of evaluation index selection, and 19 evaluation indexes such as interface and navigation, knowledge management function, reminder function and mobile terminal function are obtained. After preprocessing the obtained evaluation indexes, the evaluation factor set is determined, and then the evaluation index weight is calculated according to the index weight evaluation standard, and its consistency is tested. Finally, according to the test results, The MOOC online teaching quality evaluation model is established under the fuzzy evaluation rules. The experiment shows that the evaluation accuracy of the proposed method can reach 98%, the evaluation effect is good, and the evaluation time is short.
  Keywords: fuzzy algorithm; MOOC online teaching; quality evaluation; consistency test.
  DOI: 10.1504/IJCEELL.2024.10060327
   
  
• A Quality evaluation model of distance assisted instruction based on AHP and entropy weight method  
  by Yu Cao, Shu-wen Chen, Jie Zhao 
  Abstract: In this paper, a distance-assisted teaching quality assessment model based on AHP and entropy weight method was constructed. Construct the quality evaluation system of distance auxiliary teaching, obtain evaluation indicators, construct the evaluation index judgement matrix of AHP according to the scale of the judgement matrix, and use the square root method to calculate the quality evaluation index of distance auxiliary teaching after the consistency test of the judgement matrix through CI. The weight coefficient is used to calculate the entropy weight of each evaluation index by using the entropy weight method. The fuzzy comprehensive evaluation method is used to construct the quality evaluation model of distance-assisted teaching, and the evaluation results are obtained. The simulation experiment results show that the constructed model has higher accuracy and shorter evaluation time for remote assisted teaching quality assessment.
  Keywords: analytic hierarchy process; AHP; entropy weight method; distance assisted instruction; quality evaluation.
  DOI: 10.1504/IJCEELL.2024.10060328
   
  
• The blending teaching effect evaluation of distance education under the background of MOOC  
  by Yingyao Wang 
  Abstract: Aiming at the problems of low evaluation accuracy and large time cost of current evaluation methods, a blending teaching effect evaluation method of distance education under the background of MOOC is designed. Firstly, it constructs the blending teaching effect evaluation system of distance education under the background of MOOC from three aspects: students, teachers and the design of distance classroom teaching mode. Then, the evaluation index data is processed into consistent data with the help of normalisation method, the Euclidean distance between different index data is calculated by k-nearest neighbour algorithm, and the index data with noise is marked and removed. Finally, the evaluation matrix is used to determine the weight of the evaluation index, and the index data is put into the random forest model to obtain the relevant evaluation results. Experimental results show that this method has the comprehensive advantages of high evaluation accuracy and low time cost.
  Keywords: MOOC; distance education; blending teaching effect; assessment; index system; k-nearest neighbour algorithm; evaluation matrix.
  DOI: 10.1504/IJCEELL.2024.10060329
   
  
• Satisfaction evaluation method of composite teaching model from the perspective of MOOC concept  
  by Dingyao Liu 
  Abstract: Aiming at the problems of large consumption of evaluation time and low completeness of evaluation information in traditional evaluation methods, this paper puts forward a satisfaction evaluation method of compound teaching mode from the perspective of MOOC concept. Firstly, the MOOC curriculum model is analysed, the satisfaction evaluation index system is established for the compound teaching model, and five first-class indexes and 18 second-class indexes are obtained. Then, the evaluation matrix is established to sort the evaluation indexes of teaching satisfaction, calculate the consistency ratio of the index system, and obtain the weight of each evaluation index by means of objective assignment. Finally, cloud computing technology is used to complete the analysis of satisfaction evaluation data. The experimental results show that the time consumption of this method is between 27.62 s and 34.59 s, the completeness of evaluation information is between 0.923 and 0.951, and the accuracy of evaluation results is between 91.7% and 96.3%.
  Keywords: massive open online courses; MOOC; compound teaching mode; satisfaction; consistency ratio; objective assignment; evaluation method.
  DOI: 10.1504/IJCEELL.2024.10060330
   
  
• Study on evaluation method of modern distance teaching effect under MOOC environment  
  by Dong Wang, Xia Liu, Jun Zhuang, Ya Kang 
  Abstract: Aiming at the problems of large calculation error of index weight and low accuracy of effect evaluation in the existing teaching effect evaluation methods, this paper designs a new evaluation method of modern distance teaching effect in MOOC environment. Firstly, the reliability of indicators in different index levels is calculated by Cronbach coefficient. Then, the factor analysis method is used to determine the learning behaviour factors and construct the evaluation index of modern distance teaching effect. Finally, the comprehensive weight of the evaluation index is calculated with the help of entropy method, different processing layers are designed through dynamic clustering algorithm, and the evaluation model of modern distance teaching effect is constructed to realise the evaluation of modern distance teaching effect in MOOC environment. The experimental results show that the teaching effect evaluation accuracy of the proposed method is up to 95%, which effectively improves the accuracy of distance teaching effect evaluation.
  Keywords: MOOC environment; modern distance education; effect evaluation; factor analysis; entropy method.
  DOI: 10.1504/IJCEELL.2024.10060331
   
  
• An evaluation of college students' involvement in English learning behavior based on fuzzy comprehensive evaluation  
  by Wenfang Li 
  Abstract: To overcome the problems of large error rate in weight calculation, low accuracy and long time in traditional evaluation methods, an evaluation method of college students’ involvement in English learning behaviour based on fuzzy comprehensive evaluation was proposed. The data related to the evaluation of college students’ English learning behaviour engagement were collected, and evaluation index system was constructed by screening the evaluation indexes, and the weight of the evaluation indexes was calculated by analytic hierarchy process (AHP). The evaluation model based on fuzzy comprehensive evaluation was established by determining comment set and factor set and constructing single factor fuzzy evaluation matrix, the evaluation results were obtained. Experimental results show that the maximum error rate of evaluation index weight calculation of this method is 11.7%, the minimum is 2.1%, the average accuracy rate of input evaluation is 96.2%, and the evaluation time varies from 2.23 s to 2.45 s.
  Keywords: fuzzy comprehensive evaluation; college students’ English learning; behavioural engagement assessment; analytic hierarchy process; AHP; composition operator.
  DOI: 10.1504/IJCEELL.2024.10060332
   
  
• Study on quality evaluation method of multimedia distance education based on Data Mining  
  by Wenxia Pan 
  Abstract: Because the traditional evaluation methods have the problems of long evaluation time and low evaluation accuracy, the data mining method is used to study the quality evaluation of multimedia distance education. Firstly, the quality index system of multimedia distance education is established under the basic principles of integrity, pertinence, accuracy, representativeness, objectivity and comparability, and the evaluation indexes are obtained. Then the evaluation indexes are normalised by dimensionless processing, and the weight of each index is calculated by rough set theory. Finally, according to the calculation results; the data mining method is selected to calculate the quality evaluation results of multimedia distance education. The experimental comparison shows that the accuracy of the proposed method for multimedia distance education quality evaluation is up to 100%, the evaluation time is within 7.58s, and the evaluation effect is good.
  Keywords: data mining; multi-media; distance learning; quality evaluation; ID3 decision tree method; information entropy.
  DOI: 10.1504/IJCEELL.2024.10060333
   
  
• A dynamic evaluation of MOOC online English teaching based on decision tree algorithm  
  by Lu Qian 
  Abstract: Aiming at the problems of low evaluation accuracy, complex evaluation process and time-consuming evaluation, this paper designed a dynamic evaluation method of MOOC online English teaching based on decision tree algorithm. Firstly, the data type is determined, and the correlation and correlation degree of the data are determined by Chi-square statistics method and mutual information method. Then, the redundant data and noise are removed by calculating the data centroid distance in the data set. Finally, the attribute value of evaluation decision tree is determined by decision tree, the evaluation model is constructed, and the evaluation error is corrected by decision tree pruning method to achieve dynamic evaluation of MOOC online English teaching. Experimental results show that the proposed method has the highest accuracy of 97% and takes 1.6 s, which effectively improves the evaluation efficiency.
  Keywords: Chi-square statistical method; relevance; decision tree algorithm; MOOC online English teaching; dynamic evaluation; suppress noise.
  DOI: 10.1504/IJCEELL.2024.10060334
   
  
• A quantitative evaluation method of online teaching quality based on Data Mining  
  by Xiangzheng Diao 
  Abstract: The quantitative evaluation of online teaching quality is affected by data and has problems of high time consumption and low accuracy. Therefore, a quantitative evaluation method of online teaching quality based on data mining is designed. Firstly, through association rules in data mining, data are collected, evaluation indexes are obtained by principal component analysis, and evaluation index system is established. Then, fuzzy analytic hierarchy process (AHP) is used to calculate the weights of evaluation indexes in the evaluation index system. Finally, an evaluation model is established to realise the quantitative evaluation of online teaching quality. The experiment shows that the accuracy value of the proposed method reaches 89%, and the evaluation time is 3.8 s, which improves the accurate and efficient evaluation of online teaching quality.
  Keywords: data mining; online teaching; quality assessment; association rules; principal component analysis; fuzzy analytical hierarchy process.
  DOI: 10.1504/IJCEELL.2024.10060335
   
  
• MOOC online English teaching quality evaluation method based on fuzzy algorithm  
  by Feng Wei  
  Abstract: In order to overcome the problems of low accuracy and long evaluation time of MOOC online English teaching quality evaluation, this paper proposes a MOOC online English teaching quality evaluation method based on fuzzy algorithm. Firstly, the mean method is used to calculate the cluster centre of MOOC online English teaching quality evaluation samples and the evaluation sample selection is realised by outlier detection. Secondly, the 1-9 scale method is used to compare the importance of English teaching quality evaluation indicators, so as to realise the stratification of importance level. Construct the fuzzy discrimination matrix and calculate the weight vector of the judgement matrix. Finally, the MOOC online English teaching quality evaluation is realised by fuzzy algorithm. The experimental results show that the accuracy of this method is 98.16%, and the evaluation time is only 1.5 min.
  Keywords: variance measure; fuzzy algorithm; 1–9 scale method; fuzzy discriminant matrix; outlier detection.
  DOI: 10.1504/IJCEELL.2024.10060336
   
  
• 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