International Journal of Continuing Engineering Education and Life-Long Learning (9 papers in press)

Regular Issues

• 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: Challenges and Future Trends of Distance Learning II

• 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
   
  
• 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
   
  
• 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.
  DOI: 10.1504/IJCEELL.2024.10064518
   
  
• 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.
  DOI: 10.1504/IJCEELL.2024.10064519
   
  
• 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
   
  
• 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.
  DOI: 10.1504/IJCEELL.2024.10064520
   
  
• Data mining method of English autonomous learning behaviour 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.
  DOI: 10.1504/IJCEELL.2024.10064517
   
  
• Evaluation method of immersive situational interpretation teaching effect 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; immersive situational interpretation; teaching effect evaluation; objective weighting method; automatic word segmentation.
  DOI: 10.1504/IJCEELL.2024.10064516