sis 18: e31

Research Article

An Evaluation method for teaching effect of information technology integrated engineering management course based on data clustering

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  • @ARTICLE{10.4108/eai.26-1-2022.173160,
        author={Xiaolin Liu and Congjin Xie and Dawid Połap},
        title={An Evaluation method for teaching effect of information technology integrated engineering management course based on data clustering},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={1},
        keywords={Data clustering, Information technology, Integration, Engineering management, Course teaching, Effect evaluation},
        doi={10.4108/eai.26-1-2022.173160}
    }
    
  • Xiaolin Liu
    Congjin Xie
    Dawid Połap
    Year: 2022
    An Evaluation method for teaching effect of information technology integrated engineering management course based on data clustering
    SIS
    EAI
    DOI: 10.4108/eai.26-1-2022.173160
Xiaolin Liu1, Congjin Xie1, Dawid Połap2,*
  • 1: School of international business and management, Chongqing Institute of Foreign Studies, Chongqing, China 401120
  • 2: Faculty of Applied Mathematics/ RMS, Silesian University of Technology, Poland 44-100 Gliwice
*Contact email: Dawid.Polap@polsl.pl

Abstract

Aiming at the problems of poor evaluation effect in the teaching effect evaluation of engineering management specialty, this paper designs a new evaluation method of teaching effect of information technology Fusion Engineering Management Specialty Based on data clustering. The evaluation index system of teaching effect of information technology integration engineering management specialty is established by analytic hierarchy process. The indexes in the system mainly include five secondary indexes: teaching ability, teaching method, teaching content, teaching attitude and teaching effect; Determine the relative importance of different factors and calculate the weight vector of each index; On this basis, according to the established evaluation index system, pso-k-means algorithm is selected to evaluate the course teaching effect. PSO-k-means algorithm runs K-means clustering algorithm in each particle swarm optimization iteration, and uses the clustering result of K-means clustering algorithm as the particle fitness of the evaluation process to obtain accurate course teaching effect evaluation results. The experimental results show that the proposed method can effectively evaluate the teaching effect of information technology integration engineering management course and improve the evaluation effect.