Title: The user satisfaction evaluation of MOOC teaching platform based on multidimensional association rules

Authors: Ying Jin

Addresses: College of Cultural and Creative Industries, Changchun University of Architecture, Changchun 130000, China

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.2024.139941

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.4, pp.391 - 404

Received: 13 Jun 2022
Accepted: 30 Aug 2022
Published online: 12 Jul 2024 *

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