Title: Online learning effectiveness evaluation for college students based on social network data mining

Authors: Die Meng; Beibei Ma; Mengting Liu; Shiying Li

Addresses: College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China ' College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China ' College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China ' College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China

Abstract: To solve the problems of low information collection accuracy, recall and evaluation accuracy in traditional methods, an evaluation method of online learning effectiveness for college students based on social network data mining is proposed. A web crawler architecture is utilised to mine social network data and collect information on university student network learning. Linear regression models are employed to filter indicator data and establish an index system for evaluating learning effectiveness. The weights of indicators are determined using the analytic hierarchy process, and fuzzy evaluation vectors are obtained by combining membership degree functions. An evaluation model is constructed using fuzzy evaluation vectors and fuzzy judgement method, and evaluation indicator data are input into the model to obtain scores for learning effectiveness. Experimental results demonstrate that the maximum accuracy of information collection for the proposed method is 98.1%, the maximum recall rate is 97.9%, the mean precision is 97.67%.

Keywords: social network data mining; college students; online learning; effectiveness; evaluation; web crawler; index system; fuzzy evaluation vectors; fuzzy judgement method.

DOI: 10.1504/IJCAT.2023.138842

International Journal of Computer Applications in Technology, 2023 Vol.73 No.4, pp.323 - 332

Received: 07 Jul 2023
Accepted: 15 Dec 2023
Published online: 31 May 2024 *

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