An Improved Accurate Classification Method for Online Education Resources Based on Support Vector Machine (SVM): Algorithm and Experiment

Autor/inn/en	Quan, Zhi; Pu, Luoxi
Titel	An Improved Accurate Classification Method for Online Education Resources Based on Support Vector Machine (SVM): Algorithm and Experiment
Quelle	In: Education and Information Technologies, 28 (2023) 7, S.8097-8111 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Quan, Zhi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-11514-6
Schlagwörter	Accuracy; Classification; Internet; Open Educational Resources; Algorithms; Online Searching; Access to Education; Measurement + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Algorithm; Algorithmus; Online-Recherche; Education; Access; Bildung; Zugang; Bildungszugang; Messverfahren
Abstract	In the face of surging online education around the globe, it seems quite necessary and helpful for learners and teachers to have the plethora of online resources well sorted out beforehand. To some extent, the efficiency and accuracy of resource search and retrieval may determine the quality and influence of online education. In this research, based on the methodological framework of design science, the support vector machine (SVM) algorithm is chosen to optimise the design of an accurate resource classifier. The aim is to improve the unsatisfactory classification effect of traditional classification methods for online education resources, so that online learners can enjoy more accurate and convenient access to education resources they are seeking out of many more. For the purpose of performance evaluation, the proposed SVM-based classifier was compared with two other classification methods based on multiple neutral networks and deep learning respectively. Upon collection and pre-processing of online materials, the features of educational resources were extracted and output in the form of feature vectors. By calculating the similarity between the extracted feature vectors and the standard vectors of the set type, we obtained the classification results of online education resources for each of the three classifiers. It was found that, compared with those of the two traditional classification methods, the precision ratio and the recall ratio of the proposed classifier improved by 3.26% and 2.01% respectively. In the meantime, the proposed SVM-based classifier seems to more advantageous in performance balance with better F measurement. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01