An improved accurate classification method for online education resources based on support vector machine (SVM): Algorithm and experiment

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.

Learning Style Identification Using Semisupervised Self-Taught Labeling

“…To assess the internal performance of the model, training was conducted using k-fold cross-validation with k = 10. Although the applied SVM classifier is known for its proven performance in various classification and ability to handle complex datasets [38][39][40][41], we have empirically experimented with other types of well-known classifiers and presented the results in Table IV to justify the choice of the SVM classifier for data labelling. The results of the labeling dataset experiment for the course of Computer Skills for Humanities are presented in Table V, which summarizes the correctly and incorrectly classified instances, including specificity, precision, recall, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC).…”

Efficient Automated Cardiovascular Disease Detection Using Machine Learning

“…Using support vector machines to solve pattern recognition problems is a good idea. Today, the support vector machine is one of the most popular and accurate machine learning methods [23]. A support vector machine can also be used for classification and regression using supervised learning.…”

Influence of College Teacher’s Instructional Design on the Development of College Students’ Thinking of Innovation Multi-Algorithms Perspective Analysis

“…The SVM regression analysis algorithm is used for teaching design to help teachers better understand students ' innovative thinking level (Figure 2). According to the learning level, learning progress, and learning preference of different college students, a teaching plan is formulated to match them [35]- [38].…”