Analysis on Course Scores of Learners of Online Teaching Platforms Based on Data Mining

Zhou, Nan; Zhang, Zhaofeng; Li, Jing

doi:10.18280/isi.250508

Cited by 18 publications

(18 citation statements)

References 24 publications

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“…The Apriori algorithm aims to mine association rules with the help of frequent itemsets [20][21][22][23][24]. The basic idea of the algorithm is: first, find all frequent itemsets, whose support is greater than or equal to the predefined minimum support; then, identify the strong association rules from the frequent itemsets, which must satisfy both minimum support and minimum confidence; next, generate the rules that only contain the items in the corresponding set; after that, retain only the rules whose confidence is greater than the user-defined minimum confidence.…”

Section: Correlation Analysis Based On Apriori Algorithmmentioning

confidence: 99%

An Improved Apriori Algorithm for Association Mining Between Physical Fitness Indices of College Students

Pan

2021

Int. J. Emerg. Technol. Learn.

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The physical fitness of college students can be evaluated scientifically based on the data of physical education (PE). This paper firstly relies on the Apriori algorithm to mine the hidden correlations between the physical fitness indices from the PE data on college students, and identify the indices closely associated with the physical fitness of college students. Then, the Apriori algorithm was improved to reduce the time complexity of association rule mining. Based on the improved algorithm, it was learned that the correlation coefficients of several indices surpassed the minimum support of 0.2 and minimum confidence of 0.7, reflecting their important impacts on physical fitness. Thus, physical fitness of college students is significantly influenced by speed, endurance, flexibility, and vital capacity, but not greatly affected by height and weight. The research results provide an important guide for the test and curriculum designs of PE for college students.

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Section: Correlation Analysis Based On Apriori Algorithmmentioning

confidence: 99%

An Improved Apriori Algorithm for Association Mining Between Physical Fitness Indices of College Students

Pan

2021

Int. J. Emerg. Technol. Learn.

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“…With the maturity of data mining technology and the continuous expansion of its application fields, many university researchers have begun to study the application of data mining technology in the analysis of college students' performance. Based on the classification mining method of the K-nearest neighbor algorithm, we analyze the student performance database data, combine the SLIQ algorithm to analyze the student's performance, and establish a K-nearest neighbor algorithm model of professional ability for teachers and school education decision-makers to understand the existing problems in teaching, in order to use the performance information provided by the optimized teaching plan and decision-making [1,2]. is paper studies the application of the principal component analysis method and the Bayesian K-nearest neighbor algorithm in data mining.…”

Section: Related Workmentioning

confidence: 99%

Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm

Wang

2021

Complexity

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This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated.

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“…With the advancement of technologies and the innovation of ideas, various online learning platforms emerge one after another, becoming an important way for students to obtain learning resources and for teachers to carry out information-based teaching [4][5][6][7]. However, online teaching involves a huge number of students, who are difficult to group manually [8][9][10]. Accurately acquiring the knowledge state of students and optimizing the grouping of massive users for online cooperative learning based on the knowledge state diagnosis results is an effective way to improve the effect of online cooperative learning.…”

Section: Introductionmentioning

confidence: 99%

A Student Grouping Method for Massive Online Collaborative Learning

Wang¹,

Qi²

2022

Int. J. Emerg. Technol. Learn.

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As a brand-new learning strategy, cooperative learning can greatly improve the quality of classroom teaching and the teamwork and learning efficiency of students. The conventional student grouping models for cooperative learning take less consideration of the complementarity of students’ knowledge structures and learning interests, so they are not quite suitable for online teaching, which is provided for a huge number of students and has a large scale of learning resources and great freedom of choice. To this end, this paper studies the student grouping method for massive online collaborative learning. Firstly, the problem of identifying the student’s knowledge state was described and characterized, and a diagnosis model of students’ knowledge state simulating the cooperative learning process was constructed based on the gated recurrent neural network. The creation of an online collaborative learning scenario that considers both the knowledge state and interests of students was regarded as an NP-hard problem, and then, the enhanced particle swarm optimization algorithm was adopted to realize the grouping of students for massive online collaborative learning. The experimental results verified the effectiveness of the model constructed in this paper.

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Analysis on Course Scores of Learners of Online Teaching Platforms Based on Data Mining

Cited by 18 publications

References 24 publications

An Improved Apriori Algorithm for Association Mining Between Physical Fitness Indices of College Students

An Improved Apriori Algorithm for Association Mining Between Physical Fitness Indices of College Students

Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm

A Student Grouping Method for Massive Online Collaborative Learning

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