A review of enhancing online learning using graph-based data mining techniques

Munshi, M.; Shrimali, Tarun; Gaur, Sanjay

doi:10.1007/s00500-022-07034-7

Cited by 4 publications

(2 citation statements)

References 75 publications

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“…Based on the analysis method of online learning behavior characteristics proposed in reference [3], combined with the analysis results of the application of educational data mining in online learning by Chwen Jen Chen [4] and the experimental conclusions of M Munshi [5] to enhance the online learning effect through data mining algorithms and models, this paper puts forward a set of online learning behavior index system and analysis framework for college students, and summarizes many characteristics of different students' online learning behaviors. The overall scheme is driven by network data, involving data collection, data preprocessing, data mining, result analysis and other links and steps.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Countermeasures of College Students' English Online Learning Behavior Data Based on Data Mining Technology

Zhao

2024

Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–

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With the deepening of educational informatization, online learning methods are rapidly emerging and gradually applied in the field of higher education. Although online learning has practical advantages such as convenience, interactivity and timeliness, it also has some shortcomings such as separation of teachers and students and lagging teaching supervision and management. In this regard, based on the current teaching situation of English majors in colleges and universities, this paper will build an online learning behavior index system and analysis framework, summarize the characteristics of students' behavior, and put forward suggestions for improvement on the subsequent curriculum teaching plan. The analysis framework takes data mining technology as the core, and focuses on using Pearson coefficient to calculate the correlation between learning behavior and English learning performance. At the same time, it combines Logistic regression model to classify and predict students' learning performance, which is convenient for teachers to analyze and deal with students' learning behavior and improve the effectiveness of online learning. Practice has proved that students' operational behavior, cooperative behavior and problem-solving behavior are significantly correlated with their English performance, and learning intervals, communication times and the completion of learning processes are the main learning behavior characteristics that affect their English performance. Teachers need to take corresponding measures to intervene and provide students with necessary guidance and help.

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Section: Introductionmentioning

confidence: 99%

Analysis and Countermeasures of College Students' English Online Learning Behavior Data Based on Data Mining Technology

Zhao

2024

Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–

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“…However, the copious amount of information and data surrounding people's daily lives has both advantages and disadvantages. While accurate information can enhance lives, erroneous information can lead to faulty reasoning [2]. Therefore, data mining is essential for conducting analysis.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Application Research of E-Commerce Financial Management Based on T-DPC Optimization Algorithm

Wang,

Shan

2024

Decis. Mak. Appl. Manag. Eng.

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Given the intricate, multifaceted nature of financial data in e-commerce enterprises, this article presents a T-DPC algorithm for analyzing financial management in these businesses. The algorithm utilizes the t-SNE method to reduce the dimensionality of financial data, whilst also implementing an enhanced DPC algorithm based on the K-nearest neighbor concept to analyze financial data clusters. The results show that the F-measure metrics of the DPC algorithm optimized by t-SNE improve 16.7% and 3.07% over the DPC algorithm after testing on the PID and Wine datasets, and its running time is faster than the DPC algorithm on the Aggregation, D31, and R15 datasets by 16.2. Therefore, the algorithm has reference significance for the financial analysis of e-commerce enterprises.

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Incremental Update Algorithm of Athlete Physical Training Information Under Dynamic Iterative Sampling

Chen,

Huang

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A review of enhancing online learning using graph-based data mining techniques

Cited by 4 publications

References 75 publications

Analysis and Countermeasures of College Students' English Online Learning Behavior Data Based on Data Mining Technology

Analysis and Countermeasures of College Students' English Online Learning Behavior Data Based on Data Mining Technology

Analysis and Application Research of E-Commerce Financial Management Based on T-DPC Optimization Algorithm

Incremental Update Algorithm of Athlete Physical Training Information Under Dynamic Iterative Sampling

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