Open Access

E-learning Based Data Mining System for Chinese Teaching Information

Shujing Han

Shujing Han

Faculty of International Business and Tourism Management, Ningbo PolytechnicNingbo, China
Search for this author on
Sciendo|Google Scholar
Han, Shujing
, 
Suwei Gao

Suwei Gao

Research Institute of China Rural Policy and Practice, Ningbo UniversityNingbo, China
Search for this author on
Sciendo|Google Scholar
Gao, Suwei
 and 
Tianhe Yin

Tianhe Yin

  • School of Statistics and Data Science, Ningbo University of TechnologyNingbo, China
  • Ningbo Academy of Private Economy ResearchNingbo, China
Search for this author on
Sciendo|Google Scholar
Yin, Tianhe
  
Feb 27, 2025
E-learning Based Data Mining System for Chinese Teaching Information's Cover Image
About this article
Previous Article
Next Article
Cite
Download Cover
Published Online: Feb 27, 2025
Received: Sep 27, 2024
Accepted: Jan 19, 2025
DOI: https://doi.org/10.2478/amns-2025-0103
Keywords
© 2025 Shujing Han et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Data mining foundations.
Data mining foundations.

Figure 2.

College Chinese test data mining system flowchart
College Chinese test data mining system flowchart

Figure 3.

Experimental folding of the maximum number of antecedent words setting in a line graph.
Experimental folding of the maximum number of antecedent words setting in a line graph.

Figure 4.

Decision tree data mining conceptual diagram.
Decision tree data mining conceptual diagram.

Figure 5.

Design of an IoT-based Chinese language learning system
Design of an IoT-based Chinese language learning system

Figure 6.

Distribution of IoT achievement data for Chinese education
Distribution of IoT achievement data for Chinese education

Figure 7.

Number of classrooms distributed among several scenarios
Number of classrooms distributed among several scenarios

Figure 8.

Comparing Chinese language programs on a gradient basis, encouraging decision trees and using additional techniques
Comparing Chinese language programs on a gradient basis, encouraging decision trees and using additional techniques

Results of layering and percentage of each knowledge point_

Hierarchical Aggregate The number of knowledge points The number of knowledge points Total users in the system (%)
Level 1 4406 Tautological Restatement 4406 100
Information induction 4406 100
Implicit Meaning Reasoning 4406 100
Meaning comprehension 4405 99.98
Lexical judgement 4405 99.98
Inquiries 4315 97.93
Fixed Phrase Application 4287 97.3
Verb Collocation 4233 96.07
Related Words Application 4141 93.99
Level 2 3324 Non-predicate forms of Verbs 3319 99.61
Verb tenses and moods 3381 99.49
Level 3 2969 Analysis and application of parallel structures 2197 74
Noun collocations 2022 60.63
Others 1800 60.42
Pronouns 1794 99.65
Level 4 2969 Adjective Collocation 298 99.33
Prepositions as compounds and prepositional expressions 298 99.33
Unanalyzed Commonly used articles and times 1 Unanalyzed
Language:
English
Publication timeframe:
1 times per year
Journal Subjects:
Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
Journal RSS Feed