Open Access

Design and Implementation of a Computational Model for the Enhancement of College Students’ Independent Learning Ability Supported by Big Data

Lanyan Yang

Lanyan Yang

Guizhou University of CommerceGuiyang, China
Search for this author on
Sciendo|Google Scholar
Yang, Lanyan
  
Mar 21, 2025
Design and Implementation of a Computational Model for the Enhancement of College Students’ Independent Learning Ability Supported by Big Data's Cover Image
About this article
Previous Article
Next Article
Cite
Download Cover
Published Online: Mar 21, 2025
Received: Nov 10, 2024
Accepted: Feb 16, 2025
DOI: https://doi.org/10.2478/amns-2025-0645
Keywords
© 2025 Lanyan Yang, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

The college students’ self-learning index hierarchy
The college students’ self-learning index hierarchy

Figure 2.

The college students’ self-learning ability prediction algorithm process
The college students’ self-learning ability prediction algorithm process

Figure 3.

The model’s self-learning ability and the prediction accuracy
The model’s self-learning ability and the prediction accuracy

Figure 4.

The autonomic learning ability of the model and the prediction error
The autonomic learning ability of the model and the prediction error

Figure 5.

Model fitting precision and prediction accuracy contrast
Model fitting precision and prediction accuracy contrast

Figure 6.

Comparison of the prediction efficiency of autonomous learning ability
Comparison of the prediction efficiency of autonomous learning ability

Figure 7.

Evaluation of independent learning ability of the experimental group
Evaluation of independent learning ability of the experimental group

Figure 8.

Control group autonomous learning ability level evaluation
Control group autonomous learning ability level evaluation

The analysis of the difference between the motivation and the motivation

Dimension content Pretest-Posttest N Mean SD T P
SE Pretest 40 2.774 0.486 -2.145 0.000
Posttest 40 3.133 0.246
IG Pretest 40 2.094 0.176 -3.425 0.000
Posttest 40 4.475 0.455
LC Pretest 40 2.027 0.173 -4.124 0.002
Posttest 40 4.292 0.327
LSM Pretest 40 2.051 0.088 -2.442 0.001
Posttest 40 4.082 0.371
LEG Pretest 40 2.857 0.182 -3.124 0.000
Posttest 40 3.035 0.411
LA Pretest 40 2.655 0.298 -1.454 0.003
Posttest 40 3.647 0.24

Analysis of differential survey of learning strategies

Dimension content Pretest-Posttest N Mean SD T P
General Approach Pretest 40 2.032 0.459 -3.142 0.000
Posttest 40 3.749 0.43
LH Pretest 40 2.008 0.003 -3.104 0.002
Posttest 40 3.054 0.334
LP Pretest 40 2.987 0.194 -3.242 0.000
Posttest 40 4.482 0.424
LS Pretest 40 2.317 0.029 -2.024 0.004
Posttest 40 4.283 0.024
LE Pretest 40 2.309 0.082 -3.114 0.003
Posttest 40 3.523 0.411
LM Pretest 40 2.103 0.48 -1.324 0.000
Posttest 40 3.845 0.095

Test data statistics

College type Key university First batch universities Second batch universities Independent college Vocational school
Test data quantity 1500 2600 4700 1100 4500
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