<abstract xmlns="http://www.w3.org/1999/xhtml">

This study develops a framework for personalized learning path planning that incorporates individual students’ learning behaviors, current learner status, and mastery of knowledge points with varying degrees of difficulty. Utilizing a big data-driven model, the framework calculates the difficulty of each knowledge point specifically tailored for individual students. Subsequently, it assesses the learners’ current status through an analysis of historical learning records, enabling the strategic planning of personalized educational trajectories. Finally, the correlation analysis and independence test were used to compare the differences in students’ academic performance, learning confidence, and learning needs between personalized learning and traditional learning modes. After using the personalized learning model, the student achievement outcome data showed F(1, 145) = 41.805, p = 0.013, η2 = 0.213. There was a significant difference in the personalized learning model on the students’ student confidence, F(1, 120) = 7.364, p = 0.021, η2 = 0.007. The customized learning pathway designed in this paper can promote the achievement of personalized learning goals for students. 
</abstract>

This study develops a framework for personalized learning path planning that incorporates individual students’ learning behaviors, current learner status, and mastery of knowledge points with varying degrees of difficulty. Utilizing a big data-driven model, the framework calculates the difficulty of each knowledge point specifically tailored for individual students. Subsequently, it assesses the learners’ current status through an analysis of historical learning records, enabling the strategic planning of personalized educational trajectories. Finally, the correlation analysis and independence test were used to compare the differences in students’ academic performance, learning confidence, and learning needs between personalized learning and traditional learning modes. After using the personalized learning model, the student achievement outcome data showed F(1, 145) = 41.805, p = 0.013, η2 = 0.213. There was a significant difference in the personalized learning model on the students’ student confidence, F(1, 120) = 7.364, p = 0.021, η2 = 0.007. The customized learning pathway designed in this paper can promote the achievement of personalized learning goals for students.

Design of Students’ Personalized Learning Paths under the Integration and Development of Technology and Basic Education

School of Education, Shandong Women's University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study develops a framework for personalized learning path planning that incorporates individual students’ learning behaviors, current learner status, and...

Design of Students’ Personalized Learning Paths under the Integration and Development of Technology and Basic Education

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 14. März 2024

Akzeptiert: 28. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1737

Schlüsselwörter
Correlation, Test of independence, Analysis of variance, Big data technology, Personalized learning

© 2024 Juanjuan Zang., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Design of Students’ Personalized Learning Paths under the Integration and Development of Technology and Basic Education

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 14. März 2024

Akzeptiert: 28. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1737

SchlüsselwörterCorrelation, Test of independence, Analysis of variance, Big data technology, Personalized learning

© 2024 Juanjuan Zang., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Correlation, Test of independence, Analysis of variance, Big data technology, Personalized learning