Research on Learning Path Recommendation in Intelligent Learning

Wenjing Dong () and Xuedong Chen ()
Additional contact information
Wenjing Dong: Beijing Jiaotong University
Xuedong Chen: Beijing Jiaotong University

A chapter in LISS 2020, 2021, pp 361-372 from Springer

Abstract: Abstract This paper aims at the stability problem of Ant Colony Algorithm in learning path recommendation. First of all,explore how to select intelligent algorithm to realize learning path recommendation in intelligent learning; then, based on Ant Colony Algorithm, the basic information, learning style and knowledge level of learners and the expression form and difficulty coefficient of learning objects are considered to recommend learning path; finally, the value of volatilization factor ρ of Ant Colony Algorithm is adjusted, the simulation experiment is carried out by using control variable method, and the best volatilization factor is selected to improve the stability of the algorithm.

Keywords: Intelligent learning; Ant colony algorithm; Learning path recommendation (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-33-4359-7_26

Ordering information: This item can be ordered from
http://www.springer.com/9789813343597

DOI: 10.1007/978-981-33-4359-7_26

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().