Personalized Intelligent Tutoring System for Programming Education Integrating Multi-modal Data and Knowledge Graph
Abstract
This study analyzes the data generated by programmers and conducts behavioral modeling, which helps teachers understand their programming abilities, learning styles, knowledge mastery and other programming learning characteristics, and helps teachers build a learning portrait of programmers to support their personalized programming learning. Personalized intelligent tutoring system promotes implementation of teaching decisions, supports efficient education management, and has high application value in “human-machine collaboration” education.
Keywords
Programming education; Personalization; Intelligent tutoring system
Full Text:
PDFReferences
[1] Xu Z,Qin H ,Yang S , AS Maryam . A New Approach for Resource Recommendation in the Fog-Based IoT Using a Hybrid Algorithm[J]. The Computer Journal, 2021,189(10):1-16.
[2] Gu T,Chen H,Bin C , L Chang,W Chen.Neighborhood Attentional Memory Networks for Recommendation Systems[J]. Scientific Programming, 2021,20(1):1-10 .
[3] Liang X,Yin J.Recommendation Algorithm for Equilibrium of Teaching Resources in Physical Education Network Based on Trust Relationship[J].Journal of Internet Technology, 2022,23(1):133-141.
[4] Zhao K.A Novel Method for Integration of Online Educational Resources via Teaching Information Remote Scheduling[J]. Mathematical Problems in Engineering, 2021, 21(41):1-6.
DOI: http://dx.doi.org/10.18686/ahe.v8i5.13452
Refbacks
- There are currently no refbacks.
