Effective Diagnostic Feedback for Online Multiple-Choice Questions - Artificial Intelligence Applications and Innovations - Part I (AIAI 2012) Access content directly
Conference Papers Year : 2012

Effective Diagnostic Feedback for Online Multiple-Choice Questions

Ruisheng Guo
  • Function : Author
  • PersonId : 1008042
Dominic Palmer-Brown
  • Function : Author
  • PersonId : 1008043
Sin Wee Lee
  • Function : Author
  • PersonId : 1008044
Fang Fang Cai
  • Function : Author
  • PersonId : 1008045

Abstract

When students attempt MCQs (Multiple-Choice Questions) they generate invaluable information which can form the basis for understanding their learning behaviours. In this research, the information is collected and automatically analysed to provide customized, diagnostic feedback to support students’ learning. This is achieved within a web-based system, incorporating the SDNN (Snap-drift neural network) based analysis of students’ responses to MCQs. This paper presents the results of a large trial of the method and the system which demonstrates the effectiveness of the feedback in guiding students towards a better understanding of particular concepts.
Fichier principal
Vignette du fichier
978-3-642-33409-2_33_Chapter.pdf (439.9 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01521425 , version 1 (11-05-2017)

Licence

Attribution

Identifiers

Cite

Ruisheng Guo, Dominic Palmer-Brown, Sin Wee Lee, Fang Fang Cai. Effective Diagnostic Feedback for Online Multiple-Choice Questions. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.316-326, ⟨10.1007/978-3-642-33409-2_33⟩. ⟨hal-01521425⟩
72 View
729 Download

Altmetric

Share

Gmail Facebook X LinkedIn More