Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data

Vellanki, Pratibha, Duong, Thi, Gupta, Sunil, Venkatesh, Svetha and Phung, Quoc-Dinh 2017, Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data, Knowledge and information systems, vol. 51, no. 1, pp. 127-157, doi: 10.1007/s10115-016-0971-7.

Attached Files
Name Description MIMEType Size Downloads

Title Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data
Author(s) Vellanki, Pratibha
Duong, Thi
Gupta, SunilORCID iD for Gupta, Sunil
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh
Journal name Knowledge and information systems
Volume number 51
Issue number 1
Start page 127
End page 157
Total pages 31
Publisher Springer
Place of publication London, Eng.
Publication date 2017-04
ISSN 0219-1377
Summary The spectrum nature and heterogeneity within autism spectrum disorders (ASD) pose as a challenge for treatment. Personalisation of syllabus for children with ASD can improve the efficacy of learning by adjusting the number of opportunities and deciding the course of syllabus. We research the data-motivated approach in an attempt to disentangle this heterogeneity for personalisation of syllabus. With the help of technology and a structured syllabus, collecting data while a child with ASD masters the skills is made possible. The performance data collected are, however, growing and contain missing elements based on the pace and the course each child takes while navigating through the syllabus. Bayesian nonparametric methods are known for automatically discovering the number of latent components and their parameters when the model involves higher complexity. We propose a nonparametric Bayesian matrix factorisation model that discovers learning patterns and the way participants associate with them. Our model is built upon the linear Poisson gamma model (LPGM) with an Indian buffet process prior and extended to incorporate data with missing elements. In this paper, for the first time we have presented learning patterns deduced automatically from data mining and machine learning methods using intervention data recorded for over 500 children with ASD. We compare the results with non-negative matrix factorisation and K-means, which being parametric, not only require us to specify the number of learning patterns in advance, but also do not have a principle approach to deal with missing data. The F1 score observed over varying degree of similarity measure (Jaccard Index) suggests that LPGM yields the best outcome. By observing these patterns with additional knowledge regarding the syllabus it may be possible to observe the progress and dynamically modify the syllabus for improved learning.
Language eng
DOI 10.1007/s10115-016-0971-7
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Springer-Verlag London
Persistent URL

Document type: Journal Article
Collection: Centre for Pattern Recognition and Data Analytics
Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 193 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Thu, 18 Aug 2016, 13:35:43 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact