TITLE:
Classification of Attribute Mastery Patterns Using Deep Learning
AUTHORS:
Dezhi Chen, Congcong Yan
KEYWORDS:
Cognitive Diagnosis Assessment, Deep Learning, Attribute Mastery Pattern, Classification
JOURNAL NAME:
Open Journal of Modelling and Simulation,
Vol.9 No.2,
April
30,
2021
ABSTRACT: It is very important to identify the attribute mastery patterns of the
examinee in cognitive diagnosis assessment. There are many methods to classify
the attribute mastery patterns and many studies have been done to diagnose what
the individuals have mastered and or Montel
Carl Computer Simulation is used to study the classification of the attribute
mastery patterns by Deep Learning. Four results were found. Firstly, Deep
Learning can be used to classify the attribute mastery patterns efficiently.
Secondly, the complication of the structures will decrease the accuracy of the
classification. The order of the influence is linear, convergent, unstructured
and divergent. It means that the divergent is the most complicated, and the
accuracy of this structure is the lowest among the four structures. Thirdly,
with the increasing rates of the slipping and guessing, the accuracy of the
classification decreased in verse, which is the same as the existing research
results. At last, the results are influenced by the sample size of the
training, and the proper sample size is in need of deeper discussion.