Accomplishments
Random Forest Algorithm for Learner's Confusion detection using Behavioural features
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Category
Articles
Authors
Kavita Kelkar & Jagdish Bakal
Publisher
Springer
Publishing Date
01-Jan-2020
volume
https://www.springer.com/serie
Issue
springer proceedings
Pages
521-529
- Abstract
This paper presents random forest algorithm to detect confusion affective state in virtual learning systems. The learning contents and examination questions are based on Bloom’s taxonomy cognitive levels. Proposed methodology captures learner’s behavioral interaction features. The confusion detection accuracy is above 90%.
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KAVITA KELKAR. (2019).
Behavioral Feature Analysis For Learner Affect Identification.
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