Deriving Association between Student Comprehension and Facial Expressions using Class Association Rule Mining

G.Sofia

Deriving Association between Student Comprehension and Facial Expressions using Class Association Rule Mining

G.Sofia * §

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mmdsadiq

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Bharathiar University

Deriving Association between Student Comprehension and Facial Expressions using Class Association Rule Mining

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Abstract

The scope of this study was to discover the association between facial expressions of students in an academic lecture and the level of comprehension shown by their expressions. This study focused on finding the relationship between the specific elements of learner’s behavior for the different emotional states and the relevant expression that could be observed from individual students. The experimentation was done through surveying quantitative observations of the lecturers in the classroom in which the behavior of students are recorded and were statistically analyzed. The main aim of this paper is to derive association rules that represent relationships between input conditions and results of domain experiments. Hence the relationship between the physical behaviors that are linked to emotional state with the student’s comprehension is being formulated in the form of rules. We present Predictive Apriori algorithm that is able to find all valid class association rules with high accuracy. The rules derived by Predictive Apriori are pruned by objective and subjective measures.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

G.Sofia, mmdsadiq. 2013. "Deriving Association between Student Comprehension and Facial Expressions using Class Association Rule Mining". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 13 (GJCST Volume 13 Issue C6).

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GJCST Volume 13 Issue C6

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

class association rules facial expression objective measure predictive apriori algorithm pruning subjective measure

Classification

GJCST-C Classification H.2.8

Submission ReceivedDecember 13, 2012
Peer Review Double Blind
Handling Editor
Accepted January 4, 2013
Published January 15, 2013

Version of record

v1.2

Issue date

June 4, 2013

Language

English

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Article Matrices

Total Score: 147

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Dr. M. Mohamed Sathik, G.Sofia (PhD/Dr. count: 1)

View Count (all-time): 355

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Publish Date: 2013 01, Tue

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Total Views: 3.6K

Total Downloads: 218

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