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

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G.Sofia
G.Sofia
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Dr. M. Mohamed Sathik
Dr. M. Mohamed Sathik
1 Bharathiar University

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GJCST Volume 13 Issue C6

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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.

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.

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Not applicable for this article.

G.Sofia. 2013. \u201cDeriving Association between Student Comprehension and Facial Expressions using Class Association Rule Mining\u201d. 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
Pg. 27- 33
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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v1.2

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June 4, 2013

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English

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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.

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

Dr. M. Mohamed Sathik
Dr. M. Mohamed Sathik
G.Sofia
G.Sofia Bharathiar University

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