Non Verbal Communication for Analyzing the Malpractice in Examinations– An Overview

  • Authors

    • Kujani T
    • Sathya T
    • Bhuvanya R
    • Uma S
    2018-04-18
    https://doi.org/10.14419/ijet.v7i2.20.13302
  • Machine learning, gesture recognition, collaborative learning, Feature Extraction, Social signal Processing.

  • Abstract

    Nonverbal communication can specify the psychosomatic behavior of people involved in interpersonal communication. Many researchers have specified the importance of gesture intimation. In the paper, we shall apply the previously used computer vision hardware and Machine Learning techniques for capturing the postures students undergoing examination in the classroom atmosphere. The main       intention is to classify the people who involved in misbehavior such as copying, prompting answers, sharing the answer scripts and any other such practices. Current situation prevailing is though a physical monitor, invigilator is available in the exam hall the students     attempt to misbehave in various ways mentioned above. We discuss about the techniques to be employed to get an analysis of the       behavior of each student involved in exam.

     

  • References

    1. [1] Andrea Stevenson Won, Jeremy N. Bailenson, and Joris H. Janssen, Automatic Detection of Nonverbal Behavior Predicts Learning in Dyadic Interactions, IEEE Transactions On Affective Computing, Vol. 5, No. 2,2014.

      [2] P. Ekman and W. Friesen. Facial Action Coding System: ATechnique for the Measurement of Facial Movement. Consulting Psychologists Press, Palo Alto, CA, 1978.

      [3] A. M. Kessell and B. Tversky, “Gestures for thinking and explaining,†in Proc. Cogn. Sci. Soc. Meetings, 2005, p. 2498.

      [4] T. Dragon, I. Arroyo, B. Woolf, W. Burleson, R. el Kaliouby, and H. Eydgahi, “Viewing student affect and learning through classroom observation and physical sensors,†in Intelligent Tutoring System. Berlin, Germany: Springer, 2008, pp. 29–39.

      [5] Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data mining: A prediction for student's performance using classification method. World Journal of Computer Application and Technology, 2(2), 43-47.

      [6] Jr. Joseph J. LaViola , A Survey of Hand Posture and Gesture RecognitionTechniques and Technology.

      [7] S. K. D’Mello and A. Graesser, “Automatic detection of learner’s affect from gross body language,†Appl. Artif. Intell., vol. 23, no. 2, pp. 123–150, 2009.

      [8] M. LaFrance and M. Broadbent, “Group rapport: Posture sharing as a nonverbal indicator,†Group Organization Stud., vol. 1, pp. 328–333, 1976.

      [9] Gwen C. Littlewort, Marian Stewart Bartlett, Kang Lee,Automatic Coding of Facial Expressions Displayed During Posed and Genuine Pain, Image and Vision Computing, 27(12) p. 1741-1844.

      [10] S. Mota and R. W. Picard, “Automated posture analysis for detecting learner’s interest level,†in Proc. IEEE Conf. Comp. Vision Pattern Recog. Workshop, 2003, vol. 5, p. 49.

      [11] GiotaStratou ; Louis-Philippe Morency MultiSense—Context-Aware Nonverbal Behavior Analysis Framework: A Psychological Distress Use Case, in IEEE Transactions on Affective Computing, Volume: 8 Issue: 2, 2017.

      [12] Daniel Gatica-Perez, Alessandro Vinciarelli and Jean-Marc Odobez, Chapter-12, Nonverbal Behavior Analysis.

      [13] A. Kleinsmith, T. Fushimi, N. Bianchi-Berthouze, “An incremental and interactive affective posture recognition system,†in Proc. Int. Workshop Adapt. Interact. Style Affect. Factors, Conjunction Int. Conf. User Model., 2005, pp. 1–13.

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  • How to Cite

    T, K., T, S., R, B., & S, U. (2018). Non Verbal Communication for Analyzing the Malpractice in Examinations– An Overview. International Journal of Engineering & Technology, 7(2.20), 227-229. https://doi.org/10.14419/ijet.v7i2.20.13302

    Received date: 2018-05-26

    Accepted date: 2018-05-26

    Published date: 2018-04-18