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Impact of Disruptive Technology on Juvenile Disruptive Behavior in Classroom

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Advances in Computing and Data Sciences (ICACDS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 906))

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Abstract

Bestowing to Christensen [1] (https://www.revolvy.com/main/index.php?s=Clayton+M.+Christensen&item_type), Disruptive Technology (DT) as an evolving technology which relocates the existing technology. Mobile computing, smart phones, cloud computing, social networking are some of the disruptive technologies that displaces conventional technologies. In near future the technology might transform the life style and there are 12 Disruptive Technologies (DT’s) that are identified [2] (https://www.mckinsey.com/mgi/overview). Disruptive behaviour among juvenile in India, particularly in Bangalore (Karnataka) has become a great encounter for both parents and teachers. This study, first and foremost in this field of DT and intends to explore critically the influence of disruptive technology and disruptive behaviour among the school children. With the help of independent variables, such as demographic. Sociocultural, economic, political, environmental, infrastructural, and in addition the disruptive technological factors that are influencing the disruptive behaviour among school children are tested. The big five dimensions of personality, often referred to as “Big 5” personality traits J.M. Digman [3] (extraversion, agreeableness, openness, conscientiousness, and neuroticism) adopted to test the personality of the child. Primary data are to be used through a rigorous drop-off survey. The confirmatory factor analysis (CFA) approach is exploited to generate the results with the help of software IBM SPSS AMOS (Analysis of Moment Structures). Structural Equation Modelling (SEM) has been deployed to evaluate the original and modification indices of the model, which further establishes the improvement in SEM’s effectiveness. The model establishes the significant impact of Disruptive Technology on class room behaviour. The targeted sample is 2000, includes high school children from 8th to 10th standard (both private and public), family members and teachers, and the response was 1679. Recommendation and policy implications are henceforth provided.

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Notes

  1. 1.

    https://books.google.co.in/books?isbn=0080516661.

  2. 2.

    https://euacademic.org/UploadArticle/1247.pdf.

  3. 3.

    https://indiankanoon.org/doc/161538964/.

  4. 4.

    journals.sagepub.com/doi/abs/https://doi.org/10.1177/0973184913411184.

  5. 5.

    https://indiankanoon.org/doc/814655/.

  6. 6.

    https://spst.up.nic.in/28-09-2017.pdf.

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Authors and Affiliations

  1. Bangalore, India

    Vani Ramesh

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  1. Vani Ramesh
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Correspondence to Vani Ramesh .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Information Technology, Solan, India

    P. K. Gupta

  3. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. Institute of Information Theory and Automation, Prague 8, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, Canada

    Tuncer Ören

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Ramesh, V. (2018). Impact of Disruptive Technology on Juvenile Disruptive Behavior in Classroom. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_23

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  • DOI: https://doi.org/10.1007/978-981-13-1813-9_23

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  • Online ISBN: 978-981-13-1813-9

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