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Distributed Cognition Learning Theories on Trainers Capacity Building in Use of Autotronic Tools for Skill Acquisition in Nigeria Tertiary Institutions

Received: 6 October 2021     Accepted: 2 November 2021     Published: 24 December 2021
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Abstract

The major aim of this study was to investigate the effects of Distributed Cognition Learning Theories on Trainers Capacity Building in the use of Autotronic tools for Skill acquisition in Nigeria Tertiary Institutions. In the study which was experimental with pre-test and post-test control groups of 84 (having n=44 as experimental and n=40 as control) participants made up of automobile/autotronic engineering trainers in Nigeria tertiary institutions. Instruments used for data collection was: Autotronic Simulator Interest Inventory (ASII), Autotronic Demonstrator Perception Inventory (ADPI) and Auto Diagnostic Competence Test (ADCT) to determine trainers capacity building needs using Improvement Needed Index (INI). In describing the instruments used for data collection, ASII, ADPI and ADCT were designed to seek information concerning application of Distributed Cognition Learning Theories (DCLT) approaches of physical prototypes, design analysis tools and computer-aided design models. The ASII and ADPI have 24 items while ADCT has 12 items and were analysed appling mean and standard deviation. Also, the score guide sheet was based on a four-point rating scale used by ratters. The Pearson Product Moment Coefficient of the test instrument was used to determine the reliability coefficient of 0.79. The data collected was in line with the research questions and hypotheses which were analysed. In taking decisions for items on interest, perception and competence, any mean of 3.50 and above are regarded as highly important and high performance, 2.50 - 3.49 mean are moderately important and moderate performance while mean of less than 1.50 are regarded as not important and very low performance. Analysis of variance (ANOVA) was applied to test the null hypothesis at 0.05 level of significance and with the use of MATLAB for the graphical representations of the mean ratings that was used to answer the research questions. DCLT and Autotronic tools showed changes in the interest, perception and competence of Trainers was found in the process. The following recommendations are made: Review of curriculum to incorporate activities that reflect DCLT for imparting autotronic engineering with modern skill; trainers to be periodically encouraged to embark on capacity building training as the government is expected to equip the departments with the latest autotronic technology facilities.

Published in American Journal of Mechanical and Materials Engineering (Volume 5, Issue 4)
DOI 10.11648/j.ajmme.20210504.12
Page(s) 60-70
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Tertiary Institutions, Autotronic Technology, Simulators, Demonstrators, Auto Diagnostic Tools, Skill Acquisition, Capacity Building, Distributed Cognition Learning Theories

References
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[2] Austin, F. (2019). How to Use an Automotive Diagnostic Tool. The best way to interpret the trouble codes of a basic automotive diagnostic tool. The Drive. Brookline Media Inc.
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[5] Ezeama, A. O., Obe, P. I. and Ede, E. O. (2016). Assessment of Capacity Building Needs among Motor Vehicle Mechanics Trainers for the use of Auto Scan Tools. Nigerian Journal of Technology (NIJOTECH), Vol. 35, No. 4, pp. 805-813.
[6] Hadi D. (2014). On the Practicability of Skill Acquisition Theory in Second Language Acquisition Contexts.
[7] Haruna, A., Yahaya U. and Tijani A. A. (2015). Autotronics Course- An Innovative Approach in Modern Automotive Technology Education in Africa for Sustainable Development. International Journal of Science and Engineering Research, vol. 6 issue 1 pages 1620-1623.
[8] MacKenzie, J., and Polvere, R. (2019). A TVET Glossary: Some Key Terms. In: R. Maclean, R. and Wilson, D. N., ed., International Handbook of Education for the Changing World of Work - Bridging Academic and Vocational Learning. Bonn: Springer, pp. 63-73.
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[11] Oladiti, Y. O. and Thompson, E. N. (2016). The Effects of Projector Mediated and Demonstration Methods on Students’ Academic Performance in Basic Electronics in Technical Colleges in Lagos State. Unpublished B. Sc. Ed. Project, Yaba College of Technology, Yaba, Lagos In Affiliation with University of Nigeria, Nsukka.
[12] Olaitan, S. O., Araribe, M. O. and Nwobu V. I. (2009). Capacity building needs of teachers of agriculture for effective teaching in upper basic schools in Abia state. Paper presented at the annual conference of Nigerian vocational association (NVA) on quality assurance in vocational technical education, University of Nigeria, Nsukka on the theme “Teacher preparation and vision 20:20:20 in Nigeria”.
[13] Puspitasari, S. (2019). Educational Mismatch dan Pengaruhnya terhadap Pendapatan Lulusan Sekolah Menengah Kejuruan di Indonesia. Konferensi Nasional Ilmu Administrasi, 1, 9, pp. 1-8.
[14] Rousse, B. S. (2019). Revisiting the Six Stages of Skill Acquisition (with Stuart Dreyfus). Available online: (PDF) Revisiting the Six Stages of Skill Acquisition (with Stuart Dreyfus) | B. Scot Rousse - Academia.edu.
[15] Schunn, C., and Silk, E. (2011). Learning Theories for Engineering and Technology Education. In: Barak M., Hacker M. (eds) Fostering Human Development Through Engineering and Technology Education. International Technology Education Studies, vol 6. Sense Publishers. https://doi.org/10.1007/978-94-6091-549-9_1.
[16] Schwab, K. (2016). The Fourth Industrial Revolution. Switzerland: World Economic Forum, pp. 7.
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[19] Solesi, A. O., Sani, M. A., Niemogha, G. I., Alakija, K. S., Akinpelumi, B. A., Onyeka, S. U., Arinze, G. A., Goyol, J. S. A., Akinsipe, A. O., Dung, R. C., Awodele, O. O. and Shogunle, A. D. (2014). Appraisal of Skill Acquisition Centers in Nigeria. Report of Research & Curriculum Development Department, Industrial Training Fund (ITF) Jos, Nigeria.
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[22] Wagiran and Yudha Ari Purnama, (2020). Automotive Service Industry Needs of Autotronic Competencies and those Prepared by the Education. International Journal of Mechanical Engineering and Technology. 11 (4), pp. 34-43. http://iaeme.com/Home/issue/bnnJSyoJHhL29gY01up3EypxSxoJyhY0ciqKWhLJksqKOfo2Sxpl9WFx1SIP9JG0kIGHIsZGSsFIAGIHIsAP9WFx1SIS8kZI8jAS8jZQHhpTEz.pdf (researchbib.com).
Cite This Article
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    Osita Anthony Ezeama, George Besheng Akuchu, Rita Nneka Ezeama, Pauline Ijeoma Obe, Victor Ikechukwu Oguejiofor, et al. (2021). Distributed Cognition Learning Theories on Trainers Capacity Building in Use of Autotronic Tools for Skill Acquisition in Nigeria Tertiary Institutions. American Journal of Mechanical and Materials Engineering, 5(4), 60-70. https://doi.org/10.11648/j.ajmme.20210504.12

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    ACS Style

    Osita Anthony Ezeama; George Besheng Akuchu; Rita Nneka Ezeama; Pauline Ijeoma Obe; Victor Ikechukwu Oguejiofor, et al. Distributed Cognition Learning Theories on Trainers Capacity Building in Use of Autotronic Tools for Skill Acquisition in Nigeria Tertiary Institutions. Am. J. Mech. Mater. Eng. 2021, 5(4), 60-70. doi: 10.11648/j.ajmme.20210504.12

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    AMA Style

    Osita Anthony Ezeama, George Besheng Akuchu, Rita Nneka Ezeama, Pauline Ijeoma Obe, Victor Ikechukwu Oguejiofor, et al. Distributed Cognition Learning Theories on Trainers Capacity Building in Use of Autotronic Tools for Skill Acquisition in Nigeria Tertiary Institutions. Am J Mech Mater Eng. 2021;5(4):60-70. doi: 10.11648/j.ajmme.20210504.12

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  • @article{10.11648/j.ajmme.20210504.12,
      author = {Osita Anthony Ezeama and George Besheng Akuchu and Rita Nneka Ezeama and Pauline Ijeoma Obe and Victor Ikechukwu Oguejiofor and Paul Awo Nwobasi},
      title = {Distributed Cognition Learning Theories on Trainers Capacity Building in Use of Autotronic Tools for Skill Acquisition in Nigeria Tertiary Institutions},
      journal = {American Journal of Mechanical and Materials Engineering},
      volume = {5},
      number = {4},
      pages = {60-70},
      doi = {10.11648/j.ajmme.20210504.12},
      url = {https://doi.org/10.11648/j.ajmme.20210504.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmme.20210504.12},
      abstract = {The major aim of this study was to investigate the effects of Distributed Cognition Learning Theories on Trainers Capacity Building in the use of Autotronic tools for Skill acquisition in Nigeria Tertiary Institutions. In the study which was experimental with pre-test and post-test control groups of 84 (having n=44 as experimental and n=40 as control) participants made up of automobile/autotronic engineering trainers in Nigeria tertiary institutions. Instruments used for data collection was: Autotronic Simulator Interest Inventory (ASII), Autotronic Demonstrator Perception Inventory (ADPI) and Auto Diagnostic Competence Test (ADCT) to determine trainers capacity building needs using Improvement Needed Index (INI). In describing the instruments used for data collection, ASII, ADPI and ADCT were designed to seek information concerning application of Distributed Cognition Learning Theories (DCLT) approaches of physical prototypes, design analysis tools and computer-aided design models. The ASII and ADPI have 24 items while ADCT has 12 items and were analysed appling mean and standard deviation. Also, the score guide sheet was based on a four-point rating scale used by ratters. The Pearson Product Moment Coefficient of the test instrument was used to determine the reliability coefficient of 0.79. The data collected was in line with the research questions and hypotheses which were analysed. In taking decisions for items on interest, perception and competence, any mean of 3.50 and above are regarded as highly important and high performance, 2.50 - 3.49 mean are moderately important and moderate performance while mean of less than 1.50 are regarded as not important and very low performance. Analysis of variance (ANOVA) was applied to test the null hypothesis at 0.05 level of significance and with the use of MATLAB for the graphical representations of the mean ratings that was used to answer the research questions. DCLT and Autotronic tools showed changes in the interest, perception and competence of Trainers was found in the process. The following recommendations are made: Review of curriculum to incorporate activities that reflect DCLT for imparting autotronic engineering with modern skill; trainers to be periodically encouraged to embark on capacity building training as the government is expected to equip the departments with the latest autotronic technology facilities.},
     year = {2021}
    }
    

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    T1  - Distributed Cognition Learning Theories on Trainers Capacity Building in Use of Autotronic Tools for Skill Acquisition in Nigeria Tertiary Institutions
    AU  - Osita Anthony Ezeama
    AU  - George Besheng Akuchu
    AU  - Rita Nneka Ezeama
    AU  - Pauline Ijeoma Obe
    AU  - Victor Ikechukwu Oguejiofor
    AU  - Paul Awo Nwobasi
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    PY  - 2021
    N1  - https://doi.org/10.11648/j.ajmme.20210504.12
    DO  - 10.11648/j.ajmme.20210504.12
    T2  - American Journal of Mechanical and Materials Engineering
    JF  - American Journal of Mechanical and Materials Engineering
    JO  - American Journal of Mechanical and Materials Engineering
    SP  - 60
    EP  - 70
    PB  - Science Publishing Group
    SN  - 2639-9652
    UR  - https://doi.org/10.11648/j.ajmme.20210504.12
    AB  - The major aim of this study was to investigate the effects of Distributed Cognition Learning Theories on Trainers Capacity Building in the use of Autotronic tools for Skill acquisition in Nigeria Tertiary Institutions. In the study which was experimental with pre-test and post-test control groups of 84 (having n=44 as experimental and n=40 as control) participants made up of automobile/autotronic engineering trainers in Nigeria tertiary institutions. Instruments used for data collection was: Autotronic Simulator Interest Inventory (ASII), Autotronic Demonstrator Perception Inventory (ADPI) and Auto Diagnostic Competence Test (ADCT) to determine trainers capacity building needs using Improvement Needed Index (INI). In describing the instruments used for data collection, ASII, ADPI and ADCT were designed to seek information concerning application of Distributed Cognition Learning Theories (DCLT) approaches of physical prototypes, design analysis tools and computer-aided design models. The ASII and ADPI have 24 items while ADCT has 12 items and were analysed appling mean and standard deviation. Also, the score guide sheet was based on a four-point rating scale used by ratters. The Pearson Product Moment Coefficient of the test instrument was used to determine the reliability coefficient of 0.79. The data collected was in line with the research questions and hypotheses which were analysed. In taking decisions for items on interest, perception and competence, any mean of 3.50 and above are regarded as highly important and high performance, 2.50 - 3.49 mean are moderately important and moderate performance while mean of less than 1.50 are regarded as not important and very low performance. Analysis of variance (ANOVA) was applied to test the null hypothesis at 0.05 level of significance and with the use of MATLAB for the graphical representations of the mean ratings that was used to answer the research questions. DCLT and Autotronic tools showed changes in the interest, perception and competence of Trainers was found in the process. The following recommendations are made: Review of curriculum to incorporate activities that reflect DCLT for imparting autotronic engineering with modern skill; trainers to be periodically encouraged to embark on capacity building training as the government is expected to equip the departments with the latest autotronic technology facilities.
    VL  - 5
    IS  - 4
    ER  - 

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Author Information
  • Department of Mechatronic Engineering, University of Nigeria, Nsukka, Nigeria

  • Department of Mathematics, University of Nigeria, Nsukka, Nigeria

  • Department of Education Economics, University of Nigeria, Nsukka, Nigeria

  • Department of Industrial Technical Education, University of Nigeria, Nsukka, Nigeria

  • Department of Industrial Technical Education, University of Nigeria, Nsukka, Nigeria

  • Department of Technology and Vocational, Ebonyi State University, Abakaliki, Nigeria

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