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Article
Engineering of Convergence and Divergence and Its Modeling in the Production of Knowledge
Author(s)
Manuel Vicente Castillo Guilarte
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DOI:10.17265/1537-1514/2016.04.003
Affiliation(s)
Manuel Vicente Castillo Guilarte, Ph.D., full professor, Department of System Engineering, National Experimental Polytechnic University (UNEXPO), Caracas, Venezuela.
ABSTRACT
Once started the Doctorate of Management Research and Development (DGID) at the Central University of Venezuela, and having been approved by the National Council of Universities since 2008, at the Seminar on Management of Science and Technology at two courses in year 2015, it was possible to form a population of analysis with 88 participants; during the first two class sessions of each course, each trainee was required curricular synthesis and the issue/summary of the research work, along with a short exposure, and simultaneously with the application of an open survey had as a general question: Do you know how to generate or produce new knowledge? Getting increasingly negative response, which partially explains the reasons why in Venezuela the performance of graduates at doctoral level reaches an average of 10% of tuition income, situation known as the phenomenon of all but dissertation (Gascón, 2008). Taking it as a case study in which the DGID enrollment in seven years was 120 trainees, who in five years were required to obtain the maximum degree, the national problem in which the same scenario is repeated evidence (Castillo, 2014), since 2015, there will have only 11 graduates. From a descriptive methodology, case study from a survey with a deductive and inductive analysis, aiming to research the use of the theory of Engineering Convergence and Divergence, to define and propose a new model to establish guidance patterns for the production of knowledge, is obtained as a result of the investigation, the defined model will establish order, sequence, and systematization of the problem, theories, and solutions in time, so to collaborate with an increase in knowledge production, hoping to contribute to the partial solution to the problem of national character.
KEYWORDS
convergence engineering, generation of knowledge, syndrome quite less thesis
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References
Aparicio, M. (2010). The evaluation of the quality of the university system and employment in their articulation: Towards a transdisciplinario systemic paradigm.
Baskerville, R., Pawlowski, S., & McLean, E. (2000). Enterprise resource planning and organizational knowledge: Patterns of convergence and divergence. In Proceedings of the Twenty First International Conference on Information Systems, pp. 396-406.
Berkowitz, G. (2014). Convergent and divergent thinking. Encyclopedia of Special Education.
Boccardo, R. (2006). Creative in engineering design. USB. Venezuela: Editorial Equinoccio.
Castillo, M. (2010). Prospects of teaching profession in universities and its relationship with the engineering in Venezuela. Eighth LACCEI Latin American and Caribbean Conference for Engineering and Technology. Arequipa Peru. Paper 163.
Castillo, M. (2014). Engineering of convergence and divergence and modeling in the production of knowledge. Presented at the VII International Meeting Management Research and Development (AVEGID) Granada-Cartuja-Spain. Paper 2-1.
Cropley, A. (2006). In praise of convergent thinking. Creativity Research Journal, 18(3), 391-404.
De Bono, E. (1970). Lateral thinking a text book of creativity. UK: Mica Management.
Del Río Pérez, J. (2007). Review of psychological research on creativity observatory (OBS).
Gardener, H. (1995). The multiple intelligence. Harvard University.
Gascón, Y. (2008). Syndrome all but dissertation, without thesis “TMT” as a factor in/Uential in the research work. Journal Copérnico, 1(9), 57.
Goleman, D. (1996). Emotional intelligence. España: Editorial Kairos.
González, J. (2008). Convergence, divergence and accreditation in engineering education. Journal of Higher Education, 37(147), 89-105.
Guilford, J. P. (1957). Creative abilities in the arts. Psychological Review, 64(2), 110.
Harris, D. (2013). A model of the productivity gap: Convergence or divergence. US: Stanford University.
Humberto, E. (2011). How do you write a thesis. Barcelona: Editorial Gedisa.
Hurtado, J. (2008). Formulating research objectives. Venezuela: Quirón.
Kim, K. H., & Pierce, R. A. (2013). Convergent versus divergent thinking. In Encyclopedia of creativity, invention, innovation and entrepreneurship (pp. 245-250). New York: Springer.
Martínez, M. (2002). The emerging paradigm. México: Tillas.
Morin, E. (2004). Introduction to complex thought. Barcelona: Gedisa.
Rodríguez, M. (2013). Everything but thesis syndrome (TMT). A phenomenology in postgraduate studies, graduate research, electronics journal. UNERG, No. 3, pp. 1-6.
Sanmamed, M. G. (2013). Convergence vs. divergence diferences in the desigm of practicum. A Practcum for Integral Training of Students. Coruña University.
Vidler, D. (1974). Convergent and divergent thinking, test-anxiety, and curiosity. The Journal of Experimental Education, 43(2), 79-85.
Wedel, A. (2008). Modeling simultaneous convergence and divergence of linguistic features. Arizona University.
Ziman, J. (1986). Introduction to the study of science. Barcelona: Arial.