Hypothetico-deductive Thinking Model: Candidate Theory and Mechanism for Didactic Transposition and Teaching of the Experimental Sciences
Keywords:
Hypothetico-deductive thinking theory, Didactic transposition, Partial Least Squares, Structural Equation Modelling, SmartPLS.Abstract
The aim of this study was to construct and investigate the statistical and psychometric properties of the theorized model of the hypothetico-deductive thinking theory as candidate theory for didactic transposition, teaching and learning of thinking and reasoning. The survey involved 150 students of the Upper Sixth Science and Terminale D classes of both subsystems of education in Cameroon. A five point Likert scale questionnaire type was used to obtain data on students’ spontaneous views about the instructional, educational and life skills aspects of the thoracic and vertebral column of the human skeleton and their applicability to problem-solving. Data collected was used to specify a Partial Least Squares Structural Equation Model (PLS-SEM) for the theory. Its statistical and psychometric robustness was investigated using SmartPLS V.2 M3.
Assessment of the measurement model revealed very significant relationships and very high cross loading regression weights between indicators and their corresponding latent constructs. Also, path model assessment revealed very strong discriminant validity, reliability and Cronbach’s alpha statistics. Association between latent constructs were also strongly significant with very adequate predictive relevance and effect sizes. However, the data showed that the model was inadequate in accounting for the variances observed in the dependent constructs judging from the very low variance explained statistic. Within limits of this study, the model furnished convincing statistic and psychometric evidence for further specifications in view of proclaiming the candidate theory as theory for didactic transposition and teaching how to think and reason.
References
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[25] Efron, B. (1981). “Nonparametric Estimates of Standard Error: The Jack-knife, the Bootstrap, and other Re-sampling Methods”. Biometrika. [online]. 68(3), pp 589-599. Available: http://biomet.oxfordjournals.org/content/68/3/589.abstract. [Jan. 5, 2016].
[26] Ping, R. A. (2009). "Is there any way to improve Average Variance Extracted (AVE) in a Latent Variable (LV) X (Revised)?" [on-line paper]. Available:
http://home.att.net/~rpingjr/ImprovAVE1.doc). [Jan. 5, 2016].
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[28] Hair F.J., Sarstedt M., Ringle M.C. & Mena J.A. (2012). “An assessment of the use of partial least squares structural equation modelling in marketing research”. Journal of the Academy of Marketing Science.[online] 40(3), pp 414-433.
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[32] Straub, D.W. & Welke, R.J. (Dec. 1998). “Coping with systems risks: Security planning models for management decision making”. MIS Quarterly. [online] 22(4), 441-469. Available:
http://www.jstor.org/stable/249551. [Jul. 29, 2015].
[33] Lowry, P. B., & Gaskin, J. (2014, June). Partial Least Squares (PLS) Structural Equation Model (SEM) for Building and Testing Causal Theory: When to Choose It and How to Use It. IEEE Transactions on Professonal Communication, 57(2), 123-146. [Mar. 17, 2016].
[34] Hair, Jr., Black, J. F., Babin, W. C., Anderson, B. J. & Tatham, R. L.. Multivariate Data Analysis. (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall, 2006, pp. 150-230. Available: https://www.education.umd.edu/EDMS/courses/syllabi/Spring2006. [Jul. 1, 2015].
[2] Bloom B.S. Taxonomy of Educational Objectives, Handbook I: Cognitive Domain. In L.E. Klopfer, Ed. Student Behavior and Science Content Categories and Subcategories for a Science Program, University of Pittsburgh, Pittsburgh, 1970, pp. 6-11.
[3] Anderson, L.W. (Ed.), Krathwohl, D.R. (Ed.), Airasian, P.W., Cruikshank, K.A., Mayer, R.E., Pintrich, P.R., Raths, J. & Wittrock, M.C. A taxonomy for learning, teaching, and assessing: A revision of Bloom's Taxonomy of Educational Objectives (Complete edition). New York: Pearson, 2000.
[4] Krathwohl, D.R. (Autumn 2002). “A Revision of Bloom's Taxonomy: An Overview. Theory into practice”. College of Education, The Ohio State University. [online]. 41(4), pp. 212-218. Available:
http://www.tandfonline.com/loi/htip20. [Nov. 8, 2015].
[5] Klopfer L.E. Student Behaviour and Science Content Categories and Subcategories for a Science Program. Pittsburg University Press, Pittsburg, (1970), pp. 1-62.
[6] Meyer D.E., & Kieras, D.E. (1997a). “A computational theory of executive cognitive processes and multiple-task performance: I. Basic mechanisms”. Psychological Review. [online]. 104(1), pp.3 – 65. Available: http://www.umich.edu/~bcalab/documents/MeyerKieras1997a.pdf . [Dec. 5, 2015].
[7] Hall R. H. (2009). “Information Processing Theory”. [Electronic Version]. Missouri University of Science & Technology. Retrieved April 24th, 2009, from:
http://web.mst.edu/~rhall/ed_psych/info.html.
[8] Snyder & Snyder. “Teaching thinking and problem solving skills”. Teaching Critical Thinking: Selected Readings. Nault. R. Ed. Canada, CELTA, 2004, pp. 7-16. [online]. Available:
http://www.ilearnincambodia.net. [Nov. 8, 2015].
[9] Costa A. L., Barell J., Perkins D.N. & Salomon G., Clarke J.H., Fogarty R., McTighe J., et al. “Teaching for Thinking” in Developing Minds: A resource Book for Teaching Thinking. Revised edition, vol.1. Costa A.L; Ed. Association for Supervision and Curriculum Development. Alexandria, VA, 1991, pp.193-266. Available: http://files.eric.ed.gov/fulltext/ED332166. [Nov. 8, 2015]
[10] Hyerle D. “Visual tools for mapping minds” in A. L. Costa (Ed.), Developing minds: A resource book for teaching thinking. Alexandria, VA: Association for Supervision and Curriculum Development, 2001, pp. 401- 407.
[11] Beyer B. K. “Practical strategies for the direct teaching of thinking skills” in Developing minds: A resource book for teaching thinking. Revised edition, vol.1. Costa A.L; Ed. Association for Supervision and Curriculum Development. Alexandria, VA, 1991, pp.274-279. Available: http://files.eric.ed.gov/fulltext/ED332166. [Nov. 8, 2015] (pp. 35-40).
[12] Hatcher, D. L., & Spencer, L. A. Reasoning and Writing: From Critical Thinking to Composition. 3rd. ed. Boston: American Press, 2005, pp. 6-40.
[13] Noumi, E. (Nov. 2013). “Competency-Based Approach: A More Active, More Innovating and More Durable Training; Case of the Lesson on the Cell in 1st Year of the Higher Teachers’ Training College of the University of Yaoundé I”. Crown Research in Education. [online] 3, 13-21. Available: http://www.crownjournal.org/. [Nov. 11, 2014].
[14] Nditafon G.N. & Noumi E. (Oct. 2014). « Enhancing the Construction and Appropriation of Scientific Knowledge for Reinvestment in Seeking Solutions to Problems in New Contexts: Case of a Lesson on Sexual Reproduction in Flowering Plants among Second Year Learners of General Secondary Education in Yaoundé, Cameroon”. International Journal of Science and Research (IJSR).[online]. 3(10), pp. 692-695. Available on: www.ijsr.net. [Oct. 25, 2014].
[15] Noumi E. & Nditafon G. (Nov. 2015). « Didactic Transposition of the Alternative Knowledge of the Brazilian Telenovela “O Clone” to Knowledge Taught”. International Journal of Science and Research (IJSR.) [online]. 4, pp. 508-519. Available on: www.ijsr.net. [Nov. 25, 2014].
[16] Chin W.W., Marcolin B.L. & Newsted P.R. “A Partial Least Squares latent variable modelling approach for measuring interaction effects: Results from a Monte Carlo Simulation study and an electronic e-mail emotion/adoption study” in Proc. 17th International Conference on Information Systems, Clevaland, OH. USA, 1996, pp. 21 - 41.
[17] Haenlein, M. & Kaplan A. M. “A beginner’s guide to partial least squares analysis”. Understanding Statistics.[online]. 3(4), pp. 283–297. Available:
http://avesbiodiv.mncn.csic.es/estadistica/curso2011/regm35.pdf. [Dec. 6, 2015].
[18] Statsoft (2013). Structural Equation Modelling, Statsoft Electronic Statistics Textbook. http://www.statsoft.com/textbook/structural-equation-modeling/. . [Dec. 3, 2015].
[19] Wong, K.K. (2013). “Partial Least Squares Structural Equation Modelling (PLS-SEM): Techniques Using SmartPLS”. Marketing Bulletin, 2013, 24, Technical Note 1.[online]. Available: http://marketing-bulletin.massey.ac.nz. [Feb. 3, 2016].
[20] Bass B.M., Avolio B.J., Jung D. I. & Berson Y. (2003). “Predicting Unit Performance by Assessing Transformational and Transactional Leadership”. Journal of Applied Psychology. [online]. 88(2), pp. 207–218. Available: http://www.apa.org/pubs/journals/releases/apl-882207.pdf. [Feb. 3, 2016].
[21] Sosik J.J., Kahai S.S. & Piovoso M.J. (Feb. 2009). “Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research”. Emerald Management Reviews: Group & Organization Management. [online]. 34(1), 5-36. Available: http://gom.sagepub.com/content/34/1/5.full.pdf+html. [Feb. 3,2016].
[22] Chin W.W., Marcolin, B.L. & Newsted, P.R. (Jun. 2003). “A Partial Least Squares latent variable modelling approach for measuring interaction effects: Results from a Monte Carlo Simulation study and an electronic e-mail emotion/adoption study”. Information Systems Research. [online]. 14(2), pp. 189 – 217. Available: http://pubsonline.informs.org/doi/pdf/10.1287/isre.14.2.189.16018. [Feb. 3, 2016].
[23] Hulland J. (Feb. 1999). “Use of Partial Least Squares (PLS) in Strategic Management Research: A Review of Four Recent Studies”. Strategic Management Journal. [online]. 20(2), pp. 195-224. Available: https://www.jstor.org/stable/i355641. [Jan. 5, 2016].
[24] Mothia J., Noumi E., & Nditafon G. (Nov. 2015). “Developing Thinking Skills among Third Grade (Class 5) Pupils in Some Elementary Practising Schools in Edea, Cameroon Using Lessons on the Human Skeleton”. Open Access Library Journal2 e2071. [online]. 2(11), pp 1-15. Available: http://dx.doi.org/10.4236/oalib.1102071. [Nov. 15, 2015].
[25] Efron, B. (1981). “Nonparametric Estimates of Standard Error: The Jack-knife, the Bootstrap, and other Re-sampling Methods”. Biometrika. [online]. 68(3), pp 589-599. Available: http://biomet.oxfordjournals.org/content/68/3/589.abstract. [Jan. 5, 2016].
[26] Ping, R. A. (2009). "Is there any way to improve Average Variance Extracted (AVE) in a Latent Variable (LV) X (Revised)?" [on-line paper]. Available:
http://home.att.net/~rpingjr/ImprovAVE1.doc). [Jan. 5, 2016].
[27] Cohen, J. (1992 Jul). A Power Primer. Psychological Bulletin.[online]. 112(1), pp 155-159. Available: http://psycnet.apa.org/?&fa=main.doiLanding&doi=10.1037/0033-2909. [Jan. 5, 2016].
[28] Hair F.J., Sarstedt M., Ringle M.C. & Mena J.A. (2012). “An assessment of the use of partial least squares structural equation modelling in marketing research”. Journal of the Academy of Marketing Science.[online] 40(3), pp 414-433.
Available:https://papers.ssrn.com/so13/papers.cfm?abstract_id=2176430 [Dec. 3, 2015].
[29] Hair, J.F., Huit, T.M., Ringle, C.M. & Sarstedt M.A. (2014). Primer on Partial Least Squares Structural Equation Modelling (PLS-SEM). Los Angeles: SAGE.
[30] Hair Jr, J.F., Hult, G.T.M., Ringle, C.M. & Sarstedt, M. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd Ed. [online]. Thousand Oaks, California: Sage Publications. Available: https://uk.sagepub.com/en-gb/afr/a-primer-on-partial-least-squares-structural-equation-modeling-pls-sem/book244583. [Mar. 17, 2016].
[31] Fornell, C. & Larcker, D.F. (May 1981). "Evaluating Structural Equation Models with Unobservable Variables and Measurement Error." Journal of Marketing Research [online]. 18, 39-50. Available: https://deepblue.lib.umich.edu/bitstream/handle/2027.42/35622. [Mar. 17, 2016].
[32] Straub, D.W. & Welke, R.J. (Dec. 1998). “Coping with systems risks: Security planning models for management decision making”. MIS Quarterly. [online] 22(4), 441-469. Available:
http://www.jstor.org/stable/249551. [Jul. 29, 2015].
[33] Lowry, P. B., & Gaskin, J. (2014, June). Partial Least Squares (PLS) Structural Equation Model (SEM) for Building and Testing Causal Theory: When to Choose It and How to Use It. IEEE Transactions on Professonal Communication, 57(2), 123-146. [Mar. 17, 2016].
[34] Hair, Jr., Black, J. F., Babin, W. C., Anderson, B. J. & Tatham, R. L.. Multivariate Data Analysis. (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall, 2006, pp. 150-230. Available: https://www.education.umd.edu/EDMS/courses/syllabi/Spring2006. [Jul. 1, 2015].
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Published
2016-10-11
How to Cite
N. Nditafon, G., Noumi, E., B. Nkeck, S. R., & N. Nchia, L. (2016). Hypothetico-deductive Thinking Model: Candidate Theory and Mechanism for Didactic Transposition and Teaching of the Experimental Sciences. American Scientific Research Journal for Engineering, Technology, and Sciences, 26(1), 185–203. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/2099
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