Multivariable Analysis Methods On Identifying Factors and Groups of Students in the Environment of the Discovery Learning/Constructivistic Approach Using Cognitive Tools
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Submitted Apr 29, 2019
Published Apr 29, 2019
Published Apr 29, 2019
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This paper studies the environment of the discovery learning/constructivistic approach using cognitive tools regarding students’ performance in tests involving different kinds of learning and in the final formal examinations and students’ attitudes towards the approach in Mathematics’ higher education. In particular the paper aims in identifying factors regarding students’ scores and attitudes affected by the approach and groups of students with similar characteristics based on these factors. Data was obtained by a study realized at the Department of Statistics and Insurance Sciences of the University of Piraeus, concerning the application of the discovery learning/constructivistic approach using Mathematica on the course Calculus (Functions of multiple variables). Multivariable analysis methods are used in the data analysis, in particular factor analysis in identifying factors and cluster analysis in identifying groups of students with similar characteristics, in combination with inferential statistics’ methods. The statistical package SPSS was used for the data analysis.
Keywords: Discovery Learning/Constructivistic Approach, Cognitive Tools, Factor Analysis, Cluster Analysis
References
[1] K. Korres, “Students’ Attitudes towards Discovery Learning / Constructivistic Approach using Computers as Cognitive Tools in Higher Mathematics Education”, EJERS, European Journal of Engineering Research and Science Special Issue: CIE 2017, 2018.
[2] D. Jonassen, Computers as Mindtools for Schools: Engaging Critical Thinking (2nd Edition), New Jersey: Prentice Hall, Inc, 2000.
[3] K. Korres, A Teaching Approach of lessons in Sciences with the use of new. technologies (in Greek), Doctoral Dissertation, University of Piraeus, Department of Statistics and Insurance Sciences, 2007.
[4] M. Schneider and F. Preckel, “Variables associated with achievement in higher education: A systematic review of meta-analyses”, Psychological Bulletin, 143 (6), 2017, p. 565-600.
[5] C. D’Angelo, D. Rutstein, C. Harris, G. Haertel, R. Bernard and E. Borokhovski, Simulations for STEM Learning: Systematic Review and Meta-Analysis, Report Overview, SRI Education and Bill & Melinda Gates Foundation, 2014.
[6] B. R. Belland, A. E. Walker, N. Ju Kim and M. Lefler, “Synthesizing Results From Empirical Research on Computer-Based Scaffolding in STEM Education: A Meta-Analysis”, Review of Educational Research, 87, (2), 2017, p. 309– 344.
[7] K. Korres, “Discovery learning – constructivistic approach using Mathematica as a cognitive tool: Which form of application is more effective in university Mathematics’ courses?”, MIBES Transactions, 11(1), 2017, p. 48-62.
[8] L. Cohen, L. Manion and K. Morrison, Research methods in education, Routledge, 2013.
[9] K. Korres, Statistical packages of Quantitative Research (in Greek), Notes for the course “Statistical packages of Quantitative Research”, Postgraduate Program STEM, ASPETE, 2016.
[10] J. C. F. De Winter, D. Dodou and P. A. Wieringa, “Exploratory Factor Analysis with Small Sample Sizes”, Multivariate Behavioral Research, 44 (2), 2009, p. 147-181.
[2] D. Jonassen, Computers as Mindtools for Schools: Engaging Critical Thinking (2nd Edition), New Jersey: Prentice Hall, Inc, 2000.
[3] K. Korres, A Teaching Approach of lessons in Sciences with the use of new. technologies (in Greek), Doctoral Dissertation, University of Piraeus, Department of Statistics and Insurance Sciences, 2007.
[4] M. Schneider and F. Preckel, “Variables associated with achievement in higher education: A systematic review of meta-analyses”, Psychological Bulletin, 143 (6), 2017, p. 565-600.
[5] C. D’Angelo, D. Rutstein, C. Harris, G. Haertel, R. Bernard and E. Borokhovski, Simulations for STEM Learning: Systematic Review and Meta-Analysis, Report Overview, SRI Education and Bill & Melinda Gates Foundation, 2014.
[6] B. R. Belland, A. E. Walker, N. Ju Kim and M. Lefler, “Synthesizing Results From Empirical Research on Computer-Based Scaffolding in STEM Education: A Meta-Analysis”, Review of Educational Research, 87, (2), 2017, p. 309– 344.
[7] K. Korres, “Discovery learning – constructivistic approach using Mathematica as a cognitive tool: Which form of application is more effective in university Mathematics’ courses?”, MIBES Transactions, 11(1), 2017, p. 48-62.
[8] L. Cohen, L. Manion and K. Morrison, Research methods in education, Routledge, 2013.
[9] K. Korres, Statistical packages of Quantitative Research (in Greek), Notes for the course “Statistical packages of Quantitative Research”, Postgraduate Program STEM, ASPETE, 2016.
[10] J. C. F. De Winter, D. Dodou and P. A. Wieringa, “Exploratory Factor Analysis with Small Sample Sizes”, Multivariate Behavioral Research, 44 (2), 2009, p. 147-181.
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How to Cite
[1]
Korres, K. 2019. Multivariable Analysis Methods On Identifying Factors and Groups of Students in the Environment of the Discovery Learning/Constructivistic Approach Using Cognitive Tools. European Journal of Engineering and Technology Research. CIE (Apr. 2019), 7-12. DOI:https://doi.org/10.24018/ejers.2019.0.CIE.1289.
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