International Teacher Education Conference 2014 1

International Teacher Education Conference 2014
ACO30
ANFBH
AUN10
AAD+1
BG=0.77
CC=0.20
BG=0.32
CC1=0.13
CC2=0.12
CC3=0.41
AAD+2
ASU-1
BG=0.66
CC=0.21
Graph 3. Causal model for arithmetic
Discussion
From the graph of the causal model for language/data competence, one can see three measuring variables
which are causes: hyphenation into syllables (DHYSY), telling the stories (DTEST) and distinguishing numbers
and letters (DNULE). All causal competences or the coefficients of causal competence are 0.39 – 0.56. The only
small causal competences are when effect is colour recognition (RECO). Colour recognition (RECO) has great
previous knowledge competence of 0.92.
From the graph of causal model for geometry, one can see only one cause relation above and under
(GABUN). From this cause there is one cause line for relations and two cause lines for recognizing triangle
(GTRIA) and recognising rectangle (GRECT). From this model for geometry, one can see most difficult relation
left – right (GLERI), because the task is connected with the part of one’s body.
From the graph of causal model for arithmetic, there are three causes: counting to 30 (ACO30), knowing the
number of fingers on both hands (ANFBH) and understanding the numbers to 10 (AUN10). Also, one can
observe cause line of arithmetic operations. Final effect is adding +2 (AAD+2) with small background
competence 0.32 and grate causal competence from cause subtracting – 1 (ASU–1) of 0.41.
For the purpose of further research, it is necessary to increase statistical set or the number of children
included. Test materials must be standardized and must allow for higher gradation of results. The study should
include more measuring variables in data competence (collecting data, measuring and information technology)
and geometry competence (lines, planes and simple bodies). New research in mathematical teaching and
learning has to be included in the research (Sharma M. C. 2012.).
Special thanks goes to Lešin, G. & Hrkač, A. from kindergarten, and their previous research (Tepeš, B.,
Lešin, G. & Hrkač, A. 2013).
References
Economopoulos, K., & Murray, K. (2004), Mathematical Thinking at Grade K, Scott Foresman
Kilman, M.(2006), Mathematical Thinking at Grade 1, Scott Foresman
Kindergarten M. Sachs (2011), Zagreb, http://www.dvmilanasachsa.hr/
MZOS (2013), Preschool Education, Programmes for Learning in Kindergarten, http://public.mzos.hr/
Pellet, J. P. & Elisseef, A. (2007), A Partial Correlation – Based Algorithm for Causal Structure Discovery
with Continous Variables, in Berthold at all. (eds), Advances in Intelligent Data Analysis VII, 7 th International
Symposium on Intelligent Data Analysis, pp. 229 – 123
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