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High Ability and Learner Characteristics

Year 2013, Volume: 6 Issue: 1, - , 01.12.2012

Abstract

The outstandingly able learner has been conceptualised, in terms of test and examination performance, as the learner showing superior academic performance which is markedly better than that of peers and in ways regarded as of value by wider society. In Kuwait, such superior examination performance leads to a classification regarded as being ‘gifted’. This study looks at the inter-correlations between performance in various subjects in examinations and then considers how examination performance correlates with measures of working memory capacity, extent of field dependency, extent of divergency and visual-spatial abilities. A very large sample of grade 7 Kuwaiti students (aged ~13) was involved, the sample being selected in such a way that it contained a high proportion of those regarded as ‘gifted’ under the procedures used in Kuwait. While specific learner characteristics have been related to examination performance, this study brings four different characteristics together to gain a picture of the way these characteristics may be seen in those who perform extremely well in examinations. Principal components analysis using varimax rotation, was used to look at the examination data and one factor accounted for 87% of the variance. A consideration of the examination papers led to the conclusion that the national examinations tested only recall-recognition. It was also found that those who performed best in all six subjects tended to be those who are highly divergent and strongly visual-spatial as well as those tending to have higher working memory capacities and being more field independent. The inter-correlations between the various learner characteristics are explained in terms of the way the brain is known to process information. The implications of the findings for assessment and for the way high ability is considered are discussed

References

  • Al-Ahmadi, F. and Oraif, F. (2009). Working memory capacity, confidence and scientific thinking. Research in Science and Technological Education, 27(2): 225-243
  • Al-Enezi, D. (2008). A Study of Learning Mathematics Related to Some Cognitive
  • Factors and to Attitudes. PhD Thesis, Glasgow: University of Glasgow. Al-Qasmi, S. (2006). Problem solving in biology at university level. PhD Thesis, Glasgow: University of Glasgow.
  • Alamolhodaei, H ( 1996). A Study in higher education calculus and students’ learning styles. PhD Thesis, Glasgow: University of Glasgow.
  • Ali, A.A. (2008). Perceptions, difficulties and working memory capacity related to mathematics performance. MSc Thesis, Glasgow: University of Glasgow. [accessible at: http://theses.gla.ac.uk/441/]
  • Baddeley, A. (1986). Working Memory. Oxford: Clarendon Press.
  • Baddeley, A.D. (2002). Is working memory still working, european psychologist, 7(2): 85Bahar, M. (1999). Investigation of biology students’ cognitive structure through word association tests, mind maps and structural communication grids. PhD Thesis, Glasgow: University of Glasgow.
  • Bahar and Hansell (2000) The relationship between some psychological factors and their effect on the performance of grid questions and word association Tests, Educational Psychology, 20(3): 349-364.
  • Dai, D.Y. (2010). The nature and nurture of giftedness: a new framework for understanding gifted education, New York, London: Teachers’ College Press.
  • Danili, E. and Reid, N. (2004). Some strategies to improve performance in school chemistry, based on two cognitive factors, Research in Science and Technological Education. 22(2): 203-226.
  • Danili, E. and Reid, N. (2006). Some potential factors affecting pupils’ performance, Chemistry Education Research and Practice. 7(2): 64-83.
  • Davis, G.A., Rimm, S.B. and Siegle, D. (2011). Education of the Gifted and talented children, Upper Saddle River, NJ: Pearson.
  • El-Banna, H . (1987). The development of a predictive theory of science education based upon information processing theory. PhD Thesis, Glasgow: University of Glasgow.
  • Frasier, M and Carland, J. (1982). Dictionary of Gifted, Talented, and Creative
  • Education Terms. New York: Trillium Press. Gardner, H. (1993). Frames of mind: the theory of multiple intelligences. (2 nd Edn), London: Fontana Press.
  • Gathercole, S. Lamont, E. and Alloway, T. (2006). Working memory in the classroom.
  • In: Pickering, S. (Ed) Working Memory and Education. New York: Elsevier. Golon, A. (2004). Raising Topsy-turvy kids: Successfully parenting your visual-spatial child. Denver: Deleon Publishing.
  • Higbee, K. (1977). Your memory: how it work and how to improve it. London: Englewood Cliffs: Prentice-Hall, Inc.
  • Hindal, H. (2007). Cognitive characteristics of students in middle schools in State of
  • Kuwait, with emphasis on high achievement, PhD Thesis, Glasgow: University of Glasgow. Hindal, H., Reid, N. and Badgaish, M. (2009). Working memory, performance and learner characteristics, Research in Science and Technological Education, 27(2): 18720
  • Hudson, L (1962). Intelligence, divergence and potential originality, Nature, 601-2.
  • Hudson, L. (1967). Contrary imaginations: a psychological study of the english schoolboy. London: Penguin Books.
  • Hudson, L. (1968). Frames of mind: Ability, perception and self-perception in the arts and science. New York: Norton.
  • Johnson, N. (1996). Look closer: Visual thinking skills and activities. Ohio: Pieces of Learning
  • Johnstone, A.H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7: 75-83.
  • Johnstone, A.H. (1997). Chemistry teaching-science or alchemy? Journal of chemical education. 74(3): 262-268.
  • Johnstone, A.H. (2000). Teaching of chemistry – logical or psychological? Chemistry education: Research and Practice in Europe. 1(1): 9-15.
  • Johnstone, A.H. and Al-Naeme, F. (1991). Filling a curriculum gap in chemistry.
  • International Journal of Science Education. 17(2): 219-232. Johnstone, A.H. and Ambusaidi, A. (2000). Fixed response questions: What are we testing? Chemistry Education: Research and Practice in Europe. 1(3): 323-328.
  • Johnstone, A.H. and El-Banna, H. (1986). Capacities, demands and processes: a predictive model for science education, Education in Chemistry,. 23(3): 80-84.
  • Johnstone, A.H. and El-Banna, H. (1989). Understanding learning difficulties – a predictive research model. Studies in Higher Education. 14(2): 159-68.
  • Johnstone, A.H. and Otis, K. (2006). Concept mapping in problem based learning: a cautionary tale. Chemical Education Research and Practice. 7(2): 84-95.
  • Johnstone, A.H., Watt, A. and Zaman, T. (1998). The Students’ attitude and cognition change to a physics laboratory. Physics Education, 33(1): 22-29.
  • Ngoi, M and Vondracek, M. (2004). Working with gifted science students in a public high school environment: One School's Approach. Journal of Secondary Gifted Education. 15(4): 141-148.
  • Onwumere, O. (2009). Difficulties in understanding mathematics an approach related to working memory and field dependency, PhD Thesis, Glasgow: University of
  • Glasgow (http://theses.gla.ac.uk/1278/).
  • Pascual-Leone, J. (1970). A mathematical model for the transition rule in piaget’s developmental stages. Acta Psychologica. 32: 301-345.
  • Reid, N. (2003). Getting started in pedagogical research in higher education: Hull: Higher Education Academy. (now also online: http://www.heacademy.ac.uk/physsci/home/pedagogicthemes/pedagogicresearch).
  • Reid, N. (2006). Thoughts on Attitude Measurement, Research in Science and Technological Education, 24(1): 3-27.
  • Reid, N. (2008). A Scientific approach to the teaching of chemistry, (the royal society of chemistry nyholm lecture, 2006-2007). Chemistry Education Research and Practice. 9(1): 51-9.
  • Reid, N. (2009a). Making science learning accessible, stimulating and enjoyable: what does research evidence tell us? Journal of Science Education, 10(1): 43-44.
  • Reid, N. (2009b). The concept of working memory. Research in Science and Technological Education. 27(2):131-138.
  • Reid, N. (2009c). Working memory and science education. Research in Science and Technological Education. 27(2): 245-250.
  • Reid, N. (2010). A scientific approach to the teaching of chemistry, Chemistry in Action, 90, 29-44.
  • Reid, N. and Yang, M-J. (2002). Open-ended problem solving in school chemistry: a preliminary investigation. International Journal Science Education. 24(12): 131313
  • Robinson, K. (2011). Out of our minds - learning to be creative, Chichester, Capstone Publishing, Ltd.
  • Silverman, L. (1989). The visual-spatial learner. Preventing School Failure, 34: 15-20
  • Silverman, L. (2002). Upside-down brilliance: The Visual-Spatial Learner, Under the Title, “Making Quick Work of Math Facts,” 302-305. Denver: DeLeon
  • Silverman, L. (2003). The visual-spatial learner: An introduction, Soundview School Dolphin News. 6-7.
  • Simonton, D. (2005). Putting the gift back into giftedness: the genetics of talent development. Gifted and Talented International. 1: 15-18.
  • Smith, C.M.M. ed. (2006). Including the gifted and talented : making inclusion work for more gifted and able learners. London: Routledge.
  • Sousa, D. (2003). How the gifted brain learns. California: Corwin Press, Inc.
  • Sternberg, R. (2004). Wisdom as a form of giftedness. In: Sternberg, R (Ed) Definitions and Conceptions of Giftedness. California: Corwin Press.
  • Sternberg, R and Grigorenko, E. (1995). Styles of thinking in the school. European
  • Journal of High Ability. 6(2), 1-9. Terrell S.T., (2002). The use of cognitive style as a predictor of membership in middle and High School Programs for the academically gifted, Paper presented at the Annual meeting of the American Educational Research Association, New Orleans, Louisiana.
  • Tinajero, C.and Paramo, M. (1997). Field dependence-independence and academic achievement: a reexamination of their relationship. British Journal of Educational Psychology. 67(2): 199-212.
  • Tsaparlis, G. (2005). Non-algorithmic quantitative problem solving in university physical chemistry: a correlation study of the role of selective cognitive factors.
  • Research in Science and Technological Education. 23(2): 125-148.

High Ability and Learner Characteristics

Year 2013, Volume: 6 Issue: 1, - , 01.12.2012

Abstract

-

References

  • Al-Ahmadi, F. and Oraif, F. (2009). Working memory capacity, confidence and scientific thinking. Research in Science and Technological Education, 27(2): 225-243
  • Al-Enezi, D. (2008). A Study of Learning Mathematics Related to Some Cognitive
  • Factors and to Attitudes. PhD Thesis, Glasgow: University of Glasgow. Al-Qasmi, S. (2006). Problem solving in biology at university level. PhD Thesis, Glasgow: University of Glasgow.
  • Alamolhodaei, H ( 1996). A Study in higher education calculus and students’ learning styles. PhD Thesis, Glasgow: University of Glasgow.
  • Ali, A.A. (2008). Perceptions, difficulties and working memory capacity related to mathematics performance. MSc Thesis, Glasgow: University of Glasgow. [accessible at: http://theses.gla.ac.uk/441/]
  • Baddeley, A. (1986). Working Memory. Oxford: Clarendon Press.
  • Baddeley, A.D. (2002). Is working memory still working, european psychologist, 7(2): 85Bahar, M. (1999). Investigation of biology students’ cognitive structure through word association tests, mind maps and structural communication grids. PhD Thesis, Glasgow: University of Glasgow.
  • Bahar and Hansell (2000) The relationship between some psychological factors and their effect on the performance of grid questions and word association Tests, Educational Psychology, 20(3): 349-364.
  • Dai, D.Y. (2010). The nature and nurture of giftedness: a new framework for understanding gifted education, New York, London: Teachers’ College Press.
  • Danili, E. and Reid, N. (2004). Some strategies to improve performance in school chemistry, based on two cognitive factors, Research in Science and Technological Education. 22(2): 203-226.
  • Danili, E. and Reid, N. (2006). Some potential factors affecting pupils’ performance, Chemistry Education Research and Practice. 7(2): 64-83.
  • Davis, G.A., Rimm, S.B. and Siegle, D. (2011). Education of the Gifted and talented children, Upper Saddle River, NJ: Pearson.
  • El-Banna, H . (1987). The development of a predictive theory of science education based upon information processing theory. PhD Thesis, Glasgow: University of Glasgow.
  • Frasier, M and Carland, J. (1982). Dictionary of Gifted, Talented, and Creative
  • Education Terms. New York: Trillium Press. Gardner, H. (1993). Frames of mind: the theory of multiple intelligences. (2 nd Edn), London: Fontana Press.
  • Gathercole, S. Lamont, E. and Alloway, T. (2006). Working memory in the classroom.
  • In: Pickering, S. (Ed) Working Memory and Education. New York: Elsevier. Golon, A. (2004). Raising Topsy-turvy kids: Successfully parenting your visual-spatial child. Denver: Deleon Publishing.
  • Higbee, K. (1977). Your memory: how it work and how to improve it. London: Englewood Cliffs: Prentice-Hall, Inc.
  • Hindal, H. (2007). Cognitive characteristics of students in middle schools in State of
  • Kuwait, with emphasis on high achievement, PhD Thesis, Glasgow: University of Glasgow. Hindal, H., Reid, N. and Badgaish, M. (2009). Working memory, performance and learner characteristics, Research in Science and Technological Education, 27(2): 18720
  • Hudson, L (1962). Intelligence, divergence and potential originality, Nature, 601-2.
  • Hudson, L. (1967). Contrary imaginations: a psychological study of the english schoolboy. London: Penguin Books.
  • Hudson, L. (1968). Frames of mind: Ability, perception and self-perception in the arts and science. New York: Norton.
  • Johnson, N. (1996). Look closer: Visual thinking skills and activities. Ohio: Pieces of Learning
  • Johnstone, A.H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7: 75-83.
  • Johnstone, A.H. (1997). Chemistry teaching-science or alchemy? Journal of chemical education. 74(3): 262-268.
  • Johnstone, A.H. (2000). Teaching of chemistry – logical or psychological? Chemistry education: Research and Practice in Europe. 1(1): 9-15.
  • Johnstone, A.H. and Al-Naeme, F. (1991). Filling a curriculum gap in chemistry.
  • International Journal of Science Education. 17(2): 219-232. Johnstone, A.H. and Ambusaidi, A. (2000). Fixed response questions: What are we testing? Chemistry Education: Research and Practice in Europe. 1(3): 323-328.
  • Johnstone, A.H. and El-Banna, H. (1986). Capacities, demands and processes: a predictive model for science education, Education in Chemistry,. 23(3): 80-84.
  • Johnstone, A.H. and El-Banna, H. (1989). Understanding learning difficulties – a predictive research model. Studies in Higher Education. 14(2): 159-68.
  • Johnstone, A.H. and Otis, K. (2006). Concept mapping in problem based learning: a cautionary tale. Chemical Education Research and Practice. 7(2): 84-95.
  • Johnstone, A.H., Watt, A. and Zaman, T. (1998). The Students’ attitude and cognition change to a physics laboratory. Physics Education, 33(1): 22-29.
  • Ngoi, M and Vondracek, M. (2004). Working with gifted science students in a public high school environment: One School's Approach. Journal of Secondary Gifted Education. 15(4): 141-148.
  • Onwumere, O. (2009). Difficulties in understanding mathematics an approach related to working memory and field dependency, PhD Thesis, Glasgow: University of
  • Glasgow (http://theses.gla.ac.uk/1278/).
  • Pascual-Leone, J. (1970). A mathematical model for the transition rule in piaget’s developmental stages. Acta Psychologica. 32: 301-345.
  • Reid, N. (2003). Getting started in pedagogical research in higher education: Hull: Higher Education Academy. (now also online: http://www.heacademy.ac.uk/physsci/home/pedagogicthemes/pedagogicresearch).
  • Reid, N. (2006). Thoughts on Attitude Measurement, Research in Science and Technological Education, 24(1): 3-27.
  • Reid, N. (2008). A Scientific approach to the teaching of chemistry, (the royal society of chemistry nyholm lecture, 2006-2007). Chemistry Education Research and Practice. 9(1): 51-9.
  • Reid, N. (2009a). Making science learning accessible, stimulating and enjoyable: what does research evidence tell us? Journal of Science Education, 10(1): 43-44.
  • Reid, N. (2009b). The concept of working memory. Research in Science and Technological Education. 27(2):131-138.
  • Reid, N. (2009c). Working memory and science education. Research in Science and Technological Education. 27(2): 245-250.
  • Reid, N. (2010). A scientific approach to the teaching of chemistry, Chemistry in Action, 90, 29-44.
  • Reid, N. and Yang, M-J. (2002). Open-ended problem solving in school chemistry: a preliminary investigation. International Journal Science Education. 24(12): 131313
  • Robinson, K. (2011). Out of our minds - learning to be creative, Chichester, Capstone Publishing, Ltd.
  • Silverman, L. (1989). The visual-spatial learner. Preventing School Failure, 34: 15-20
  • Silverman, L. (2002). Upside-down brilliance: The Visual-Spatial Learner, Under the Title, “Making Quick Work of Math Facts,” 302-305. Denver: DeLeon
  • Silverman, L. (2003). The visual-spatial learner: An introduction, Soundview School Dolphin News. 6-7.
  • Simonton, D. (2005). Putting the gift back into giftedness: the genetics of talent development. Gifted and Talented International. 1: 15-18.
  • Smith, C.M.M. ed. (2006). Including the gifted and talented : making inclusion work for more gifted and able learners. London: Routledge.
  • Sousa, D. (2003). How the gifted brain learns. California: Corwin Press, Inc.
  • Sternberg, R. (2004). Wisdom as a form of giftedness. In: Sternberg, R (Ed) Definitions and Conceptions of Giftedness. California: Corwin Press.
  • Sternberg, R and Grigorenko, E. (1995). Styles of thinking in the school. European
  • Journal of High Ability. 6(2), 1-9. Terrell S.T., (2002). The use of cognitive style as a predictor of membership in middle and High School Programs for the academically gifted, Paper presented at the Annual meeting of the American Educational Research Association, New Orleans, Louisiana.
  • Tinajero, C.and Paramo, M. (1997). Field dependence-independence and academic achievement: a reexamination of their relationship. British Journal of Educational Psychology. 67(2): 199-212.
  • Tsaparlis, G. (2005). Non-algorithmic quantitative problem solving in university physical chemistry: a correlation study of the role of selective cognitive factors.
  • Research in Science and Technological Education. 23(2): 125-148.
There are 58 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Huda Hindal This is me

Norman Reid This is me

Rex Whitehead This is me

Publication Date December 1, 2012
Published in Issue Year 2013 Volume: 6 Issue: 1

Cite

APA Hindal, H., Reid, N., & Whitehead, R. (2012). High Ability and Learner Characteristics. International Journal of Instruction, 6(1).
AMA Hindal H, Reid N, Whitehead R. High Ability and Learner Characteristics. International Journal of Instruction. December 2012;6(1).
Chicago Hindal, Huda, Norman Reid, and Rex Whitehead. “High Ability and Learner Characteristics”. International Journal of Instruction 6, no. 1 (December 2012).
EndNote Hindal H, Reid N, Whitehead R (December 1, 2012) High Ability and Learner Characteristics. International Journal of Instruction 6 1
IEEE H. Hindal, N. Reid, and R. Whitehead, “High Ability and Learner Characteristics”, International Journal of Instruction, vol. 6, no. 1, 2012.
ISNAD Hindal, Huda et al. “High Ability and Learner Characteristics”. International Journal of Instruction 6/1 (December 2012).
JAMA Hindal H, Reid N, Whitehead R. High Ability and Learner Characteristics. International Journal of Instruction. 2012;6.
MLA Hindal, Huda et al. “High Ability and Learner Characteristics”. International Journal of Instruction, vol. 6, no. 1, 2012.
Vancouver Hindal H, Reid N, Whitehead R. High Ability and Learner Characteristics. International Journal of Instruction. 2012;6(1).