Araştırma Makalesi

This study utilizes an evaluation model AHP (analytic hierarchy process) which prioritized the relative weights of three general subtypes of mathematical disability (MD), Semantic Memory, Procedural, and Visuospatial in order to analyze and explain underlying cognitive and performance features of FCAT (Florida Comprehensive Assessment Test) benchmarks and corresponding items for grades 6-8 in all mathematics categories. For this purpose, extensive review of the literature has been conducted on mathematical disability to determine subtypes of learning disability in mathematics. Afterwards, a multi-step AHP approach is adopted to obtain the relative weights of criteria (subtypes of learning disability) by linking the independent evaluations of four content area experts for each benchmark. The results indicate that semantic memory deficiency is the dominant subtype of mathematical learning disability on vast majority of benchmarks. Another important finding of this study is that effect of visuospatial deficiency increases from grade 6 to grade 8. In addition, effect of procedural deficiency does not show big variability among reporting categories, although it has the highest effect on number categories.

- Ackerman, P.T., & Dykman, R.A. (1995). Reading-disabled students with and without comorbid arithmetic disability. Developmental Neuropsychology, 11, 351–371.
- Brown T.C., Peterson G.L. (2009). An enquiry into the method of paired comparison. U.S. Department of Agriculture, General Technical Reports. RMRS-GTR-216WWW, Fort Collins, CO, USA
- Chan, A.P.C, Ho, D. C.K., and Tam, C. M. (2001). Design and build project success factors: Multivariate analysis. Journal of construction engineering and management, 127 (2), 93-100.
- Chamodrakas, I., Batis, D., & Martakos, D. (2010). Supplier selection in electronic marketplaces using satisficing and fuzzy AHP. Expert Systems with Applications, 37, 490-498.
- Choo, E.U., Schoner, B., Wedley, W.C. (1999). Interpretation of criteria weights in multi-criteria decision making. Computers and Industrial Engineering, 37: 527-541.
- Forman, E. & Peniwati, K. (1998). Aggregating individual judgments and priorities with the Analytic Hierarchy Process. European Journal of Operational Research, p.108,165−169.
- Geary, D.C. (2004). Mathematics and learning disabilities. Journal of Learning Disabilities, 37, 4–15.
- Geary, D. C., Hoard, M. K., Byrd-Craven, J., & Desoto, M. (2004). Strategy choices in simple and complex addition: Contributions of working memory and counting knowledge for children with mathematical disability. Journal of Experimental Child Psychology, 88, 121–151.
- Geary, D.C. (2003). Learning disabilities in arithmetic: Problem solving differences and cognitive deficits. In H.L. Swanson, K. Harris, & S. Graham (Eds.), Handbook of learning disabilities (pp. 199–212). New York: Guilford.
- Geary, D. C. (1993). Mathematical disabilities: Cognition, neuropsychological and genetic components. Psychological Bulletin, 114, 345–362.
- Ginevičius, R. (2008). Normalization of quantities of various dimensions, Journal of business economics and management, 9(1): 79-86.
- Goldman , S. R., Pellegrino , J. W., & Mertz , D. L. (1988). Extended practice of basic addition facts: Strategy changes in learning disabled students. Cognition and Instruction, 5, 223-265.
- Haghighi, M., Divandari, A., & Keimasi, M. (2010). The impact of 3D e-readiness on e-banking development in Iran: A fuzzy AHP analysis. Expert Systems with Applications, 37, 4084-4093.
- Hitch, G. J. & McAuley, E. (1991). Working memory in children with specific arithmetical learning difficulties. British Journal of Psychology, 82: 375–386.
- Hwang, C.L., & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Berlin/Heidelberg/New-York: Springer Verlag.
- Jordan, N.C., & Montani, T.O. (1997). Cognitive arithmetic and problem solving: A comparison of children with specific and general mathematics difficulties. Journal of Learning Disabilities, 30, 624–634.
- Li, T.S., & Huang, H. (2009). Applying TRIZ and Fuzzy AHP to develop innovative design for automated manufacturing systems. Expert Systems with Applications, 36, 8302-8312.
- Pan, N. (2009). Selecting an appropriate excavation construction method based on qualitative assessments. Expert Systems with Applications, 36, 5481-5490.
- Ramanathan, R. & Ganesh, L.S.(1994). Group Preference Aggregation Methods employed in AHP: An Evaluation and Intrinsic Process for Deriving Members' Weightages,European Journal of Operational Research, 79, p. 249−265.
- Saaty, T.L.(2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences. Volume 1, Number 1/2008 Pages83-98.
- Saaty, T.L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 15 (3), 234-281.
- Saaty. T.L., 1980. The Analytic Hierarchy Process, McGraw-Hill, New York, NY.
- Saaty, T.L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48 1.
- Seçme, N. Y., Bayrakdaroglu, A., & Kahraman, C. (2009). Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS. Expert Systems with Applications, 36, 11699-11709.
- Sen, C. G., & Çinar, G. (2010). Evaluation and pre-allocation of operators with multiple skills: A combined fuzzy AHP and max-min approach. Expert Systems with Applications, 37, 2043-2053.
- Triantaphyllou, E. (2000). Multi-Criteria Decision Making Methods: a Comparative Study. Kluwer Academic Publishers, Boston.
- Yang, C.L., Chuang, S.P., & Huang, R.H. (2009). Manufacturing evaluation system based on AHP/ANP approach for wafer fabricating industry. Expert Systems with Applications, 36, 11369-11377.

This study utilizes an evaluation model AHP (analytic hierarchy process) which prioritized the relative weights of three general subtypes of mathematical disability (MD), Semantic Memory, Procedural, and Visuospatial in order to analyze and explain underlying cognitive and performance features of FCAT (Florida Comprehensive Assessment Test) benchmarks and corresponding items for grades 6-8 in all mathematics categories. For this purpose, extensive review of the literature has been conducted on mathematical disability to determine subtypes of learning disability in mathematics. Afterwards, a multi-step AHP approach is adopted to obtain the relative weights of criteria (subtypes of learning disability) by linking the independent evaluations of four content area experts for each benchmark. The results indicate that semantic memory deficiency is the dominant subtype of mathematical learning disability on vast majority of benchmarks. Another important finding of this study is that effect of visuospatial deficiency increases from grade 6 to grade 8. In addition, effect of procedural deficiency does not show big variability among reporting categories, although it has the highest effect on number categories.

- Ackerman, P.T., & Dykman, R.A. (1995). Reading-disabled students with and without comorbid arithmetic disability. Developmental Neuropsychology, 11, 351–371.
- Brown T.C., Peterson G.L. (2009). An enquiry into the method of paired comparison. U.S. Department of Agriculture, General Technical Reports. RMRS-GTR-216WWW, Fort Collins, CO, USA
- Chan, A.P.C, Ho, D. C.K., and Tam, C. M. (2001). Design and build project success factors: Multivariate analysis. Journal of construction engineering and management, 127 (2), 93-100.
- Chamodrakas, I., Batis, D., & Martakos, D. (2010). Supplier selection in electronic marketplaces using satisficing and fuzzy AHP. Expert Systems with Applications, 37, 490-498.
- Choo, E.U., Schoner, B., Wedley, W.C. (1999). Interpretation of criteria weights in multi-criteria decision making. Computers and Industrial Engineering, 37: 527-541.
- Forman, E. & Peniwati, K. (1998). Aggregating individual judgments and priorities with the Analytic Hierarchy Process. European Journal of Operational Research, p.108,165−169.
- Geary, D.C. (2004). Mathematics and learning disabilities. Journal of Learning Disabilities, 37, 4–15.
- Geary, D. C., Hoard, M. K., Byrd-Craven, J., & Desoto, M. (2004). Strategy choices in simple and complex addition: Contributions of working memory and counting knowledge for children with mathematical disability. Journal of Experimental Child Psychology, 88, 121–151.
- Geary, D.C. (2003). Learning disabilities in arithmetic: Problem solving differences and cognitive deficits. In H.L. Swanson, K. Harris, & S. Graham (Eds.), Handbook of learning disabilities (pp. 199–212). New York: Guilford.
- Geary, D. C. (1993). Mathematical disabilities: Cognition, neuropsychological and genetic components. Psychological Bulletin, 114, 345–362.
- Ginevičius, R. (2008). Normalization of quantities of various dimensions, Journal of business economics and management, 9(1): 79-86.
- Goldman , S. R., Pellegrino , J. W., & Mertz , D. L. (1988). Extended practice of basic addition facts: Strategy changes in learning disabled students. Cognition and Instruction, 5, 223-265.
- Haghighi, M., Divandari, A., & Keimasi, M. (2010). The impact of 3D e-readiness on e-banking development in Iran: A fuzzy AHP analysis. Expert Systems with Applications, 37, 4084-4093.
- Hitch, G. J. & McAuley, E. (1991). Working memory in children with specific arithmetical learning difficulties. British Journal of Psychology, 82: 375–386.
- Hwang, C.L., & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Berlin/Heidelberg/New-York: Springer Verlag.
- Jordan, N.C., & Montani, T.O. (1997). Cognitive arithmetic and problem solving: A comparison of children with specific and general mathematics difficulties. Journal of Learning Disabilities, 30, 624–634.
- Li, T.S., & Huang, H. (2009). Applying TRIZ and Fuzzy AHP to develop innovative design for automated manufacturing systems. Expert Systems with Applications, 36, 8302-8312.
- Pan, N. (2009). Selecting an appropriate excavation construction method based on qualitative assessments. Expert Systems with Applications, 36, 5481-5490.
- Ramanathan, R. & Ganesh, L.S.(1994). Group Preference Aggregation Methods employed in AHP: An Evaluation and Intrinsic Process for Deriving Members' Weightages,European Journal of Operational Research, 79, p. 249−265.
- Saaty, T.L.(2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences. Volume 1, Number 1/2008 Pages83-98.
- Saaty, T.L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 15 (3), 234-281.
- Saaty. T.L., 1980. The Analytic Hierarchy Process, McGraw-Hill, New York, NY.
- Saaty, T.L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48 1.
- Seçme, N. Y., Bayrakdaroglu, A., & Kahraman, C. (2009). Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS. Expert Systems with Applications, 36, 11699-11709.
- Sen, C. G., & Çinar, G. (2010). Evaluation and pre-allocation of operators with multiple skills: A combined fuzzy AHP and max-min approach. Expert Systems with Applications, 37, 2043-2053.
- Triantaphyllou, E. (2000). Multi-Criteria Decision Making Methods: a Comparative Study. Kluwer Academic Publishers, Boston.
- Yang, C.L., Chuang, S.P., & Huang, R.H. (2009). Manufacturing evaluation system based on AHP/ANP approach for wafer fabricating industry. Expert Systems with Applications, 36, 11369-11377.

Toplam 27 adet kaynakça vardır.

Konular | Eğitim Üzerine Çalışmalar |
---|---|

Bölüm | Makaleler |

Yazarlar | |

Yayımlanma Tarihi | 20 Aralık 2016 |

Gönderilme Tarihi | 21 Mart 2017 |

Kabul Tarihi | 1 Eylül 2016 |

Yayımlandığı Sayı | Yıl 2016 Cilt: 2 Sayı: 2 |

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