Patterns of Cognitive Strengths and Weaknesses and Relationships to Math Errors

2016 ◽  
Vol 35 (1-2) ◽  
pp. 155-167 ◽  
Author(s):  
Taylor Koriakin ◽  
Erica White ◽  
Kristina C. Breaux ◽  
Emily DeBiase ◽  
Rebecca O’Brien ◽  
...  
Keyword(s):  
Processing Speed ◽  
Listening Comprehension ◽  
Educational Achievement ◽  
Object Naming ◽  
Simple Addition ◽  
Cognitive Patterns ◽  
Geometry Measurement ◽  
Math Calculation ◽  
Math Concepts

This study investigated cognitive patterns of strengths and weaknesses (PSW) and their relationship to patterns of math errors on the Kaufman Test of Educational Achievement (KTEA-3). Participants, ages 5 to 18, were selected from the KTEA-3 standardization sample if they met one of two PSW profiles: high crystallized ability (Gc) paired with low processing speed/long-term retrieval (Gs/Glr; n = 375) or high Gs/Glr paired with low Gc ( n = 309). Estimates of Gc and Gs/Glr were based on five KTEA-3 subtests that measure either Gc (e.g., Listening Comprehension) or Gs/Glr (e.g., Object Naming Facility). The two groups were then compared on math error factors. Significant differences favored the High-Gc group for factors that measure math calculation, basic math concepts, and complex computation. However, the two groups did not differ in their errors on factors that measure geometry/measurement or simple addition. Results indicated that students with different PSW profiles also differed in the kinds of errors they made on math tests.

Do Cognitive Patterns of Strengths and Weaknesses Differentially Predict Errors on Reading, Writing, and Spelling?

2016 ◽  
Vol 35 (1-2) ◽  
pp. 186-205 ◽  
Author(s):  
Xiaochen Liu ◽  
Lavinia Marchis ◽  
Emily DeBiase ◽  
Kristina C. Breaux ◽  
Troy Courville ◽  
...  
Keyword(s):  
Processing Speed ◽  
Phonological Processing ◽  
Educational Achievement ◽  
Orthographic Processing ◽  
Cognitive Profiles ◽  
Word Decoding ◽  
High Processing Speed ◽  
Error Factor ◽  
Cognitive Patterns

This study investigated the relationship between specific cognitive patterns of strengths and weaknesses (PSWs) and the errors children make in reading, writing, and spelling tests from the Kaufman Test of Educational Achievement–Third Edition (KTEA-3). Participants were selected from the KTEA-3 standardization sample based on five cognitive profiles: High Crystallized Ability paired with Low Processing Speed and Long-Term Retrieval (High Gc), Low Crystallized Ability paired with High Processing Speed and Long-Term Retrieval (High Gs/ Glr), Low Orthographic Processing (Low OP), Low Phonological Processing (Low PP), and Low Phonological Processing paired with Low Orthographic Processing (Low PP_OP). Error factor scores for all five groups were compared on Reading Comprehension and Written Expression; the first four groups were compared on Letter & Word Recognition, Nonsense Word Decoding, and Spelling, and the first three groups were compared on Phonological Processing. Significant differences were noted among the patterns of errors demonstrated by the five groups. Findings support the notion that students with diverse cognitive PSWs display different patterns of errors on tests of academic achievement.

Biological Gender Differences in Students’ Errors on Mathematics Achievement Tests

2016 ◽  
Vol 35 (1-2) ◽  
pp. 47-56 ◽  
Author(s):  
Christie Stewart ◽  
Melissa M. Root ◽  
Taylor Koriakin ◽  
Dowon Choi ◽  
Sarah R. Luria ◽  
...  
Keyword(s):  
Gender Differences ◽  
Mathematics Achievement ◽  
Stereotype Threat ◽  
Educational Achievement ◽  
Age Group ◽  
Significance Level ◽  
Geometric Concepts ◽  
Error Factor ◽  
Math Calculation ◽  
Math Concepts

This study investigated developmental gender differences in mathematics achievement, using the child and adolescent portion (ages 6-19 years) of the Kaufman Test of Educational Achievement–Third Edition (KTEA-3). Participants were divided into two age categories: 6 to 11 and 12 to 19. Error categories within the Math Concepts & Applications and Math Computation subtests of the KTEA-3 were factor analyzed and revealed five error factors. Multiple ANOVA of the error factor scores showed that, across both age categories, female and male mean scores were not significantly different across four error factors: math calculation, geometric concepts, basic math concepts, and addition. They were significantly different on the complex math problems error factor, with males performing better at the p < .05 significance level for the 6 to 11 age group and at the p < .001 significance level for the 12 to 19 age group. Implications in light of gender stereotype threat are discussed.

Retrieval from Memory or Procedural Strategies for Addition Problems

2011 ◽  
Vol 70 (1) ◽  
pp. 35-39 ◽  
Author(s):  
Muriel Fanget ◽  
Catherine Thevenot ◽  
Caroline Castel ◽  
Michel Fayol
Keyword(s):  
Reaction Times ◽  
Long Term Memory ◽  
Verbal Reports ◽  
Term Memory ◽  
Simple Addition ◽  
Memory Traces ◽  
Addition Problem ◽  
Retrieval From Memory ◽  
Calculation Procedures

In this study, we used a paradigm recently developed ( Thevenot, Fanget, & Fayol, 2007 ) to determine whether 10-year-old children solve simple addition problems by retrieval of the answer from long-term memory or by calculation procedures. Our paradigm is unique in that it does not rely on reaction times or verbal reports, which are known to potentially bias the results, especially in children. Rather, it takes advantage of the fact that calculation procedures degrade the memory traces of the operands, so that it is more difficult to recognize them when they have been involved in the solution of an addition problem by calculation rather than by retrieval. The present study sharpens the current conclusions in the literature and shows that, when the sum of addition problems is up to 10, children mainly use retrieval, but when it is greater than 10, they mainly use calculation procedures.

Evidence-Based Cognitive Rehabilitation for People With Multiple Sclerosis: A Systematic Review

2021 ◽  
Vol 75 (Supplement_2) ◽  
pp. 7512520385p1-7512520385p1
Author(s):  
Yu-Chih Chen ◽  
Szu-Wei Chen ◽  
Britney Ferri
Keyword(s):  
Processing Speed ◽  
Cognitive Skills ◽  
Cognitive Rehabilitation ◽  
Evidence Based ◽  
Long Term Outcomes ◽  
Noninvasive Brain Stimulation ◽  
Future Studies ◽  
Cognitive Improvement ◽  
Computer Based

Abstract Date Presented 04/22/21 Computer-based programs, conventional cognitive rehabilitation, neurologic music therapy, and noninvasive brain stimulation are effective in improving memory and learning, processing speed, language, executive function, or general cognitive skills. However, the retention of treatment effects and the generalizability of the cognitive improvement to the daily occupations are not clear. Including a predetermined single outcome, functional and long-term outcomes are needed in future studies. Primary Author and Speaker: Yu-Chih Chen Contributing Authors: Nicole Gerhardt, Christina Calhoun Thielen, Winnie Dunn, and Mary Jane Mulcahey

Educational Achievement, Employment, Smoking, Marital, and Insurance Statuses in Long-term Survivors of Childhood Malignant Solid Tumors

2013 ◽  
Vol 35 (2) ◽  
pp. 129-133 ◽  
Author(s):  
Begül Yağci-Küpeli ◽  
Bilgehan Yalçin ◽  
Serhan Küpeli ◽  
Ali Varan ◽  
Canan Akyüz ◽  
...  
Keyword(s):  
Solid Tumors ◽  
Educational Achievement ◽  
Long Term Survivors

High Thinking Processes (HTP): Elements of Curricula and Teaching Able-Learners

2002 ◽  
Vol 16 (3) ◽  
pp. 225-240 ◽  
Author(s):  
Shlomo Kaniel
Keyword(s):  
Learning Communities ◽  
Processing Speed ◽  
Mental Image ◽  
Advanced Technology ◽  
Long Term Memory ◽  
Differential Instruction ◽  
Challenging Environment ◽  
Thinking Processes ◽  
Metacognitive Teaching

Acquiring High Thinking Processes (HTP) seems to be the solution for greater challenge in the present and for preparing able learners to the technological dynamic future. In dealing with this, the article is divided into two main chapters: a) HTP as a goal (what to teach) b) the different elements needed to accomplish this (how to do it), a) The components of HTP as a goal are: control and enhancement of long term memory and working memory; automatization and regulation of processing speed; flexibility; openness to information; developing strategies of listening, speaking, reading and writing; inference and reasoning processes; decision making using metacognition; positive transfer; description and justification of mental processes; mental image; independent learner and using HTP for values like tolerance, b) There are four main elements to develop such a goal: 1) well organized and justified curricula with appropriate tasks, 2) metacognitive teaching that emphasizes processes, integration, feedback, differential instruction and efficiency, 3) challenging environment, learning communities and advanced technology, 4) training teachers for HTP expertise.

Big Data

2017 ◽  
Vol 7 (2) ◽  
pp. 1-23 ◽  
Author(s):  
Stephen H. Kaisler ◽  
William H. Money ◽  
Frank Armour ◽  
J. Alberto Espinosa
Keyword(s):  
Big Data ◽  
Processing Speed ◽  
Previous Analysis ◽  
Decision Makers ◽  
Data Transport ◽  
Processing Technologies ◽  
Research Paradigms ◽  
On Line ◽  
Near Term

Big Data refers to data volumes in the range of exabytes (1018th) requiring processing from distributed on-line storage systems with thousands of processors, mainframes or supercomputers where processing speed is measured in GFLOPS. The rate at which data are being collected are accelerating and will approach the zettabyte/year range. Other attributes of Bi Data are also concurrently expanding including variety/variability, velocity, value, and vital concerns for veracity. Storage and data transport technology issues may be solvable in the near-term. However, these communication, quantity management, and processing technologies also represent long-term challenges that require research, paradigms and analytical practices. This paper extends the authors' previous analysis of the issues and challenges with Big Data. It presents a table that contrasts their previous research finding and projects with the state of Big Data today, and their projections of what managers and decision makers will (or should) seek to accomplish as the Big Data universe continues to expand and evolve.

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