This presentation presents an update of the "CHC COG-ACH correlates research synthesis" project described and hosted at IQ's Corner (www.intelligencetesting.blogspot.com) and IAP (www.iapsych.com). The viewer should first read the background materials regarding this project at these sites (how to access is also included in first slide). The current slides present my preliminary analysis and conclusions of the relations between CHC cognitive abilities and basic reading skills, reading comprehension, basic math skills, and math reasoning as a function of age (developmental status). The results are part of a manuscript that is in preparation. Revisit IQ's Corner to keep abreast of updates.
This research study module published by NCETM was developed by Anne Watson based on the paper Growth Points in Understanding of Function published in Mathematics Education Research Journal.
This document provides an example of a selective testing scenario using the Woodcock-Johnson III (WJ III) battery for a student referred for basic reading skills difficulties between the ages of 6-8. It outlines a branching path of additional tests that could be administered within the cognitive domain of Crystallized Intelligence (Gc) based on initial test results to further evaluate broad versus narrow abilities. The path includes examining Listening Comprehension (Gc-LS), General Information (Gc-K0), and Academic Knowledge through comparison of cluster scores.
Assessing Complex Problem Solving In The Classroom Meeting Challenges And Op...Emily Smith
The document discusses challenges of assessing complex problem solving in classrooms, noting that today's "digital native" students have different characteristics than those typically used in prior microworld studies; it describes the Genetics Lab microworld which was designed with intuitive interfaces and game-like elements to engage students, and its development incorporated multiple usability studies to ensure it functioned well for students.
Assessment For Learning In Immersive And Virtual Environments Evidence-Cent...Sabrina Green
1) The document discusses a research program called Assessment for Learning in Immersive Virtual Environments (ALIVE) that examines how 3D immersive virtual environments can be used to provide formative feedback to students.
2) Specifically, the project explores using 3D virtual environments to assess middle school students' science inquiry skills through formative feedback.
3) The goal of the research is to understand how formative feedback in virtual environments affects students' academic achievement, agency, and ability to self-regulate their learning. It aims to contribute evidence for how virtual environments can improve STEM education outcomes.
The document summarizes a study that examined the psychometric properties of a shortened version of the Motivated Strategies for Learning Questionnaire (MSLQ) for junior high school students. The study involved two samples of students in Singapore. An initial exploratory factor analysis identified issues with some items, which were removed. A confirmatory factor analysis on the remaining items with a second sample supported a five-factor model consisting of motivational beliefs and cognitive/self-regulation strategies scales. Tests for validity and invariance across gender also supported the modified measurement model of the MSLQ for assessing motivation and learning strategies of junior high students.
The Evolution of the Cattell-Horn-Carrol (CHC) Theory of Intelligence: Schne...Kevin McGrew
This presentation is based on Schneider, W. J., & McGrew, K. S. (in press). The Cattell-Horn-Carroll Theory of Cognitive Abilities. This presentation includes a portion of key material to be published in a forthcoming CHC update/revision chapter-->In D. P. Flanagan & Erin M .McDonough (Eds.), Contemporary intellectual assessment: Theories, tests and issues (4thed.,) New York: Guilford Press.
This is only a small amount of the chapter. Also, I have inserted some new material related to test interpretation that is not included in the to-be-published chapter. The tentative date for publication of the Flanagan book is spring 2018.
This research study module published by NCETM was developed by Anne Watson based on the paper Growth Points in Understanding of Function published in Mathematics Education Research Journal.
This document provides an example of a selective testing scenario using the Woodcock-Johnson III (WJ III) battery for a student referred for basic reading skills difficulties between the ages of 6-8. It outlines a branching path of additional tests that could be administered within the cognitive domain of Crystallized Intelligence (Gc) based on initial test results to further evaluate broad versus narrow abilities. The path includes examining Listening Comprehension (Gc-LS), General Information (Gc-K0), and Academic Knowledge through comparison of cluster scores.
Assessing Complex Problem Solving In The Classroom Meeting Challenges And Op...Emily Smith
The document discusses challenges of assessing complex problem solving in classrooms, noting that today's "digital native" students have different characteristics than those typically used in prior microworld studies; it describes the Genetics Lab microworld which was designed with intuitive interfaces and game-like elements to engage students, and its development incorporated multiple usability studies to ensure it functioned well for students.
Assessment For Learning In Immersive And Virtual Environments Evidence-Cent...Sabrina Green
1) The document discusses a research program called Assessment for Learning in Immersive Virtual Environments (ALIVE) that examines how 3D immersive virtual environments can be used to provide formative feedback to students.
2) Specifically, the project explores using 3D virtual environments to assess middle school students' science inquiry skills through formative feedback.
3) The goal of the research is to understand how formative feedback in virtual environments affects students' academic achievement, agency, and ability to self-regulate their learning. It aims to contribute evidence for how virtual environments can improve STEM education outcomes.
The document summarizes a study that examined the psychometric properties of a shortened version of the Motivated Strategies for Learning Questionnaire (MSLQ) for junior high school students. The study involved two samples of students in Singapore. An initial exploratory factor analysis identified issues with some items, which were removed. A confirmatory factor analysis on the remaining items with a second sample supported a five-factor model consisting of motivational beliefs and cognitive/self-regulation strategies scales. Tests for validity and invariance across gender also supported the modified measurement model of the MSLQ for assessing motivation and learning strategies of junior high students.
The Evolution of the Cattell-Horn-Carrol (CHC) Theory of Intelligence: Schne...Kevin McGrew
This presentation is based on Schneider, W. J., & McGrew, K. S. (in press). The Cattell-Horn-Carroll Theory of Cognitive Abilities. This presentation includes a portion of key material to be published in a forthcoming CHC update/revision chapter-->In D. P. Flanagan & Erin M .McDonough (Eds.), Contemporary intellectual assessment: Theories, tests and issues (4thed.,) New York: Guilford Press.
This is only a small amount of the chapter. Also, I have inserted some new material related to test interpretation that is not included in the to-be-published chapter. The tentative date for publication of the Flanagan book is spring 2018.
The document discusses different methods for analyzing pre-test and post-test data from physics courses, including analyzing normalized average gain (<g>) and calculating effect sizes. It reports on a study of 62 introductory physics courses that found:
(1) Traditional lecture courses had an average normalized gain of 0.23, while interactive engagement courses averaged 0.48;
(2) Calculated effect size was higher for interactive engagement courses (d=2.18) than traditional courses (d=0.88);
(3) Normalized average gain provides a useful measure for comparing course effectiveness across groups with varying initial knowledge.
EDUC8102-6 MD7Assgn4: Research Application Paper #1. eckchela
This is Walden University course (EDUC8102-6) MD7Assgn4: Research Application Paper #1. The purpose of the paper is to analyze two research articles relevant to the field of education. It is written in APA format and includes references. Most universities submit higher-education assignments to turnitin; so, remember to paraphrase. Enjoy your discovery!
Cluster analysis of the WJ III Battery: Implications for CHC test interpreta...Kevin McGrew
The document summarizes a cluster analysis of 50 tests from the WJ III cognitive and achievement battery. The analysis revealed clusters that supported most major CHC broad abilities, with some exceptions. It also generated some potential new findings, such as possible subfactors within broad abilities like Gf, Grw, and Gs, as well as intermediate dimensions like temporal processing. The analysis provides further evidence for the CHC model while also suggesting areas for potential refinement or extension.
The Model of Achievement Competence Motivation (MACM): Part A Introduction o...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the first (Part A) in the series. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
Comparing the latent structure of the Mini-Mental State Examination among you...Eugenia Savvidou
Aim: The aim of the study was the comparison of the cognitive functioning between young children and older adults through the investigation of the latent structure qualitative changes in Mini-Mental State Examination (MMSE) from age to age, using Confirmatory Factor Analyses (CFA) and testing a conventional two-factor model and/or a unidimensional model of MMSE. Method: The sample consisted of 42 kindergarten and 56 elementary school students (age range: 5-8 years) and 118 new-old adults and 27 old-old adults (age range: 61-88 years) whose cognitive functioning was examined in MMSE. Results: Regarding the new-old adults group, CFA indicated that individual variability across MMSE measured variables (total scores for each of the five subsets) can be modeled by a two-factor model. The patterns of two-factor and one-factor MMSE structure were not verified for the groups of kindergarten students, elementary school students, and old-old adults. Conclusion: The results support the hypothesis of “retrogenesis”.
Using Semantics of Textbook Highlights to Predict Student Comprehension and K...Sergey Sosnovsky
The document presents a framework for using the semantics of student textbook highlights to predict comprehension and knowledge retention. It uses semantic embeddings to encode highlighted sentences, compares them to questions, and uses the match scores in a model. It finds that augmenting a baseline model with highlighting features improves predictions of question accuracy, especially for held-out students. A semantic encoding of highlights performed better than a positional encoding. The approach works well across different levels of conceptual difficulty as defined by Bloom's taxonomy.
Part I: Beyond the CHC tipping point: Back to the futureKevin McGrew
The document discusses the evolution of intelligence testing and theory from early waves focused on general intelligence to more recent waves incorporating contemporary cognitive ability theories like Cattell-Horn-Carroll (CHC). It argues that a tipping point was reached around 2001-2003 when CHC theory became widely adopted in intelligence test development and interpretation, aligning tests more closely with empirical research. Future directions may focus on integrating psychometric and information processing approaches using new statistical methods.
Relationships between diversity of classification ensembles and single class ...Ecway Technologies
This document discusses the relationship between diversity in classification ensembles and single-class performance measures for class imbalance problems. It first analyzes when and why ensemble diversity, as measured by Q-statistic, can improve overall accuracy based on classification patterns. It then extends this analysis to single-class performance measures like recall, precision and F-measure. Through theoretical analysis, it identifies six situations of how diversity may impact these measures. Finally, extensive experiments on artificial and real-world datasets with skewed class distributions show strong correlations between diversity and the discussed performance measures. Diversity generally has a positive impact on the minority class and is beneficial to the overall performance in terms of AUC and G-mean.
Dotnet relationships between diversity of classification ensembles and singl...Ecway Technologies
This document discusses the relationship between diversity in classification ensembles and single-class performance measures for class imbalance problems. It first analyzes when and why ensemble diversity, as measured by Q-statistic, can improve overall accuracy based on classification patterns. It then extends this analysis to consider diversity's impact on single-class performance measures like recall, precision and F-measure. The paper presents six situations of how diversity may affect these measures based on theoretical analysis. Finally, extensive experiments on artificial and real-world datasets with skewed class distributions show strong correlations between diversity and the discussed performance measures. In general, diversity has a positive impact on the minority class and also benefits overall performance in terms of AUC and G-mean.
Java relationships between diversity of classification ensembles and single-...ecwayerode
This document discusses the relationship between diversity in classification ensembles and single-class performance measures for class imbalance problems. It first analyzes when and why ensemble diversity, as measured by Q-statistic, can improve overall accuracy based on classification patterns. It then extends this analysis to consider diversity's impact on single-class performance measures like recall, precision and F-measure. Theoretical analysis identifies six situations of how diversity may affect these measures. Extensive experiments on artificial and real-world datasets with skewed class distributions find strong correlations between diversity and the discussed performance measures. Diversity generally has a positive impact on the minority class and is beneficial to overall performance in terms of AUC and G-mean.
Java relationships between diversity of classification ensembles and single-...Ecway Technologies
This document discusses the relationship between diversity in classification ensembles and single-class performance measures for class imbalance problems. It first analyzes when and why ensemble diversity, as measured by Q-statistic, can improve overall accuracy based on classification patterns. It then extends this analysis to consider diversity's impact on single-class performance measures like recall, precision and F-measure. The paper presents six situations of how diversity may affect these measures based on theoretical analysis. Finally, extensive experiments on artificial and real-world datasets with skewed class distributions show strong correlations between diversity and the discussed performance measures. In general, diversity has a positive impact on the minority class and is beneficial to the overall performance in terms of AUC and G-mean.
Relationships between diversity of classification ensembles and single class ...Ecway Technologies
This document discusses the relationship between diversity in classification ensembles and single-class performance measures for class imbalance problems. It first analyzes when and why ensemble diversity, as measured by Q-statistic, can improve overall accuracy based on classification patterns. It then extends this analysis to consider diversity's impact on single-class performance measures like recall, precision and F-measure. The document presents six situations of how diversity may affect these measures based on theoretical analysis. Finally, it reports results of experimental studies on artificial and real-world datasets that show diversity has a generally positive impact on the minority class performance and overall metrics like AUC and G-mean.
Learning with me Mate: Analytics of Social Networks in Higher EducationDragan Gasevic
Effects of social interactions are reported in research on higher education to lead to positive outcomes such as higher levels of internalization, sense of community, academic achievement, metacognition, and student retention. The role of social networks has especially been emphasized in research due to the availability of theoretical foundations and analytic methods to investigate their effects in higher education. The increased use of technologies in education allows for the collection of large and rich datasets about social networks which call for the use of novel analytics methods. This talk will first give a brief overview of the existing work on and lessons learned from some well-known studies on social networks in higher education in diverse situations from face-to-face to massive open online courses. The talk will then identify critical challenges that require immediate attention in order for the study of social networks to make a sustainable impact on learning and teaching. The most important take away from the talk will be that
- computational aspects of the study of social networks need to be integrated deeply with theory, research and practice,
- novel methods for the study of critical dimensions (discourse, structure and dynamics) that shape network formation and network effects are necessary, and
- innovative instructional approaches are essential to address the changing conditions created by contemporary educational and technological contexts.
Writing your research aims and proposal activity sheetRhianWynWilliams
The document discusses strengths and weaknesses of a proposed qualitative research methodology for a dissertation proposal exploring how IT professionals manage work-life balance and how work-life balance provisions impact their intention to leave an organization. The methodology is strengthened by qualitative research's ability to understand complex situations from multiple perspectives and understand reasons and social contexts behind participant responses. However, qualitative research is criticized for potential researcher bias and subjectivity. The document also notes the researcher's lack of statistical expertise as further justification for a qualitative approach.
The presentation will highlight changing demands (from a sharp focus on access to concerns about throughput) and responses related to admission to higher education, and the research underpinning such responses. Beginning in the late 1980s, the paper traces the development of assessment procedures n the ‘dynamic’ testing tradition (responding to the need to test for ‘potential’ and widen access). The paper ends with a discussion of the National Benchmark Tests Project (responding the need to places students in appropriate curricula and improve throughput), focusing on the research and approaches underlying these tests as well as the findings and some implications both for schooling and higher education.
Presented by A/Prof. Nan Yeld & Robert Prince
Hierarchical clustering and topology for psychometric validationColleen Farrelly
From my graduate work and extended to the field of education.
Citation of paper from which presentation was derived:
Farrelly, C. M., Schwartz, S. J., Amodeo, A. L., Feaster, D. J., Steinley, D. L., Meca, A., & Picariello, S. (2017). The Analysis of Bridging Constructs with Hierarchical Clustering Methods: An application to identity. Journal of Research in Personality.
A Cognitive Tutor For Genetics Problem Solving Learning Gains And Student Mo...Tony Lisko
This document describes a new intelligent tutoring system called the Genetics Cognitive Tutor. The tutor supports students in solving complex genetics problems through individualized feedback and guidance. It is underpinned by a cognitive model of genetics problem-solving that allows it to interpret student work, provide step-by-step feedback, and maintain a model of each student's knowledge. Evaluations found students using the tutor gained an average of two letter grades in learning and the tutor's model of student knowledge accurately predicted test performance. The tutor provides a more personalized learning experience than other genetics software by combining problem-solving activities with context-sensitive guidance.
1) The purpose of this study was to examine the relationship between visual static models and students' written solutions to fraction problems using a large sample of student work.
2) The results indicate that common student errors relate to how students interpret the given model or their own model of the situation. Students' flexibility with visual models is related to successful written solutions.
3) Researchers hypothesize that exposure to varied mathematical representations influences students' ability to flexibly use static visual representations. Students need a solid understanding of real-world situations to successfully create and interpret visual models.
The Model of Achievement Competence Motivation (MACM) Part E: Crossing the R...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the fifth (Part E) in the series. It is brief...only 11 slides. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
The Model of Achievement Competence Motivation (MACM): Part D: The volition ...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the fourth (Part D) in the series. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
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The document discusses different methods for analyzing pre-test and post-test data from physics courses, including analyzing normalized average gain (<g>) and calculating effect sizes. It reports on a study of 62 introductory physics courses that found:
(1) Traditional lecture courses had an average normalized gain of 0.23, while interactive engagement courses averaged 0.48;
(2) Calculated effect size was higher for interactive engagement courses (d=2.18) than traditional courses (d=0.88);
(3) Normalized average gain provides a useful measure for comparing course effectiveness across groups with varying initial knowledge.
EDUC8102-6 MD7Assgn4: Research Application Paper #1. eckchela
This is Walden University course (EDUC8102-6) MD7Assgn4: Research Application Paper #1. The purpose of the paper is to analyze two research articles relevant to the field of education. It is written in APA format and includes references. Most universities submit higher-education assignments to turnitin; so, remember to paraphrase. Enjoy your discovery!
Cluster analysis of the WJ III Battery: Implications for CHC test interpreta...Kevin McGrew
The document summarizes a cluster analysis of 50 tests from the WJ III cognitive and achievement battery. The analysis revealed clusters that supported most major CHC broad abilities, with some exceptions. It also generated some potential new findings, such as possible subfactors within broad abilities like Gf, Grw, and Gs, as well as intermediate dimensions like temporal processing. The analysis provides further evidence for the CHC model while also suggesting areas for potential refinement or extension.
The Model of Achievement Competence Motivation (MACM): Part A Introduction o...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the first (Part A) in the series. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
Comparing the latent structure of the Mini-Mental State Examination among you...Eugenia Savvidou
Aim: The aim of the study was the comparison of the cognitive functioning between young children and older adults through the investigation of the latent structure qualitative changes in Mini-Mental State Examination (MMSE) from age to age, using Confirmatory Factor Analyses (CFA) and testing a conventional two-factor model and/or a unidimensional model of MMSE. Method: The sample consisted of 42 kindergarten and 56 elementary school students (age range: 5-8 years) and 118 new-old adults and 27 old-old adults (age range: 61-88 years) whose cognitive functioning was examined in MMSE. Results: Regarding the new-old adults group, CFA indicated that individual variability across MMSE measured variables (total scores for each of the five subsets) can be modeled by a two-factor model. The patterns of two-factor and one-factor MMSE structure were not verified for the groups of kindergarten students, elementary school students, and old-old adults. Conclusion: The results support the hypothesis of “retrogenesis”.
Using Semantics of Textbook Highlights to Predict Student Comprehension and K...Sergey Sosnovsky
The document presents a framework for using the semantics of student textbook highlights to predict comprehension and knowledge retention. It uses semantic embeddings to encode highlighted sentences, compares them to questions, and uses the match scores in a model. It finds that augmenting a baseline model with highlighting features improves predictions of question accuracy, especially for held-out students. A semantic encoding of highlights performed better than a positional encoding. The approach works well across different levels of conceptual difficulty as defined by Bloom's taxonomy.
Part I: Beyond the CHC tipping point: Back to the futureKevin McGrew
The document discusses the evolution of intelligence testing and theory from early waves focused on general intelligence to more recent waves incorporating contemporary cognitive ability theories like Cattell-Horn-Carroll (CHC). It argues that a tipping point was reached around 2001-2003 when CHC theory became widely adopted in intelligence test development and interpretation, aligning tests more closely with empirical research. Future directions may focus on integrating psychometric and information processing approaches using new statistical methods.
Relationships between diversity of classification ensembles and single class ...Ecway Technologies
This document discusses the relationship between diversity in classification ensembles and single-class performance measures for class imbalance problems. It first analyzes when and why ensemble diversity, as measured by Q-statistic, can improve overall accuracy based on classification patterns. It then extends this analysis to single-class performance measures like recall, precision and F-measure. Through theoretical analysis, it identifies six situations of how diversity may impact these measures. Finally, extensive experiments on artificial and real-world datasets with skewed class distributions show strong correlations between diversity and the discussed performance measures. Diversity generally has a positive impact on the minority class and is beneficial to the overall performance in terms of AUC and G-mean.
Dotnet relationships between diversity of classification ensembles and singl...Ecway Technologies
This document discusses the relationship between diversity in classification ensembles and single-class performance measures for class imbalance problems. It first analyzes when and why ensemble diversity, as measured by Q-statistic, can improve overall accuracy based on classification patterns. It then extends this analysis to consider diversity's impact on single-class performance measures like recall, precision and F-measure. The paper presents six situations of how diversity may affect these measures based on theoretical analysis. Finally, extensive experiments on artificial and real-world datasets with skewed class distributions show strong correlations between diversity and the discussed performance measures. In general, diversity has a positive impact on the minority class and also benefits overall performance in terms of AUC and G-mean.
Java relationships between diversity of classification ensembles and single-...ecwayerode
This document discusses the relationship between diversity in classification ensembles and single-class performance measures for class imbalance problems. It first analyzes when and why ensemble diversity, as measured by Q-statistic, can improve overall accuracy based on classification patterns. It then extends this analysis to consider diversity's impact on single-class performance measures like recall, precision and F-measure. Theoretical analysis identifies six situations of how diversity may affect these measures. Extensive experiments on artificial and real-world datasets with skewed class distributions find strong correlations between diversity and the discussed performance measures. Diversity generally has a positive impact on the minority class and is beneficial to overall performance in terms of AUC and G-mean.
Java relationships between diversity of classification ensembles and single-...Ecway Technologies
This document discusses the relationship between diversity in classification ensembles and single-class performance measures for class imbalance problems. It first analyzes when and why ensemble diversity, as measured by Q-statistic, can improve overall accuracy based on classification patterns. It then extends this analysis to consider diversity's impact on single-class performance measures like recall, precision and F-measure. The paper presents six situations of how diversity may affect these measures based on theoretical analysis. Finally, extensive experiments on artificial and real-world datasets with skewed class distributions show strong correlations between diversity and the discussed performance measures. In general, diversity has a positive impact on the minority class and is beneficial to the overall performance in terms of AUC and G-mean.
Relationships between diversity of classification ensembles and single class ...Ecway Technologies
This document discusses the relationship between diversity in classification ensembles and single-class performance measures for class imbalance problems. It first analyzes when and why ensemble diversity, as measured by Q-statistic, can improve overall accuracy based on classification patterns. It then extends this analysis to consider diversity's impact on single-class performance measures like recall, precision and F-measure. The document presents six situations of how diversity may affect these measures based on theoretical analysis. Finally, it reports results of experimental studies on artificial and real-world datasets that show diversity has a generally positive impact on the minority class performance and overall metrics like AUC and G-mean.
Learning with me Mate: Analytics of Social Networks in Higher EducationDragan Gasevic
Effects of social interactions are reported in research on higher education to lead to positive outcomes such as higher levels of internalization, sense of community, academic achievement, metacognition, and student retention. The role of social networks has especially been emphasized in research due to the availability of theoretical foundations and analytic methods to investigate their effects in higher education. The increased use of technologies in education allows for the collection of large and rich datasets about social networks which call for the use of novel analytics methods. This talk will first give a brief overview of the existing work on and lessons learned from some well-known studies on social networks in higher education in diverse situations from face-to-face to massive open online courses. The talk will then identify critical challenges that require immediate attention in order for the study of social networks to make a sustainable impact on learning and teaching. The most important take away from the talk will be that
- computational aspects of the study of social networks need to be integrated deeply with theory, research and practice,
- novel methods for the study of critical dimensions (discourse, structure and dynamics) that shape network formation and network effects are necessary, and
- innovative instructional approaches are essential to address the changing conditions created by contemporary educational and technological contexts.
Writing your research aims and proposal activity sheetRhianWynWilliams
The document discusses strengths and weaknesses of a proposed qualitative research methodology for a dissertation proposal exploring how IT professionals manage work-life balance and how work-life balance provisions impact their intention to leave an organization. The methodology is strengthened by qualitative research's ability to understand complex situations from multiple perspectives and understand reasons and social contexts behind participant responses. However, qualitative research is criticized for potential researcher bias and subjectivity. The document also notes the researcher's lack of statistical expertise as further justification for a qualitative approach.
The presentation will highlight changing demands (from a sharp focus on access to concerns about throughput) and responses related to admission to higher education, and the research underpinning such responses. Beginning in the late 1980s, the paper traces the development of assessment procedures n the ‘dynamic’ testing tradition (responding to the need to test for ‘potential’ and widen access). The paper ends with a discussion of the National Benchmark Tests Project (responding the need to places students in appropriate curricula and improve throughput), focusing on the research and approaches underlying these tests as well as the findings and some implications both for schooling and higher education.
Presented by A/Prof. Nan Yeld & Robert Prince
Hierarchical clustering and topology for psychometric validationColleen Farrelly
From my graduate work and extended to the field of education.
Citation of paper from which presentation was derived:
Farrelly, C. M., Schwartz, S. J., Amodeo, A. L., Feaster, D. J., Steinley, D. L., Meca, A., & Picariello, S. (2017). The Analysis of Bridging Constructs with Hierarchical Clustering Methods: An application to identity. Journal of Research in Personality.
A Cognitive Tutor For Genetics Problem Solving Learning Gains And Student Mo...Tony Lisko
This document describes a new intelligent tutoring system called the Genetics Cognitive Tutor. The tutor supports students in solving complex genetics problems through individualized feedback and guidance. It is underpinned by a cognitive model of genetics problem-solving that allows it to interpret student work, provide step-by-step feedback, and maintain a model of each student's knowledge. Evaluations found students using the tutor gained an average of two letter grades in learning and the tutor's model of student knowledge accurately predicted test performance. The tutor provides a more personalized learning experience than other genetics software by combining problem-solving activities with context-sensitive guidance.
1) The purpose of this study was to examine the relationship between visual static models and students' written solutions to fraction problems using a large sample of student work.
2) The results indicate that common student errors relate to how students interpret the given model or their own model of the situation. Students' flexibility with visual models is related to successful written solutions.
3) Researchers hypothesize that exposure to varied mathematical representations influences students' ability to flexibly use static visual representations. Students need a solid understanding of real-world situations to successfully create and interpret visual models.
The Model of Achievement Competence Motivation (MACM) Part E: Crossing the R...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the fifth (Part E) in the series. It is brief...only 11 slides. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
The Model of Achievement Competence Motivation (MACM): Part D: The volition ...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the fourth (Part D) in the series. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
The Model of Achievement Competence Motivation (MACM) Part C: The motivation...Kevin McGrew
The document provides an overview of the Model of Achievement Competence Motivation (MACM) which defines key motivation domains. The MACM model integrates affective and conative construct domains and defines three major motivation domains: achievement orientations, interests and task values, and self-beliefs. Each domain is further defined by constructs such as intrinsic motivation, academic goal orientation, need for cognition, academic self-efficacy, and locus of control. The model represents motivational processes as occurring in preparatory, deliberation, and action commitment stages focused on goals, reasons for goals, and expectancies of competence.
The Model of Achievement Competence Motivation (MACM): Part B - An overview ...Kevin McGrew
The Model of Achievement Competence Motivation (MACM) is a series of slide modules. This is the second (Part B) in the series. The modules will serve as supplemental materials to "The Model of Achievement Competence Motivation (MACM)--Standing on the shoulders of giants" (McGrew, in press, 2021 - in a forthcoming special issue on motivation in the Canadian Journal of School Psychology)
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1. The document discusses recent research on how executive functions fit within the Cattell-Horn-Carroll (CHC) theory of cognitive abilities and their relationship to general intelligence (g).
2. Studies have found that white matter integrity, processing speed (Gt), and executive functions are strongly related to general fluid intelligence (Gf) and that processing speed may mediate the relationship between white matter integrity and intelligence.
3. The Parietal-Frontal Integration Theory (P-FIT) model proposes that the frontal and parietal lobes are important for constructs like working memory, attention, and executive functions that relate to general intelligence.
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1. National Association of School Psychology (NASP) Conference Feb 27, 2009 Boston, MA Kevin S. McGrew, PhD Woodcock-Munoz Foundation (WMF) University of Minnesota (Ed. Psych.) CHC cognitive and achievement relations research synthesis: What we’ve learned from 20 years of research (Formative materials…..this presentation is not yet completed)
2. Conflict of interest disclosure for Kevin McGrew A large portion of the research summarized in the project summarized in this presentation has been based on the WJ-R and WJ III. Dr. Kevin McGrew has a financial interest in the WJ III as a co-author of the WJ III Battery
3. CHC Cognitive-Achievement Relations: What We Have Learned From the Past 20 Years of Research Kevin S. McGrew Woodcock-Muñoz Foundation University of Minnesota Barbara J. Wendling Woodcock-Muñoz Foundation (manuscript in preparation, 2009) Click the above image to visit the actual internet-based EWOK (evolving web of knowledge) in order to learn more about the research project that is the basis of the current set of slides If that does not work go to IQ’s Corner Blog ( www.intelligencetesting.blogspot.com ) and click on CHC COG-ACH res. synthesis link under IQ’s Corner Information section It is strongly recommended that the viewer first review this background information prior to attempting to review and understand the results in this current set of slides
4. Prior CHC Cog-Ach Relations Research Syntheses Included in Cross-Battery Texts
9. Benson, N. (2009). Integrating psychometric and information processing perspectives to clarify the process of mathematical reasoning . Manuscript in preparation. Benson, N. (2008) . Cattell-Horn-Carroll cognitive abilities and reading achievement. Journal of Psychoeducational Assessment, 26 (1), 27-41. Evans, J. J., Floyd, R. G., McGrew, K. S., & Leforgee, M. H. (2002) . The relations between measures of Cattell-Horn-Carroll (CHC) cognitive abilities and reading achievement during childhood and adolescence. School Psychology Review, 31 (2), 246-262. Flanagan, D. P. (2000) . Wechsler-based CHC cross-battery assessment and reading achievement: Strengthening the validity of interpretations drawn from Wechsler test scores. School Psychology Quarterly, 15 (3), 295-329. Floyd, R. G., Bergeron, R., & Alfonso, V. C. (2006) . Cattell-Horn-Carroll cognitive ability profiles of poor comprehenders. Reading and Writing, 19 (5), 427-456. Floyd, R. G., Evans, J. J., & McGrew, K. S. (2003) . Relations between measures of Cattell- Horn- Carroll (CHC) cognitive abilities and mathematics achievement across the school- age years. Psychology in the Schools, 40 (2), 155-171. Floyd, R. G., Keith, T. Z., Taub, G. E., & McGrew, K. S. (2007) . Cattell-Horn-Carroll cognitive abilities and their effects on reading decoding skills: g has indirect effects, more specific abilities have direct effects. School Psychology Quarterly, 22 (2), 200-233. Ganci, M. (2004 ). The diagnostic validity of a developmental neuropsychological assessment (NEPSY) - Wechsler Intelligence Scale for Children-third edition (WISC-III) based cross battery assessment . Retrieved from ProQuest UMI Dissertation Publishing (UMI Microform 3150999) . Hale, J. B., Fiorello, C. A., Dumont, R., Willis, J. O., Rackley, C., & Elliott, C. (2008 ). Differential Ability Scales-Second Edition (Neuro) psychological predictor of math performance for typical children and children with math disabilities. Psychology in the Schools, 45 (9), 838- 858. Keith, T. Z. (1999) . Effects of general and specific abilities on student achievement: Similarities and differences across ethnic groups. School Psychology Quarterly, 14 (3), 239-262. McGrew, K. (2007) . Prediction of WJ III reading and math achievement by WJ III cognitive and language tests . Unpublished data analysis available at IQs Corner Blog. Studies included in research synthesis (continued on next page)
10. McGrew, K. (2008) . Cattell-Horn-Carroll g and specific ability effects on reading and math: Reanalysis of Phelps et al. (2007). Unpublished data analysis available at IQs Corner Blog. McGrew, K. S. (1993 ). The relationship between the WJ-R Gf-Gc cognitive clusters and reading achievement across the lifespan. Journal of Psychoeducational Assessment, Monograph Series: WJ R Monograph, 39-53. McGrew, K. S., Flanagan, D. P., Keith, T. Z., & Vanderwood, M. (1997). Beyond g: The impact of Gf-Gc specific cognitive abilities research on the future use and interpretation of intelligence tests in the schools . School Psychology Review, 26 (2), 189-210. McGrew, K. S., & Hessler, G. L. (1995) . The relationship between the WJ-R Gf-Gc cognitive clusters and mathematics achievement across the life-span. Journal of Psychoeducational Assessment, 13 , 21-38. Miller, B. D. (2001) . Using Cattell-Horn-Carroll cross-battery assessment to predict reading achievement in learning disabled middle school students . Retrieved from ProQuest UMI Dissertation Publishing (UMI Microform 9997281). Proctor, B. E., Floyd, R. G., & Shaver, R. B. (2005) . Cattell-Horn-Carroll broad cognitive ability profiles of low math achievers. Psychology in the Schools, 42 (1), 1-12. Taub, G. E., Floyd, R. G., Keith, T. Z., & McGrew, K. S. (2008) . Effects of general and broad cognitive abilities on mathematics achievement. School Psychology Quarterly, 23 (2), 187- 198. Vanderwood, M. L., McGrew, K. S., Flanagan, D. P., & Keith, T. Z. (2002) . The contribution of general and specific cognitive abilities to reading achievement. Learning and Individual Differences, 13 , 159-188. Studies included in research synthesis (cont)
16. Visual-graphic explanations of the primary research designs that have been used in the contemporary CHC Cog-Ach relations research studies are presented next (and are the basis of “analysis method” in Table 1--prior slide)
17. Figure 1(a): Simultaneous or Standard Multiple Regression ( MR ) -Manifest/Measured Variables ( MV) - rectangles -no g (full scale IQ) included -Cog. IVs either at broad and/or narrow stratum levels -Ach DVs either at broad and/or narrow stratum levels -b1..bn th represent regression weights Te Cog. Test or Cluster 1 Te Cog. Test or Cluster 2 Te Cog. Test or Cluster 4 Te Cog. Test or Cluster 3 Te Cog. Test or Cluster 5 Te Cog. Test or Cluster 6 Te Cog. Test or Cluster 7 Te Cog. Test or Cluster 8 Te Cog. Test or Cluster n th Ach. Test or Cluster (Note : Double headed arrows representing correlations between all pairs of cog. test or cluster variables omitted for readability purposes) Independent Variables (IV) – Cog. Dependent Variable (DV) – Ach. b1 b2 b4 b3 b6 b5 b7 b8 bn th
18. Figure 1(a): Simultaneous or Standard Multiple Regression ( MR ) : Example
19. Figure 1(a): Simultaneous or Standard Multiple Regression ( MR ) : Example
20. Figure 1(b): Structural Equation Modeling ( SEM ) – Type 1 -Manifest/Measured Variables ( MV) – rectangles -Latent Variables (LV) - ovals - g (conceptually similar to full scale IQ) included -LVs at general (thick linked oval), broad (regular thickness oval) , and/or narrow (dashed oval) strata levels for Cog. IVs and broad and narrow strata for Ach DVs -dashed arrows represent factor loading paths (the measurement model) -solid arrows represent causal effect paths (the structural model) -g has d irect effect on Brd Ach and indirect effects on Ach LV1, LV2, LV3, and LV4 (mediated through) g Brd Ach. - g has indirect effects on Ach LV4 (mediated through g Cog LV2) and Ach LV1 (mediated through g Cog LV1) -Cog LV1 Ach LV1 and Cog LV2 Ach LV4 are examples of specific ability direct effects (Note : Residuals and significant correlations between residuals are omitted from the diagram for readability purposes Te Cog. Test 1 Te Cog. Test 2 Te Cog. Test 4 Te Cog. Test 3 Te Cog. Test 5 Te Cog. Test 6 Te Cog. Test 7 Te Cog. Test 8 Te Cog. Test n th Independent Variables (IV) – Cog. Dependent Variables (DV) -- Ach Cog LV2 Cog LV n th g Cog LV1 Ach LV1 Te Ach. Test 1 Te Ach. Test 2 Te Ach. Test 4 Te Ach. Test 3 Brd Ach Ach LV3 Ach LV4 Ach LV2 Cog LV3
21.
22. Figure 1(c): Structural Equation Modeling ( SEM ) – Type 2 -Manifest/Measured Variables ( MV) – rectangles -Latent Variables (LV) - ovals - g (conceptually similar to full scale IQ) included -LVs at general (thick linked oval), broad (regular thickness oval) , and / or narrow (dashed oval) stratum levels for Cog. IVs but only at narrow stratum for Ach. DVs -dashed arrows represent factor loading paths (the measurement model) -solid arrows represent causal effect paths (the structural model) - g has no direct effect on Ach LV1 but has indirect effect on Ach LV1 (mediated through) g Cog LV1 -Cog LV1 Ach LV1 is an example of a specific ability direct effect (Note : Residuals and significant correlations between residuals are omitted from the diagram for readability purposes Te Cog. Test 1 Te Cog. Test 2 Te Cog. Test 4 Te Cog. Test 3 Te Cog. Test 5 Te Cog. Test 6 Te Cog. Test 7 Te Cog. Test 8 Te Cog. Test n th Independent Variables (IV)) – Cog. Dependent Variable (DV) – Ach. Cog LV2 Cog LV n th g Cog LV1 Te Ach. Test 2 Te Ach. Test 1 Ach LV1 Cog LV3
23. Figure 1(c): Structural Equation Modeling ( SEM ) – Type 2 Example
24. Figure 1(d): Structural Equation Modeling ( SEM ) – Type 3 -Manifest/Measured Variables ( MV) – rectangles -Latent Variables (LV) - ovals - g (conceptually similar to full scale IQ) included -LVs at general (thick linked oval), broad (regular thickness oval) , and / or narrow (dashed oval) stratum levels for Cog. IVs but only at narrow stratum for Ach. DVs -dashed arrows represent factor loading paths (the measurement model) -solid arrows represent causal effect paths (the structural model) - g has direct effect on all Ach LVs - g has indirect effects on Ach LV1; mediated through; g Cog LV n th Cog LV3; g Cog LV2 - g has indirect effects on Ach LV2; mediated through; g Cog LV n th Cog LV3 Ach LV1; g Cog LV2 Ach LV1; g Ach LV1 - g has indirect effects on Ach LV3; mediated through; g Cog LV n th Cog LV3 Ach LV1 Ach LV2; g Cog LV2 Ach LV1 Ach LV2; g Ach LV1 Ach LV2; g Ach LV2 -Cog LV2 Ach LV1 and Cog LV3 Ach LV1 are examples of a specific ability direct effects (Note : Residuals and significant correlations between residuals are omitted from the diagram for readability purposes Te Cog. Test 1 Te Cog. Test 2 Te Cog. Test 4 Te Cog. Test 3 Te Cog. Test 5 Te Cog. Test 6 Te Cog. Test 7 Te Cog. Test 8 Te Cog. Test n th Independent Variables (IV)) – Cog. Dependent Variable (DV) – Ach. Cog LV2 Cog LV n th g Cog LV1 Cog LV3 Te Ach. Test 2 Te Ach. Test 1 Ach LV1 Te Ach. Test 4 Te Ach. Test 3 Ach LV2 Te Ach. Test 6 Te Ach. Test 5 Ach LV3
25. Figure 1(d): Structural Equation Modeling ( SEM ) – Type 3 Example
26. BR Ga Ga: PC Ga: US/UR Gc: K0 Gc: LD/VL Gc: LS Gf: RQ Glr Glr: MA Gs Gs: P Gsm Gsm: MS Gsm: MW g CHC Ability 0 20 40 60 80 100 % sign. samples 14-19 yrs (n =9 ) 9-13 yrs (n =14) 6 to 8 yrs (n =14) % significant samples in prediction of Basic Reading Skills by CHC abilities by three age groups Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure 4-3-2 7 9 8 3-3 14 14 9 11 3 4 14 4 2 2 7 8 7 8 7 6 5 6 6 4 10 9 6 5 4-4 9-8-5
27. BR Ga Ga: PC Gc: K0 Gc: LD/VL Gc: LS Gf Gf: RQ Glr Glr: MM Glr: NA Gs Gs: P Gsm Gsm: MS Gsm: MW Gv: MV g 0 20 40 60 80 100 % sign. samples CHC Ability % significant samples in prediction of Reading Comprehension by CHC abilities by three age groups 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure 5-4-3 4 7 9 8 7 11-13-8 3-3-3 3-3-3 4 2 2 4 6 2-2 3 3 4 7 7 7 5 3 3 8-8 5 3-3-3 5-6-3 2
28. Ga: PC Gc: LD/VL Gc: LS Gc: VL Gf Gf: RQ Gf: RG Glr: MA Glr: MM Glr: NA Gs Gs: AC/EF Gs: P Gsm Gsm: MW Gv: SS g 0 20 40 60 80 100 % sign. samples CHC Ability % significant samples in prediction of Basic Math Skills by CHC abilities by three age groups 14-19 yrs (n=10) 9-13 yrs (n=12) 6 to 8 yrs (n = 12) Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure 4-4 4 12 12 10 2-2-2 2 10 9 8 2-2-2 2-2-2 3 2 2-2-2 8 7 6 2 2 2 4-4-4 4 5-5-5 5-5-3 2
29. BM Ga: PC Ga: US/UR Gc: K0 Gc: LD/VL Gc: LS Gf Gf: RQ Gf: RG Glr: MA Glr: MM Gs Gs: AC/EF Gs: P Gsm Gsm: MS Gsm: MW g 0 20 40 60 80 100 % sign. samples CHC Ability % significant samples in prediction of Math Reasoning by CHC abilities by three age groups 14-19 yrs (n=12) 9-13 yrs (n=14) 6 to 8 yrs (n = 13) Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure 2-3-3 6-6 5 2 13 14 10 2-2 2 2 2-2 12 11 8 2-2-2 2 4 2 8 8 2 5 6 5 5-5 3 5 5-5-5 6-7-5
30. Basic Reading Skills: Consistency of findings Lets break it down to better understand the results and conclusions
31. The following BRS study coding tables served as the “raw material” for the subsequent visual-graphic figures used in drawing conclusions. Original (and more readable) copies of these tables are available at the original EWOK site (see first slide)
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37. BR Ga Ga: PC Ga: US/UR Gc: K0 Gc: LD/VL Gc: LS Gf: RQ Glr Glr: MA Gs Gs: P Gsm Gsm: MS Gsm: MW g CHC Ability 0 20 40 60 80 100 % sign. samples 14-19 yrs (n =9 ) 9-13 yrs (n =14) 6 to 8 yrs (n =14) % significant samples in prediction of Basic Reading Skills by CHC abilities by three age groups Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure 4-3-2 7 9 8 3-3 14 14 9 11 3 4 14 4 2 2 7 8 7 8 7 6 5 6 6 4 10 9 6 5 4-4 9-8-5
45. Ga: PC Gc: K0 Gc: LD/VL Gc: LS 0 20 40 60 80 100 % sign. samples 14-19 yrs (n =9 ) 9-13 yrs (n =14) 6 to 8 yrs (n =14) Basic Reading Skills: Consistency of findings summary 9 8 11 14 14 9 3 14 4 4 Gsm 6 6 4 10 9 6 5 4-4 Gsm: MS Gsm: MW Gs Gs: P 7 8 7 6 5 9-8-5 4-3-2 7 Ga 3-3 Ga: US/UR 2 2 Gf: RQ Glr Glr: MA 7 8 BR g Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign Gv Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure
46. Reading Comprehension: Consistency of findings Lets break it down to better understand the results and conclusions
47. The following three RC study coding tables served as the “raw material” for the subsequent visual-graphic figures used in drawing conclusions. Original (and more readable) copies of these tables are available at the original EWOK site (see first slide)
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51. BR Ga Ga: PC Gc: K0 Gc: LD/VL Gc: LS Gf Gf: RQ Glr Glr: MM Glr: NA Gs Gs: P Gsm Gsm: MS Gsm: MW Gv: MV g 0 20 40 60 80 100 % sign. samples CHC Ability % significant samples in prediction of Reading Comprehension by CHC abilities by three age groups 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure 5-4-3 4 7 9 8 7 11-13-8 3-3-3 3-3-3 4 2 2 4 6 2-2 3 3 4 7 7 7 5 3 3 8-8 5 3-3-3 5-6-3 2
58. Gv BR Ga Ga: PC Gc: K0 Gc: LD/VL Gc: LS Gf Gf: RQ Glr Glr: MM Glr: NA Gs Gs: P Gsm Gsm: MS Gsm: MW Gv: MV g 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure 5-4-3 4 7 9 8 7 11-13-8 3-3-3 3-3-3 4 2 2 4 6 2-2 3 3 4 7 7 7 5 3 3 8-8 5 3-3-3 5-6-3 2 Reading Comprehension: Consistency of findings summary Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign
60. Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Gc : BRS and RC comparisons Gc: K0 Gc: LD/VL Gc: LS 3-3-3 3-3-3 11-13-8 Gc: K0 Gc: LD/VL Gc: LS 14 14 9 3 14 4 4 BRS RC
61. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Ga Ga: PC 9 7 8 4 7 Ga: PC 9 8 11 Ga : BRS and RC comparisons BRS RC 3-3 Ga: US/UR Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign 7 Ga
62. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Gs: P 7 7 5 Gs 7 4 Gs Gs: P 7 8 7 6 5 Gs : BRS and RC comparisons BRS RC Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign
63. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Gsm 3 3 Gsm: MW 3-3-3 Gsm: MS 5 8-8 Gsm 6 6 4 10 9 6 5 4-4 Gsm: MS Gsm: MW BRS RC Gsm : BRS and RC comparisons Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign
64. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Glr Glr: MM Glr: NA 2-2 3 6 4 3 Glr Glr: MA 7 8 Glr : BRS and RC comparisons BRS RC Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign
65. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Gf Gf: RQ 2 2 4 2 2 Gf: RQ Gf : BRS and RC comparisons Red = high Blue = medium Green = low Light purple = Not sign. or tentative/speculative BRS RC
66. Gv Gv 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Gv: MV 2 BRS RC Gv : BRS and RC comparisons Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign
67. Basic Math Skills: Consistency of findings Lets break it down to better understand the results and conclusions
68. The following three BMS study coding tables served as the “raw material” for the subsequent visual-graphic figures used in drawing conclusions. Original (and more readable) copies of these tables are available at the original EWOK site (see first slide)
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72. Ga: PC Gc: LD/VL Gc: LS Gc: VL Gf Gf: RQ Gf: RG Glr: MA Glr: MM Glr: NA Gs Gs: AC/EF Gs: P Gsm Gsm: MW Gv: SS g 0 20 40 60 80 100 % sign. samples CHC Ability % significant samples in prediction of Basic Math Skills by CHC abilities by three age groups 14-19 yrs (n=10) 9-13 yrs (n=12) 6 to 8 yrs (n = 12) Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure 4-4 4 12 12 10 2-2-2 2 10 9 8 2-2-2 2-2-2 3 2 2-2-2 8 7 6 2 2 2 4-4-4 4 5-5-5 5-5-3 2
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78. Basic Math Skills: Consistency of findings Lets put it all back together
79. Ga: PC Gc: LD/VL Gc: LS Gc: VL Gf Gf: RQ Gf: RG Glr: MA Glr: MM Glr: NA Gs Gs: AC/EF Gs: P Gsm Gsm: MW Gv: SS g 0 20 40 60 80 100 % sign. samples Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure 4-4 4 12 12 10 2-2-2 2 10 9 8 2-2-2 2-2-2 3 2 2-2-2 8 7 6 2 2 2 4-4-4 4 5-5-5 5-5-3 2 Basic Math Skills: Consistency of findings summary Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign Gv 14-19 yrs (n=10) 9-13 yrs (n=12) 6 to 8 yrs (n = 12)
80. Math Reasoning: Consistency of findings Lets break it down to better understand the results and conclusions
81. The following three MR study coding tables served as the “raw material” for the subsequent visual-graphic figures used in drawing conclusions. Original (and more readable) copies of these tables are available at the original EWOK site (see first slide)
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85. BM Ga: PC Ga: US/UR Gc: K0 Gc: LD/VL Gc: LS Gf Gf: RQ Gf: RG Glr: MA Glr: MM Gs Gs: AC/EF Gs: P Gsm Gsm: MS Gsm: MW g 0 20 40 60 80 100 % sign. samples CHC Ability % significant samples in prediction of Math Reasoning by CHC abilities by three age groups 14-19 yrs (n=12) 9-13 yrs (n=14) 6 to 8 yrs (n = 13) Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure 2-3-3 6-6 5 2 13 14 10 2-2 2 2 2-2 12 11 8 2-2-2 2 4 2 8 8 2 5 6 5 5-5 3 5 5-5-5 6-7-5
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91. Math Reasoning: Consistency of findings Lets put it all back together
92. % sign. samples Note: Included CHC abilities had to have at least 2 sample results reported (number above each bar is the number of samples included).CHC abilities with “% significant samples” less than 20% excluded from figure 2-3-3 6-6 5 2 13 14 10 2-2 2 2 2-2 12 11 8 2-2-2 2 4 2 8 8 2 5 6 5 5-5 3 5 5-5-5 6-7-5 Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign BM Ga: PC Ga: US/UR Gc: K0 Gc: LD/VL Gc: LS Gf Gf: RQ Gf: RG Glr: MA Glr: MM Gs Gs: AC/EF Gs: P Gsm Gsm: MS Gsm: MW g 0 20 40 60 80 100 14-19 yrs (n=12) 9-13 yrs (n=14) 6 to 8 yrs (n = 13)
94. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Gc : BMS and MR comparisons BMS MR Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign Gc: LS Gc: VL 12 10 Gc: LD/VL 2-2-2 2 12 Gc: K0 Gc: LS 14 10 2-2 2 2 2-2 Gc: LD/VL 13
95. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Ga : BMS and MR comparisons BMS MR Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign Ga: PC 4-4 4 Ga: PC Ga: US/UR 5 2 6-6
96. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Gs : BMS and MR comparisons BMS MR Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign Gs Gs: AC/EF Gs: P 8 7 6 2 2 2 4-4-4 Gs Gs: AC/EF Gs: P 8 8 2 5 6 5
97. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) BMS MR Gsm : BMS and MR comparisons Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign Gsm Gsm: MW 5-5-5 4 Gsm Gsm: MS Gsm: MW 5-5 5-5-5 3 5
98. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Glr : BMS and MR comparisons BMS MR Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign Glr: MA Glr: MM Glr: NA 2 2-2-2 3 Glr: MA Glr: MM 4 2
99. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) Gf : BMS and MR comparisons Red = high Blue = medium Green = low Light purple = Not sign. or tentative/speculative BMS MR Gf Gf: RQ Gf: RG 10 9 8 2-2-2 2-2-2 Gf Gf: RQ Gf: RG 12 11 8 2-2-2 2
100. 0 20 40 60 80 100 % sign. samples 14-19 yrs (n=8) 9-13 yrs (n=13) 6 to 8 yrs (n =11) BMS MR Gv : BMS and MR comparisons Red = high Blue = medium Green = low Light purple = tentative/speculative Gray = not cons. sign Gv Gv Gv: SS 2
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102. Broad Domain Markers Basic Reading Skills – ages 6 to 8 Gc Crystallized Intelligence Gsm Short-Term Memory Ga Auditory Processing Gs Processing Speed Glr Long-Term Retrieval Short-term Memory Working Memory Processing Speed Perceptual Speed-DS Comp-Knowledge Listening Comp. Phonemic Awareness Phonemic Awareness 3 Most Relevant WJ III Clusters Long-term Retrieval Associative Memory-DS Cognitive Fluency Work Mem (MW) Lang. Dev. (LD) Listen. Ability (LS) Gen. Info. (K0) Lex. Know. (VL) Phonetic Coding (PC) Perc. Speed (P) Narrow Domain Markers Assoc. Mem. (MA) Naming Fac. (NA) Numbers Reversed (MW) Understanding Dir (MW/LS ) Aud. Working. Mem. (MW) Visual Matching (P) Verbal Comp. (LD/VL) Oral Comp. (LS) General Info (K0) Picture Vocab. (VL) Snd. Aware. (PC/MW) Snd. Blending (PC) Most Relevant WJ III Tests Vis.-Aud.-Lrng. (MA) Rapid. Pic. Nam. (NA) Retrieval Fluency (FI) (NA) Research foundation: From McGrew @ Wendling (2009) CHC COG-ACH relations research synthesis (prior slides) Bridge research – real world practice WJ III clusters/test selected based on McGrew & Wendling (2009) plus expert-knowledge and clinical experience with WJ III battery
103. Work Mem (MW) Memory Span (MS) Lang. Dev. (LD) General Info (K0) Lex. Know. (VL) Phonetic Coding (PC) Spch-Snd Discrim/ Res to ASD (US/UR) Perc. Speed (P) Narrow Domain Markers Short-term Memory Working Memory Memory Span-DS Processing Speed Perceptual Speed-DS Comp-Knowledge Phonemic Awareness Phonemic Awareness 3 Most Relevant WJ III Clusters Gc Crystallized Intelligence Gsm Short-Term Memory Ga Auditory Processing Gs Processing Speed Broad Domain Markers Num Reversed (MW) Mem. for Words (MS) Understanding Dir. (MW/LS) Visual Matching (P) Verbal Comp. (LD/VL) General Info (K0) Picture Vocab. (VL) Snd. Aware. (PC/MW) Snd. Blending (PC) Snd. Pat.-Voice (US/UR)-DS Most Relevant WJ III Tests Basic Reading Skills ages 9 to 13 Research foundation: From McGrew @ Wendling (2009) CHC COG-ACH relations research synthesis (prior slides) Bridge research – real world practice WJ III clusters/test selected based on McGrew & Wendling (2009) plus expert-knowledge and clinical experience with WJ III battery
104. Work Mem (MW) Memory Span (MS) Lang. Dev. (LD) General Info (K0) Lex. Know. (VL) Perc. Speed (P) Narrow Domain Markers Short-term Memory Working Memory Memory Span-DS Perceptual Speed-DS Comp-Knowledge Phonemic Awareness Phonemic Awareness 3 Most Relevant WJ III Clusters Gc Crystallized Intelligence Gsm Short-Term Memory Ga Auditory Processing Gs Processing Speed Broad Domain Markers Num. Reversed (MW) Mem. for Words (MS) Mem. for Sent. (MS)-DS Aud. Work. Mem. (MW) Visual Matching (P) Verbal Comp. (LD/VL ) General Info (K0) Picture Vocab. (VL) Snd. Aware. (PC/MW) Snd. Blending (PC) Snd. Pat-Voice (US/UR)-DS Most Relevant WJ III Tests Basic Reading Skills ages 14 to 19 Phonetic Coding (PC) Spch-Snd Discrim/ Res to ASD (US/UR) Research foundation: From McGrew @ Wendling (2009) CHC COG-ACH relations research synthesis (prior slides) Bridge research – real world practice WJ III clusters/test selected based on McGrew & Wendling (2009) plus expert-knowledge and clinical experience with WJ III battery
105. Work Mem (MW) Narrow Domain Markers Working Memory Most Relevant WJ III Clusters Broad Domain Markers Numbers Rev. (MW) Under. Dir. (MW/LS) Mem. for Sent (MS/LS)-DS Aud. Work. Memory (MW) Most Relevant WJ III Tests Reading Comp. ages 6 to 8 Gc Crystallized Intelligence Gsm Short-Term Memory Ga Auditory Processing Gs Processing Speed Perc. Speed (P) Perceptual Speed-DS Visual Matching (P) Lang. Dev. (LD) Listen. Ability (LS) General Info (K0) Lex. Know. (VL) Comp-Knowledge Listening Comp. Verbal Comp. (LD/VL) General Info (K0) Oral Comprehension (LS) Picture Vocab. (VL) Phonetic Coding (PC) Auditory Processing Phonemic Awareness Phonemic Awareness 3 Snd. Aware. (PC/MW) Snd. Blending (PC) Incomplete Words (PC) Glr Long-Term Retrieval Assoc. Memory (MA ) Naming Facility (NA) Associative Memory-DS Cognitive Fluency Vis.-Aud.-Lrng (MA) Rapid. Pic. Nam. (NA ) Retrieval Fluency (FI) (NA) Research foundation: From McGrew @ Wendling (2009) CHC COG-ACH relations research synthesis (prior slides) Bridge research – real world practice WJ III clusters/test selected based on McGrew & Wendling (2009) plus expert-knowledge and clinical experience with WJ III battery
106. Narrow Domain Markers Most Relevant WJ III Clusters Broad Domain Markers Most Relevant WJ III Tests Reading Comp. ages 9 to 13 Gc Crystallized Intelligence Gsm Short-Term Memory Ga Auditory Processing Glr Long-Term Retrieval Gs Processing Speed Perc. Speed (P) Perceptual Speed-DS Visual Matching (P) Lang. Dev. (LD) General Info (K0) Listen. Ability (LS) Lex. Know. (VL) Comp-Knowledge Listening Comp. Verbal Comp. (LD/VL) General Info (K0) Picture Vocab. (VL) Oral Comp. (LS) Phonetic Coding (PC) Phonemic Awareness Phonemic Awareness 3 Snd. Aware. (PC/MW) Sound Blending (PC) Naming Fac. (NA) Meaningful Mem. (MM) Long-term Retrieval Cognitive Fluency Story Recall (MM/LS) Vis.-Aud.-Lrng (MA) Rapid. Pic. Nam. (NA) Retrieval Fluency (FI) (NA) Work. Mem. (MW) Working Memory Under. Dir (MW/LS) Mem. for Sent. (MS/LS) Aud. Wrk. Memory (MW) Numbers Reversed (MW) Research foundation: From McGrew @ Wendling (2009) CHC COG-ACH relations research synthesis (prior slides) Bridge research – real world practice WJ III clusters/test selected based on McGrew & Wendling (2009) plus expert-knowledge and clinical experience with WJ III battery
107. Work Mem (MW) Memory Span (MS) Lang. Dev. (LD) General Info (K0) Lex. Know. (VL) Listen. Ability (LS) Phonetic Coding (PC) Narrow Domain Markers Working Memory Memory Span Comp-Knowledge Listening Comp. Phonemic Awareness Phonemic Awareness 3 Most Relevant WJ III Clusters Broad Domain Markers Verbal Comp. (LD/VL) General Info (K0) Oral Comp. (LS) Picture Vocab. (VL) Snd. Aware. (PC/MW) Sound Blending (PC) Most Relevant WJ III Tests Understanding Dir (MW/LS) Mem. for Sent. (MS/LS) Numbers Reversed (MW) Aud. Work. Mem. (MW ) Reading Comp. ages 14 to 19 Gc Crystallized Intelligence Gsm Short-Term Memory Ga Auditory Processing Glr Long-Term Retrieval Gs Processing Speed Perc. Speed (P) Perceptual Speed-DS Visual Matching (P) Meaningful Mem. (MM) Naming Facility (NA) Story Recall (MM/LS) Rapid. Pic. Nam. (NA) Retrieval Fluency (FI) (NA) Cognitive Fluency Research foundation: From McGrew @ Wendling (2009) CHC COG-ACH relations research synthesis (prior slides) Bridge research – real world practice WJ III clusters/test selected based on McGrew & Wendling (2009) plus expert-knowledge and clinical experience with WJ III battery
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117. Most of the “action” appears to be at the narrow (vs broad) CHC ability level Important conclusion
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119. Some CHC abilities appear to be important across reading AND math – domain general Important conclusion
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121. Some CHC abilities appear to be differentially important for reading vs math– domain specific Important conclusion
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125. Are some CHC abilities more important at certain ages – developmentally specific ? Important conclusion
126. WJ-R/III has allowed investigation of “developmental” aspects of CHC COG-ACH relations
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128. CHC Selective Referral-Focused Assessment Worksheet (McGrew, 2009) Age/grade: _____ Academic referral concern ___________________________________________ CHC res. syn. based Non-CHC res. based Broad/Narrow CHC Abilities Referral Relevant domain? Selective/focused set of starting tests Selective/focused possible additional tests Gsm Memory Span (MS) Y N Working Memory (MW) Y N Gs Perceptual Speed (P) Y N Number Facility (N) Y N Glr Associative Memory (MA) Y N Naming Facility (NA) Y N Meaningful Memory (MM) Y N Gc Language Development (LD) Y N General Information (K0) Y N Listening Ability (LS) Y N Lexical Knowledge (VL) Y N Ga Phonetic Coding (PC) Y N Spch-Snd Disc/Res to ASD (US/UR) Gf Gen. Seq. Reasoning (RG) Y N Quantitative Reasoning (RQ) Y N EF Vigilance/inhibition/planning/ concentration, self-regulation, etc Y N Gkn Domain-specific knowledge (__) Y N Gv Visualization (Vz)/Spat Rel (SR)/ Visual Memory (MV)/Imagery (IM) Y N ??? Orthographic processing (???) Y N
129. “ Tests do not think for themselves, nor do they directly communicate with patients. Like a stethoscope, a blood pressure gauge, or an MRI scan, a psychological test is a dumb tool , and the worth of the tool cannot be separated from the sophistication of the clinician who draws inferences from it and then communicates with patients and professionals” Meyer et al. (2001). Psychological testing and psychological assessment. American Psychologist,
130. WJ III branching test scenarios PSYCHOLOGIST GENERAL’S WARNING: These are NOT to be used in a cookbook manner. The examples are intended to demonstrate modeled logical and decision-making. All cases are unique. Referral-based focused testing is a non-linear iterative cognitive testing hypothesis method based on the skills and expertise of the clinician. (“We are the instrument”; K. McGrew; date unknown) Stay tuned……..research-based selective testing tree examples are under development and will be shared at NASP 2009 (Feb 23-March 1; Boston) and posted for viewing after the conference