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Modern Learning Theories and Mathematics Education The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305H050035 to Carnegie Mellon University. The opinions expressed are those of the author and do not represent views of the Institute or the U.S. Department of Education.
Like many developmental psychologists, most of my early research was theoretical (Definition: “Without any likely application”) Over time, my kids (and granting agencies) motivated me to think harder about ways in which the research could be applied to important educational problems without sacrificing rigor One outcome has been my current research applying theories of numerical cognition to improving low-income preschoolers’ mathematical understanding A Little Personal Background
Another outcome has been to increase my interest in broader issues of application, i.e., educational policy issues This growing interest in applications led me to abandon my traditional “just say no” policy regarding commissions and panels and accept appointment to the National Mathematics Advisory Panel (NMAP). Main role was in learning processes group The present talk combines perspectives gained from doing the applied research and from participating in the learning processes group of NMAP
“ 9. Encouraging results have been obtained for a variety of instructional programs developed to improve the mathematical knowledge of preschoolers and kindergartners, especially those from low-income backgrounds. There are effective techniques – derived from scientific research on learning – that could be put to work in the classroom today to improve children’s mathematical knowledge.” “14. Children’s goals and beliefs about learning are related to their mathematics performance. . . When children believe that their efforts to learn make them ‘smarter,’ they show greater persistence in mathematics learning.”  Conclusions of NMAP:
[object Object],[object Object],[object Object],Theoretical Background: The Centrality of Numerical Magnitude Representations
The Number Line Task 0 100 71
Number Presented Number Presented Number Presented Progression from Log to Linear Representation — 0-100 Range (Siegler & Booth, 2004)
Progression from Log to Linear Representation — 0-1,000 Range (Siegler & Opfer, 2003) Sixth Graders Number Presented R 2 lin  = .97 Number Presented Median Estimate Second Graders R 2 log  = .95
[object Object],The Centrality of Numerical Magnitude Representations
Correlations Among Linearity of Magnitude Representations on Three Estimation Tasks   (Booth & Siegler, 2006) ** p  < .01; * p  < .05 Grade Task Measurement Numerosity 2 nd   Number line .65** .55* Measurement .54** 4 th Number line .84** .70** Measurement .60**
Correlations Between Linearity of Estimation and Math Achievement  (Booth & Siegler, 2006) Estimation Task Grade ** p  < .01; * p  < .05 Number Line Measurement Numerosity 2 nd .53** .62** .48** 4 th .47* .54** .35
Causal Evidence: External Magnitude Representations and Arithmetic Learning (Booth & Siegler, 2008)   ** p  < .01 ** % Correct Sums
Issue in Mathematics Education:  Low-Income Children Lag Behind in Mathematical Proficiency Even Before They Enter School ,[object Object],[object Object]
[object Object],[object Object]
Applying Theory to Educational Problem Might inadequate representations of numerical magnitudes underlie low-income children’s poor numerical performance?
[object Object],[object Object],Applied Goal Raised New Theoretical Question: What Leads Anyone to Form Initial Linear Representation?
[object Object],[object Object],[object Object],Playing Board Games
Chutes and Ladders
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Key Properties of Board Games Like Chutes and Ladders
Number Board Game
Color Board Game
[object Object],[object Object],[object Object],Effects of Playing the Number Board Game (Ramani & Siegler, 2008)
[object Object],[object Object],[object Object],[object Object],[object Object],Methods
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Numerical Magnitude Comparison M % Correct * * * p  < .001
Counting M Mean Counts Without Error * * * p  < .001
Number Line Estimation: Linearity of Individual Children’s Estimates M Mean R 2 lin * * * p  < .001
Numeral Identification M * * % Correct * p  < .001
Percent Correct Addition Answers (Siegler & Ramani, 2009) M % Correct * p  < .05 *
Experiment 2:  Game Playing Outside the Lab ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Percent of Children Who Had Played Each Type of Game M * * % of Children * * p  < .01
Correlations Between Game Playing Experience and Numerical Knowledge Among Head Start Children M ** p  < .01; * p  < .05 Number Line Linearity Magnitude Comparison Counting Numeral Identification No. of Board Games .38** .26** .20* .25** No. of Card Games .18 .28** .11 .13 No. of Video Games .21* .02 .00 .07
Correlations Between Playing Chutes and Ladders and Numerical Knowledge M * p  < .05 Number Line Linearity Magnitude Comparison Counting Numeral Identification Played Chutes & Ladders .20* .18 .19* .24*
[object Object],Conclusion 15 from NMAP: “ Teachers and developers of instructional materials sometimes assume that children need to be a certain age to learn certain mathematical ideas. However, a major research finding is that what is developmentally appropriate is largely contingent on prior opportunities to learn. Claims that children of particular ages cannot learn certain content because they are too young have consistently been shown to be wrong.”
[object Object],[object Object],Funding agencies have generously supported research on learning principles and on small scale programs that implement these principles. As always, we need more research, but some of the research is now sufficiently advanced for broad implementation, at least on an experimental basis. The challenge for the field of mathematics education is how to use the programs and principles to improve educational practice.
 

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Modern Learning Theories and Mathematics Education - Robert Siegler

  • 1. Modern Learning Theories and Mathematics Education The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305H050035 to Carnegie Mellon University. The opinions expressed are those of the author and do not represent views of the Institute or the U.S. Department of Education.
  • 2. Like many developmental psychologists, most of my early research was theoretical (Definition: “Without any likely application”) Over time, my kids (and granting agencies) motivated me to think harder about ways in which the research could be applied to important educational problems without sacrificing rigor One outcome has been my current research applying theories of numerical cognition to improving low-income preschoolers’ mathematical understanding A Little Personal Background
  • 3. Another outcome has been to increase my interest in broader issues of application, i.e., educational policy issues This growing interest in applications led me to abandon my traditional “just say no” policy regarding commissions and panels and accept appointment to the National Mathematics Advisory Panel (NMAP). Main role was in learning processes group The present talk combines perspectives gained from doing the applied research and from participating in the learning processes group of NMAP
  • 4. “ 9. Encouraging results have been obtained for a variety of instructional programs developed to improve the mathematical knowledge of preschoolers and kindergartners, especially those from low-income backgrounds. There are effective techniques – derived from scientific research on learning – that could be put to work in the classroom today to improve children’s mathematical knowledge.” “14. Children’s goals and beliefs about learning are related to their mathematics performance. . . When children believe that their efforts to learn make them ‘smarter,’ they show greater persistence in mathematics learning.” Conclusions of NMAP:
  • 5.
  • 6. The Number Line Task 0 100 71
  • 7. Number Presented Number Presented Number Presented Progression from Log to Linear Representation — 0-100 Range (Siegler & Booth, 2004)
  • 8. Progression from Log to Linear Representation — 0-1,000 Range (Siegler & Opfer, 2003) Sixth Graders Number Presented R 2 lin = .97 Number Presented Median Estimate Second Graders R 2 log = .95
  • 9.
  • 10. Correlations Among Linearity of Magnitude Representations on Three Estimation Tasks (Booth & Siegler, 2006) ** p < .01; * p < .05 Grade Task Measurement Numerosity 2 nd Number line .65** .55* Measurement .54** 4 th Number line .84** .70** Measurement .60**
  • 11. Correlations Between Linearity of Estimation and Math Achievement (Booth & Siegler, 2006) Estimation Task Grade ** p < .01; * p < .05 Number Line Measurement Numerosity 2 nd .53** .62** .48** 4 th .47* .54** .35
  • 12. Causal Evidence: External Magnitude Representations and Arithmetic Learning (Booth & Siegler, 2008) ** p < .01 ** % Correct Sums
  • 13.
  • 14.
  • 15. Applying Theory to Educational Problem Might inadequate representations of numerical magnitudes underlie low-income children’s poor numerical performance?
  • 16.
  • 17.
  • 19.
  • 22.
  • 23.
  • 24.
  • 25. Numerical Magnitude Comparison M % Correct * * * p < .001
  • 26. Counting M Mean Counts Without Error * * * p < .001
  • 27. Number Line Estimation: Linearity of Individual Children’s Estimates M Mean R 2 lin * * * p < .001
  • 28. Numeral Identification M * * % Correct * p < .001
  • 29. Percent Correct Addition Answers (Siegler & Ramani, 2009) M % Correct * p < .05 *
  • 30.
  • 31. Percent of Children Who Had Played Each Type of Game M * * % of Children * * p < .01
  • 32. Correlations Between Game Playing Experience and Numerical Knowledge Among Head Start Children M ** p < .01; * p < .05 Number Line Linearity Magnitude Comparison Counting Numeral Identification No. of Board Games .38** .26** .20* .25** No. of Card Games .18 .28** .11 .13 No. of Video Games .21* .02 .00 .07
  • 33. Correlations Between Playing Chutes and Ladders and Numerical Knowledge M * p < .05 Number Line Linearity Magnitude Comparison Counting Numeral Identification Played Chutes & Ladders .20* .18 .19* .24*
  • 34.
  • 35.
  • 36.