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Equivalence and Equa-on Solving with Mul-ple Tools: Toward a Local Instruc-onal Theory Nicole L. Fonger, Ph.D. Research Associate, North Carolina State University Friday Ins-tute for Educa-onal Innova-on PMENA 2013 Chicago, IL November 1417

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Page 1: Equivalence+and+Equaon+Solving+ with+Mul …...Equivalence+and+Equaon+Solving+ with+Mul-ple+Tools:+Toward+a+ Local+Instruc-onal+Theory+ NicoleL.+Fonger,Ph.D.+ Research+Associate,+North+CarolinaState+University+

Equivalence  and  Equa-on  Solving  with  Mul-ple  Tools:  Toward  a    Local  Instruc-onal  Theory  

Nicole  L.  Fonger,  Ph.D.  Research  Associate,  North  Carolina  State  University  

Friday  Ins-tute  for  Educa-onal  Innova-on    

PME-­‐NA  2013  Chicago,  IL  

November  14-­‐17  

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Focus  

•  Purpose:  Conjecture,  test,  and  revise  an  instruc-onal  theory  for  students’  representa-onal  fluency  in  solving  equa-ons  with  CAS  and  paper-­‐and-­‐pencil.    

•  Research  Ques-on:  What  processes  of  learning  and  means  of  support  seem  to  facilitate  students’  change  in  RF  in  a  combined  CAS  and  paper  and  pencil  environment?    

•  Themes:  Representa=onal  fluency,  equivalence  and  equa=on  solving,  mul=ple  tools  

 

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BACKGROUND  &  MOTIVATION  

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Star-ng  Points  

•  Representa-ons—fluency  is  an  indicator  of  conceptual  understanding  (Na-onal  Research  Council,  2001;  Kieran,  2007)  

•  Tools—the  coordinated  use  of  tools  can  support  co-­‐development  of  technique  and  theory  (Kieran  &  Drijvers,  2006)  

•  Mathema-cs—Understanding  “=”  ma_ers  for  students’  abili-es  to  solve  equa-ons  (Knuth,  Stephens,  McNeil,  Alibali,  2006)  

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Fluency  

•  Students  demonstrate  shortcomings  in  all  or  some  aspects  of  representa-onal  fluency  in  solving  problems  involving  linear  equa-ons  (e.g.  ,Bieda  &  Nathan,  2009;  Huntley,  Marcus,  Kahan,  &  Miller,  2007;  Moschkovich,  Schoenfeld,  Arcavi,  1993).  

•  For  non-­‐rou-ne  tasks,  students  demonstrate  difficul-es  in  coordina-ng  informa-on  across  graphs  and  symbols,  hence  have  superficial  connec-ons  (Huntley  et  al.,  2007;  Knuth,  2000).  

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Mul-ple  Tools  

•  Cul-va-ng  opportuni-es  to  learn  with  mul-ple  tools  and  methods  is  valued  (NCTM,  2000;  

CCSSI,  2010),  and  needed  for  beginning  algebra  (e.g.,  Heid  &  Blume,  2008;  Kieran  &  Yerushalmy,  2004).    

•  More  research  is  needed  to  specify  the  roles  of  representa-ons  and  a  balance  between  CAS  and  paper-­‐and-­‐pencil  tools  (e.g.,  Kieran  &  Saldnaha,  2008).    

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Mathema-cs  

•  We  expect  students  to  jus-fy  solving  linear  equa-ons  by  reasoning  about  equivalence  of  equa-ons  and  to  use  appropriate  tools  (CCSSI,  2010).    

•  Students  need  to  understand  the  equal  sign  as  expressing  a  rela-on  to  be  successful  in  symbolic  manipula-on  of  equa-ons  (Knuth,  Stephens,  McNeil,  &  Alibali,  2006).  

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Mo-va-on  

•  Linking  research  and  prac-ce  is  a  priority  (Arbaugh,    Herbel-­‐Eisenmann,  Ramirez,  Knuth,  Kranendonk,  Quander,  2010)  

•  Cul-va-ng  opportuni-es  to  learn  with  mul-ple  tools  and  methods  is  valued  (NCTM,  2000;  CCSSI,  2010),  

and  needed  for  beginning  algebra  (Heid  &  Blume,  2008;  Kieran  &  Yerushalmy,  2004).  

•  More  research  is  needed  to  specify  the  roles  of  representa-ons  and  a  balance  between  CAS  and  paper-­‐and-­‐pencil  tools  (Kieran  &  Saldnaha,  2008).    

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CONCEPTUAL  &  THEORETICAL  FRAMEWORKS  

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Conceptual  Frame  on  Learning  

•  Interpre-ve  lens  on  classroom  ac-vity  (Cobb  &  Yackel,  1996)  

•  Individual  cogni-on  and  ac-vity  emerge  as  classroom  prac-ces  are  nego-ated  

 

Psychological   Social  Individual  cogni-on  and  ac-vity  

Classroom  mathema-cal  prac-ces  

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Theore-cal  Frame  on  Ac-vity  

•  Coordinated  tool  use  (Ar-gue,  2002;  Kieran  &  Drijvers,  2006)  

•  Organizes  task  design  for  specific  content  by  intent  of  tool  use  and  theore-cal  backing  based  on  learning  

 

Tasks   Technique   Theory  

What—the  mathema-cal  ac-vity  guided  by  learning  goal  

How—accomplish  tasks  with  a  tool,  approach,  representa-on  

Why—disciplinary  discourse  that  underpins  a  sequence  of  goal-­‐directed  tasks  

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A  DESIGN  RESEARCH  STUDY  

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Phases  of  Design  Experiment  

I.  Prepare  for  experiment  (Cobb,  2000;  Simon,  1995);  

design  tasks  based  on  progression  (Kieran  &  Drijvers,  2006;  Kieran  &  Sfard,  1999)  

II.  Conduct  collabora-ve  teaching  experiment  (Cobb,  2000);  engage  in  ongoing  analyses,  thought  experiments  (Gravemeijer  &  Cobb,  2006)  

III.  Retrospec-ve  analyses  of  case  studies,  classroom  condi-ons,  revised  theory  (Cobb  et  al.,  2003;  Gravemeijer  &  Cobb,  2006;  Stake,  1995)  

(Gravemeijer  &  Cobb,  2006)  

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Design  to  Link  Research  and  Prac-ce  

Gravemeijer  and  Cobb  (2006,  p.  28):  Reflexive  rela-on  between  theory  and  prac-ce  

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Mathema-cal  Learning  Goals  

I.  Develop  representa-onal  fluency  with  linear  expressions  and  equa-ons.  

II.  Use  representa-ons  of  linear  expressions  and  equa-ons  to  solve  problems.  

III.  Understand  the  meaning  of  the  equal  sign  as  a  rela-onship  between  expressions.  

IV.  Understand  solving  equa-ons  as  a  process  of  reasoning  and  explain  that  reasoning.  

(CCSSI, 2010; Knuth, Stevens, McNeil, Alibali, 2006)

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Assump-ons  

•  A  more  adaptable  understanding  is  possible  with  the  rule  of  four—create,  interpret,  connect  (e.g.,  Huntley  et  al.,  2007;  Janvier,  1987).  

•  Students  need  to  come  to  understand  the  meaning  of  “=”  as  an  equivalence  rela-on  (Knuth  et  al.,  2006).  

•  Coordina-ng  equivalence  of  expressions  and    solving  equa-ons  is  significant  to  build  meaning  with  mul-ple  tools  (Chazan  &  Yerushalmy,  2003;  Kieran  &  Drivers,  2006).  

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Collabora-ve  Teaching  Experiment  

•  All  ac-vi-es  were  created  in  close  communica-on  and  considera-on  of  district  planning  guides.  

•  The  teacher  taught  all  lessons;  the  researcher  was  a  par-cipant  observer  (field  notes,  technical  assistant).  

•  Daily  debriefing  sessions/thought  experiments  about  alignment  between  enactment  and  goals.  

•  Revision  and  crea-on  of  subsequent  ac-vi-es  were  based  experimenta-on  and  reflec-on.  

 

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Retrospec-ve  Analyses  

•  The  data  analysis  method  resembled  a  constant  compara-ve  method  (Glaser  &  Strauss,  1967).    

•  Conjectures  about  the  instruc-onal  theory  from  ongoing  analysis  were  confirmed/refuted;  then  tested  against  the  next  episode.    

•  Process  of  confirming  and  refu-ng  conjectures  was  repeated  un-l  all  teaching  episodes  were  analyzed  in  chronological  order.    

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RESULTS  

Processes  of  learning  and  means  of  suppor=ng  learning—Gravemeijer  &  Cobb  (2006)  

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Emerging  Local  Instruc-onal  Theory  

•  Task.  An  ac-vity  sequence  was  constructed  from  empirical  studies  on  students’  understanding  of  the  equal  sign  and  representa-onal  fluency.  

•  Technique.  An  ac-vity  structure  guided  the  coordinated  use  of  techniques  with  both  paper-­‐and-­‐pencil  and  computer  algebra  systems.  

•  Theory.  A  learning  progression  was  conjectured  based  on  literature,  revised  for  a  mul--­‐representa-onal  lens,  then  tested  and  revised  throughout  the  teaching  experiment.  

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Chapter Enacted Revised

1 Multiple Representations of Equivalent Expressions

The “Cartesian Connection” in Graphs, Symbols, Tables, and Words

Equivalent Expressions and Non-Equivalent Expressions in Graphs, Tables, Symbols, and Words

2

Equations are Equivalence Relations that are Sometimes, Always, or Never True

Equations are Equivalence Relations that are Sometimes, Always, or Never True

3

Solving Linear Equations with Multiple Representations

Identifying Solution Sets of Linear Equations in Graphs, Tables, Symbols, and Words

Equivalent Equations Have the Same Solution Sets

Revised  Sequence  of  Ac-vi-es  (Tasks)  

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Suppor-ng  Representa-onal  Fluency  

•  Transla-on  involves  crea-on  and  interpreta-on  (Janvier,  1987)  

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An  Ac-vity  Structure  

•  Ac-on-­‐consequence  principle  (Dick  &  Hollebrands,  2011)  •  Ac-on  on  objects  (Moschkovich,  Schoenfeld,  Arcavi,  1993)  

•  Reflec-on,  CAS  Check,  and  reconciling  CAS  and  paper-­‐and-­‐pencil  (Kieran  &  Saldanha,  2008)  

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A  Learning  Progression  

•  Descrip-on  of  students’  progress  from  informal  understandings  to  more  sophis-cated  “big  ideas”  based  on  research  on  student  learning  (Confrey,  Maloney,  Nguyen,  Mojica,  &  Myers,  2009)  

•  Star-ng  points  –  Linking  arithme-c  and  algebra    –  Equivalence  of  expressions  

•  Ending  points  –  Representa-onal  fluency  in  solving  equa-ons  –  Linking  expressions,  equa-ons,  and  func-ons  

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Kieran  &  Drijvers  (2006)  

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Expression1  =  Expression2  

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Interpret  CAS  Inscrip-ons  “No  cause  its  [sic]  a  word”  –  Annie      “[Y]es  because  if  you  put  the  numbers  and  variable  together  it  makes  sense”  –  Bryon      “I  think  it  does  because  the  CAS  says  the  two  equa-ons  are  equil  [sic]”  –  Carlos  

Technique  &  Theory  

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D1*:  Solu=ons  to  equa=ons  can  be  determined  by  equality  of  expressions.    

•  “Students  must  understand  the  equal  sign  as  expressing  a  rela-on  in  order  to  make  sense  of  the  transforma-ons  performed  on  such  an  equa-on”  (Knuth,  et  al.,  2006,  p.  229).    

•  Ms.  L:  “If  two  lines  cross  how  many  solu-ons  do  you  have?  [gestures  an  X  with  hands]”  Student:  “One”  Ms.  L:  “You’re  going  to  have  no  solu-ons  when  they’re  parallel  [gestures  |  |  with  hands  and  points  to  graph  of  5x+7+x=6x]…  what  was  the  third  case?”  Student:  “Always”  Ms.  L:  “Always  …  infinite  solu-ons  [points  to  graph  of  -­‐1(x+4)=-­‐x-­‐4].”  

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“Solve  each  equa-on  for  the  variable.  Show  your  work.  Check  your  solu-on.    

–r  –  5  =  –  (r  +  5).”    Ms.  L:  Looks  like  he  used  the  distribuKve  property.  […]  And  he  ended  up  with  0  =  0.  What  does  zero  equals  zero  mean?  [interrup-on  and  inaudible  student  response]  Ms.  L:  Infinite  solu-ons.  Why?  Why  is  it  infinite  solu-ons?  Cause  it  means  the  same  thing  on  either  side  of  the  equal  sign.  Look  back  at  this  step  right  here.  What  do  you  see  about  the  expression  on  the  lez  and  the  expression  on  the  right?    Ethan:  They're  the  same.  Ms.  L:  They're  exactly  the  same.  So  you  have  equivalent  expressions.  If  your  expressions  on  either  side  of  the  equals  sign  are  exactly  the  same  that  means  you  have  the  same  line,  you  have  exactly  the  same  line,  so  infinite  solu-ons  on  number  2.    

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Kieran  &  Sfard  (1999);    Davis  (2005)  

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f(x)=ax+b  vs.  g(x)=cx+d  to  Solve  

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Iden-fying  Solu-ons:  -­‐7x+13=4x-­‐9  Ms.  L:  What  happened  to  those  lines?    Katrina:  They  overlap.  Ms.  L:  They  cross  don't  they?  They  sure  are  intersec-ng.  […]  And  if  I  look  at  my  graph  can  I  figure  out  what  value  of  x  they're  going  to  cross  at?    Student:  Yeah.    Ms.  L:  About  where?  Abila:  Nega-ve  one  and  two.  Ms.  L:  So  x  =  2,  y  =  –1.  What  if  I  look  at  the  table?  Davon:  You  can  check.  Ms.  L:  That's  the  way  we  do  it,  too.  So  if  I  go  to  my  table,  Control  T,  go  to  your  table,  and  look  where  my  y-­‐values  are  the  same,  OK,  that's  the  only  place  where  the  y-­‐values  are  the  same.  Right  here  [gestures  to  table]  x  =  2,  y  =  –1.  That's  the  only  place.  So  one  solu-on.    

technique  

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CONCLUSIONS  &  DISCUSSION  

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Design  Research:  Method  and  Evalua-on  of  Outcomes  

•  Revision  and  tes-ng  over  -me  in  several  cycles  

•  Design  principles  for  coordina-ng  mul-ple  tools  (predict-­‐act-­‐reflect-­‐reconcile)  more  stable  than  progression  of  learning  (remains  a  conjecture).  

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Improvements  to  Future  Design  

•  Integra-on  of  task-­‐techniques-­‐theory  (e.g.,  see  Stephan  &  Cobb,  2013)  

•  Too  large  of  conceptual  shizs  proposed  without  adequate  -me  to  coordinate  perspec-ves—concep-ons  of  algebra  and  mul-ple  representa-ons.  

•  Shiz  focus  of  thought  experiments  from  reflec-on  on  theory  in  prac-ce  to  imagining  hypothe-cal  learning  trajectories  (Simon,  1995).  

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Expanding  Instruc-onal  Theory  

“An  instruc=onal  theory  is  more  than  learning  goals  and  a  means  of  support  for  students,  it  also  includes  a  means  of  support  for  the  instructor  to  determine  the  alignment/efficacy  of  the  goal  outcomes  and  student  supports  (i.e.,  a  theory  of  evidence  or  assessment).”  (Reviewer  comment)  

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Ongoing  Research  

•  Adaptability  in  mul-ple  contexts  – pre-­‐service  secondary  teachers    – middle  grades  students  

•  Usability  and  Prac-cality  – design  principles  –  learning  theory  –  integra-on  of  task-­‐technique-­‐theory  

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References  Arbaugh,  F.,  Herbel-­‐Eisenmann,  B.,  Ramierez,  N.,  Knuth,  E.,  Kranendonk,  H.,  &  Quander,  J.  R.  (2010).  Linking  research  and  prac=ce:  The  NCTM  research  

agenda  conference  report.  Bieda,  K.  N.,  &  Nathan,  M.  J.  (2009).  Representa-onal  disfluency  in  algebra:  evidence  from  student  gestures  and  speech.  ZDM,  41(5),  637-­‐650.    Chazan,  D.,  &  Yerushalmy,  M.  (2003).  On  apprecia-ng  the  cogni-ve  complexity  of  school  algebra:  Research  on  algebra  learning  and  direc-ons  of  curricular  

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Cobb,  P.  (2000).  Conduc-ng  teaching  experiments  in  collabora-on  with  teachers.  In  A.  E.  Kelly  &  R.  A.  Lesh  (Eds.),  Handbook  of  research  design  in  mathema=cs  and  science  educa=on  (pp.  307-­‐333).  Mahwah,  NJ:  Lawrence  Erlbaum  Associates.  

Cobb,  P.  (2003).  Inves-ga-ng  students'  reasoning  about  linear  measurement  as  a  paradigm  case  of  design  research.  Journal  for  Research  in  Mathema=cs  Educa=on,  Monograph  no.  12,  1-­‐16.    

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References,  cont.  Huntley,  M.  A.,  Marcus,  R.,  Kahan,  J.,  &  Miller,  J.  L.  (2007).  Inves-ga-ng  high-­‐school  students'  reasoning  strategies  when  they  solve  linear  equa-ons.  The  Journal  of  

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(Ed.),  Second  Handbook  of  Research  on  Mathema=cs  Teaching  and  Learning  (Vol.  2,  pp.  707-­‐762).  Charlo_e,  NC:  Informa-on  Age.  Kieran,  C.,  &  Drijvers,  P.  (2006).  The  co-­‐emergence  of  machine  techniques,  paper-­‐and-­‐pencil  techniques,  and  theore-cal  reflec-on:  A  study  of  CAS  use  in  secondary  

school  algebra.  Interna=onal  Journal  of  Computers  for  Mathema=cal  Learning,  11,  205-­‐263.    Kieran,  C.,  &  Saldanha,  L.  (2008).  Designing  tasks  for  the  codevelopment  of  conceptual  and  technical  knowledge  in  CAS  ac-vity.  In  G.  W.  Blume  &  M.  K.  Heid  (Eds.),  

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mathema=cs  (pp.  115-­‐155).  Washington,  D.  C.:  Na-onal  Academy  Press.  Ri_le-­‐Johnson,  B.,  Ma_hews,  P.  G.,  Taylor,  R.  S.,  &  McEldoon,  K.  L.  (2010,  December  20).  Assessing  Knowledge  of  Mathema-cal  Equivalence:  A  Construct-­‐Modeling  

Approach.  Journal  of  Educa=onal  Psychology,  Advance  online  publica=on.  doi:  10.1037/a0021334.    Simon,  M.  A.  (1995).  Reconstruc-ng  mathema-cs  pedagogy  from  a  construc-vist  perspec-ve.  Journal  for  Research  in  Mathema=cs  Educa=on,  26(2),  114-­‐145.    Stake,  R.  E.  (1995).  The  art  of  case  study  research.  Thousand  Oaks,  CA:  Sage.  Sztajn,  P.,  Confrey,  J.,  Wilson,  P.  H.,  &  Edgington,  C.  (2012).  Learning  trajectory  based  instruc-on:  Toward  a  theory  of  instruc-on.  Educa=onal  Researcher,  41(5),  

147-­‐156.    

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Thank  you.      

Nicole  L.  Fonger,  Ph.D.  [email protected]  

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