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Presentation Mathmaster 031210

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Christian Bokhove
Christian Bokhove

Presentation for Mathmaster students of Utrecht University, december 3rd 2010, by Christian Bokhove

FormationTechnologie
1  sur  27
Use of ICT for acquiring, practicing and assessing algebraic expertise Christian Bokhove [email_address] www.fi.uu.nl/~christianb
Context ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem statement ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overview In what way can the use of ICT support acquiring, practicing and assessing relevant mathematical skills? Assessment - Formative (for) v Summative (of) - Feedback (Black & Wiliam, 1998) ICT tool use - Instrumentation - Task, technology, theory (Chevallard, 1991) Algebraic expertise - Basic skills - Symbol Sense: gestalt, pattern salience, local salience, circularity (Arcavi, 1994)
Mathematical proficiency ,[object Object]
Sfard ,[object Object],[object Object],[object Object],[object Object],[object Object]
Tall: procept
Algebraic expertise ,[object Object],[object Object],[object Object],[object Object]
Extension of Gestalt ,[object Object],[object Object],Bokhove, C., & Drijvers, P. (2010). Symbol sense behavior in digital activities. In press. For the Learning of Mathematics . Gestalt view: pattern salience, local salience and strategic decision  
Design research (Tessmer)
Prelim: criteria for tools ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Bokhove, C., & Drijvers, P. (2010). Digital tools for algebra education: criteria and evaluation.  International Journal of Computers for Mathematical Learning, 15(1), 45-62.  ( link )
1st cycle: 1-to-1s Qual. analysis (video, camtasia, atlas TI) Symbol Sense Quality of tool Feedback 6 multihour think-aloud 1-to-1 sessions with 17/18 year olds I want to know what’s going on in their minds
2nd cycle ,[object Object],[object Object],[object Object]
Design choices ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Bokhove, C. (2010). Implementing feedback in a digital tool for symbol sense. . International Journal for Technology in Mathematics Education . 17 (3)
Crises ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Digital mathematical environment http://www.fi.uu.nl/dwo/voho , developer: Peter Boon
Authoring tasks CUSTOM CORRECTIVE FEEDBACK DESCRIPTIVE BUG-RELATED HINTS
Example student work
In action
Another student example
3rd cycle ,[object Object],[object Object],[object Object],[object Object]
Data collection & analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Excel
 
Analyzing log data C4.5 Decision trees
Future? ,[object Object]
Discussion ,[object Object],[object Object],[object Object],[object Object],[object Object]
Selected references ,[object Object],[object Object],[object Object],[object Object],[object Object]

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Presentation Mathmaster 031210

  • 1. Use of ICT for acquiring, practicing and assessing algebraic expertise Christian Bokhove [email_address] www.fi.uu.nl/~christianb
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  • 4. Overview In what way can the use of ICT support acquiring, practicing and assessing relevant mathematical skills? Assessment - Formative (for) v Summative (of) - Feedback (Black & Wiliam, 1998) ICT tool use - Instrumentation - Task, technology, theory (Chevallard, 1991) Algebraic expertise - Basic skills - Symbol Sense: gestalt, pattern salience, local salience, circularity (Arcavi, 1994)
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  • 12. 1st cycle: 1-to-1s Qual. analysis (video, camtasia, atlas TI) Symbol Sense Quality of tool Feedback 6 multihour think-aloud 1-to-1 sessions with 17/18 year olds I want to know what’s going on in their minds
  • 13.
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  • 16. Digital mathematical environment http://www.fi.uu.nl/dwo/voho , developer: Peter Boon
  • 17. Authoring tasks CUSTOM CORRECTIVE FEEDBACK DESCRIPTIVE BUG-RELATED HINTS
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  • 22.
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  • 24. Analyzing log data C4.5 Decision trees
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  • 27.