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This presentation is about a spreadsheet tool to help student learning. Five tests were organized in which students expressed their certitude to choose the right answer to MCQs. After the test results were given about number of correct and incorrect answers for each degree of certitude. With these data students analyzed, commented and regulate their performances.
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Excel spreadsheet to suppor t veterinar y student’s
metacognition
Jean-Loup CastaigneIFRES — LabSET
University of Liege, Belgium
EARLI — SIG 16 Metacognition, 2nd Biennial Conference — Friday 21 July 2006
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Liege, the Vet facultyGeographical situation
Who am I?
What teaching am I involved with?
Reproduction of the cow and the mare
5th year out of 6 — students’ mean age of 24
390 students — 75% of girls — 10% of students with interest in cattle and horses.
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Excel spreadsheet to support veterinary
student’s metacognition
3
Outline
• Theoretical background
• Presentation of the tool
• Survey
• Conclusions
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
“Regulative” metacognitionBernadette Noël (1991)
Metacognitive judgment
Metacognitive process: being aware
Metacognitive decision: decide to or not to regulate
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Excel spreadsheet to support veterinary
student’s metacognition Outline
• Theoretical background
• Presentation of the tool
• Survey
• Conclusions
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Teaching
Study
formative test 1
September
Methodology (1 year)
Teaching Studyformative
test 2December
certification test 1 (January)
TeachingStudyformative test 2February
StudyOralexam(June)
Teaching Study certification test 2 (March)
6from Edgar P. Jacobs 1956 - The Yellow "M"
ISBN 2-87097-010-2
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006 7
Weight of each testFormative test #1
Formative test #2
Certification test #1
Formative test #3
Certification test #2
Final oral examination
100%
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Pay-off for MCQ“cognitive” score
Correct Answer (CA): +1Wrong Answer (WA): -0,5No answer (NoA): -0,25
Freedom to use, or not toconfidence marking for MCQself-estimation for open questions
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Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006 9
Hypothesis
If confidence marking does NOT influence the results of the test, students will be more sincere giving the confidence they associate with the chosen answer.
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Are the students sincere?
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Certitude
0-25% 25-50%
50-70%
70-85%
85-95%
95-100%Référen
ce12,5 % 37,5 % 60 % 77,5 % 90 % 98,5 %
cochez 0 1 2 3 4 5RC +13 +16 +17 +18 +19 +20RI +4 +3 +2 +0 -6 -20
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
In certification tests including confidence marking, I choose maximum 60% and less if I doubt because for an incorrect answer with a confidence of 80% ou 100% penalize my score.
Survey (n=369)
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Confidence
0 % 20 % 40 % 60 % 80 % 100 %Thick 0 1 2 3 4 5
CA +13 +16 +17 +18 +19 +20
IA +4 +3 +2 +0 -6 -20
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006 12
5 metacognitive measures
Individual test with comments
Global view of a# 5 testsHelp
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006 13
Computing and drawing
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Spectral distribution
no a
nsw
er
Incorrect answers
Confidence : from -100 to -0
Correct answers
Confidence : from 0 to +100
Num
ber
of a
nsw
ers
Jans (1999)
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Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Three types of knowledge situation
misinformed
uninformed
informed
Spectral distribution
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Hunt (1993)
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Spectral distribution
no a
nsw
er
misinformed
Confidence : from -100 to -0
informed
Confidence : from 0 to +100
Num
ber
of a
nsw
ers
16
uninformed
Hunt (1993) Jans (1999)
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Spectral distribution
no a
nsw
er
misinformed
Confidence : from -100 to -0
informed
Confidence : from 0 to +100
Num
ber
of a
nsw
ers
Hunt (1993)
17
uninformed
Jans (1999)
DangerousKonwledge
UsableKnowledge
unusable knowledge
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006Confidence : from -100 to -0Confidence : from 0 to +100
Num
ber
of a
nsw
ers
Four zonesUnawareness
Usableknowledge
Leclercq 2003
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Mid knowledge
Dangerousknowledge
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Drawing of graphs
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Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
adapted "om Leclercq & Gi$es 1995Chosen confidence
Perc
enta
ge o
f CA
Realism graphRegarding all MCQ for which you choose a confidence of 40%
For those MCQ you’ve reached a score of 63% of correct answers
You’ve succeeded more (63%) than you estimate (40%): thus in the class of 40% you underestimate your level
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Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Objective judgments & analysis
Comments: evaluated fieldsCognitive field
Metacognitive field
Subjective judgments & analysis
Metacognitive regulation
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Excel spreadsheet to support veterinary
student’s metacognition Outline
• Theoretical background
• Presentation of the tool
• Survey
• Conclusions
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
I’ve progressed in metacognition only because I progressed in knowledge in reproduction
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Cognition vs. Metacognition
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Filling the spreadsheet pushed me to study more reproduction
Regulation “product”
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Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Regulation “process”
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Because of what I’ve learned filling the spreadsheet I’ve change my method of working
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Because of what I’ve learned filling the spreadsheet I’ve changed the way I use confidence marking
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Regulation “process”
Excel spreadsheet to support veterinary
student’s metacognition Outline
• Theoretical background
• Presentation of the tool
• Survey
• Conclusions
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Conclusion
29
ProductsProductsProducts ProcessessProcessessProcessesspre per post pre per post
Judgment ✔
Analysis ✔ ✔
Regulation ✔
Noël (1991)
“Regulative” metacognition
✔
✔
✔
✔
✔
✔
✔✔
Jean-Loup Castaigne, EARLI-SIG Metacognition, Cambridge 2006
Conclusion“ I’m not sure that this system helps us to understand better success or failure. It is very complex and it doesn’t matter commenting numbers or graphs, it’s always theoretical…”
“I was really dubious regarding the efficiency of this system but my own progresses proves me that it was hugely profitable.”
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Thank you for your attention.
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