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Edm2015presentation
1. Students changing their answers, based on what
their friends say
Sameer Bhatnagar 1 Michel Desmarais 1
C. Whittaker 2 N. Lasry 3 M. Dugdale 3 K. Lenton 4 E. Charles 2
1Polytechnique Montreal
2Dawson College
3John Abbott College
4Vanier College
June 25, 2015
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 1 / 22
2. What is this all about?
Introducing a new data set
Demonstrating its potential for EDM
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 2 / 22
3. Outline
1 Glossary
2 Data
3 Results
Answer Changes
Gender Differences
Peer Voting
4 Future Work
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 3 / 22
5. Glossary
Peer Instruction
Classroom Activity popularized by Eric Mazur of Harvard
DALITE
Web Based Learning Environment for Peer Instruction at Home
Let’s give the system a try!
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 4 / 22
6. Outline
1 Glossary
2 Data
3 Results
Answer Changes
Gender Differences
Peer Voting
4 Future Work
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 5 / 22
7. The Data Set: from the classroom
118 students from three different colleges
Four different teachers, five different groups
Final Grade for the course (Freshman Year Physics)
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 6 / 22
8. The Data Set: from DALITE
7100 Student-item pairs
Each student item pair includes
First Answer
Rationale
Second Answer
How many votes the rationale received
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 7 / 22
9. Outline
1 Glossary
2 Data
3 Results
Answer Changes
Gender Differences
Peer Voting
4 Future Work
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 8 / 22
10. Strong students as likely to go the wrong way
0.0
0.1
0.2
0.3
0.4
bottom top
Final grade partitioned at median
P(Right−−>Wrong)
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11. Weak students as likely to go the right way
0.0
0.2
0.4
0.6
0.8
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Final grade partitioned at median
P(Wrong−−>Right)
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 10 / 22
12. Don’t Use the Tool Alone!
Which group never talked about DALITE in class?
0.0
0.2
0.4
0.6
cw09 cw10 KJL MD NL
Different Groups
P(SwitchingAnswer)
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 11 / 22
13. Gender Gap in Physics
0.4
0.6
0.8
1.0
f m
Gender
P(RightAnsweronFIRSTAttempt)
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 12 / 22
14. Gender Gap in Physics except if you let them
change their minds
0.4
0.6
0.8
1.0
f m
Gender
P(RightAnsweronSECONDattempt)
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15. No Gender Gap in Votes Earned
0.0
0.2
0.4
0.6
f m
Gender
AverageNumberofVotesEarned
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16. Strong students do earn more votes
0.0
0.1
0.2
0.3
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Final grade partitioned at median
AverageVotesearnedoverthesemester
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17. Strong, even when Wrong
0.0
0.2
0.4
0.6
bottom top
Final grade partitioned at median
AverageVotesearnedoverthesemester
forWRONGANSWER
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18. Outline
1 Glossary
2 Data
3 Results
Answer Changes
Gender Differences
Peer Voting
4 Future Work
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19. Future Work
NLP on Rationales
Automated Essay Scoring
Topic Modeling
Collaborative Filtering with voting data
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20. Welcome to The Big Leagues
License to HarvardX
Next Step: Your edX MOOC!
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 19 / 22
21. What is this all about?
Introducing a new data set
Demonstrating its potential for EDM
S.Bhatnagar, M. Desmarais (Polytechnique Montreal) DALITE 20 / 22