Personality Types and Student
Performance in an Introductory Physics Course
Jason J.B. Harlow, David M. Harrison, Andrew Meyertholen
and Brian Wilson - Department of Physics, University of Toronto
and Michael Justason - Faculty of Engineering, McMaster
University
Abstract: We measured the personality type of the students in a large
introductory physics course of mostly life science students using the True Colors
instrument. We found large correlations of personality type with performance on
the Pre-Course Force Concept Inventory (FCI), both term tests, the Post-Course
FCI, and the final examination. We also saw correlations with the normalized
gain on the FCI. The personality profile of the students in this course is very
different from the profile of the physics faculty, and also very different from the
profile of students taking the introductory physics course intended for physics
majors and specialists.
True Colors
https://truecolorsintl.com/
• Each of us has a different and unique personality;
however, there are commonalities that we share.
• True Colors® is an attempt to identify various
personality styles and label them with 4 colours.
• Each colour is associated with certain personality
traits or behaviours.
• Everyone has some degree of each colour, but one
colour is predominant.
Try it Yourself!!
A+H+K+N+S
D+E+L+P+Q
C+F+J+O+R
B+G+I+M+T
Gold
• “Guardian”
• MBTI: Sensing /
Judging
• Love to plan,
detail-oriented,
trustworthy
Blue
• “Idealist”
• MBTI: Intuition /
Feeling
• Mediators,
Optimistic,
Passionate
Green Orange
• “Rational”
• MBTI: Intuition /
Thinking
• Intellectual,
idea-person,
philosophical
• “Artisan”
• MBTI: Sensing /
Perceiving
• Playful, energetic,
risk-taker
Us
Our First-Year Life
Sciences Students
Intro. To Physics Fall 2016
• First half of a two-semester course for non-physics
science students
• Newtonian mechanics up to oscillations and waves
• ~1000 students; 800 in MW 11am lecture in
Convocation Hall, 200 in MW 5pm lecture
• Weekly 2-hour “Labratorials” with PER-based
pedagogy. Students work in teams of 4, share the
mark, 18:1 student:teacher ratio
• We give pre- and post-course FCI: “Force Concept
Inventory”. Typical gains are ~0.4
• Students were not told of their FCI results or True
Color results
Pre-course Force Concept
Inventory (FCI)
All
Blue
Colours
Number with
Colour
Gold
Green Orange
879
162
419
200
98
Mean Pre FCI 50%
41%
49%
57%
50%
Standard error
in mean
0.8%
1.6%
1.1%
1.8%
2.4%
sigma Green
- Blue
+6.6
Final Mark in Course
All
Colors
Blue
Gold
Green
Orange
count
585
91
274
145
75
mean
73
67
74
77
71
Standard
error in mean
0.6
1.6
0.8
1.2
1.5
sig Green Blue
4.8
Dropout Rate of the Course
All
Colors
Original
879
Number
Number who
293
dropped
Percent
33%
dropped
dropped %
2%
error
sigma Green −2.5
Blue
Blue
Gold
Green Orange
162
419
200
98
70
145
55
23
43%
35%
28%
23%
5%
3%
4%
5%
Gain on the FCI over 1 semester
All
Colours
Blue
Gold
Number
Pre&Post
564
90
262
139
73
Mean Gain
+0.33
+0.26
+0.31
+0.42
+0.32
err in gain
0.03
0.06
0.04
0.07
0.09
sigma Green
- Blue
1.701
Green Orange
Conclusions
• Results of a quick self-reflection on “True Colors®”
personality traits correlate highly with learning in
physics
• Green students, on average, significantly outperform
blue students on summative and formative
assessments, with gold and orange in between
• Orange students are the least likely group to drop the
course
• Physics faculty and students are mostly green, while our
non-physics students are more diverse
• Our teaching methods and assessment tools may be
biased to benefit personality types similar to our own
Future Work
• How can we reduce the green-blue performance gap?
• The “Blue” personality type is warm, friendly,
compassionate, people-oriented, empathetic, kind, and
interested in social causes – can we adjust our teaching
style in minor ways to reach out to these students more
effectively?
• This summer we are forming “all-green” and “all-blue”
focus groups. An outside facilitator will investigate how
students interact with each other and the material
from the course
• This fall we are planning to try using personality data to
assign seating in teams of 4. Is it better to randomly
mix personality types, or to group similar personality
types together?