Andrew Mason, Ph.D. Kenneth Heller, Leon Hsu, Anne Loyle-Langholz, Qing Xu University of Minnesota,...
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Transcript of Andrew Mason, Ph.D. Kenneth Heller, Leon Hsu, Anne Loyle-Langholz, Qing Xu University of Minnesota,...
Andrew Mason, Ph.D.Kenneth Heller, Leon Hsu, Anne Loyle-Langholz, Qing Xu University of Minnesota, Twin CitiesMAAPT Spring 2011 MeetingSt. Mary’s University, Winona, MN4/30/2011
Supported by NSF DUE #0230830 and DUE #0715615 and by the University of Minnesota
Background Demonstration Preliminary findings
▪ Initial calibration data▪ Currently: Scoring using Rubric for Problem
Solving (Docktor 2009) Methodology for further study
Use coaches in a calculus-based physics course for scientists and engineers Students are asked to volunteer; volunteers
are paid for their participation
Assign students into 2 matched groups Variables for matching: background information, e.g.
HS physics & math level, FCI/CLASS/math pretests
Study #1 (Fall 2010) ~40 students, 1 lecture session of intro
calculus-based class (~20 for each group)
Subset of coaches available – energy (8), momentum (7) – done over 4 weeks (~4 per week)
Retention rate: high in fall (18 of 21) for 15 coaches 2 others completed 12 of 15 All students found them useful
▪ Does this hold for other sections?▪ What will happen with a larger set of coaches?
Student preference for each type Faculty tend to disagree with students
(found type 1 tedious) 1 student initially preferred type 1 but
switched to type 3 after gaining familiarity with physics
Most useful 2nd most useful
Least useful
Type 1 13 3 1
Type 2 0 9 9
Type 3 4 5 8
• Average time range to complete between coaches: between 20 and 40 minutes
Percentage of correct answers seems to correlate with background info
2 sampled “A” students: 80-90% 2 sampled “C” students: 60-70% Students tend to stay on task
Only 2 of 18 had at least one break of more than 5 minutes for a question
Time between questions(one type 1 coach)
Distribution suggests students are taking the tutors seriously Median time = 4.58
s
0
100
200
300
400
500
0 5 10 15 20 25 30F
requ
ency
Time (seconds)
Rubric developed to evaluate student problem solutions Validity, reliability have been tested 2 raters use, discuss with each other until >90%
agreement Five rubric categories (established by
research on expert and novice problem solvers) Useful Description Physics Approach Specific Application of Physics Mathematical Procedures Logical Progression
Study #2 (Spring 2011) 9 students, 1 lecture session of intro calculus-based
class Coaches available for 4 segments: kinematics (3),
dynamics (4), COE (8), COM (7)
Good time to establish baseline of rubric scores for general class Eventual comparison with computer coach
users 2 expert raters – PER researchers 23 students (3 tiers according to pretests);
eventually will expand to ~40 13 problems (8 from quizzes, 5 from final)
- Standard error bars are on the order of +/- 1 to +/- 2 out of 5
Raters: 2 experts/PER researchers
~90 students, 1 lecture session of intro calculus-based class (~45 for each group) Comparison group given equal face time
with problems used in coaches
All coaches available (kinematics, dynamics, energy, momentum, rotational motion) 8 x 5 = 40 total
Short-term questions: Will students use coaches? How will students use them? (keystroke
function) Do they improve students’ problem solving
skills with respect to baseline scores? (rubric scoring of quizzes)
Longer-term questions: Are they adaptable to be used in teaching
other physics courses? Possible software/AI development – refine
beta versions; make it more able to follow student preferences
Can this software be modified by instructors to fit a different problem solving framework?
Summary Initial results seem to have much to say; need to be
expounded upon Currently examining a baseline of exam
performance to compare to future data from computer coach users
Website: http://groups.physics.umn.edu/physed Can look at research, previous talks and publications Try out the coaches! Give us feedback!http://groups.physics.umn.edu/physed/prototypes.html
Treatment and Comparison groups• Treatment group – computer coaching (on Web,
outside of class), 4 problems per week • Comparison group – normal class setting
Data collection• Diagnostic pretests and posttests• Written solutions on quizzes & final exam
• 2×4+5=13 for each student • Problem-solving interviews with students
Can look at individual time on each question for each student
A few questions take some time regardless of student Entering answer into calculator
Can look for patterns in other questions
Chi, Feltovich and Glaser, “Categorization and representation of physics problems by experts and novices,” 1981.
Collins, Brown and Newman, “Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics,” 1989.
Docktor and K. Heller, “Robust assessment instrument for student problem solving,” 2009; also see J. Docktor, dissertation, 2009.
P. Heller and K. Heller, “The Competent Problem Solver: A Strategy for Solving Problems in Physics,” 1995.
P. Heller and Hollabaugh, “Teaching Problem Solving Through Cooperative Grouping. Part 2: Designing Problems and Structuring Groups,” 1992.
Hsu, Heller, Mason, and Xu, Summer AAPT presentation, Portland, OR, 2010.
Larkin, McDermott, D. Simon and H. Simon, “Expert and Novice Performance in Solving Physics Problems,” 1980.
Newell and Simon, “Human Problem Solving,” 1972.
Palincsar and Brown, “Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities,” 1984.
Polya, “How to Solve It,” 1945; 1957. Polya, “Mathematical Discovery,” 1962. Reif and Scott, “Teaching Scientific Thinking
Skills: Students and Computers Coaching Each Other,” 1999; also see L. Scott dissertation, 2001.