immex: providing insight into problem solving using technology charlie cox preliminary oral defense
TRANSCRIPT
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IMMEX: Providing Insight into Problem
Solving Using Technology
Charlie Cox
Preliminary Oral Defense
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Outline
What is IMMEX? Why use IMMEX? Proposed project
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What is IMMEX?
Originally developed for the UCLA medical school.
Internet – Based Software Novel Assessment Tool
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IMMEX Problems
Case – Based Non-precribed strategies Immediate Assessment
http://ublib.buffalo.edu/libraries/projects/cases/case.htmlWilkerson, L. & Feletti, G. (1989). Problem – Based Learning: One Approach to Increasing Student
Participation, In A.F. Lucas (Ed.), The Department Chairperson’s Role in En hancing College
Teaching, New Directions for Teaching and Learning, 37, 51 – 66.
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Prolog
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Problem Space
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What is unique about IMMEX?
Immediate feedback Tracks HTML movements
Search Path Map
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Search Path Map
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Strategy Interpretations
Most commonly 3 strategy - types can be identified: Limited Efficient Prolific
Artificial Neural Networks
Hidden Markov Models
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Limited Search Path Map
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Additional Example
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Prolific Search Path Map
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Efficient Search Path Map
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Strategy Analysis using ANNs
Pattern Recognition Uses a 6 x 6 grid for pattern recognition
Stevens, R.H., Ikeda, J., Casillas, A., Palacio-Cayetano, J., and Clyman, S., (1999) Artificial Neural
Network-Based Performance Assessments, Computers in Human Behavior, 15: 295 – 314.
Stevens, R. and Palacio-Cayetano, J., (2003) Design and Performance Frameworks for Constructing
Problem-Solving Simulations, Cell Biology Education, 2: 162 – 179.
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A Node From Hazmat
0
0.2
0.4
0.6
0.8
1
1.2
Problem Space Item
P
R
O
B
A
B
I
L
I
T
Y
Background
Flame, Solubility,Conductivity
Litmus
HCl,NaOH
Precipitins
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Hidden Markov Modeling
Used to study student transitions when working problems.
Predictive Modeling
Soller, A., and Lesgold, A. (2003). A Computational Approach to Analyzing Online KnowledgeSharing Interaction. Proceedings of Artificial Intelligence Education, 2003, Australia. 253.
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HMM Probabilities
Initial
Transition
aij = Pr[qt+1=Sj|qt=Si]
Emission or Observational Probabilities
bj(k) = Pr(vk at t|qt = Si]
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Stevens et. al. ITS, 2004, accepted
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Transition Table
To State:
1 2 3 4 5
1 0.99 0.00 0.00 0.00 0.00
2 0.05 0.55 0.02 0.07 0.31
From State: 3 0.06 0.34 0.22 0.33 0.05
4 0.03 0.01 0.01 0.94 0.01
5 0.02 0.03 0.00 0.00 0.95
Stevens et. al., ITS, 2004, accepted
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Transition Summary
Stevens et. al., ITS, 2004, accepted
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Framing, Transitioning, Stabilization
StateStrategy
Sequence Description Trajectory
1 32 33 28 33 33
Limited test selections, few background resources Localized
2 12 18 24 20 1
Many tests becoming fewer with progress. Progressive
3 5 24 6 18 Many test selections. Localized
4 4 22 33 33Resource extensive
going to data. Shifting
5 4 6 14 25 30 19
Shifts between data rich and data lean strategies. Shifting
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Item Response Theory
Examines each item individually as it is added and removed.
1 - parameter logistic Student ability Item Difficulty
€
logP(xj=1P(xj=0 ⎛ ⎝ ⎜ ⎜
⎞ ⎠ ⎟ ⎟=θ−bj
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Research Objectives
Does teaching style influence problem solving?Passive vs. Active Methods
IRT and States will be compared
Cooper, M.M. (1995), Cooperative Learning: An Approach for Large Enrollment Courses Journal of Chemical Education. 162.
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Research Objectives
• Stabilization• Gender Effects• Period of Time for Stabilization
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Research Objectives
The Effect of Formal Reasoning on Problem Solving using the GALT test.
Spatial Ability
Bunce, Diane M.; Hutchinson, Kira D. J. Chem. Educ. 1993, 70, 183.Herron, D. The Chemistry Classrom: Formulas for Successful
Teaching, American Chemical Society, Washington DC, 1995.
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Transfer of Knowledge
Problem Similarities Teaching Style Gender
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IMMEX Problems for the Study
In - Stereo OrganoMech Lewis Structure Finding Carbon’s Neighbors TLC Separation Specta Analysis
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In - Stereo Figures
Line Structure
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Lewis Structure
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Newman Projection
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Sawhorse Projection
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3 - D Ball and Stick Figure
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3 - D Wireframe
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3 - D Stick Model
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Plugin
Rotation in 3 - D space Switch between the possible 3 - D figures
while rotating.
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TLC Problem -- Unknown Analysis
Unknown identification based upon TLC analyses.
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Additional Information
Developing Solvent Stationary Phase Solvent Volume Visualization Method Library
Rf Values Examples of TLC of a Known Compound
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Spectra Analysis
1H, 13C, IR, and MS data is available for students to elucidate an unknown’s structure.
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Spectra Analysis Continued
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Additional Spectra Analysis INFO
1. Example Spectra a. Benzaldehyde b. 2-butanone c. Benzene d. Butylamine e. Propionic Acid2. Correlation Tables3. Library
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Preliminary Results
Lewis Structure (Male and Female Strategies)State
1.00 2.00 3.00 4.00 5.00 Total
Count 66 11 6 38 9 130ExpectedCount
58.2 7.8 20.2 28.3 15.6 130.0
Female
% withinGender
50.8% 8.5% 4.6% 29.2% 6.9% 100.0%
Count 113 13 56 49 39 270ExpectedCount
120.8 16.2 41.9 58.7 32.4 270.0
Gender
Male
% withinGender
41.9% 4.8% 20.7% 18.1% 14.4% 100.0%
Count 179 24 62 87 48 400ExpectedCount
179.0 24.0 62.0 87.0 48.0 400.0
Total
% withinGender
44.8% 6.0% 15.5% 21.8% 12.0% 100.0%
p < 0.000
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Lewis Structure Performance
Correct
.00 1.00 Total
Count 49 81 130ExpectedCount
54.3 75.7 130.0
Female
% withinGender
37.7% 62.3% 100.0%
Count 118 152 270ExpectedCount
112.7 157.3 270.0
Gender
Male
% withinGender
43.7% 56.3% 100.0%
Count 167 233 400ExpectedCount
167.0 233.0 400.0
Total
% withinGender
41.8% 58.3% 100.0%
p = 0.151
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Stabilization
0.00 1.00
Bars show Means
1.00 2.00 3.00 4.00 5.00
State
1.00
2.00
3.00
4.00
Step
1.00 2.00 3.00 4.00 5.00
State
Female Male
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Spectra Analysis (GPA & State)
1 2 3 4 5
State
2.00
2.50
3.00
3.50
4.00
GPA
??
??
? ?
State Description1 Random Type Testing. Around 50% of the students used prolific type strategies
and view excessive information while others viewed little information2 Involved normal testing with extensive use of available hints and library materials.3 Normal testing with the use of available hints and little use of examples and the
library.4 Involved increased use of examples and hints.5 This is described as more random testing with some use of all materials, but not
in a very high frequency (i.e. ~50%)
Sum ofSquares df Mean Square F Sig.
Between Groups 4.950 4 1.237 6.421 .000Within Groups 63.212 328 .193Total 68.162 332
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Interventions -- Collaborative Grouping
Anova: SingleFactor
SUMMARYGroups Count Sum Average Variance
Before 9504.0000 385836.0900 40.5972 93.7453Group 3901.0000 161179.7300 41.3175 127.1533After Group 8727.0000 358399.1600 41.0679 102.2055
ANOVASource ofVariation SS df MS F P-value F crit
Between Groups 1795.0939 2.0000 897.5470 8.7167 0.0002 2.9961Within Groups 2278604.4645 22129.0000 102.9692
Total 2280399.5584 22131.0000
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State Distribution -- Collaborative Grouping
State 1 2 3 4 5 Totals
Observed 390 236 167 41 14Prior to grouping
Expected 37308 282.7 138.6 33.7 19.3848
Observed 287 276 84 20 21Post grouping
Expected 303.2 229.3 112.4 27.3 15.7688
Totals 677 512 251 61 35 1536(Chi Square=38.624, p<0.001)
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In - Stereo