 Noyce Conference, May 2013
Funded by NSF Grant: Robert Noyce Scholarship Program





Only four undergraduate STEM majors were licensed through GMU since 2004
All four pre-service teachers were Earth
Science majors
President Obama has challenged us to educate 100,000 new teachers
In the current financial crisis in the US, we need to help promising new STEM teachers





Twice a year we accept applications from faculty for learning assistants
Advertise student learning assistant positions through posters and College of Science broadcast emails
Spring semester we had more than twice as many requests for LAs as we could fund
Spring semester we had 90 student applications for 28 positions.




New LAs must attend the Teaching and
Learning seminar once a week
All LAs meet weekly with their supervisor
All LAs are provided time for preparation, which may include the following:
 Attend the class for which they are LAs
 Work through homework assignments that students in the course are doing
 Ask mentors questions about any topic that they do not understand

•
•
•
•
•
•
•
•
•
•
•
Participate in weekly LA Seminar in their first semester as an LA
Meet weekly with their course mentor
Facilitate math oral reviews for Business
Calculus, Calculus with Algebra, Calculus I, Calculus II,
Quantitative Reasoning
Facilitate Biology oral reviews for Cell Structure and Function
Conduct on-line peer tutoring
Create on-line modules to assist in student learning
Provide review sessions prior to tests
Provide peer to peer instruction in help sessions
Work with small groups during classes
Assist students in labs
Teach mini-lessons









Sexual Harassment Prevention Training
Learning Styles
Constructivism
Importance of Discourse
Student Centered learning
Wait time
How to write and use rubrics
How to facilitate oral reviews





LAs have genuine teaching experiences
LAs work with faculty who are excited about teaching and learning
Students learn some basic educational principles at the Teaching and Learning
Seminar
LAs become an integral part of their department’s teaching effort




Grant pays for 10 LAs/year
Accelerator funds 12-18 additional
Community college has 11-12 LAs




Year 1:
Began with 1 math Noyce – looking for a job now
Second semester – added Chemistry Noyce
Next fall – 7 confirmed Noyce, possibly 3 more – all former LAs



When asked “what would you tell a student who was thinking about being an LA?” One LA answered,:
“Go for it! If you believe you have any future in education this is a must have collegiate experience!”

Another claimed: “I would tell them to definitely try it. The experience was great and taught me a lot; I really think it educated me an equal amount that I educated others. Being an LA gives you experience, patience, knowledge, and public speaking skills.”

Another explained that as a K-12 student, he wanted to be tennis pro and “anything but math.” He still has visions of being a tennis pro, but now his aspirations include being a mathematician and a teacher.


The result of having Taylor in the lab was an increase in the number of A grades compared to Fall 2012. In Fall 2012 37% of the enrolled students earned A’s. In Spring 2013, when
Taylor was assisting, the percentage of students earning A’s increased to 42%. Similarly, in Fall 2012, 27% of enrolled students failed to pass the course. In Spring 2013, this number dropped significantly to 15%.”

“Stephen was phenomenal. He arranged weekly oral reviews to help the Business Calculus students, and his attendance was amazing. Students really appreciated the help in understanding the material.”





Ungraded, voluntary
Often cited by students as most important aid to learning
Small groups of 3-5 students for an hour
Emphasis on conceptual questions
◦ Why would you use linearization?
◦ What does it look like on a graph?
◦ From the graph, what kind of functions will give the best results?
◦ Does it matter where you center the linearization?

1.
Vehicle for getting students to discuss mathematics and other sciences
typically pattern match without understanding
-need to put understanding in their own words
-need others to correct and clarify misconceptions
-then they need to “say it again!” to convince themselves that they understand
-teachers often have ah-ha moments
- excellent training for LAs in student-centered teaching

Placement Groups
0-18
19-21
22-26
27-30
N
622
639
1245
372
Mean
1.0400
1.7236
2.3332
3.0946
Std. Deviation
1.13777
1.21572
1.17252
.97526

Compare Exam Scores
No Orals Exam 1
Orals Exam 1
No Orals Exam 2
Orals Exam 2
No Orals Exam 3
Orals Exam 3
N
333
134
298
162
318
138
Average
75.1
81.6
74.5
79.8
64.4
73.9
St. Dev
15.0
10.4
15.4
12.6
19.1
15.7

Fall 2007 – Course Grade (4 point scale) by Number of Orals
No. of Orals No. of Students Mean Standard Deviation
0
1
2
3
243
63
81
69
1.935
2.089
2.542
2.841
1.33
1.15
1.03
.85

TEST 1 Instructor 1 Instructor 2 Instructor 3
Average Median Average Median Average Median
No orals 79 81 73 80 74 80 orals 83 86 82 86 87 90


Email questions: mnelso15@gmu.edu





Students learn the importance of understanding the basic concepts in order to be able to apply those concepts to novel situations
Students learn better ways of studying
Students work harder because they believe their instructors are invested in their success.
All of the above improvements increase with the number of orals in which students participate






Students agreement increased significantly on:
Item 8 – I am not satisfied until I understand why something works the way it does. (p=.042)
Item 11 – I study math to learn things that will be useful in my life outside of school. (p=.012)
Item 16 – To understand math I talk about it with friends and other students. (p=.002)
Item 23 – Mathematical formulas express meaningful relationships among measurable things or amounts. (p=.001)
Item 36 – When studying something new in math, I compare it to what I already know rather than just memorizing the way it was presented. (p=.028)



Item 7 – There is usually only one correct way to solve a math problem. (p=.037)
Item 18 – If I don't remember a mathematical method needed to solve a problem on a test, there's nothing else I can do. (p=.007)
*Students answers to all other questions were not significantly different pre/post


















More time/slower pace
◦ Comprehensive exam after two semesters
◦ Workshops add 2 hours/week
◦ 1. Workshops
◦ 2. Review sessions

35
30
25
20
15
10
5
0
Calculus I Calculus 2
9 year AVG, Calc1 and 2 2007-2008






Helps me understand the hard concepts
Helps me determine what I know and don’t know for the upcoming test
It clarifies things I was unclear about
It gives me confidence before the written test
It helps to hear how other students think about some of these things







Developing better motivation measures
Examining the “caring” effect
Using orals in other venues
◦ Mechanical Engineering: Component Design
◦ Aerospace: Statics
◦ High school algebra
Teaching students to run their own orals in
Calculus III

12
10
16
14
8
6
4
2
0
0.0
GRADE
1.0
2.0
3.0
4.0
Std. Dev = 1.02
Mean = 2.5
N = 34.00
40
30
60
50
20
10
0
0.0
GRADE
1.0
2.0
3.0
4.0
Std. Dev = .79
Mean = .4
N = 69.00


Regular students
140
REGVS134: 2.00
120
100
80
60
40
20
0
0 10 placement score
20 30 40

Treatment students
140
REGVS134: 1.00
120
100
80
60
40
20
0 placement score
10 20 30


groups

Comparison
Score deviation
Mean difference
Effect size
Treatment
Regular
97.37
73.98
24.221
27.809
23.39 .84 st. dev.

Group
Treatment
Regular
Subgroup Final Exam Conceptual Procedural Placement
At-risk 93 9.2 42.6 14.15
Not-at-risk 98.2
All students 95.4
8.4
8.9
44.7
43.4
20.3
16.5
At-risk 52
Not-at-risk 79
All students 74
3.25
6.7
6
22
33.3
31
13.99
23.4
21

GROUPS
Treatment At-risk
Control At-risk
Treatment Not At-risk
Control Not At-risk
Treatment At-risk
Control Not At-risk
Mean Exam
Score
Standard
Deviation
93.13
52.23
98.30
77.61
93.13
77.61
27.41
24.41
25.30
26.69
27.41
26.69
Mean
Difference
40.9
20.59
15.52
Effect Size
1.49
.77
.57

GROUP At-risk students taking final
Mean course grade
Treatment at-risk
Regular at-risk
16
61
Standard deviation
2.34 (C+)
1.30
1.06
.79 (D-)
% at-risk who took
Calculus II
56%
20%
Of the at-risk who took
Calculus II,
% who passed
89%
80%

GROUPS
Treatment
N = 34
Control
N = 615
Percent of
Students
At-risk
62%
16%
Percent of at-risk
No longer at CU
30%
45%







Randomly selected 1 of my 2 large classes – coin flip before semester began
Trained all Calculus I TAs and 2 Noyce to do orals
Provided orals questions each time
Each TA did 1 and each Noyce fellow did 2
I facilitated the rest
About 50% of the class participated

GROUPS
Test 1
Test 2
Test 3
Control
74
65
65
Treatment
82
67
72

GROUPS
Percent taking
Quiz
Control
68.5
Treatment
85



As reported by a student from control class
“It’s like a different class…I want to be in that class next semester. Which class will get those things next semester? I want to be in there.”
When asked why, “They are really into it.
Everyone is answering your questions.
They’re really excited about it. It’s not like our class.”







Offered orals to all APPM 1350 students
My class had over 50% attendance
Some classes as low as 20%
Analyzed results using Answer Tree
Complications due to 30 students took finalbecause of snow storm
Major question is effect of motivation – compare to Workshop and Review sessions

TEST
1
2
3
NO ORALS
Failure Rate
12.5%
13%
13.1%
ORALS
Failure Rate
10%
9%
8.5%
NO ORALS
Average
75
74
64
ORALS
Average
81
79
73

Typical Failure Rate
Failure Rate for Fall 2007
30-33%
22%

Year
2006
2007
2008
Failure Rate
31%
27%
17%









We have been given CCLI Phase II grant
Implementation in all Calculus I and II classes
Implementation in UCCS Calculus classes
Implementation in high school algebra classes
Implementation in Mech E Component Design class
Implementation in Aerospace E classes Fall 09
Broader participation by TAs and LAs in facilitating orals
Observations of TAs and LAs to ensure fidelity of treatment










Is control group same as all previous
Covariance due to placement scores
Counterfactuals
◦ Class size: 35-42 vs 48-142
 Compared to 48 person class
◦ Time on task
 Workshop students had same time-on-task
 Treatment had entire year’s material on final
Common final exam
Enrollment and success in Calculus II
Retention at the University

◦ At-risk placement test
◦ Students scoring
◦ considered at-risk treatment students
Treatment
Students
34 person
classes
Control
Students
48-142 person classes
Two -semester One –semester
62% at-risk 16% at-risk
Mean Place
16.5
Mean Place
21.4





We hope to f’ll scale up to all Calculus I classes
Orals will take place in recitations and workshops
AND before each midterm
On-line homework will free TAs to contribute more time to orals
Analyses will examine effect on
◦ Overall class
◦ Women and minorities
◦ Students whose placement scores designate them at-risk





Is the conceptual framework basically there?
What needs to be eliminated?
What needs to be reworked?
Suggestions PLEASE!

◦ At-risk determined by
30 question placement test
◦ Students scoring below 18 are considered at-risk
◦ All but two treatment students who were not designated “at-risk” by the placement test were in the class because they failed the first test in the regular class (20-30%) and dropped back to the treatment class
Treatment
Students
34 person class
Two semester
Control
Students
96-140 person class
One – semester



Study based on constructivist view of learning
Mathematics reform movement is an embodiment of constructivism
◦ Emphasis on:
*Vygotsky’s notion of ZPD
*Scaffolding
*Discourse
*Formative Assessment / misconception theory

TEST
1
2
3
Workshop
47%
52%
43%
Non-workshop
25%
26%
26.9%

TEST
1
2
3
ORALS
Failure Rate
10%
9%
8.5%
NO ORALS
Failure Rate
12.5%
13%
13.1%