Chabot College
Program Review Report
Check one:
_X_ SLO Portion of Upcoming ’16-’17
Program Review
(Submitted May 2015 in Preparation for Oct 2015)
___ Revision to ’15-’16 Program Review
(Originally Submitted Oct 2014)
___ Revision to ’14-’15 Program Review
(Originally Submitted Oct 2013)
Submitted on 5/1/15
Contact: Robert Yest
1
Appendix B: “Closing the Loop” Course-Level Assessment Reflections.
Course
Semester assessment data gathered
Number of sections offered in the semester
Number of sections assessed
Percentage of sections assessed
Semester held “Closing the Loop” discussion
Faculty members involved in “Closing the Loop” discussion
MTH 122
Fall 2014
1
1
100%
Spring 2015
1 (bring up at a MMMM to increase
the number?)
Form Instructions:
 Complete a separate Appendix B2 form for each Course-Level assessment reported in this
Program Review. These courses should be listed in Appendix B1: Student Learning Outcomes
Assessment Reporting Schedule.
 Part I: CLO Data Reporting. For each CLO, obtain Class Achievement data in aggregate for all
sections assessed in eLumen.
 Part II: CLO Reflections. Based on student success reported in Part I, reflect on the individual
CLO.
 Part III: Course Reflection. In reviewing all the CLOs and your findings, reflect on the course as
a whole.
PART I: COURSE-LEVEL OUTCOMES – DATA RESULTS
CONSIDER THE COURSE-LEVEL OUTCOMES INDIVIDUALLY (THE
NUMBER OF CLOS WILL DIFFER BY COURSE)
(CLO) 1: (Critical Thinking) Analyze mathematical
Defined Target
Scores*
(CLO Goal)
70%
Actual Scores**
(eLumen data)
88.9%
problems critically using logical methodology.
 If more CLOs are listed for the course, add another row to the table.
* Defined Target Scores: What scores in eLumen from your students would indicate success for this
CLO? (Example: 75% of the class scored either 3 or 4)
**Actual scores: What is the actual percent of students that meet defined target based on the eLumen
data collected in this assessment cycle?
In this course, ALEKS is used as the primary mode of instruction delivery. In ALEKS, students are
periodically given progress assessments on the topics that they have mastered since the last
assessment. All assessments are not previously announced and are taken on an honors system with no
time limit. For each assessment, we gather the percent of topics mastered that students demonstrated
that they retained through the assessment. We average the percent for all students’ assessments.
2
PART II: COURSE- LEVEL OUTCOME REFLECTIONS
A. COURSE-LEVEL OUTCOME (CLO) 1:
1. How do your current scores match with your above target for student success in this course
level outcome?
The above target was set at 70% because most instructors set a passing grade at 70%. The
actual results at 88.9% exceeded the target.
2. Reflection: Based on the data gathered, and considering your teaching experiences and
your discussions with other faculty, what reflections and insights do you have?
The interesting question is that, if the assessment results are so good in ALEKS, why are
students grade so poor in math in general? I think the answer lies in the pace at which the
students learn. In ALEKS, students set the pace. For each topic, the material is provided for
students to study directly relevant to the topic at hand. There is no number of topics that
the students have to get through; they just have to put in the time. While some may say
that this does not make the student accountable for achievement, there’s little evidence
that students would try to cheat the system by not being productive while logged in. MTH
122 does not satisfy any prerequisites. Students choose to take this class to build their
skills because they recognize the need to. The only thing that student can get out of MTH
122 is the chance to reassess before the 6 months waiting period is up, and it seems very
unlikely that a student would pay $70 for ALEKS for 18 weeks for an opportunity to retake
the assessment early and not actually study.
The fact that students can retain well on “pop tests” in ALEKS on the topics they learned
when there is no time limit is an indication that perhaps theirs is a mismatch in the pace of
curriculum set up and what developmental math students can handle for long term
learning, not just learning for the test. Based on the student’s work pattern in ALEKS, it
appears that students often find it difficult to put in as much time as they know they need
to put in, for whatever reason, but for the work they were able to put in, the retention was
good. However, students don’t get that feedback in their regular math class via the benefit
of an online assessment system. By using ALEKS, students can achieve good retention
under ALEKS’s mastery learning format, where students have to get certain number of
questions correct in a row before the topic is considered mastered. A mastered topic not
answered correctly on the assessment test gets unlisted, and the student has to rework
that topic again. I think for developmental students, knowing that they have something to
show for the amount of time that they put in is a great motivator. Students know when
they don’t put in enough time for a class because of work or other issues, but the data
from ALEKS shows that they are capable of learning math skills when the topics are well
sequenced to be within the student’s zone of proximal development.
The situation working in ALEKS described above contrasts with how in a regular class most
students who fall behind are trying to keep up in class with the difficult material when they
haven’t mastered the skills needed to learn best but have no way of helping themselves
sort out what they really need to concentrate on first. These students are so focused on
the next assessment that the teacher will give that they don’t spend the solid amount of
3
time on the fundamentals needed for success. It seems that, to break the cycle of
developmental students’ focusing on short term learning goals, these students can benefit
from a mastery learning, open-entry-open-exit format in which they don’t have to start
from scratch when they repeat the course.
4
PART III: COURSE REFLECTIONS AND FUTURE PLANS
1. What changes were made to your course based on the previous assessment cycle, the prior
Closing the Loop reflections and other faculty discussions?
None. This is the first time the course is assessed.
2. Based on the current assessment and reflections, what course-level and programmatic
strengths have the assessment reflections revealed? What actions has your discipline
determined might be taken as a result of your reflections, discussions, and insights?
The student’s achievements on the progress assessments in ALEKS reveal that ALEKS is the
right product to help students focus on learning and not on grade. Having the assessment
managed by the computer, the students get a better understanding of their math skills
level and the quality of their learning. Students often don’t practice the best long-term
learning trajectory when the grade on their next quiz or test is on the line. Thus, MTH 122
should continue with the way it is if the goal is to help students prepare for their next
math course by helping them focus on long-term learning/retention.
3. What is the nature of the planned actions (please check all that apply)?
X – Curricular
X – Pedagogical
 Resource based
 Change to CLO or rubric
 Change to assessment methods
 Other: ___________________________________________________
5
Appendix B: “Closing the Loop” Course-Level Assessment Reflections.
Course
Semester assessment data gathered
Number of sections offered in the semester
Number of sections assessed
Percentage of sections assessed
Semester held “Closing the Loop” discussion
Faculty members involved in “Closing the Loop” discussion
Math 33
Fall 14
1
1
100%
Spring 15
Matt Davis, Christine Coreno,
Robert Yest, Dan Quigley, Anita
Wah, Miranda Brasleton, Kyle
Ishibashi
Form Instructions:
 Complete a separate Appendix B2 form for each Course-Level assessment reported in this
Program Review. These courses should be listed in Appendix B1: Student Learning Outcomes
Assessment Reporting Schedule.
 Part I: CLO Data Reporting. For each CLO, obtain Class Achievement data in aggregate for all
sections assessed in eLumen.
 Part II: CLO Reflections. Based on student success reported in Part I, reflect on the individual
CLO.
 Part III: Course Reflection. In reviewing all the CLOs and your findings, reflect on the course as
a whole.
PART I: COURSE-LEVEL OUTCOMES – DATA RESULTS
CONSIDER THE COURSE-LEVEL OUTCOMES INDIVIDUALLY (THE
NUMBER OF CLOS WILL DIFFER BY COURSE)
Defined Target
Scores*
(CLO Goal)
(CLO) 1: See Attachment
See Attachment
Actual Scores**
(data from
eLumen or your
own tracking)
See Attachment
(CLO) 2: See Attachment
See Attachment
See Attachment
(CLO) 3: See Attachment
See Attachment
See Attachment
(CLO) 4: See Attachment
See Attachment
See Attachment
 If more CLOs are listed for the course, add another row to the table.
* Defined Target Scores: What scores in eLumen from your students would indicate success for this
CLO? (Example: 75% of the class scored either 3 or 4)
**Actual scores: What is the actual percent of students that meet defined target based on the eLumen
(or your own) data collected in this assessment cycle?
6
PART II: COURSE- LEVEL OUTCOME REFLECTIONS
A. COURSE-LEVEL OUTCOME (CLO) 1:
3. How do your current scores match with your above target for student success in this course
level outcome?
See Attachment
4. Reflection: Based on the data gathered, and considering your teaching experiences and
your discussions with other faculty, what reflections and insights do you have?
See Attachment
B. COURSE-LEVEL OUTCOME (CLO) 2:
1. How do your current scores match with your above target for student success in this course
level outcome?
See Attachment
2. Reflection: Based on the data gathered, and considering your teaching experiences and
your discussions with other faculty, what reflections and insights do you have?
See Attachment
C. COURSE-LEVEL OUTCOME (CLO) 3:
1. How do your current scores match with your above target for student success in this course
level outcome?
See Attachment
2. Reflection: Based on the data gathered, and considering your teaching experiences and
your discussions with other faculty, what reflections and insights do you have?
See Attachment
D. COURSE-LEVEL OUTCOME (CLO) 4:
1. How do your current scores match with your above target for student success in this course
level outcome?
See Attachment
2. Reflection: Based on the data gathered, and considering your teaching experiences and
your discussions with other faculty, what reflections and insights do you have?
See Attachment
E. COURSE-LEVEL OUTCOME (CLO) 5: ADD IF NEEDED.
7
PART III: COURSE REFLECTIONS AND FUTURE PLANS
4. What changes were made to your course based on the previous assessment cycle, the prior
Closing the Loop reflections and other faculty discussions?
See Attachment
5. Based on the current assessment and reflections, what course-level and programmatic
strengths have the assessment reflections revealed? What actions has your discipline
determined might be taken as a result of your reflections, discussions, and insights?
See Attachment
6. What is the nature of the planned actions (please check all that apply)?
 Curricular
 Pedagogical
 Resource based
 Change to CLO or rubric
 Change to assessment methods
 Other:_________________________________________________________________
8
Appendix C: Program Learning Outcomes
Considering your feedback, findings, and/or information that has arisen from the course level
discussions, please reflect on each of your Program Level Outcomes.
Program: __Math AA, AS, AS-T____

PLO #1: Analyze mathematical problems critically using logical methodology.

PLO #2: Communicate mathematical ideas, understand definitions, and interpret concepts.

PLO #3: Increase confidence in understanding mathematical concepts, communicating ideas and
thinking analytically.
What questions or investigations arose as a result of these reflections or discussions?
See Attachment
What program-level strengths have the assessment reflections revealed?
See Attachment
What actions has your discipline determined might be taken to enhance the learning of
students completing your program?
See Attachment
9
Student Learning Outcomes for the Math Subdivision
After one year since we adopted the new process, the Math Subdivision has continued meeting
to discuss student success—by sharing best practices for teaching math at various levels in a
series of robust discussions.
These monthly meetings are a resounding success. While success to many is only seen in
student success, we feel that the enthusiastic reaction to the shared ideas with commitment to
try to implement the suggestions in our classrooms is what we feel to be success. Validation of
student success will come later in our next assessment cycle.
We met throughout the academic year on the last Tuesday of each month. During this past
year, we discussed approximately 40% of the topics laid out documented in last Program
Review. Below is that list. The yellow highlights indicate those topics discussed.
Program Level



Multiple New Full Time Faculty Members – Robert Yest and Charlene Wieser presented
the case for 5 new hires. The math subdivision did get 2 new hires. THIS IS STILL AN
ISSUE. With new faculty members retiring, we are still struggling to backfill those
positions that we lost due to recent retirements.
Technology as a Tool for Instruction – Adolf Oliver presented his online program to help
students’ learning. It blends an ongoing assessment with material allowing for repeated
material addressing students’ weekness.
Placement Exam Revision
Curriculum





Math 36/37 into 20 Transitions
Math 20 to prepare for Math 1, 2, 3, 4, 6, and 8.
Converting Math 103/104 into Non-Credit
Using results to compare old 65/55 to new 65/55 sequence. – While not enough data is
available to sufficiently compare the two sequences. We did discuss the current issues.
The main one here is the new textbook. We analyzed the sequence of sections to try to
present the best approach to the topics. During this detailed discussion we developed a
suggested approach for 1) Functions: Domains, Ranges, Inverses, etc., 2) Variation, 3)
Graphing Equations from the Point Slope forms, 4) Exponential and Log functions, 5)
Rational Functions, and 6) Translations. What surprised us was how a simple discussion
of order of sections presented could lead to a robust discussion on how to prepare and
introduce the topic to the students in an effective way. For the discussion of functions
we also included how to build on the concept from the algebra sequence into 37
(Trigonometry) and 20 (Pre-Calculus).
Success in 53/43 vs. 65/55/43 compared to 53 vs. 65/55
10
Pathway Level





Definition of Functions (See Above)
Inverse Functions and their Domains and Ranges (See Above)
Polar Coordinates
Visualizing Topics
Interpretations / Applications
Course Level





Percents
Variation (See Above)
Pt-Slope Equations (See Above)
Taylor Series
Binomial Distribution
In addition to the Program Review’s list from last year, we included new topics that focus on
the student themselves:
Student Level


Learning/Study Skills – Matt Davis led a discussion to where we discussed many
different approaches to encourage students to improve their study skills. This includes
detailing the many different forms studying can take. We created a list of suggestions
from each of us to pass on to the student. This gives students suggestions beyond
simply doing homework. Also, we shared our incentives to motivate students to
improve on previous mistakes rather than dwell on them.
Critical Thinking – In March Doris Hanhan and Robert Yest participated in Chabot’s
discussion for measuring and assessing the College Wide Learning Goal, Critical Thinking.
Doris took the recommendations and led the healthy discussion on how to implement
the suggestions from that meeting. The broad scope of ideas offered a multi-faceted
approach, from types of questions to ask on a test, to how to approach a problem in
class, to using tests scoring as a tool to understand what why we ask those types of
questions getting students to analyze the material conceptually and away from the
problem itself.
Since last Program Review, two courses were assessed: Math 33 and Math 122. Math 122 was
assessed, and its Closing the Loop is included above.
Math 33 was surveyed in Fall 14 in the same manner as the other math courses. We discussed
33 in context of all our courses, and the results are included below. What we found was that
the issues to be addressed were in line and included in the list we determined last year.
11
It was brought up about prerequisites. Seeing that the target audience for 33 is Business
students and non-STEM GE students, we felt that Math 53 could serve as a prerequisite. Also
we would like to partner with our Business faculty to determine how best to market this course.
We will be investigating that in the future year.
Results for Student Learning Outcomes for Math 2014 including 33
Course
Survey
Ques #
37
37
37
37
37
37
37
37
37
37
20
20
20
20
20
20
20
20
20
20
1
1
1
1
1
1
1
1
1
1
2
2
2
10
4
8
6
5
7
1
9
3
2
10
5
3
7
4
6
1
8
9
2
1
6
10
7
9
5
2
8
4
3
5
2
8
Type of Question
Graph a Polar Equation
Finding Areas of Plane Regions
* Domains & Ranges of Inverse Trig Fns
Solving Trigonometric Equations
Graphing Trigonometric Functions
* Finding Trig Function Values w/ Identities
Triangle Congruence Proofs
Law of Sines and Law of Cosines
Solving Triangles Using Right Trangle Thms
Angle Measures Using Basic Theorems
Converting Polar Equations
Polar Graphing
Right Triangle Geometry
* Range of 1-1 Functions
* Translations and Transformaitons
Polynomial Graphing
Log Equations
Sequences
Series
Polynomial Factoring
* Epsilon Delta
* Mean Value Theorem
Volumes
Concavity
* Riemann Sum
Implicit Differentiation
Continuity
Integral
Computation of Derivative
Definition of Derivative
Taylor Series
* Natural Logarithm (Calculus) Definition
Inverse Trigonometric Derivatives
12
# of Low
Total
Percent
Outliers Students
(**)
53
36
32
24
22
22
18
12
5
3
35
33
27
27
26
25
20
14
11
4
66
40
31
19
19
16
14
3
2
1
50
37
25
100
100
100
100
100
100
100
100
100
100
86
86
86
86
86
86
86
86
86
86
87
87
87
87
87
87
87
87
87
87
84
84
84
53.00%
36.00%
32.00%
24.00%
22.00%
22.00%
18.00%
12.00%
5.00%
3.00%
40.70%
38.37%
31.40%
31.40%
30.23%
29.07%
23.26%
16.28%
12.79%
4.65%
75.86%
45.98%
35.63%
21.84%
21.84%
18.39%
16.09%
3.45%
2.30%
1.15%
59.52%
44.05%
29.76%
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
9
7
1
4
10
6
3
9
10
5
4
8
6
1
7
2
3
10
1
4
6
9
3
8
2
7
5
Interval of Convergence
Polar Area
* Geometric Series Convergence
Trigonometric Integrals
Improper Integral
L'Hopital's Rule
Integration by Parts
Divergence Theorem
* Line Integral Applications
Optimization
* Gradient Properties
Green's Theorem
Volumes
3D Geometry
Spherical Integration
Tangent Vectors
Partial Derivatives
Laplace Transformations
* Existence and Uniqueness Theorem
Exact DE
* Definition of Fundamental Set
Power Series Solutions
First Order Linear DE
Higher Order Linear Differential Equaitons
Verifying Solutions
Variation of Parameters
IVP
22
20
11
11
10
8
2
32
27
23
21
12
7
5
5
3
3
16
15
12
8
7
3
3
1
1
0
84
84
84
84
84
84
84
58
58
58
58
58
58
58
58
58
58
25
25
25
25
25
25
25
25
25
25
26.19%
23.81%
13.10%
13.10%
11.90%
9.52%
2.38%
55.17%
46.55%
39.66%
36.21%
20.69%
12.07%
8.62%
8.62%
5.17%
5.17%
64.00%
60.00%
48.00%
32.00%
28.00%
12.00%
12.00%
4.00%
4.00%
0.00%
6
6
6
6
6
6
6
6
6
6
8
8
8
8
8
9
5
8
10
7
6
1
4
2
3
4
6
7
3
10
Orthonormal Bases
Rank and Nullity of a Matrix
Linear Transformations
Eigenvectors and Eigenvalues
* Definition of Vector Spaces
Column Spaces
Gauss-Jordan Elimination Method
* Linear Independence
Inverse Matrices
Determinant
* Countability
Modular Arithmetic
* Proof by Contradiction
Sets
Counting Techniques
18
11
11
9
8
5
4
3
1
1
13
12
11
10
10
34
34
34
34
34
34
34
34
34
34
26
26
26
26
26
52.94%
32.35%
32.35%
26.47%
23.53%
14.71%
11.76%
8.82%
2.94%
2.94%
50.00%
46.15%
42.31%
38.46%
38.46%
13
8
8
8
8
8
103
103
103
103
103
103
103
103
103
103
104
104
104
104
104
104
104
104
104
104
65
65
65
65
65
65
65
65
65
65
55
55
55
55
55
55
55
9
2
5
8
1
3
10
8
7
9
1
5
6
2
4
5
8
4
10
7
9
2
3
1
6
9
5
6
10
4
8
7
2
3
1
9
6
7
8
3
10
4
Discrete Probability
Symbolic Logic
Euclidean Algorithm
RSA Encription
Rules of Inference
Graphing Fractions
* Proportions
Simplification of Fractions
Percents
* Contrasting Different Measurments
Words to Decimal Conversion
Decimal Division
Rounding
Adding Fractions
Decimal Subrtraction
Application of Roots
Volume
Circular Computations
* Simplifying vs. Evaluating
Percents
* Interpretaion of Computations
Evaluating Expressions
Linear Equations
Order of Operations
Square Roots
Percents
Solving System of Equations
Equations of Lines from Points
* Vertical Slopes
Simplifying Rational Expressions
Graphing Lines
* Explain "Canceling"
Algebra of Polynomials
Factor Trinomials
Linear Equations
Logrithmic Functions
Exponential Applications
* Definition of Function
Exponential Graphing
Inverses
Rationalizing Denominators
Complex Numbers
14
5
4
3
3
0
26
26
24
17
10
8
8
6
4
3
65
55
47
38
34
30
25
9
8
7
91
78
55
49
46
40
35
29
11
7
127
119
84
81
66
61
43
26
26
26
26
26
49
49
49
49
49
49
49
49
49
49
143
143
143
143
143
143
143
143
143
143
192
192
192
192
192
192
192
192
192
192
280
280
280
280
280
280
280
19.23%
15.38%
11.54%
11.54%
0.00%
53.06%
53.06%
48.98%
34.69%
20.41%
16.33%
16.33%
12.24%
8.16%
6.12%
45.45%
38.46%
32.87%
26.57%
23.78%
20.98%
17.48%
6.29%
5.59%
4.90%
47.40%
40.63%
28.65%
25.52%
23.96%
20.83%
18.23%
15.10%
5.73%
3.65%
45.36%
42.50%
30.00%
28.93%
23.57%
21.79%
15.36%
55
55
55
33
33
33
33
33
33
33
33
33
33
31
31
31
31
31
31
31
31
31
31
5
2
1
5
2
6
9
3
10
1
8
4
7
9
5
2
8
3
4
1
6
10
7
Radical Equations
Absolute Value Inequalities
Quadratic Formula
Payments into an Account
Matrix Addition / Transposes
Probability
Discrete Random Variables
Gauss-Jordan Elimination Method
System of Linear Inequality
Matrix Multiplication
* Reduced Row Eshelon
Compound Interest
Application of Probability
Exponential Models
* Determining Domains
Binomial Expansion
* Quadratic Modeling
Logarithmic Equations
Graphing of Rational Functions
* Interpret Graphs
Geometric Series
Rational Inequalities
Rational Equations
39
11
4
6
5
5
5
4
4
3
3
2
2
26
25
23
23
22
22
20
18
18
6
280
280
280
18
18
18
18
18
18
18
18
18
18
93
93
93
93
93
93
93
93
93
93
13.93%
3.93%
1.43%
33.33%
27.78%
27.78%
27.78%
22.22%
22.22%
16.67%
16.67%
11.11%
11.11%
27.96%
26.88%
24.73%
24.73%
23.66%
23.66%
21.51%
19.35%
19.35%
6.45%
15
15
15
15
15
15
15
15
15
15
16
16
16
16
16
16
16
16
16
7
8
5
10
4
9
1
6
2
3
8
9
10
7
2
4
6
1
3
Exponential Models
Using Derivatives To Sketch A Graph
Related Rates
* Intermediate Value Theorem
Differentiation Rules For Exp And Logs
* Continuity At A Point
Evaluate Limits
Cancavity And Inflection Points
Equations Of Tangent Lines
Find Derivatives Using Differentiation Rules
Continuous Random Variable Probabilities
Optimization Problems
Related Rates
Taylor Series Representation
Improper Integral
Double Intervals
Separable Differential Equations
Integration By Parts
Partial Derivatives
25
18
16
15
11
11
9
5
3
2
4
4
4
3
2
1
1
0
0
46
46
46
46
46
46
46
46
46
46
6
6
6
6
6
6
6
6
6
54.35%
39.13%
34.78%
32.61%
23.91%
23.91%
19.57%
10.87%
6.52%
4.35%
66.67%
66.67%
66.67%
50.00%
33.33%
16.67%
16.67%
0.00%
0.00%
15
16
54
54
54
54
54
54
54
54
54
54
53
53
53
53
53
53
53
53
53
53
5
10
3
5
8
6
4
1
2
7
9
10
4
1
3
5
9
6
7
8
2
Differentiate A Trigonometric Functions
Variation
Applications of System of Equations
Rates
* Choosing an Appropriate Model
Exponential Models
Interpretation of Functional Models
* Interpret Linear Models
Equations for Parallel Lines
Exponential Equations
Quadratic Graphing
Variation
Empirical Rule for Normal Distributions
Geometry and Measurement
* Interpreting the Slope of a Line
Dimensional Analysis
Exponential Models
Linear Models for Real Situations
Function Notation
Scatterplots
Mean and Median
0
33
26
20
14
13
8
7
5
5
5
46
38
32
21
20
19
18
12
7
5
6
59
59
59
59
59
59
59
59
59
59
97
97
97
97
97
97
97
97
97
97
0.00%
55.93%
44.07%
33.90%
23.73%
22.03%
13.56%
11.86%
8.47%
8.47%
8.47%
47.42%
39.18%
32.99%
21.65%
20.62%
19.59%
18.56%
12.37%
7.22%
5.15%
43
43
43
43
43
43
43
43
43
43
5
7
10
3
9
8
4
1
2
6
* Linear Regression
Binomial Distribution
Hypothesis Testing
* Interpreting Plots
Confidence Intervals
Normal Distribution
Using Box Plots
* Types of Studies
Finding Measures ofCcenter and Spread
Conditional Probability
146
106
96
94
91
61
52
45
42
34
357
357
357
357
357
357
357
357
357
357
40.90%
29.69%
26.89%
26.33%
25.49%
17.09%
14.57%
12.61%
11.76%
9.52%
16
Expected Grade
Course
37
20
1
2
3
4
6
8
33
31
15
16
54
53
43
55
65
103
104
# Students
96
85
87
80
56
24
32
26
18
91
45
6
59
90
343
275
186
47
137
A
19.8%
27.1%
18.4%
22.5%
37.5%
41.7%
59.4%
15.4%
16.7%
20.9%
20.0%
33.3%
6.8%
11.1%
31.8%
18.5%
10.8%
19.1%
19.0%
B
38.5%
27.1%
41.4%
38.8%
33.9%
33.3%
28.1%
57.7%
33.3%
41.8%
37.8%
33.3%
35.6%
31.1%
37.9%
34.2%
38.2%
42.6%
34.3%
17
C
32.3%
32.9%
29.9%
36.3%
26.8%
16.7%
9.4%
23.1%
27.8%
29.7%
35.6%
16.7%
44.1%
41.1%
25.7%
42.2%
43.0%
31.9%
34.3%
D
5.2%
9.4%
9.2%
1.3%
1.8%
8.3%
3.1%
3.8%
16.7%
5.5%
4.4%
0.0%
6.8%
11.1%
2.9%
4.0%
6.5%
0.0%
9.5%
F
4.2%
3.5%
1.1%
1.3%
0.0%
0.0%
0.0%
0.0%
5.6%
2.2%
2.2%
16.7%
6.8%
5.6%
1.7%
1.1%
1.6%
6.4%
2.9%
Confidence in Math
Course
37
20
1
2
3
4
6
8
33
31
15
16
54
53
43
55
65
103
104
# Students
95
84
87
83
56
25
33
26
18
93
46
6
59
93
348
275
185
47
134
Improved
71.6%
46.4%
66.7%
72.3%
69.6%
80.0%
75.8%
88.5%
72.2%
62.4%
78.3%
33.3%
62.7%
61.3%
74.4%
68.7%
68.6%
72.3%
67.9%
18
Same
18.9%
41.7%
24.1%
21.7%
23.2%
12.0%
21.2%
7.7%
16.7%
30.1%
17.4%
50.0%
28.8%
23.7%
18.7%
24.7%
23.2%
27.7%
23.9%
Worse
9.5%
11.9%
9.2%
6.0%
7.1%
8.0%
3.0%
3.8%
11.1%
7.5%
4.3%
16.7%
8.5%
15.1%
6.9%
6.5%
8.1%
0.0%
8.2%