Teaching Portfolio
2010
Kevin D. Donohue
Professor
Electrical and Computer Engineering
Department
University of Kentucky
Lexington, KY 40506-0046
http://www.engr.uky.edu/~donohue/
Table of Contents
Subject
Page
Teaching Evaluation ............................................................................................................1
Changes to Portfolio since Last Review: .......................................................................1
Philosophy and Goals ....................................................................................................1
Course Characteristics ...................................................................................................1
Teaching Performance and Outcome Assessment .........................................................2
EE 101 Electrical Engineering Professions Seminar(outcome assessment) ............2
EE 211 Circuits I (outcome assessment) .................................................................4
EE221 Circuits II (outcome assessment) .................................................................6
EE222 Electrical Engineering Laboratory I .............................................................8
EE307 Circuit Analysis with Applications ..............................................................8
EE380 Microcomputer Organization .......................................................................9
EE421 Signals and Systems I ..................................................................................9
EE422 Signals and Systems II .................................................................................9
EE462G Electronic Circuits Laboratory (outcome assessment) ............................12
EE 499 Senior Design (outcome assessment) ........................................................16
EE 513 Audio Signals and Systems .......................................................................17
EE611 Deterministic Systems................................................................................19
EE630 Digital Signal Processing ...........................................................................20
EE 639 Adv. Topics in Com. and Signal Processing (outcome assessment) ........20
Student Evaluation Summary ............................................................................................22
Advising Evaluation...........................................................................................................22
Appendix A - Student Evaluations of Courses and Teaching............................................23
Teaching Evaluation:
Changes to portfolio since last review:
The feedback and improvement discussions have changed for EE221, EE422G, EE513,
and EE611. These were the courses I taught in the last 2 years.
Philosophy and Goals:
My teaching assignment consists of undergraduate and graduate courses in the electrical
engineering department with specialty courses in the signals and systems area (statistical
signal processing, digital signal processing (DSP), and communication systems). These
courses emphasize applying mathematical models for relating information to signals and
systems, and using these models to solve engineering problems in control,
communications, and signal processing.
I believe my most significant activities are directed at generating excitement and genuine
interest in the electrical engineering field, and instilling a confidence that they can
contribute to the profession and change the world as others have in the past. This will
hopefully motivate them to dream and set goals, work hard and become self learners as
they deal with the challenges presented to them in the classroom. The confidence comes
in meeting the challenges I create for the students. These challenges require the student
master and apply basic knowledge and skill sets as described in the course outcomes.
Projects are used to assess the more complex skills involving synthesis and provide the
student with the confidence that they can apply their skills to solve open-ended problems.
I hope to introduce a history based course on electrical engineering at some point in my
career, or add it to one of the freshmen courses. I think it can be made stimulating for all
students, and provide knowledge of where the field has been, where it can go, and how
they can be a part. But limitations on my own time and a crowded curriculum make this
an unlikely prospect for the near future. For now I tend to intersperse my lectures with
stories of some the peculiar characters that have shaped our profession and changed the
world (Faraday, Maxwell, Galvani, Franklin, Edison, Steinmetz, Tesla, Weiner, Shannon,
Shockley, …) .
Course Characteristics:
My classroom activities are strongly influenced by student feedback, which I get directly
from the students or through the teacher/course evaluations. As a result of student
feedback over the years, the characteristics of my classroom are as follows. I give many
quizzes throughout the course rather than a few midterms. The quizzes are graded by me
are returned promptly with comments. I assign, collect, and grade homework
(homework assignments are graded by the teaching assistant, if one is provided for the
course). I give projects that involve students working in groups. I make class materials
available on the web (see http://www.engr.uky.edu/~donohue/courses.html). I do a lot of
1
information broadcasts using class email lists the college of engineering computing
services set up for me. I make students orally present project results or explain
homework solutions to the rest of the class.
In laboratory courses I emphasize experimental design skills and written explanations
(how to write and communicate technical information). Therefore, I try not to provide a
lot of detail in the lab assignment on how to make a measurement. This frustrates some
students; however I am hopeful that through the struggle they will learn instrumentation
and problem-solving skills applicable to broader setting. I encourage writing more
concisely through the use of figures, graphs, and equations. I think by now most of my
students see the value in developing their writing skills and appear to be improving at it.
Teaching Performance and Outcome Assessment
In order to determine how well students are achieving the course outcomes, I have started
a system (since Spring 1999) for grading each assignment according to the outcomes
listed for the course. If a single assignment has multiple outcomes, it will be graded in
separate components and recorded as separate components in the class spreadsheet. So
each column relates to the performance of one outcome. Scores are then averaged over
all students and a class grade assigned for each outcome. This way I can track student
outcome achievements with change I make in the classroom from semester to semester.
The courses taught before 1999 just have a brief description of the course with a list of
student evaluations on the teaching quality and course value for each time I taught the
course.
EE 101 Electrical Engineering Professions Seminar
Subjects Covered: Professional practice, growth, conduct, ethics, computers in
electrical engineering, the University computer system, careers in engineers, and
professional societies.
This is a one hour seminar course (taken Pass/Fail) designed to help freshmen become
familiar with the electrical engineering profession and learn basic skill for enhancing
their time as an undergraduate at the University of Kentucky.
Teacher/Course Evaluation (by student):
EE101 Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Fall 2000
92
3.2
3.5
Fall 2001
102
3.1
3.4
Fall 2002
107
3.2
3.4
Course Outcome Assessment:
2
Two evaluations of course outcomes are performed. One is a self-evaluation that reflects
the student confidence level, and the other is by the instructor derived from the average
score on course assignments related to each outcome.
Course Outcome List:
1. Know the academic requirement for an undergraduate degree in Electrical Engineering.
2. Understand ethical and professional issues associated with the electrical engineering profession.
3. Able to use word processing spreadsheets, computer networks, literature search techniques, email, and very simple electric networks.
Student Self-Assessment:
Students rated their own ability relative to each course outcome on a scale from 1 (no
confidence) to 5 (most confident). The mean was taken over all students and converted
to a percentage (out of 5), which is reported in the table below in terms of percent
100*(score-1)/4.
Table EE101 Student Confidence Level Percentage (student self-assessment)
Course Outcome
Semester Respondents
1
2
3
Fall 00
52
87.5
85
90
Fall 01
52
-85
85
Fall 02
35
82
80
77.5
The dropping trend in Outcome 3 is not good. I need to give more assignments
associated with this outcome. I originally dropped some because I did not want the
course to burden the freshmen over their more important academic subjects; however I
probably need to give smaller and possibly in-class assignments so students can gain
more confidence in their abilities with these outcomes.
Assessment by Instructor:
The following assessment tools were used throughout the class:
 Homework assignments
 In-class assignments
Table EE101 Student Performance Level Percentage (faculty assessed)
Course Outcome
Semester Enrollments
1
2
3
Fall 00
91
50
75
75
Fall 01
102
-75
62.5
Fall 02
107
-87
87
Feedback and Improvement:
While I felt their performance has improved, it is hard to report a finer scale with a passfail grading system. I can only go by number of acceptable assignments turned in. The
greater weight of outcome assessment for this course has to be the student self
assessments rather than the faculty numbers. The increase in performance for outcomes
3
in the Fall 02 is most likely the result of giving few assignments. Student confidence
level has held steady.
For outcome 2, I am finding that my senior exit interviews reveal almost 100% of our
students remember seeing the IEEE code of ethics. Most had seen this for the first time
in EE101. In 1997 though 2000 many of our graduating seniors (more than 50%) did not
recalled seeing the IEEE code of ethics before.
I also feedback information from interviews I do with students on probation and the
senior interview to help advise students on strategies for successfully completing the
curriculum. I continue to provide this information to all faculty and especially those
teaching EE101. Over the last 6 year we have seen over a 100% improvement in
retention rates. Will EE101 was on the only factor, I do think it played a significant part
in this success.
EE 211 Circuits I
Subjects Covered: AC and DC analysis of linear circuits including transient and steadystate analysis.
This is the students’ first technical course in electrical engineering. The focus is on
fundamental circuit analysis and circuit simulation software.
Teacher/Course Evaluation (by student):
EE211 Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Spring 02
33
3.6
3.8
Fall 01
55
3.7
3.8
Spring 01
27
3.8
3.9
Fall 00
45
3.5
3.6
Spring 00
29
3.6
3.7
Course Outcome Evaluation
Two evaluations of course outcome are performed. One is a student self-evaluation that
reflects the student confidence level, and the other is by the instructor derived from the
average score on course assignments related to each outcome.
Outcome list:
1.
2.
3.
4.
5.
Analyze simple resistive circuits including those containing operational amplifiers and
controlled sources with loop and nodal analysis
Analyze RLC circuits containing switches, independent sources, dependent sources, resistors,
capacitors, inductors, and operational amplifiers for transient response using loop and nodal
analysis
Analyze RLC circuits with sinusoidal excitation sources for steady-state response using loop
and nodal analysis
Compute Thévenin and Norton equivalent circuits
Use SPICE (computer simulation package) to compute voltages, currents, transient responses,
and sinusoidal steady-state responses
4
Student Self-Assessment:
Students rated their own ability relative to each course outcome on a scale from 1 (no
confidence) to 5 (most confident). The mean was taken over all students and converted
to a percentage (out of 5), which is reported in the table below.
Table EE211 Student Confidence Level Percentage
Course Outcome
Semester Respondents
1
2
3
Spring 00
Fall 00
23
88
80
88
Spring 01
17
90
76
82
Fall 01
34
90
80
82
Spring 02
25
84
76
78
4
5
76
82
76
72
90
90
90
84
Assessment by Instructor:
The following assessment tools were used throughout the class:
 Homework assignments
 In-class and take-home quizzes
 Final exam
The complex assignments that involved multiple outcomes were graded and recorded in
components. The average score was computed over all students and assessment tools
related to each outcome. The percentage for each outcome is listed in the table below.
Table EE211 Student Performance Level Percentage
Course Outcome
Semester
Enrollment
1
2
3
Spring 00
29
81
74
75
Fall 00
23
88
71
72
Spring 01
27
82
84
84
Fall 01
45
80
79
81
Spring 02
29
85
80
71
4
48
60
74
76
83
5
81
73
87
90
78
Feedback and improvement:
When I started to collect these statistics I realized the outcome number 4 was in definite
need of improvement with a 48% average score on problems related to this outcome. I
began to focus on this component, giving more class time to it and more homework
problems. As a result there has been a steady improvement in this outcome until it is now
comparable to the other outcomes. Student confidence in this outcome is still low,
however. This indicates that while I may have been successful in teaching the mechanics
of the problem, they lack the understanding and purpose of the analysis and confidence. I
will work on this next time I teach the course. Outcomes 2 and 3 are now of my greatest
5
concern. The next time I teach the course I will focus on better examples for the lecture.
I will also work on special exercises to use during recitation.
EE221 Circuits II
Subjects covered: Transfer functions of circuits, singularity functions, differential
equation representations and solutions for circuits, two-port parameter representations of
circuits, and design project organization.
My main addition to the course was a hearing aid design project which I added in 1994
and is still being used in this course to develop and evaluate student outcomes related to
open-ended design and team work. The web pages of Drs. Gedney and Donohue have
various version of this project described. I also have a link so that students can evaluate
their own hearing loss and develop the filters and amps to compensate for their own
deviation from normal hearing. Recently I have been stressing the Engineering
notebooks in preparation for senior design and it utility in industry.
Teacher/Course Evaluation (by student):
EE221 Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Fall 2010
34
NYA
NYA
Spring 2010
34
3.5
3.1
Fall 2008
19
3.5
3.4
Spring 2007
33
3.2
3.2
Spring 2005
43
3.3
3.1
Fall 98
34
3.4
3.5
Fall 97
44 Team Taught 50%
3.0
3.2
Summer 97
19
No Evaluation
No Evaluation
Fall 96
41
3.5
3.6
Summer 96
19
No Evaluation
No Evaluation
Spring 96
59
3.3
3.2
Fall 95
61
3.2
3.3
Fall 94
26
3.5
3.6
Summer 94
10
No Evaluation
No Evaluation
Spring 93
71
3.4
3.6
Spring 92
58
3.5
3.5
Fall 91
51
3.6
3.6
In 2001 the outcomes for this course changed, so materials on assessments prior to 2001
have been removed. Below are current outcomes along with their assessments.
Course Outcome Evaluation
Two outcome evaluations are performed. One is a self-evaluation that reflects the student
confidence level and the other is by the instructor derived from the average score on
course assignments related to each outcome.
6
Outcome list:
1.
2.
3.
4.
5.
6.
7.
8.
9.
Perform an AC steady-state power analysis on single-phase circuits.
Perform an AC steady-state power analysis on three-phase circuits.
Analyze circuits containing mutual inductance and ideal transformers.
Derive transfer functions (variable-frequency response) from circuits containing independent
sources, dependent sources, resistors, capacitors, inductors, operational amplifiers, transformers,
and mutual inductance elements.
Derive two-port parameters from circuits containing resistive and impedance elements.
Use SPICE to compute circuit voltages, currents, and transfer functions.
Describe a solution with functional block diagrams (top-down design approach).
Work as a team to formulate and solve an engineering problem.
Use computer programs (such as MATLAB and SPICE) for optimizing design parameters and
verify design performance.
Student Self-Assessment:
Students rated their own ability relative to each course outcome on a scale from 1 (no
confidence) to 5 (most confident). The mean was taken over all students and converted
to a percentage (out of 5) reported in the table below. These are converted to percentages
(100% = 5, 0% = 1) and indicated in the tables below.
Table EE221 Student Confidence Level Percentage (self-assessment)
Semester
Respondents
Spring 05
Spring 07
Fall 08
Spring 10
Fall 10
Mean
Standard
Deviation
34
27
19
34
34
1
82
88
90
85
NAY
86.25
2
77.5
76
67.5
75
NAY
74.00
3
77.5
80
82.5
70
NAY
77.50
3.50
4.45
5.40
Course Outcome
4
5
6
77.5
77.5
77.5
84
74
90
87.5
85
77.5
75
75
80
NAY
NAY
NAY
81.00 77.88 81.25
5.76
4.97
5.95
7
77.5
88
85
87.5
NAY
84.50
8
77.5
86
87.5
85
NAY
84.00
9
75
84
77.5
80
NAY
79.13
Mean
77.72
83.33
82.22
79.17
4.85
4.45
3.84
4.80
80.61
NAY => Not Available Yet, IP=> In Progress
Assessment by Instructor:
The following assessment tools were used throughout the class:
 Homework assignments
 Team-design project
 In-class and take-home quizzes
 Final exam
The complex assignments that involved multiple outcomes were graded and recorded in
components. The average score was computed over all students and assessment tools
related to each outcome. The percentage for each outcome is listed in the table below.
Table EE221 Student Performance Level Percentage (faculty-assessment)
Semester
Enrollment
Spring 05
Spring 07
43
33
1
81.8
81.4
2
71.7
76.6
3
79.0
74.3
Course Outcome
4
5
6
79.6
76.5
77.4
80.4
78.8
78.7
7
7
84.3
91.1
8
90.6
94.7
9
85.0
96.3
Mean
80.66
83.59
Fall 08
Spring 10
Fall 10
Mean
Standard
Deviation
19
34
34
90.3
83.18
74.69 75.93 78.88 75.09 86.10 76.55 97.84 87.63 89.86
73
75.6
80
75.6
80
87.6
90
96.5
67.35
73.12
77.50
80.84
72.20
79.10
83.51
86.75
93.84
78.73
74.63
77.77
78.53
78.43
81.23
88.47
93.93
90.53
82.51
79.36
82.16
4.97
2.59
3.04
2.57
1.78
5.55
3.65
3.02
5.65
4.14
Feedback and Improvement: With tracking over 5 year we can identify some patterns
of interest. First off it is interesting that that student assessments and faculty assessment
of outcomes track pretty close, especially the far right column showing the means over all
the outcomes. I think this speaks to the fact that the students take a quiz every week and
get weekly feedback on how they are performing before taking the final exam. The most
recent faculty assessments of student output show some drops in basic phasor analysis.
This is for the most part review from Circuits1. Students complained that they did not
cover it in Circuits 1, but this was not the case. I will not make any changes based on
that, unless I see a trend. I have been emphasizing the project notebook more and
grading more harshly, which is the reason the teamwork outcome appears to be dropping
for outcome 8. I think it was a combination of an unusually low-level class (just opposite
of the Spring 07 class) so again I will not make changes based on that. Overall the
outcome ratings remain positive. Much of the variation I think is explain by the natural
variation in the level of the students in the classes. I am thinking of being less aggressive
with the project next time so the students can focus more on the circuit solving skill of
outcomes 1-6.
EE222 Electrical Engineering Laboratory I
Subjects covered: Basic measurement and characterization of DC and AC voltages,
currents, and power. Enrollment: 45 Students.
EE222 Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Fall 96
44
3.2
3.6
EE307 Circuit Analysis with Applications
Subjects covered: Basic AC and DC circuit analysis techniques (mesh and nodal
analysis with circuit elements comprised of resistors, capacitors, inductors, op amps, and
transformers). Examples in instrumentation, electro-mechanical, and power transfer
circuits.
EE307 Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Summer 96
40
No Evaluation
No Evaluation
Summer 95
41
No Evaluation
No Evaluation
8
EE380 Microcomputer Organization
Subjects covered: General Architecture of Microcomputer, 8086 instruction set and
programming concepts
EE380 Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Fall 93
41
2.9
3.0
Spring 93
47
3.3
3.1
EE421G Signals and Systems I
Subjects covered: Modeling and analysis of signals and systems using convolution,
Fourier series, Fourier Transform bandwidth, basic filter design, modulation techniques,
random variables and random processes and spectral density.
EE421G Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Spring 98
29
3.2
3.0
Spring 97
50
3.1
3.4
EE422G Signals and Systems Laboratory
Prior to Fall 2007, this course was the second signal and systems course that covered
discrete-time system models, Laplace and z-transforms, system block-diagrams, feedback
analysis, and digital filter design. However after surveying employers, students,
engineers from industry in the signals and systems area, there was a general trend in the
feedback for students to have more experience at translating the theoretical knowledge to
practical problems and implementing these concepts with programs/hardware. As a
result we changed the curriculum to move probability theory out of the first signals and
systems course so it could include the topics of the old EE422G course (now in EE421G),
make probability from the mathematics department a required course, and change
EE422G to a laboratory based course where students solve problems using real and
simulated data with programs such as Matlab, Simulink, and LabVIEW. This change
also addressed the feedback from students in their desire for more experience with these
software packages.
The course is intended to run once a year and be a part of the elective laboratory set for
electrical engineering students. For the Fall 2007 and Spring 2008 semester it was taught
in a hybrid mode where I lectured during lab periods for the first 4 weeks to cover the
9
probability and discrete concepts so that students in transition (those that had the old style
EE421G) would not miss the basic materials. As a result I gave homework and quizzes
on these subjects which would not ordinarily be a part of the regular lab course. Spring
2009 will the first time it is offered with a full set of labs. Therefore, no faculty
assessments of student outcomes were performed because the assignments and
assessment were significantly different from the ones that will be implement in the
regular course. The teacher course evaluations and student self-assessments are
provided, along with the syllabus for the transition period.
EE422G Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Fall 2007
14
3.4
3.6
Spring 2008
14
3.3
3.4
Spring 2009
25
3.2
3.3
Fall 2009
17
3.6
4.0
Fall 2010
23
NAY
NAY
Course Outcome Evaluation
Two evaluations for the student course outcomes are performed. One is a self-evaluation
that reflects the student confidence level and the other is by the instructor derived from
the average score on course assignments related to each outcome.
The course outcomes are student abilities to:
1.
2.
3.
4.
5.
6.
Characterize random signals with correlation and probability density functions
Analyze discrete-time signals with the (discrete) Fast Fourier transform.
Design FIR and IIR filters based on signal and noise specifications.
Characterize system dynamics using impulse responses, transfer functions, and state-variable
representations.
Simulate signals and systems using modern computer software packages
Design experiments to estimate signal and system model parameters from input and/or output
data.
Table EE422G Student Confidence Level Percentage (student self-assessment)
Semester
Enrollments
1
5
6
Mean
Fall 2007
14
75
72.5
72.5
75
72.5
72.5
73.33
Spring 2008
14
85
77.5
77.5
47.5
82.5
72.5
73.75
Spring 2009
25
62.5
70
57.5
57.5
62.5
57.5
61.25
Fall 2009
17
77.5
77.5
77.5
72.5
80
77.5
77.08
Fall 2010
23
NAY
NAY
NAY
NAY
NAY
NAY
NAY
Mean
Standard
Deviation
Course Outcome
2
3
4
75.00
74.38 71.25
63.13
74.38
70.00
71.35
9.35
3.75
12.97
8.98
8.66
8.86
9.46
NAY => Not Available Yet, IP=> In Progress
10
Table EE422G Student Confidence Level Percentage (faculty-assessment)
Semester
Enrollments
1
5
6
Mean
Fall 2007
14
94.93
97.83
99.17
99.25
99.52
99.62
98.39
Spring 2008
14
97.10
97.50
98.33
97.04
96.92
98.50
97.57
Spring 2009
25
70.44
70.71
71.86
67.73
65.09
67.73
68.92
Fall 2009
17
85.58
93.48
94.30
90.84
90.42
94.99
91.60
Fall 2010
23
73.73
75.01
73.99
72.67
79.44
76.37
75.20
76.58
79.73 80.05
77.08
78.31
79.70
78.58
7.96
12.10 12.39
12.17
12.70
13.93
11.88
Mean
Standard
Deviation
Course Outcome
2
3
4
NAY => Not Available Yet, IP=> In Progress
Feedback and Improvement:
Years 2007 and 2008 were transition years, where we had changed this course to a lab
course for the first time and moved the discrete and laplace material into EE421G. So
some of the lab times were devoted to lectures on the material they may have missed for
taking the “old” EE421G as a prerequisite. Also several of the testing/evaluation
assignments were geared to the EE421G outcomes in the transition time. For this reason,
the means and standard deviations for each outcome of the faculty assessment (last rows
of the table) start with Spring 2009.
Note that starting with Spring 2009, the faculty assessments are much closer to the
student self assessments. This is good and primarily because assessments for whole
semester were more closely tied to the course outcomes. Spring 2009 also shows a drop
in the self-assessment ratings. This also is true in the faculty assessments as well
(relative to future semesters). I felt that this was because some students were not
motivated to put time into the lab exercises to reinforce their knowledge. Especially with
group efforts, there was a tendency for a member in a group to exploit the efforts of the
others and not work as hard. This brought the group score down (with limited penalty to
them, since they were pulled up by the others). So I instituted 2 changes in Fall 2009.
One was for each student to sign into the lab upon their arrival. If a student was late or
missing, they would be penalized for not being in the lab. This motivated all the students
in the group to be there. Also I added a final exam to test students knowledge that should
have been reinforced from engagement in the lab activities. This did boost both the self
and faculty assessment scores. I will continue on with these practices.
It is also noted that there is a large standard deviation from year to year on the faculty
assessments. This is in part due to changing TAs and the grading scale they use in
grading complex assignments, such as written lab reports. I could make the final worth
more, but I do want the grade to reflect the significant writing effort, which is more of a
team activity. Therefore, to get more uniformity on the lab exercises I will keep a
11
collection of sample assignments that I or past TAs have graded and give these to the new
TAs can get a sense of a numerical scale for the grading.
EE 462G Electronic Circuits Laboratory
Subjects Covered: Experimental exercises in the design and analysis of electronic
circuits incorporating transistors, zener diodes, integrated circuits, and operational
amplifiers.
This is now an elective lab in the electrical engineering curriculum. For students taking
this lab, it is the second required lab on circuits. The first one emphasized linear circuit
elements, and this one focuses on nonlinear circuit elements. Writing and
instrumentation skills are emphasized.
Teacher/Course Evaluation (by student):
EE462G Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Fall 2002
18
3.5
3.7
Spring 2003
45
3.5
3.8
Fall 2003
23
3.5
3.6
Summer 2004 15
3.7
3.9
Fall 2004
39
3.5
3.6
Summer 2005 8
4
4
Fall 2005
51
3.4
3.5
Spring 2006
26
Lost
Lost
Fall 2006
14
3.7
3.9
Spring 2007
30
3.6
3.7
Course Outcome Evaluation
Two evaluations for the student course outcomes are performed. One is a self-evaluation
that reflects the student confidence level and the other is by the instructor derived from
the average score on course assignments related to each outcome.
Outcome list:
1.
2.
3.
4.
5.
Analyze circuits with nonlinear elements using semiconductor characteristics.
Measure relevant quantities and parameters in electronic circuits using oscilloscopes, multimeters,
function generators, power supplies, and curve tracers.
Analyze electronic circuits with computer simulation programs (SPICE).
Describe an experimental procedure involving circuits with semiconductor devices.
Interpret experimental measurements involving circuits with semiconductor devices
12
Student Self-Assessment:
Students rated their own ability relative to each course outcome on a scale from 1 (no
confidence) to 5 (most confident). The mean was taken over all students and converted
to a percentage (5=100%, 1=0%), which is reported in the table below.
Table EE462 Student Confidence Level Percentage (student self-assessment)
Semester
Fall 2002
Spring 2003
Fall 2003
Summer 2004
Fall 2004
Summer 2005
Fall 2005
Spring 2006
Fall 2006
Spring 2007
Mean
Standard
Deviation
Respon
dents
18
45
23
15
39
4
5
Lost
7
19
1
87.5
75
67.5
90
77.5
82.5
85
Course Outcome
2
3
4
92.5
87.5
92.5
87.5
75
85
87.5
67.5
77.5
90
90
92.5
85
75
80
100
87.5
82.5
90
75
85
5
82.5
77.5
85
90
77.5
82.5
80
Mean
88.5
80
77
90.5
79
87
83
90
90
82.78
92.5
91.6
90.73
85
90
81.39
92.5
90
86.39
90
90
83.89
90
90.32
85.04
7.95
4.30
8.30
5.74
5.17
5.35
NAY => Not Available Yet, IP=> In Progress
Student Self-Evaluation Data Summary
The outcomes ratings range between 75 and 100 with an average rating of 85. The
average class-to-class outcome ratings appear to be holding steady around the mean with
a significant dip for the Fall 2003 and 2004 classes. The outcomes with the greatest
fluctuations are 1 and 3, which are tied to the pre-lab assignments (circuit analysis and
computer simulation). They have a standard deviation around 8, while the others have a
standard deviation around 5. The last 2 times the course ran, students confidence level is
has been either at or above the average within about 1 standard deviation. The Spring
2006 evaluation sheet disappeared, which was disappointing that this data were lost. A
staff member was responsible for bring the evaluation sheets to the college office and
claims they were deliver to the proper office on time.
Assessment by Instructor:
The following assessment tools were used throughout the class:
 Pre-lab assignments
 In-lab data sheets
 Lab written reports
 Final lab demonstrations
The lab reports involving multiple outcomes were graded and recorded in components
corresponding to each outcome. The average score was computed over all students and
assessment tools related to each outcome. The percentage for each outcome is listed in
the table below.
13
Table EE462 Student Performance Level Percentage (Faculty assessment)
Semester
Fall 2002
Spring 2003
Fall 2003
Summer 2004
Fall 2004
Summer 2005
Fall 2005
Spring 2006
Fall 2006
Spring 2007
Mean
Standard
Deviation
Enroll
ments
18
45
23
15
39
8
51
26
14
30
1
91
74.5
84.6
75.5
90.7
88.7
82.4
84.4
93.5
86.4
85.17
6.36
2
98.25
96.5
90.3
85.75
92.5
87.7
85
91
91.9
89.7
Course Outcome
3
4
91
87.2
80.25
97.5
81.9
90.8
76.25
86.25
82.75
89
93
84.4
77.7
75.7
93.1
94.9
87.3
95.8
95.4
95.7
90.86 86.31
4.25
7.26
5
81.75
88.5
94.3
83.9
75
85.8
76
85.1
84.1
81.8
Mean
89.84
87.45
90.00
81.53
88.39
87.92
79.36
89.70
90.52
89.80
89.73
84.83
87.45
6.71
4.79
3.86
Faculty Student Outcome Evaluation Data Summary
The outcomes ratings range between 74.5 and 97.5 with an average rating of 87 and a
standard deviation around 4. The average class-to-class outcome ratings appear to be
holding steady around the mean with a significant dip over one standard deviation for the
Fall 2005. The outcomes with the greatest fluctuations are 3 and 4, where 3 is part of the
pre-lab assignments (circuit analysis and computer simulation) and for deals with
describing lab procedures.
Data Analysis: The average numbers for both the self and faculty performance ratings
are surprisingly similar. In both cases the scores have been higher than average the last 2
times the course ran. This is encouraging and suggests that changes in instruction made
along the way have made a difference. The outcomes with the greatest standard
deviations (1 and 3 for self, 3 and 4 for faculty) show the most improvement. Overall
there is no trend or significant difference in the student or faculty ratings that suggest a
particular improvement at this time.
Feedback and improvement:
Lab infrastructure development: Over the course of teaching this lab in the first 3
years, I have guided the upgrade of oscilloscopes and curve tracer. The curve tracer was
over 18 years old and outdated, the oscilloscopes were over 4 years old and were the
oldest scopes in all our teach labs, even having less features than the oscilloscopes used
in the sophomore labs. In addition I managed to convince the department to put PCs at
all lab stations with GPIB interfaces for automated instrumentation with a LabVIEW
interface. The newer equipment has enabled new labs to be designed that involve the
students more with programming and advanced data analysis. Future improvements will
likely involve moving away from the GPIB interface to USB, and designing labs that
require more LabVIEW programming assignments.
The outcomes started off high because the grading standard was not as rigorous the first
year. The TAs do most of grading for the lab reports, and my ability to train them is
14
limited, due to time constraints and high variability in the TAs’ background. My first
priority was having students write procedures, which they seem to rank high even from
the beginning. I have been working on being more general in the lab assignment
instructions so the students are doing more experimental design and less cookbook lab
assignments. These were changes to enforce a standard that I feel is important and in the
students and professions best interest. My work to help students in their achievement of
these outcomes had not been all that successful at first as indicated from the outcomes on
both the instructor and student assessments in Fall 2005. Since then I had made several
changes and stress more of what I expect in the lab write ups with the hope of improving
performance and it has appeared to make a difference in improving Outcome 4.
I felt that many of the labs originally developed by Dr. Radun were high quality; however
they took more time that expected for a 2 credit hour lab for the students to complete and
write up. As a result students were skipping or doing substandard work on critical parts
of the lab just to get the assignment done. Since summer of 2004 I have reduced the
number of labs and now only required only one lab report and one pre-lab assignment
from each team, so that students can put more effort into the writing process (one writes
and the other edits or checks prelab work, and then they switch off for subsequent labs).
I was hoping they would learn from each other better in this approach and take more time
to do the assignments well. I also end the labs a couple of weeks early to have an open
lab where students can come in and work on things they had questions about and prepare
for the final lab practical exam. This unstructured time appears to be working as
indicated by the consistent outcome rating since then.
Another improvement over the years has been in the interpretation of the data. Students
have always rated themselves relatively low. With the new equipment I have been
lecturing more on computing statistics and relating that to the circuit and instrumentation
properties. I shifted the experimental focus on some labs to collecting large data sets and
analyzing the variability of an experimental measurement (using Matlab and LabVIEW).
The quality of the measurement (procedure) can then be judged based on repeatability.
In its current state I will focus more on upgrading equipment (a new version of B2SPICE
is greatly needed) and providing the students with opportunities for more unstructured
time in the lab where they can pursue some of their own questions that arise from the
standard experiments.
EE 499 Senior Design
Subjects Covered: A course for senior students in electrical engineering with an
emphasis on the engineering design processes requiring the creative involvement of
students in open-ended problems relating to actual designs that are appropriate to the
profession of electrical engineering.
Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Spring 99
8
3.3
3.6
15
Spring 99 Project Description: The following design project requires a good
understanding of the concepts presented in EE380, EE421, and EE422 (some of EE461).
The skills most often required for typical project tasks include programming (in Matlab,
C, and assembly language) and teamwork. This design project focuses on a system that
processes a voiced melody and synthesizes sounds in real-time to create a harmony. The
critical element for the design solution involves programming a TMS320C3x floatingpoint DSP chip. Subtasks include designing simple audio amps (microphone and speaker
circuits), and analyzing sounds from various voices and instruments.
Student Self-Assessment:
Spring 99
Students Responding Expected Grade Level of confidence
Course Outcomes
effectively work in groups to propose and
develop engineering solutions.
6
3.2
76.00%
apply previously acquired engineering principles
as well as learn new principles in solving a large
engineering design problem.
6
3.2
84.00%
communicate and thoroughly document the
results of an engineering design project to the
engineering community using a variety of media
(report, web page).
6
3.2
74.00%
Assessment tools:
Optional questions on teacher/course evaluation that asked to student the rate their own
ability to perform each of the course outcomes
Assessment by Instructor:
Course Outcomes
Spring 99
Students Evaluated Percent of total Grade Assessment Score
effectively work in groups to propose and
develop engineering solutions.
8
0.25
90.37%
apply previously acquired engineering
principles as well as learn new principles in
solving a large engineering design problem.
8
0.3
95.33%
communicate and thoroughly document the
results of an engineering design project to the
engineering community using a variety of
media (report, web page).
8
0.45
88.50%
Assessment tools:
 Group written pre proposals for solving the design problem that included a
breakdown into subtasks, a timetable for completion, and division of effort among
the team members
16




Individual oral presentations where each person explains the overall objective of
the team and their individual role in the project including the individual tasks they
need to accomplish (presentation are 50% peer rated)
Individual engineering notebooks where the students log their work on the project
Group presentation of the final product (demonstrate performance)
Group written final report documenting the design and the design process
Feedback and improvement:
This is the first time I am using this style of assessment and the first time I taught this
course. The numbers above represent a baseline score, and more data are required to
draw specific conclusions about what should be improved. Since this was my first time
teaching the course, I do have quite a few ideas on how to make it better. Students liked
learning about programming the DSP hardware and were frustrated that all students did
not get a chance to learn it (effort was divided up where some group member were
primarily working on the algorithms and supporting analog hardware). As a result I have
a have developed a set of 5 lab experiments designed to introduce the student to program
and debug the C31 DSP chip. I will use this that next time I teach a DSP related course
using the C30.
EE 513 Audio Signals and Systems (A new course)
Subjects Covered: Audio system and signal models, analysis and design of audio
systems.
I had the idea to offer a course like this for a long time. I finally followed through on it
when a group of students presented me with a petition to offer a course in audio systems.
With many interested students on hand, I decided to offer this course the following year
in the summer so as not to burden the department with one more 500-level course in the
regular year. I also felt this course was a good idea because there are currently no seniorlevel courses offered in signal processing and audio is a good application through which
to teach several popular analyses and design approaches in signal processing. Signal
processing makes up a significant part of the electrical engineering profession and with
the information age taking us to a place of “sensors everywhere,” signal processing will
continue to be an important component of the electrical engineering community.
The course was offered 3 times as an EE599 course and went through significant changes
each time. I initially wanted to cover psychoacoustics and nonlinear processing, but soon
realized that students were struggling with programming concepts as well as the linear
filter theory that they were introduced to in the signals and systems junior-level courses.
So I refocused the course on digital filter design and applied signal analysis techniques. I
had been able to fit in a very simple introduction to a Bayesian design of classifiers and
give a studio project on it that involved classify recorded sounds or words.
17
In Fall 2007 the course was finally offered as an official senior elective course EE513,
but was not listed until a week before the semester started and it conflicted with senior
design, so only 4 students signed up. Because of their graduation status and limitations
of the students’ schedules who had signed up, the department chair decided to let it run.
In the Spring 2008 it was offered again with 14 student registering. The faculty
assessments at this point are just developing and are difficult even to use as a base line.
Below is a syllabus of the last time it was offered along with the teacher course
evaluations.
Teacher/Course Evaluation (by student):
EE513 Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Fall 2007
4
3.7
4
Spring 2008
13
3.2
3.5
Spring 2009
9
3.9
3.8
Spring 2010
11
3.7
3.7
Course Outcome Evaluation
Two evaluations for the student course outcomes are performed. One is a self-evaluation
that reflects the student confidence level and the other is by the instructor derived from
the average score on course assignments related to each outcome.
Outcome list:
1.
2.
3.
4.
5.
6.
Characterize digital audio systems with difference equations and transfer functions.
Characterize digital audio signals with correlation functions and power spectra.
Design systems for processing audio data for applications such as filtering, audio effects, and
signal classification.
Know the fundamental principles of acoustic energy generation and propagation.
Program with mathematics software to implement and evaluate designs.
Work as a team to solve multi-component problems.
Table EE513 Student Confidence Level Percentage (student self-assessment)
Semester
Fall 2007
Spring 2008
Spring 2009
Spring 2010
Mean
Standard
Deviation
Enrollments
4
13
9
11
1
75
87.5
85
70
Course Outcome
2
3
4
75
87.5
37.5
77.5
80
82.5
77.5
77.5
72.5
77.5
80
72.5
79.38
76.88 81.25
66.25
81.25
84.38
Mean
72.92
81.25
81.25
77.50
78.23
8.26
1.25
19.74
4.33
5.54
7.24
4.33
NAY => Not Available Yet, IP=> In Progress
18
5
75
82.5
85
82.5
6
87.5
77.5
90
82.5
Table EE513 Student Confidence Level Percentage (faculty - assessment)
Semester
Fall 2007
Spring 2008
Spring 2009
Spring 2010
Mean
Enrollments
4
13
9
11
Standard
Deviation
1
66.13
85.14
83.77
91.70
Course Outcome
2
3
4
71.36 92.58
67.12
88.21
84.55
83.12
79.93
86.43
87.46
89.51 87.81
90.76
5
92.39
83.69
93.29
92.37
6
93.63
84.87
91.60
92.78
Mean
80.53
84.93
87.08
90.82
81.69
82.25 87.84
82.11
90.43
90.72
85.84
10.93
8.41
10.48
4.52
3.99
4.29
3.43
NAY => Not Available Yet, IP=> In Progress
Feedback and Improvement:
There appears to be a general agreement between the instructors and student selfevaluations. There is a significant increase in the outcomes (with both evaluations) after
he first offering. One big difference was that I required individual written reports from
the group projects. I think this helped student better assimilate the knowledge and skills
they learned from each studio project. Both evaluations give higher ratings to the
software and teamwork outcomes (5 and 6). This is the emphasis of the course, so it does
not surprise me. I probably need to give more short quizzes throughout the semester to
keep them focused on their analytical skills, however that would detract from the
programming efforts. So I may try a modest increase (maybe 6 total quizzes) and see if
that raises outcomes 1 through 4. In the most recent offering I did not use a textbook,
since none of the book are a good fit for this course. The outcomes did go down a little
after that, but it is within the standard deviations. So I will try it one more semester to
see if there is a negative results from that.
EE611 Deterministic Systems
Subjects covered: Linear system models and solutions for multiple-input-output
systems, System model controllability, System model observability, State-feedback
design.
Expected Student Outcomes: A student who has successfully completed this course should be able to:
1. Use transfer function and state-space representations to describe linear systems .
2. Classify systems based on their properties and descriptions, which include causality, linearity,
time-invariance, continuous-time, controllability, observability, and stability.
3. Solve for system outputs given inputs, initial conditions/states, and state-space or transfer function
descriptions.
4. Analyze and design state observer systems.
5. Analyze and design state feedback systems
6. Create computer programs (such as MATLAB, Octave, or Python) to analyze and design systems
19
Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Fall 92
37
3.5
3.7
Fall 2008
31
3.7
3.8
Fall 2009
17
3.8
3.7
EE630 Digital Signal Processing
Subjects covered: Z-transforms, Digital Filter Design, and Analog-to-Digital and
Digital-to-Analog Conversion, lab component Filter implementation on DSP hardware.
Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Fall 95
11
3.6
3.6
Fall 94
14
3.6
3.7
Fall 93
9
3.4
3.7
EE 639 Advanced Topics in Communications and Signal Processing
Subjects Covered: Advanced topics in signal processing and communications research
and design topics of current interests. A review and extension of current literature and
selected papers and reports.
Spring 2002 Subjects Covered: Statistical Modeling by Wavelets, Estimation Theory
for Signal Processing and Communications, and DSP implementation
Fall 99 Subjects Covered: Statistical Modeling by Wavelets, Estimation Theory for
Signal Processing and Communications
Spring 96 Subjects Covered: Modern Spectral Estimation and Optimal Filtering
Spring 95 Subjects Covered: Wavelets and Higher Order Statistics
Spring 94 Subjects Covered: Modern Spectral Estimation and Optimal Filtering
Student Ratings of Instructor and Course (Scale 1-4 with 4 being the best)
Term
Enrollment
Value of Course
Quality of Teaching
Spring 02
14
3.8
3.8
Fall 99
5
3.5
3.5
Spring 96
3
No Evaluation
No Evaluation
Spring 95
8
3.9
3.9
Spring 94
9
3.2
3.7
20
Student Self-Assessment:
Course Outcomes
Ability to read and critically evaluate
research in selected areas of
communications and signal processing
Ability to mathematically model
communication and signal processing
systems
Ability to design a Monte Carlo
simulation for performance analysis of
communications and signal processing
systems
Ability to implement signal processing
algorithms with modern hardware
Fall 99
Spring 02
Students Assessment Percent of Students Assessment Percent of
Evaluated Score
total Grade Evaluated
Score
total Grade
5
80.66%
27.50%
7
4
10%
5
78.81%
45.00%
7
3.86
27.5%
5
79.93%
27.50%
7
4.36
27.55
NA
NA
NA
7
4.14
35%
Assessment by Instructor:
Course Outcomes
Ability to read and critically evaluate
research in selected areas of
communications and signal processing
Ability to mathematically model
communication and signal processing
systems
Ability to design a Monte Carlo
simulation for performance analysis of
communications and signal processing
systems
Ability to implement signal processing
algorithms with modern hardware
Fall 99
Spring 02
Students Assessment Percent of Students Assessment Percent of
Evaluated Score
total Grade Evaluated
Score
total Grade
5
80.66%
27.50%
14
91%
10%
5
78.81%
45.00%
14
89%
27.5%
5
79.93%
27.50%
14
88%
27.55
NA
NA
NA
14
99%
35%
Assessment tools:
 Literature review on designated topic of interest
 A Monte Carlo simulation design homework assignment
 Oral presentations explaining a result in the literature they want to challenge or
confirm with a Monte Carlo simulation. (presentations are peer and instructor
rated)
 Written final report documenting the research and conclusions
Feedback and improvement:
Between the two times I have taught this course, I added an additional outcome as a
result of responding to an employers survey indicating that they like to see the graduates
aware of how to implement the signal processing algorithm they learn the theory on.
Therefore, NA is present in the 4th outcome for the Fall 99 class. There appears to be an
improvement in the Monte Carlo methods. I have provided better examples and teaching
21
material in the Spring 2002 class, since I was able to build on the material presented in
previous classes. I also worked harder with the students to help them find reasonable
projects to work on for the class. This may have been the reason for the increase.
Overall I think as an advanced topics course I try to cover too much since we have a
significant gap in our grad program for signal processing courses. It may best to create
an EE631 course as a sequel to EE630 and teach more fundamental concepts where they
can focus on intermediate theory required to tackle more advanced concepts.
Student Evaluation Summary:
My student evaluations have been consistently positive over my career ( ranging from 3.0
to 4.0 on a 4 point scale). This is typically equal to or greater than the college average.
However, there was a relative dip in ratings between 1997 and 1999. This I believe was
more a function of distractions from my increased administrative duties (Interim Chair)
and decreased margins in my personal life, rather than something systematic in my
teaching style. Both sources of distraction have changed significantly, and my teaching
ratings have gone back up. I take most student praise and criticisms seriously as listed in
their comments and have accommodated when it felt benefit both the students and
profession which they desired to work in.
Advising Evaluation:
While Director of Undergraduate Studies (DUGS) I was the advisor’s advisor to the
department. I advised all students who are transferring in and helped new faculty
understand the curriculum and their advising responsibilities. I regularly trained new
faculty and remind old faculty in regards to the curriculum and their advising duties. I
had not collected data to quantitatively assess my performance or the department’s
performance in this area. I was not aware of any consistent complaints from the faculty
or the students on the way I perform my advising duties as DUGS.
Since becoming a regular faculty member in 2006 I advise about 20 students every
semester. I try to ask them questions on their progress and give them opportunities to ask
questions that go beyond the curriculum and relate to the profession and their careers. I
find it rewarding and have not performed assessment on my job in this regards.
22
APPENDIX A
Student Evaluations of Courses and Teaching
From Fall 2006 through Spring 2010
Following the summary table of course and teacher ratings since starting at university of
Kentucky in Fall 1991, the original student evaluations for the last 5 years are arranged in
reverse chronological order (most recent first) with student comments first and then the
numerical ratings.
Table 1. Summary of course taught with Course/Teaching Rating
EE101Term
Enrollment
Value of Course
Fall 2000
92
3.2
Fall 2001
102
3.1
Fall 2002
107
3.2
EE211 Term Enrollment
Value of Course
Spring 02
33
3.6
Fall 01
55
3.7
Spring 01
27
3.8
Fall 00
45
3.5
Spring 00
29
3.6
EE221 Term Enrollment
Value of Course
Fall 2010
34
IP
Spring 2010
34
3.5
Fall 2008
19
3.5
Spring 2007
33
3.2
Spring 2005
43
3.3
Fall 98
34
3.4
Fall 97
44 Team Taught 50%
3.0
Summer 97
19
No Evaluation
Fall 96
41
3.5
Summer 96
19
No Evaluation
Spring 96
59
3.3
Fall 95
61
3.2
Fall 94
26
3.5
Summer 94
10
No Evaluation
Spring 93
71
3.4
Spring 92
58
3.5
Fall 91
51
3.6
EE222 Term Enrollment
Value of Course
Fall 96
44
3.2
EE307 Term Enrollment
Value of Course
23
Quality of Teaching
3.5
3.4
3.4
Quality of Teaching
3.8
3.8
3.9
3.6
3.7
Quality of Teaching
IP
3.1
3.4
3.2
3.1
3.5
3.2
No Evaluation
3.6
No Evaluation
3.2
3.3
3.6
No Evaluation
3.6
3.5
3.6
Quality of Teaching
3.6
Quality of Teaching
Summer 96
Summer 95
EE380 Term
Fall 93
Spring 93
EE421 Term
Spring 98
Spring 97
EE422 Term
Fall 2010
Fall 2009
Spring 2009
Spring 2008
Fall 2007
Fall 92
EE462GTerm
Spring 2007
Fall 2006
Spring 2006
Fall 2005
Summer 2005
Fall 2004
Summer 2004
Fall 2003
Spring 2003
Fall 2002
EE499 Term
Spring 99
EE513 Term
Spring 2010
Spring 2009
Spring 2008
Fall 2007
EE599 Term
Fall 2006
Spring 2004
Summer 2003
EE611 Term
Fall 2009
Fall 2008
Fall 92
EE630 Term
Fall 95
Fall 94
Fall 93
40
41
Enrollment
41
47
Enrollment
29
50
Enrollment
23
17
25
14
14
20
Enrollment
30
14
20
51
8
39
15
23
45
18
Enrollment
8
Enrollment
11
9
13
4
Enrollment
15
7
14
Enrollment
17
19
37
Enrollment
11
14
9
No Evaluation
No Evaluation
Value of Course
2.9
3.3
Value of Course
3.2
3.1
Value of Course
IP
3.6
3.5
3.3
3.4
3.7
Value of Course
3.6
3.7
(lost records)
3.4
4
3.5
3.7
3.5
3.5
3.5
Value of Course
3.3
Value of Course
3.7
3.9
3.2
3.7
Value of Course
3.8
3.3
3.7
Value of Course
3.5
3.7
3.5
Value of Course
3.6
3.6
3.4
24
No Evaluation
No Evaluation
Quality of Teaching
3.0
3.1
Quality of Teaching
3.0
3.4
Quality of Teaching
IP
4.0
3.3
3.4
3.6
3.7
Quality of Teaching
3.7
3.9
(lost records)
3.5
4
3.6
3.9
3.6
3.8
3.7
Quality of Teaching
3.6
Quality of Teaching
3.7
3.8
3.5
4
Quality of Teaching
3.9
3.5
3.9
Quality of Teaching
3.7
3.8
3.7
Quality of Teaching
3.6
3.7
3.7
EE 639 Term
Spring 02
Fall 99
Spring 96
Spring 95
Spring 94
Enrollment
14
5
3
8
9
Value of Course
3.8
3.5
No Evaluation
3.9
3.2
25
Quality of Teaching
3.8
3.5
No Evaluation
3.9
3.7