Teaching PortfolioTeaching...

Teaching PortfolioTeaching PortfolioTeaching PortfolioTeaching Portfolio

Ira Gerhardt

Northwestern University

October 27, 2008

Ira Gerhardt: Teaching Portfolio

2

Table of Contents

1. Teaching Philosophy..............................................................................................3

2. Evidence of Teaching.............................................................................................5 2.1. IEMS 317: Discrete Event Systems Simulation - Winter, 2008.......................................... 5

2.1.1. Sample Lesson Plan (Abbreviated), with Lecture Slides.............................................. 6 2.1.2. Sample Project Assignment, with Grading Rubric ....................................................... 8 2.1.3. Sample In-Class Activity: HAT!!................................................................................ 10 2.1.4. Review Lecture, Example Slides ................................................................................ 11

2.2. 18.03: Differential Equations - Fall, 1997 to Spring, 1999 ............................................... 13

3. Teaching Interests ............................................................................................... 15 3.1. Statistics (200/300/400 level Math or Engineering) .......................................................... 15 3.2. Stochastic Models (300/400 level IE/MS)......................................................................... 15 3.3. Operations Research (300/400 level Math or Engineering) .............................................. 16

4. Professional Development .................................................................................. 17 4.1. Graduate Teaching Certificate Program ............................................................................ 17 4.2. Teaching with Technology ................................................................................................ 17

5. Outside Evaluation.............................................................................................. 18 5.1. Course Teacher Evaluation Reports (CTECs) ................................................................... 18

5.1.1. CTEC Scores............................................................................................................... 18 5.1.2. CTEC Comments........................................................................................................ 20

5.2. Faculty Review of a Lecture .............................................................................................. 21


3

1. Teaching Philosophy I believe the classroom experience should be an interactive one, rather than one where one individual lectures while other individuals transcribe. As a student, I find the prospect of sitting for an hour or two, being lectured at with little opportunity to respond or to apply the ideas that have just been explained to me, unpleasant. As a teacher, I consider the idea of writing furiously on the blackboard, with my back to the class uninterrupted for the same hour or two, ineffective. Although one responsibility of a teacher is to introduce new facts, formulas, and concepts into the classroom setting, another responsibility is to engage their students. The best teachers I have had are the ones who talked with me, rather than those who talked at me. Not all classes are designed to be discussion-oriented. In many science, mathematics, and engineering courses, the lecturer may need to spend much of the class stating theorems and deriving formulas. However, even in this situation, interactions can be created. The teacher may ask students to provide intuition for various components of the formula, and should encourage students to provide examples to which to apply the theorem. The lecturer may introduce the necessary steps to solve a particular type of problem, but it can be the students who apply these steps to a given example. This may seem applicable only to the small-classroom setting. After all, structuring a lecture with 600 or more students as a discussion may prove difficult, but the class must still be designed to engage as many students as possible. For example, Personalized Response Systems may be utilized in a large lecture class to survey which of a set of possible solutions is the correct one. Recognizing that students register for a course for reasons ranging from genuine passion for the material to fulfilling a requirement, and creating an interactive atmosphere within the classroom, can go a long way towards allowing the students to gain from the course whatever they need. At present, I have had two opportunities to serve as an instructor in a formal setting. The first of these was as an undergraduate recitation instructor for the Mathematics Department at M.I.T., orchestrating a semi-weekly tutorial for approximately 30 students in which I assisted them in understanding the lecture material from an Ordinary Differential Equations (ODE) course. I learned two lessons from this very quickly: first, that I am not a super-genius, and second, that students do not always need a super-genius at the front of the classroom. I simply presented an example ODE problem, and then walked through the steps that led to its solution. This allowed me to connect with the students by getting them involved in recitation, whether they worked on the problems with me or in groups amongst themselves; this often resulted in the students presenting the solution to the class rather than my doing it. Recently I served as the instructor for a 300-level Simulation course that is required for Industrial Engineering majors at Northwestern University. It was my first experience being completely responsible for all aspects of course delivery, including the assessment plan, as well as resolving student conflict and assigning final grades. I learned there is much more to teaching than just deciding what material should be presented in lecture. One important issue to consider is the style of teaching. For example, I made sure to know the names of 75% of my students by the end of the first class, and I often told relevant jokes and stories to engage the students at the start of lecture. Also important to consider is the proactive nature of learning. I utilized in-class


4

activities, and provided a term-long consulting case for which the students performed tasks that mirrored the course topics as the term progressed. Finally, it is important to consider the internalization of theory through application. I structured the course to be project-oriented, so that the simulation and statistics topics presented in lecture were then used in modeling recycling centers, drive-thru windows, and machine shops. Additionally, I found that engaging my students meant talking with them, not only having one student explain the correct solution, but also having one who did not solve it correctly explain their intuition (i.e., what caused them to select the answer they did) in order to allow them to feel comfortable moving forward with the class. Teaching is difficult and rewarding, time-consuming and full of opportunities for assessment, for both my students and myself. There is much to gain from interacting with students, and there are also challenges. It is the combination of the two that makes teaching at the university level attractive to me, and it is why I feel I am, and will continue to be, a good teacher.


5

2. Evidence of Teaching I have often told students that it is by attending class that they give themselves the best opportunity to understand the material. This seems obvious, yet students do not always realize that they learn the most when they join in the dialogue that occurs between the instructor and the students when a lesson is going well. Similarly, the best way to appreciate how I teach and what I do to engage my students would be to attend a lecture, sit in on an office hour, or work on an assignment. To represent this experience, I am including material from the Simulation course I taught in Winter, 2008: a sample lesson plan (with accompanying slides), project assignment (and grading rubric), in-class activity, and a pair of slides from the end-of-term review lecture (done in Dr. Seuss-style). I am also including a hand-out I made for my students when I was an undergraduate recitation instructor with the M.I.T. Math Department. Each of these hopefully provides insight into what I strive to accomplish as a teacher: to engage the students, to take high-level material and give it a practical context to which they can relate, and to try to do it all with humor and humanity.

2.1. IEMS 317: Discrete Event Systems Simulation - Winter, 2008

During the Winter, 2008 quarter, I was selected to serve as the instructor for IEMS 317: Discrete Event Systems Simulation. This course is a degree requirement for Industrial Engineering (IE) majors at Northwestern University, and is frequently sought out by other engineering students interested in gaining familiarity with simulation techniques. The course was project-oriented; I felt strongly that the best way for the students to use the material covered in class was in the form of simulation projects similar to those they would encounter in the engineering and consulting worlds. All aspects of the class—lectures, projects, our consulting case (a term-long class-participation project designed to tie together all class topics), in-class activities, and the final exam (as well as the review lecture, done entirely in rhyme)—had the goal in mind of making the students more comfortable with doing simulation.


6

2.1.1. Sample Lesson Plan (Abbreviated), with Lecture Slides

Monday, February 4th, 2008

IEMS 317-0, Winter 2008 Lecture #12

Files to have opened at start of class:

- Announcements_Winter2008.ppt - ToPost_TheCase-NotDell_Winter2008.ppt - Output Analysis.ppt

- Announcements (see ppt)

- Humor/Stuff: Animation: grad student seeing their efforts over five years of their life come to fruition

- NotDell Case: Task #3 (lowest mean cost scenario)

- Notes: What is intuition behind results we see for darkest colors? What physical properties of a scenario make it undesirable? - Notes: Did it work? Did your model with sufficient reps meet the error requirement? (Yes it did. But you should always check.) - Notes: Why didn’t you go above (19,7)—especially since you covered below these levels so thoroughly? - Notes: Great job covering this many scenarios.

- NotDell Case: Task #4 (risk analysis)

- Notes: Why did you choose “Roundup” for the demand sample? Would ‘Round’ have been sufficient?

- Remind students this is ‘testable’ material. Next slide: ‘More on NotDell’, then introduce Task

#5 (additional input modeling). - Add’l Lecture Notes: Output Analysis.ppt (Output Analysis)

- (slide #215) note this week is HEAVY on stats (yea!!) - (slide #216)

- note that ‘measure of error’ is what drives # of reps (should know that from Project #1) - (slide #217) go through entirely

- ‘Terminating’ is MORE like reality - example of ‘time-dependent…’ is avg. # of customers in bank at specific time - ‘Steady-state’ will be Project #3—kind of like queueing models

- (slide #218) read - (slide #219) ‘steady-state’ means you don’t care when it’s going to end - (slide #220) read - (slide #221)

- IN REALITY: things change… but we model as though they don’t change - ‘stationary’: internet is bad example (always growing)


7

- ‘changeovers’ refers to NOT a flawless change

- (lesson plan abbreviated) …

Corresponding Lecture Slides


8

2.1.2. Sample Project Assignment, with Grading Rubric

IEMS 317, Winter 2008 Ira Gerhardt Project 1: Garbage Recycling Decision Approximation due: January 25, 2008 Project due: February 1, 2008

Seymour, a medium-sized Midwestern community, is planning to upgrade the incinerating system of its recycling center to keep up with increasing demand. There are three options to fuel the system: natural gas (NG), bunker oil (BO) and wood (W). Each of the three fuels involves different investment costs, operating expense rates and material cost rates.

Operating expenses are functions of the quantity of garbage processed, which is expected to be 1 million tons during the first year, and growing at a rate of roughly 10% per year thereafter. Operating expenses are $0.50/ton for NG, $0.60/ton for BO, and $1/ton for W.

The material costs also depend on the quantity of garbage processed, and in some cases the price of fuel. For W the cost is $0.10/ton processed. For BO, the cost to process a ton of garbage is $0.02P, where P is the price of a barrel of oil. The cost per ton for NG is $0.40 + $0.001G per ton, where G is the cost of 107 kilocalories of NG.

There are initial investment costs of approximately $6 million for each option. However, for NG and BO these costs must be incurred entirely before start up (call this year 0), while for W $4 million is incurred in year 0, and the remaining $2 million in year 1. Actually, the $2 million in year 1 is a worst-case scenario; far more typical are costs of around $1 million.

The new system is expected to last for 10 years. Naturally, Seymour would like to invest in the system with the lowest net present value of cost (bringing everything back to year 0). But city planners realize that the price of oil, the price of natural gas, and the growth in garbage to be processed are all variable. Therefore, they would also like to manage their risk of incurring excessive costs.

Data on the yearly average price of a barrel of oil for 1983-2008, and natural gas for 1997-2005, are in this file. Please use this data for your input models, even if you believe you have better sources.

The city planners believe that the growth in garbage will be about 10% per year, but could range from 5% to 15%. After further questioning they agreed that these are not even firm lower and upper bounds.

You will need to come up with a reasonable discount rate to use in your net present value calculation [We will not worry about modeling inflation.] Use the Excel NPV function to compute the net present value.

Approximation: Develop a spreadsheet that uses mean or most likely values for everything. Turn in two printouts of your spreadsheet, one that shows the results, and the other that displays the formulas. To display formulas Press CTRL + ` (grave accent) .

Project: Your task is to evaluate the options, make a recommendation, and explain the risks to the city planners. You must look at both the mean NPV and also assess risk in a meaningful way. Your recommendation should balance these objectives. Since accounting will also read your report, an Appendix should justify the input models you chose and the # iterations you used. The Appendix should also give the spreadsheet model with the formulas displayed.


9

Example Grading Rubric


10

2.1.3. Sample In-Class Activity: HAT!! I frequently provided my students the opportunity to be active participants in class. One technique I utilized was to have them work in small groups on brief simulation problems that would help reinforce their knowledge of the material and their familiarity with the simulation software tool Arena. I put little slips of paper with these problems in a baseball cap that I had with me in class and had a student randomly select one; the groups then had five to ten minutes to design a solution. I present examples of these problems below, with a prepared solution for each.

Modeling Moments: Set Ups A workstation handles ten different types of parts. Whenever a part arrives that is of a different type from the last part that the workstation processed there is a two-minute changeover time to alter the tooling. How can we incorporate this changeover time?

(1st file, ‘e’ view) Secret: Define global Variable ‘Last Type’. Immediately after entity creation Assign attribute ‘Part Type’ (uniform discrete), second attribute ‘Part Time’ (constant) . Use Decide to see if ‘Part Type’ is equal to ‘Last Type’, if so send on to be processed as-is, if not, Assign ‘Part Time’ equal to ‘Part Time + 2’. After processing, set ‘Last Type’ equal to ‘Part Type’.

Modeling Moments: Animating Groups of Entities In an order fulfillment center, boxes come down a conveyor from time to time. When eight boxes have collected they are moved as a group to a sorting center, and then distributed individually to the appropriate truck. How can we model this grouping and ungrouping, and animate it so we see the boxes move as a group?

(2nd file, ‘g’ view) Secret: Use Batch to make group of 8. After batch, Assign picture to GroupBox (picture of 8 boxes together). Use Separate to unbatch, Assign new picture (original picture) of individual box to each.

Modeling Moments: Skill Sets A call center has four basic tasks: taking individual customer orders, taking business customer orders, answering individual customer questions, and answering business customer questions. Operators are rated I-Order (can only take individual customer orders), I-All (can do all individual customer tasks), B-All (can do all business customer tasks), All (can do all tasks). When a customer calls and has been classified (individual or business; order or question), how can we model its request for the right kind of operator resource?

(2nd file, ‘k’ view) Secret: Uses Sets. Define a Set for each type of caller, such that the Resources in that Set are the list of operators that are capable of answering that caller’s question.


11

2.1.4. Review Lecture, Example Slides During the term, I presented a review slide at the start of any class wherein I continued a previously introduced topic. Towards the end of the term, I realized that the set of review slides effectively told the story of our class. I utilized them in the review lecture I gave the last day of class; that day (March 14, 2008) was also the release date of the movie version of Dr. Seuss’ Horton Hears a Who™. In honor of that release (and as a big Dr. Seuss fan), I performed the review lecture entirely in rhyme. The following pictures are the first two slides from this presentation, with the accompanying verses. The students received a hand-out packet with only the slides, while I utilized the Notes View in PowerPoint to perform the accompanying verses.


12

The lecture lasted approximately half an hour and consisted of an additional 14 slides, covering topics from confidence intervals to linear congruential generators and common random numbers. I feel the lecture was well received; at its conclusion, there was a round of applause from the students, and to this day, I encounter undergraduates in our department who ask if I really gave a lecture entirely in rhyme. But the greatest indicator of the usefulness of the lecture was that my students made it clear by their performance that they were well-prepared for the final exam.


13

2.2. 18.03: Differential Equations - Fall, 1997 to Spring, 1999

During my undergraduate experience, I served four semesters as a recitation instructor for 18.03: Introduction to Ordinary Differential Equations (ODEs), in the Mathematics Department at M.I.T. The focus of the class is to introduce students to differential equations (as opposed to algebraic ones), where, rather than identifying the value of a variable that satisfies a set of equations, we try to identify the function whose properties (slope, inflection, etc.) are described. ODEs are the building blocks of much of the research done in engineering, and have applications in several other areas as well. My goal as a recitation instructor was to provide the students with a multitude of example ODE problems and to review the steps necessary to solve the equations. The following page is a handout I designed to assist students in solving second-order constant coefficient ODEs where the right-hand side is non-zero, with a specific focus on identifying the pit-falls in this type of problem (e.g., see Example 3).


14


15

3. Teaching Interests It is important to try new things, especially when there is no guarantee of immediate success. I feel I have been successful as an instructor in the classes that I have already been involved with; in this section, I provide mini-syllabi for other subjects that I might have a future opportunity to teach. These include a brief list of course objectives and topics as well as a basic assessment plan. Notice that objectives, topics and assessments will vary by whether the target student population is undergraduate (200 or 300 level) or graduate (400 level).

3.1. Statistics (200/300/400 level Math or Engineering) • Course Objectives: At the completion of this course, students should …

- be comfortable with basic/advanced statistical concepts such as single and multiple factor regression, analysis of variance (ANOVA) including confidence intervals, hypothesis testing and estimation.

- be able to utilize a statistical software package (such as Minitab, SAS, SPSS, etc.) in obtaining useful insights through the analysis of data sets.

• Covered topics to include (chosen appropriate to level): - Populations vs. Samples. - Mean, variance, correlation, z-scores, and t-stats. - Confidence intervals vs. Prediction intervals. - Linear model theory and multiple regression. - ANOVA, in single and multiple factor models. - Hypothesis testing, MLE, bias, and likelihood-ratio tests.

• Sample Assessment Plan: - Weekly homework (200 & 300 levels) or biweekly problem sets (400 level) ~ 25%.

⇒ May include small quizzes to reinforce reading assignments. - Computer labs (application to data sets) ~ 10%. - Exams: Midterm ~ 20%, Final ~ 35%.

3.2. Stochastic Models (300/400 level IE/MS) • Course Objectives: At the completion of this course, students should …

- be able to model and analyze stochastic systems, with a focus on applications. - be able to calculate steady-state performance measures for stationary queueing models

and identify when these measures are relevant. - (300 level) be able to utilize a simulation software package (such as Arena, SIMUL8,

ProModel, etc.) in modeling discrete-event systems with stochastic interarrival and service/processing times.


16

• Covered topics to include (chosen appropriate to level): - Poisson processes, exponential distributions, and the memoryless property. - Renewal theory and regenerative processes. - Markov chains, Markov processes, and Semi-Markov processes. - Queueing models, ergodicity, limiting and steady-state distributions, and Little’s Law. - Random walks and Brownian motion.

• Potential texts: - Stochastic Modeling, Analysis and Simulation, Barry L. Nelson. - Adventures in Stochastic Processes, Sidney Resnick. - Stochastic Processes, Sheldon M. Ross. - Modeling and Analysis of Stochastic Systems, Vidyadhar G. Kulkarni.

• Sample Assessment Plan: - Weekly homework (200 & 300 levels) or biweekly problem sets (400 level) ~ 30%.

⇒ May include small quizzes to reinforce reading assignments. - Exams: Midterm ~ 30%, Final ~ 40%.

3.3. Operations Research (300/400 level Math or Engineering) • Course Objectives: At the completion of this course, students should …

- be able to analyze a described system and specify a mathematical model in which the objective is clearly defined (e.g., minimizing cost) subject to some identifiable constraints (e.g., resource allocation).

- be able to provide analytical proof that a gathered solution is optimal, and explain what that means in words.

- (300 level) be able to utilize an optimization package (such as Solver in Excel) in modeling systems as linear programs.

• Covered topics to include (chosen appropriate to level): - Linear programming and the simplex method. - Non-linear programming, including convexity, search techniques, Karush-Kuhn-Tucker

(KKT) conditions, and Lagrange multipliers. - Decisions and risk analysis, including sensitivity. - Game theory, including Nash equilibrium, complete/incomplete information, and

cooperation.

• Sample Assessment Plan: - Weekly homework (300 level) or biweekly problem sets (400 level) ~ 30%. - Exams: Midterm ~ 30%, Final ~ 40%.


17

4. Professional Development One belief that I hold strongly is that you can never be too prepared, never have too much training, and never be satisfied with just your present quantity of knowledge or quality of skill; that is, you must always work to learn more and to be better. In accordance with this I have become involved with two programs offered by the Searle Center for Teaching Excellence at Northwestern University: Graduate Teaching Certificate Program and Teaching with Technology. In this section, I briefly discuss these programs, each of which has provided me with a better understanding of today’s students and a greater comfort with the resources available to me as an instructor.

4.1. Graduate Teaching Certificate Program During the 2007-2008 academic year, I was a participant in the Graduate Teaching Certificate Program. This provided me the opportunity to interact with graduate students from a wide spectrum of academic departments, all of whom were interested in becoming better teachers. We met throughout the year to discuss five key aspects of teaching: course design, assessment, methodology, mentoring, and evaluation. With my math and science background, this gave me my first opportunity to interact with individuals whose background and interests were deeply rooted in the humanities. It provided for me a fresh perspective towards engaging students, organizing lectures, and assessing accomplishments. Most importantly, this program allowed me to reflect on my previous teaching experience and my thoughts on teaching as I move forward in my career. Physically I now have a certificate that provides validation of my understanding of teaching principles, but more importantly I now have a greater confidence in myself and a better idea of what I need to accomplish to provide a worthwhile experience for both myself and my students.

4.2. Teaching with Technology In the Summer, 2007 quarter, I was enrolled in Teaching with Technology. The focus of this course was to introduce teachers to options in technology that may be useful in the classroom that go beyond simplify using PowerPoint for lectures. Topics included the use of Personalized Response Systems in both small and large lectures, media services on campus (both audio and visual), and Course Management Systems (such as Blackboard). The tools I was introduced to in this class were of great use to me when I taught IEMS 317 this past Winter. Although I did use PowerPoint for my lectures, I integrated audio and video media into the lectures, and was ready from the first day to use Blackboard as a communication and organizational tool for coordinating the course.


18

5. Outside Evaluation

The most important reward to me, as a teacher, is to find that my students feel that they have gained something from the time spent in my class. This can manifest itself in many ways: a personal response to me, an improvement in their quality of work over the course of a term, or even just a handshake as they walk out of the final exam. Along these lines, I am including a summary of the scores and comments (reproduced here verbatim) from the Course Teacher Evaluation Reports, or CTECs, submitted by the students in IEMS 317 at the completion of the Winter, 2008 quarter. I am also including a faculty review of one of my lectures.

5.1. Course Teacher Evaluation Reports (CTECs)

5.1.1. CTEC Scores CTEC Online summary page Department: IEMS Industrial Engineering and Management Science Course: 317-0-01 Discrete-Event Systems Simulation Quarter: Winter 2008 Instructor: Ira Gerhardt Enrollment: 25 Responses: 18 % response: 72.00% Demographics Distro Major Minor Elective Other None

Reason for taking the class more than one can be checked

1 17 0 0 0 0

Number of Responses

6 Very High

5 4 3 2 1

Very Low

Average

Interest before taking the class 18 1 5 6 5 0 1 3.94

School wide questions

Wording of Statement Number of responses

6 (high)

5 4 3 2 1 (low)

Average

1. Provide an overall rating of the instruction. 18 9 6 2 1 0 0 5.28

2. Provide an overall rating of the course. 18 4 10 2 2 0 0 4.89

3. Estimate how much you learned in the course. 18 4 10 3 1 0 0 4.94

4. Rate the effectiveness of the course in challenging you intellectually.

18 4 9 3 1 0 1 4.72

5. Rate the effectiveness of the instructor in stimulating your interest in the subject.

18 12 3 2 0 1 0 5.39


19

Time survey question

Wording of statement Number of Responses

<3 4-7

8-11

12-15

16-19

20>

6. Estimate the average number of hours per week you spent on this course outside of class and lab time.

18 0 8 7 3 0 0

Department Specific questions

Wording of Statement Number of responses

6 (high)

5 4 3 2 1 (low)

Average

7. Rate how well the organization of the course facilitated your learning. 18 8 9 1 0 0 0 5.39

8. Rate the effectiveness and clarity with which the instructor communicated the ideas.

18 7 10 1 0 0 0 5.33

9. Rate how well the instructor was prepared and organized for each class. 18 15 2 1 0 0 0 5.78

10. Rate how well the instructor encouraged class discussion. 18 10 6 2 0 0 0 5.44

11. Rate how well the instructor answered students' questions inside and outside the classroom.

18 6 7 2 2 0 1 4.78

12. Rate how well the instructor displayed interest in students' learning and needs.

18 11 3 3 0 0 1 5.22

13. Rate how well the examinations accurately measured your achievement in the course.

17 5 6 5 0 1 0 4.82

14. Rate the usefulness of projects, if any, in your learning and applying the course material.

18 11 3 2 2 0 0 5.28

15. Rate the difficulty of the course material. 18 3 7 6 2 0 0 4.61

16. Rate the usefulness of lab/discussion section, if any, in learning and applying course material.

18 8 5 4 1 0 0 5.11

17. Rate the course materials (texts, lecture notes, handouts, and audio-visuals).

18 8 8 1 1 0 0 5.28

18. Rate how well this course gave you skills and techniques applicable to your future career.

18 8 8 2 0 0 0 5.33


20

5.1.2. CTEC Comments CTEC Online summary of comments for IEMS 317-0-01 Department: IEMS Industrial Engineering and Management Science Course: 317-0-01 Discrete-Event Systems Simulation Quarter: Winter 2008 Instructor: Ira Gerhardt Enrollment: 25 Responses: 17 % response: 68.00%

What are the primary teaching strengths of the instructor?

He is very enthusiastic about teaching. / great talker!! i was always attentive, even if i was falling asleep in the class before. he is interesting and great at making things that are boring seem not so much / Very enthusiastic and always started the class with a joke, story or "motivational poster" to engage the class. Really important to him that the class was learning and aware of what was going on. Encouraged attending office hours and asking questions. / He cared about the students and understood their level of understanding and issues they would have, allowing him to address issues that were on the students' minds. / Ira is an engaging lecturer and very approachable. He was great about responding to e-mails and was always very helpful. Also was extremely knowledgeable about the course material and always made class interesting. /

What are the primary weaknesses, if any, of the instruction?

There were no rubricks for the projects and because of this people got points off for things that they knew how to do but may have forgotten to include in the project. / Did not always answer the question that was asked, but gave other good adivce / Sometimes didn't answer questions as directly as we would have liked. Preferred to "teach us how to fish" rather than "give us a fish". This was good sometimes, not so good other times. /

Please summarize your reaction to this course focusing on the aspects that were most important to you.

ira is a great talker - intimidating!, but nice and definitely interesting. i learned a lot in the course because he really got people to be engaged. the material is definitely useful in any sort of engineering/finance job you go in to. / Great course, Ira is a really good teacher. Listen in class, and you'll be alright. / This course is really pretty good. The projects can be very time consuming and annoying, but overall, this class taught me alot and was mostly pretty interesting. Ira is an awesome lecturer, was always very helpful and is an all around good guy. Take this class with Ira if you can. / ira is always on a red-bull overdose. hes very jumpy in class and hes pretty funny at times. if you read his notes and go to class everyday, theres NO way you wont get an A. hes very helpful in office hours so go to him. good class to take.. the projects are very interesting. ok bye. / Ira was always prepared and enthusiastic for each class. He kept the class entertaining and tried to get his students to participate in class. I enjoyed the class and Ira's teaching. The projects are very helpful in learning the material, but they take a long time, so start early. /


21

5.2. Faculty Review of a Lecture

Teaching PortfolioTeaching...

Documents

Transcript of Teaching PortfolioTeaching...