(Last updated November 2023)
I teach one of the most popular courses at Penn, which is our Artificial Intelligence (AI) course. This semester (Fall 2023), the course has an enrollment of 600 students – 400 in-person, plus 200 online students who are doing a master’s from Penn Engineering Online. Even with more than 500 students in my course, I consistently receive excellent teaching reviews. I worked hard to achieve this scale and quality in my teaching.
Back in 2018, I plotted my teaching reviews against my course sizes and found an anti-correlation (Figure 1). I set myself the goal of flattening the trend so that I could continue to offer large courses and have the students’ experience be as positive as when I offer smaller, more personal courses. I undertook a variety of improvements:
I’m proud to say that I managed to eliminate the inverse correlation between class size and course reviews (Figure 2). Since dedicating myself to improving my teaching quality, my reviews have steadily gone up, and my courses are now consistently rated between ‘very good’ and ‘excellent’ (Figure 3).
I have designed two courses for Penn Engineering Online: Artificial Intelligence and Natural Language Processing. I re-designed NLP while I was on sabbatical last year to center the course on large language models. My online courses are not “watered down”. The content of the courses is identical to what I teach in my on-campus courses. In fact, my on-campus courses have improved as a result of my investing the effort I put into creating the online courses. I carefully thought out the course design. I recorded high-quality lecture videos. I designed homework assignments that are auto-graded and give students instant feedback.
I frequently take on extra teaching duties for Penn Engineering Online, and voluntary overloads. This semester I’m teaching a total of 750 students. 600 in AI, 100 in NLP, and about 20 enrolled in my PhD seminar on Large Language Models and Programming Languages (which I’m teaching for fun in collaboration with Benjamin Pierce a PL faculty member here at Penn).
I am proud of my teaching record, and the fact that my large classes get consistently excellent reviews from students.
You can read my full teaching reviews here. Below are the summary statistics.
|Colored rows indicate additional teaching beyond normal requirements.||Taught for Penn Engineering Online||Voluntary teaching overload|
|Penn teaching reviews are on a 0-4 quality scale:||0=Poor||1=Fair||2=Good||3=Very Good||4=Excellent|
Quality scale (0-4): 0=Poor, 1=Fair, 2=Good, 3=Very Good, 4=Excellent
|Term||Course Title (Number)||Students Enrolled||Course Quality||Instructor Quality|
|Fall 2023||Artificial Intelligence (CIS 4210/5210 - on campus)||384|
|Fall 2023||Artificial Intelligence (CIS 5210 - Penn Engineering Online)||196|
|Fall 2023||Natural Language Processing (CIS 5300 - Penn Engineering Online)||159|
|Fall 2023||Large Language Models and Programming Languages (CIS 8000)||20|
|Summer 2023||Artificial Intelligence (CIS 5210 - Penn Engineering Online)||107||3.4||3.5|
|Summer 2023||Natural Language Processing (CIS 5300 - Penn Engineering Online)||45||3.3||3.5|
|Fall 2022||Artificial Intelligence (CIS 4210/5210 - on campus)||363||3.3||3.5|
|Fall 2022||Artificial Intelligence (CIS 5210 - Penn Engineering Online)||94||3.4||3.6|
|Fall 2022||Research Practicum (CIS 8000)||16||3.5||3.6|
|Summer 2022||Artificial Intelligence (CIS 521 - Penn Engineering Online)||70||3.5||3.7|
|Spring 2022||Interactive Fiction and Text Generation (CIS 700-001)||53||3.3||3.5|
|Fall 2021||Artificial Intelligence (CIS 521 - MCIT Online)||234||3.2||3.4|
|Fall 2021||Artificial Intelligence (CIS 421/521 - on campus - section 1)||180||3.2||3.4|
|Fall 2021||Artificial Intelligence (CIS 421/521 - on campus - section 2)||138||3.2||3.5|
|Fall 2021||Artificial Intelligence (CIS 421/521 - online only section for foreign graduate students unable to return to campus due to the pandemic)||11||2.5||2.3|
|Summer 2021||Artificial Intelligence (CIS 521 - MCIT Online)||49||3.0||3.6|
|Spring 2021||Crowdsourcing and Human Computation (NETS 213)||146||3.0||3.3|
|Fall 2020||Artificial Intelligence (CIS 421/521)||197||3.1||3.3|
|Spring 2020||Computational Linguistics (CIS 530)||125||3.3||3.3|
|Spring 2020||Interactive Fiction and Text Generation (CIS 700-008)||23||3.1||3.3|
|Fall 2019||Artificial Intelligence (CIS 421/521)||148||3.1||3.3|
|Summer 2019||Artificial Intelligence (CIS 421/521)||36||2.9||3.0|
|Spring 2019||Computational Linguistics (CIS 530)||75||2.8||3.0|
|Spring 2019||Crowdsourcing and Human Computation (NETS 213)||59||2.5||2.7|
|Fall 2018||Artificial Intelligence (CIS 421/521)||101||2.5||2.5|
|Spring 2018||Computational Linguistics (CIS 530)||64||2.8||2.7|
|Fall 2017||Data Structures and Algorithms (CIS 121)||216||2.1||1.7|
|Fall 2016||Data Structures and Algorithms (CIS 121)||219||2.5||2.2|
|Spring 2016||Crowdsourcing and Human Computation (NETS 213)||113||2.4||2.8|
|Fall 2015||Data Structures and Algorithms (CIS 121)||174||2.2||2.2|
|Spring 2015||Machine Translation (CIS 526)||51||2.9||3.2|
|Fall 2014||Crowdsourcing and Human Computation (NETS 213)||48||3.2||3.6|
|Spring 2014||Machine Translation (CIS 526)||25||3.3||3.5|
|Fall 2013||Crowdsourcing and Human Computation (CIS 399)||26||3.1||3.5|
During the pandemic, I won the Ford Motor Company Award for Faculty Advising twice – once in 2021 and once again in 2022. This award is presented annually by the undergraduate student body in recognition of faculty dedication in helping students realize their educational, career and personal goals. I attribute this award to the care I put into teaching and the empathy that I expressed for students during the pandemic when we all of our learning rapidly changed to an online remote format. I made several accommodations to students to help mitigate the negative effects of learning online.
I have supervised 15 master’s theses and supervised dozens of independent study projects for undergraduates. I found that student interest in doing these outstripped my capacity to supervise them, so last year I experimented with offering a Research Practicum (CIS 8000 – Fall 2022) that guided students through the process of conducting research. We formed teams and worked through projects, starting with literature reviews all the way through writing and editing conference paper submissions. I’m proud to say that this experimental course resulted in 5 publications – 4 in ACL and 1 in the BEA workshop:
I am currently supervising or co-supervising 11 PhD students. In my career so far, I have graduated 9 PhD students and supervised 7 postdocs. All of them have gone on to excellent positions. I am especially proud of my mentorship of women – 5 women who I mentored now hold faculty positions. I also value the diversity of my group, and I have mentored several URM and LGBTQ students.
I addition to helping my own students negotiate their faculty offers, I often help other PhD students negotiate their first faculty offers too. I have collected nearly 100 computer science faculty via this survey, and I share my spreadsheet of data with students to help them understand their offers, and often negotiate stronger offers.