My Teaching Philosophy
Teaching is a noble profession, and it allows me to fulfill what I believe is a fundamental responsibility of every mathematician: to pass on what they have learned to another generation. During my teaching career of more than ten years, I have seen learning and teaching modalities change from purely teacher-centric to student-centric, including collaborative learning, team/project-based, problem-solving approach, and, most recent Artificial intelligence-based learning. I use a combination of the above learning methods depending on various factors such as class size/layout, the course being taught, etc. I approach each class with enthusiasm and try to maintain a balance among the following four key principles:
- each student is teachable;
- respect each student as an individual and know their learning styles and background;
- encourage collaborative learning and active participation whenever possible;
- switch between the teaching styles frequently; and
- evaluate fairly using various forms of assessments.
Courses I have taught over the years.
Math 408x: Mathematical Methods in Data Science
I designed and taught this course as a topic course (Math 495) during Fall 2020 with 32 students. This course is created to help data science enthusiasts who need Mathematics to understand many data science concepts and Math enthusiasts who need to understand data science concepts using mathematics. All the assignments are done in python. Students' survey showed that everybody wanted to see this course as a full-fledged class. So I designed this class and running it this Fall with 14 students.
Hon 322C: Using Mathematics in Data Science
This is a one-credit class I designed and taught for university honors students in the spring of 2020. The course material includes a part of DS 201 and more, depending on the students' needs. Because of the success of this class, I will be leading another version of this class ( with two credits) in the upcoming Spring 2022 semester.
DS 201: Introduction to Data Science
I have been teaching DS 201 in the Fall for three years now, and I have seen this class grow from a class with 60 students to almost 200 every semester. Students only need a background in College Algebra. We guide students with the basics of Data Science and hands-on experience with data using Python in Jupyter Notebook from Anaconda. We have guided students to do some basic supervised machine learning models such as Regression, Classification, and Clustering. Students work on various data science projects in groups and present their findings at the end of the semester.
Equity via Basic Mathematics and Data Science
Over the years, I have seen too many students drop out of STEM because they can not get through the required Calculus sequence [1, 2]. With just the help of college algebra and some coding in python, we can teach students science, data science, and modeling without them having to go through calculus. This will help, especially for those who have math anxiety. Doing data science before calculus does not reduce the importance of calculus, though. In my experience, I have seen students who did DS 201 after college algebra starting to like math and being excited about taking calculus, knowing when and how they will have to use it.
Math 365: Complex Variables and Applications, Math 385: Introduction to Partial Differential Equations
I have taught each of these classes a few times. These small classes are usually filled with Math majors or advanced engineering students who are already mathematically oriented. Lectures with tophats, weekly group projects, and individual attention I can give to students are usually enough to learn the material in these classes.
Math 267: Elementary Differential Equations and Laplace Transforms
I started coordinating Math 267 in the Fall of 2020 when we were in the middle of the pandemic. Lectures were all online, and recitations were face-to-face but optional. All the assignments were online. It was difficult for students and professors alike in these classes. We adapted as we went, and it seemed to work out. The tools like Piazza and speed grader were very important when we did this online. We are teaching this class f2f for Fall 2021 and are still trying to balance students' online class habits, use of technology while doing assignments, and written in-class assignments.
Math 165, 166, 265: Calculus Series
Until recently, these classes were taught using team-based learning and project-based methods (TBL). I used a hybrid teaching technique between TBL and lectures when I taught these classes. While TBL is very effective for students learning the material, students do not like TBL. So balancing between lectures, TBL was effective in these classes.
Math 143: Preparation for Calculus
I taught Math 143 occasionally. I used the student-centered teaching method for this class, where students see the martial first while doing group projects. The lecture followed the group projects to validate what students had learned. The homework system WeBWork and understanding retrieval tool Top Hat was used.
I taught and coordinated College Algebra from Fall 2016 to Summer 2020. We used the ALEKS homework system based on knowledge space discovery, artificial intelligence, and machine learning to find an optimal learning path for students. Traning TAs and other instructors on ALEKS and guiding nontraditional students on their learning journey through college algebra was a very enriching experience.