CS + X

This week I was introduced to the CS + X team and curriculum. The current plan is to run two courses: one for students from a local high school who have taken computer science, and the other is called Upward Bound, which identifies first-generation college students and introduces them to computer science and its impact in other fields. I’ll be designing lesson plans and activities as well as teaching and collaborating with the students for these six sessions. The “X” part of the class this summer will be linguistics, so the students will be interacting with natural language processing, machine learning, and English language. The class will culminate in a Python project that examines student written grammar with terminal cases and defined functions.

From these meetings, I have begun designing introductory python lessons that are differentiation to support the students’ previous knowledge and continue to develop their skills. The main concepts that I’ve identified that students will need to know are dictionaries, functions, for loops, if-elif-else blocks, and printing, as well as being able to read the traceback. I would like students to take ownership of their learning and be able to find solutions through the traceback because this in turn will fuel their confidence and logical reasoning skills. My introductory lesson is wrapped up in a “Silly Joke Generator” that is a Python function that takes in one keyword argument and returns a joke. I find that students are more engaged when they are working on a project that simply performing practice tasks.

Later this week, I will make “Daily Dojo” bellringers to help students maintain their Python skills as well as interact with more subfields of computer science like A.I. and NLP.

Study Skills and Belonging

This week consisted of two introductory meetings to establish the goals of the study as well as a review of the materials collected thus far. Earlier in the spring, Dr. Gertner inteviewed 16 computer science undergraduate students about their study habits, resources utilized, course perceptions and self reflections on the discrete mathematics courses and these interviews were transcribed by a program utilizing artificial intelligence.

While follow-up and clarification questions differed from student to student depending ont their responses, the common core questions throughout were as follows:

1. What resources did you use to study?
2. How do you prepare for examlets and study? Is it different or similar to how you study for other classes?
3. Do you typically study alone or with others?
4. Do you use any outside resources to study (such as YouTube, another textbook, etc)?
5. When starting a problem, do you prefer to start right away to find a solution or do you plan out your response first?
6. Do you go to office hours, if so, how do you participate?
7. When you signed up for this course, did you think it would be interesting or boring?
8. Was this a course you thought you could do well in?
9. Do you feel like you belong in computer science, and did this course positively or negatively impact that sense of self?
10. What advice would you give to future students so they could do well in this course?

We realized that while the A.I. program was helpful in most of the interview transcriptions, it is not perfect at capturing the nuance of speech and some relevant University of Illinois vocabulary such as “examlets”, which are the name of the online exams. I have begun to hand-correct these transcriptions by listening to the audio files and editing the transcription text. I am careful to make a copy of the text as to preserve the initial transcription in case we would like to reference it later.

In addition to the two introductory meetings, we began to parse through the transcribed data to identify meaningful “codes” that we would like to comment in the transcriptions to refer back to later. We have roll-up codes such as “Strategies”, “Resources”, “Self”, and “Course”. Strategy refers to any activity the student performs that helps them study, complete homework, or gain understanding of the course material: common themes so far have been reading the textbook, practicing proofs, completing previous year’s examlets. Resources are related because they are the physical objects that the students use to aid their studies, so these could be the textbook, the lecture notes, the lecture videros, or social connections like friend and peer groups. “Course” refers to students’ feedback on the layout of this asychronous online course and its “town hall” inspired office hours. The town hall nature refers to the few T.As in the front of a large room as students sit in a large classroom shouting out problems they would like to review.

The “Self” has been the most interesting so far because this code is used whenever a student is reflecting on their course perceptions or their accomplishments in the course. In the few that I’ve coded so far, there seems to be a spectrum of perceptions of students believing they would perform well despite the challenge of the material to students who describe trying their hardest and still not accomplishing the grade they wanted. I am interested in uncovering more about how this course impacted student perceptions about belonging in computer science.

General Reflections

I am pleasantly surprised at how smooth this adjustment has been, and even though I am learning a lot, I feel like I am making meaningful contributions to my team. My background in statistics and data science, my Masters in Educational Leadership, and all those scientific journals I’ve read for class and leisure along the way help me understand the process of research.

Even though the process is lengthy, I appreciate taking the time to listen to the Study Skills and Belonging interviews because I feel that I am committing this information to memory and taking note of the student’s emotions as they discuss their experiences. The A.I. captured about 80% of the text accurately, but I feel the other 20% that I am correcting will help my team and I find better insights.