Welcome to CS 106, Introduction to Scientific Computing and Modeling. This course introduces students to computer programming, emphasizing how computing can be used as a tool for scientific inquiry. Testimonials from prior students

Learning Outcomes ¶

An introduction to computing as a tool for science, emphasizing programming as a methodology for problem solving, quantitative data analysis, and simulation in science and mathematics. This includes in silico modeling of natural phenomena, precise specification of a problem, design of its algorithmic solution, testing, debugging, and maintaining software, using scripting to increase scientific productivity, and the use of existing scientific software libraries. A secondary emphasis is the discussion of breadth topics, including historical, theoretical, ethical and biblical perspectives on computing as a discipline. This course provides an alternative to CS 108, providing an introduction to computing focusing on scientific examples and applications.

Students should enroll simultaneously in CS-106L, the lab for this course.

Students successfully completing this course will demonstrate that they can:

• Program a computer (using Python) to solve simple to intermediate problems.
• Break down a problem into parts small enough to be programmed.
• Analyze and debug a program to find the syntax and logic errors in it.
• Build abstractions to model a problem.
• Explore some ways that faith informs our work in computer technology.

Instructors ¶

• Professor: Ken Arnold (ken.arnold@calvin.edu).
  • Office hours: Tuesdays 1:30-3pm, NH 298
  • Or just stop by whenever my door is open, or message me and we’ll find a time.
  • If you want to do lunch or coffee, we can find a time for that too!
  • Some weeks I might hold office hours in the Study Sessions room (SB 337); if so I’ll leave a note on the office door.
• Lab assistant: Priscilla Chen (jc253)
• Feedback Coordinator: Eduard Dehelean (end27)

Study Sessions ¶

Calvin offers a tutoring service to help you study for this course. Why participate in Study Sessions?

• 4 hours of tutoring per week
• A nice place to do homework and ask questions
• An entire room of people working through similar challenges
• A chance to make friends and connect with study partners

You should regularly attend the Study Sessions for this course early in the semester to avoid falling behind.

• Where: Science Building Lab 337
• When: Monday and Thursday, 7-9pm, starting September 8, 2025
• How: Advocate for yourself: ask clear questions, ask follow-up questions if needed, and remember that the tutors aren’t there to give you the answers, but to help you learn how to find them!

Study Sessions are drop-in, so you can come for 10 minutes or 2 hours, depending on what you need on any given day. If you find you need additional support after having attended at least 2 Study Sessions, you can request an extra hour of 1:1 Tutoring for this class. You can sign up to meet with a tutor, and you will then be provided one hour each week for the remainder of the semester in addition to the Study Sessions. Any questions or information about these (free) Support Services may be directed to Emily Bosscher (emily.bosscher@calvin.edu), or check out the Center for Student Success website.

Communication ¶

• Outside of class, we’ll communicate primarily through the Course Team on MS Teams.
  • Use General for on-topic discussions (course logistics, assignment clarifications, etc.)
  • Use Random as a chill space to drop random thoughts, share wins / losses, post links, or just chat
• Use email for personal issues (missing class, illness, etc.)

Materials ¶

Moodle is the home page for all class materials. It will include links as necessary to other materials including:

Weekly Expectations ¶

To succeed, you’ll need to complete the following assignments each week (details on Moodle).

• Before class on Monday, do the preparation assignments for the week. Details are given on Moodle, but will typically include:
  • Reading assigned sections of the text
  • Completing the associated participation exercises.
  • (on some weeks) a perspectival reading response statement, posted in a discussion forum
• In class on Monday we will do a POGIL exercise. Please bring your laptop if you have one.
• Homework is due on Tuesdays. Thees alternate between technical homework and a perspectives forum post.
• Before lab on Thursday, attempt the “A” lab exercise, which is intended as preparation. We will typically work on this together during the Wednesday class.
• During lab, complete the remaining lab exercises with a pair programming partner.

Unless otherwise stated, assignments must be submitted by 11:59PM on the due date. The Moodle calendar will list these due dates/times.

You will generally find the course staff responsive during the week but less so on the weekend, particularly Sunday, as we try to observe a Sabbath rest in our own schedules. The course schedule is designed to enable you to rest also.

Grading ¶

Some details:

• Socrative quizzes are intended to be formative assessments, helping you to learn the material. Frequent low-stakes quizzes are shown to improve academic performance. They will be graded on correctness, but we will review them together in class and you will have the opportunity to correct your answers.
• Labs will be done in pairs. They will be graded on correctness and effective collaboration.

Final projects are presented at a showcase, which occurs at our final exam time (see the University final exam schedule, which for Fall 2025 is Tuesday Dec. 16 at 6:30 p.m. Attendance at the final project showcase is mandatory.

Students may choose either to participate in weekly Perspectives forums or to do a perspectival project (such as a poster, artistic expression, essay, etc.). Students interested in the project option should discuss expectations with the instructor by the sixth week of class.

Technology in Class ¶

One of the concepts introduced at the beginning of this course is that technology is not neutral. Consequently, technology in the classroom changes things. Studies suggest that internet-enabled electronic devices in the classroom reduces student performance, even for those who don’t use devices themselves, because of distraction.

In light of those findings, many instructors opt to ban the use of electronic devices in the classroom. Instead, we will practice using it within limits.:

• You may use a smartphone or laptop to complete retrieval quizzes and polls during designated times.
• You may use laptops to take notes or attempt programming tasks as directed.
• Beyond those uses, technology use in class should be limited to critical messages only.

If you notice someone else using an electronic device in a way that could distract you or other students, please let them know. If it continues, please let me know. I will only intervene if two or more students reach out to me, so please don’t hesitate to do so.

Collaboration ¶

• If feasible, you will work in pairs during lab exercises and many in-class exercises.
  • When you work in a pair, one of the partners should submit one copy of your work with both of your names and email addresses on it. It’s wise for both partners to have copies of your shared solution.
  • Note that Outlook blocks .py attachments. Options for workarounds include:
    • use OneDrive
    • rename to .txt
    • make a .zip file (“compressed folder”)
• The project should be completed individually.
  • You may obtain help from other people, but:
    • Every character entered (beyond any template we provided) should be typed by you, and
    • For integrity, humility, and gratitude, you should acknowledge any help you receive by name in your submission.
  • Feel free to discuss ideas with us or with your classmates, but don’t copy code. The assignments in this class are designed so that it is rarely a good idea to reuse code you find on the Internet. An exception is code that we provide.

AI Use ¶

Generative AI can be very helpful for learning, but it can also take away from the productive struggle that scientists have found is required for the biochemical changes in your brain that constitute learning. What goes into your mind smoothly often slips out just as smoothly. It can also shortcut the collaboration that we want to foster. Here are a few guidelines for how to use AI thoughtfully in this class:

2. Use AI granularly: ask for one step, not a complete solution.
3. Use AI reflectively: ask for feedback on what you’ve done so far, not to make things for you or “clean up” things for you.
4. Use AI studiously: ask for questions to test your understanding.
5. Use AI skeptically: even when it’s not wrong, it may be misguided or overcomplicated. Go to the textbook or official documentation first.
6. Use AI communally: when you work together, rather than everyone chatting with their own bot, have a shared conversation positioned where everyone can see. Discuss what the bot gives you.

We provide several tools to encourage thoughtful use of AI, such as Codehelp (on Moodle) and the Thoughtful editor and add-in.

Now the policies:

1. All code and prose you submit in this class must be typed by you. Even if you’re just doing visual copy-paste (like from some documentation), it’s helpful practice. (You’ll use autocomplete in later classes.)
2. No AI on quizzes/tests, whether in-class or at-home.
3. No AI-generated code in projects.
   • Asking for feedback, coaching, or debugging help is fine (but think for at least a minute on your own first).
   • In some limited cases it might be helpful to have an AI-generated “starting point” that you build on. But you must have explicit permission from the professor to do this.
4. We highly discourage AI-generated code for preparation, lab, and homework assignments. We won’t police AI-generated code on these assignments, but your performance on quizzes/tests will almost certainly show it.
5. For forum posts in this class:
   • you may use AI to improve your thinking by:
     • critique your arguments
     • push your thinking in a new direction
     • help you identify (but not fix) editing errors
   • you may not use AI to replace your thinking, such as:
     • providing your initial ideas
     • identifying real-world examples that are intended to be drawn from your personal experience
     • generating prose (unless your outline was highly detailed–in which case, just submit your outline)
     • fixing your editing errors for you

Note also that the course staff will be using AI to improve the quality and speed of your learning:

1. Feedback on lab and homework assignments will be generated by automated systems under the supervision of the Feedback Coordinator and the instructor. For example, the Feedback Coordinator will regularly review the feedback that is being given for accuracy and pedagogical appropriateness, and adjust the tooling accordingly. Although most of this tooling uses traditional code (like you might write in this class) to generate that feedback, we are exploring the use of privacy-respecting LLMs to expand the range of feedback we can offer. The privacy of your student data will be carefully protected, and no grading decisions will be made by LLMs.
2. Improvement of course materials. For example, I frequently ask LLMs to anticipate confusing aspects of assignment instructions. I also use LLMs for strategic planning and research, e.g., searching for examples to use in class.

If you would like to opt-out of LLM-powered feedback on your work, please let the instructor know as soon as possible.

Special Circumstances ¶

Occasionally there are special circumstances that require that course policies be adjusted for a particular student. In such cases, it is the responsibility of the student to inform us of the situation as soon as possible, so that the appropriate arrangements can be made. This includes, but is not limited to, students with documented disabilities.

Calvin University is committed to providing access to all students. If you need additional accommodations to succeed in this class, please contact Disability Services in the Center for Student Success (disabilityservices@calvin.edu) as soon as possible to explore what arrangements can be made. The three of us (student, instructor, and Disability Services) will work together to come up with an appropriate solution.

We will give a grade of Incomplete (I) only in unusual circumstances that have been confirmed by the Student Life office.

Late Work ¶

• Quizzes can be made up only by documented exceptional circumstances, arranged in advance if possible.
• Project due dates are already set as late as they can be.

Wellness ¶

A wide range of things can interfere with your learning: trouble concentrating, stress/anxiety, relationship troubles, family situations, food or housing insecurity, substance use, and many more. You are encouraged to care well for yourself by keeping a consistent sleep schedule, eating well, avoiding drugs and alcohol, exercising, and taking time to relax and connect with friends and family.

Also, learning how to ask for help is an important part of the college experience. Many people on campus are eager to support you. The Center for Counseling and Wellness sees one out of five students each year and can connect you with a variety of mental health resources on and off campus. You can also reach out to the instructor, another faculty/staff, a friend, or a family member you trust for help getting connected to support. You are not alone, and help is available!

Academic Integrity in Computing ¶

Code re-use can be a valuable practice in computing, but mindlessly copying code is not an effective way to learn programming and reusing code or algorithms without attribution (even from AI systems like ChatGPT) is plagiarism. If you’ve copied code with attribution, we’ll grade you on the code you wrote, not on the code you copied. If we detect copying without attribution, i.e., plagiarism, we’ll be forced to give you a failing grade for the assignment and perhaps for the course. Additionally, we will report you to the office of Judicial Affairs, which may result in the incident being part of your student record.

A key academic virtue is honesty, and this also applies to the lectures, textbook, slides, handouts, audio/visual materials, or any other content produced by a faculty instructor. While students may reproduce course content to enhance their own learning, they may not share that content with audiences outside the course without the express permission of their faculty instructor. In particular, students may not share quiz, test, or exam content with other class sections. However, students are encouraged to share their final projects.

Calvin’s General Statement on Academic Integrity: The student-faculty relationship is based on trust and mutual respect, which can be seriously undermined by the suspicion or reality of academic dishonesty. Academic Dishonesty includes, but is not limited to, plagiarism (students plagiarize when they do not credit the sources of their writing - the words, information, ideas, or opinions of others), improper group work, reuse of a paper from another course and/or cheating on a test. Students are encouraged to speak to their faculty member with specific questions related to academic dishonesty. For further clarification or information, please visit https://calvin.edu/directory/policies/academic-integrity-policy

Hospitality ¶

I intend for this course to serve students of all backgrounds, whether they are new to programming or experienced. Please join me in creating a class that respects everyone in it.

Past versions and Acknowledgments ¶

This course was previously offered by Dr. Victor Norman, and many of the materials were originally developed by him.

This course also includes material from prior versions of CS 108, co-taught by Prof Arnold and Prof Vander Linden.