CSC 161 Imperative Problem Solving

Fall 2025

Synopsis: This class broadens your means of "computational thinking" as a method of solving problems. Using robotics (sensing and control) as a vehicle for exploration, you will learn additional important concepts from computer science. With daily labs that help you become progressively fluent in a low-level programming language and its data representations, you will practice the design and analysis of algorithms, recognizing and creating abstractions, and manipulating important information structures. You also get to make robots sing and dance!

Instructor:	Jerod Weinman
Office:	Noyce 3825
Phone:	x9812
E-mail:	[weinman]
Mentor:
Section 01	Chloe
Section 02	Anna

Course web page:

http://weinman.cs.grinnell.edu/courses/CSC161/2025F

Class meetings:

 

Section 01

MWF 8:30-9:50 am, Science 3815

Section 02

MWF 1:00-2:20 pm, Science 3815

Contents

english 1  Accommodations
2  Overview
3  Textbooks
4  Schedule of topics
5  Assignments and activities
    5.1  Reading
    5.2  Module Laboratories and Projects
        5.2.1  Laboratory sessions
        5.2.2  Projects
    5.3  Short Quizzes
    5.4  Homework assignments
    5.5  Exams
6  Electronic Devices
7  Partner responsibilities and Collegiality
8  Grading
9  Attendance
10  Academic honesty
    10.1  Policy on AI-based Tools
        10.1.1  Minimal Exclusive Exception
11  Deadlines
12  Getting help
    12.1  Peer educators
    12.2  Discussion with Piazza
    12.3  Office hours
    12.4  Email

1  Accommodations

If you have any disability that requires accommodations, please meet with me right away so that we can work together to find accommodations that meet your learning needs. You will also need to provide documentation of your disability to the Office of Disability Resources , located in Steiner Hall 209 (x3089 or [access]). Please also note that I also require your accommodations. The chemical fragrances found in lotions, after shave, body sprays, scented laundry products, perfume, cologne, etc. make many people who suffer with asthma, allergies, environmental sensitivities, cancer, and migraines much sicker. I am sensitive to many chemicals you may not even notice, so please try to avoid using such scented products before coming to class and especially if you visit my office.   Grinnell College is committed to compliance with Title IX and to supporting the academic success of pregnant and parenting students and students with pregnancy related conditions. If you are a pregnant student, have pregnancy related conditions, or are a parenting student who wishes to request reasonable related supportive measures from the College under Title IX, please email the Title IX Coordinator at titleix@grinnell.edu. The Title IX Coordinator will work with Disability Resources and your professors to provide reasonable supportive measures in support of your education while pregnant or as a parent under Title IX.

2  Overview

The official catalog description reads:

This section of CSC 161 will utilize robotics as an application domain in studying imperative problem solving, data representation, and memory management. Additional topics will include assertions and invariants, data abstraction, linked data structures, an introduction to the GNU/Linux operating system, and programming the low-level, imperative language C. The course will utilize a workshop style, in which students will frequently work collaboratively on a series of problems. Includes formal laboratory work.

This means that you'll be introduced to the C programming language, learning how to adequately describe and decompose problems of a computational nature so that you can effectively tell a computer the steps it should take to solve the problem. We will study some beginning concepts that make this process possible, easier to undertake, and often elegant. Our major objectives for this course include:

• Understanding more fundamentals of computer science: algorithms, data structures, and abstraction.
• Developing proficiency with the practice of computer programming (design, documentation, development, testing, and debugging) in a low-level programming language, C.
• Learning about solving problems in the imperative paradigm.
• Enhancing general thinking and learning skills.

Practically speaking, our topics will include:

• imperative problem solving: top-down design, common algorithms, assertions
• C programming: syntax and semantics, control structures, functions, parameters, macro processing, compiling, linking, program organization
• data concepts: data abstraction, integer and floating-point representation, string representation, arrays, structures, linked list data structures, stacks, and queues
• machine-level issues: data representation, pointers, memory management
• GNU/Linux operating system: terminal commands and software development tools

*  Why take it? Understanding another computational paradigm will increase your problem-solving abilities. Learning how your programs connect to the underlying representation and physical machine will give insight to their behaviors and limits. Oh, and programming robots is fun.

3  Textbooks

Students in this course should have a ready reference for the C programming language. Some labs include only a brief discussion of a topic, and students will need to do additional reading to understand the general context and the details of the material. Use of the textbook can be an effective means to study this material in appropriate depth.

K. N. King, C Programming: A Modern Approach, Second Edition, W. W. Norton, 2008, ISBN 978-0393979503.

Additional copies are available in the CS Learning Center (Noyce 3828) as well as on reserve in the Kistle Science Library (Noyce 2102). The course web page provides a significant list of additional important references.

4  Schedule of topics

We will work through eight primary modules, each with an integrative project. See the web page schedule for additional details.

Module 0:

Getting Started: Linux terminal environment, C language, Scribbler 2 Robots, and Program Development (Weeks 0-2)

Module 1:

Types, Computational Models, Conditionals, Loops, and Robot Motion (Weeks 2-3)

Module 10:

Arrays, Pointers, Functions, Testing, and Debugging (Weeks 4-5)

Data Representation:

Bits, Positive and Negative Integers, and Floating Point Numbers (Weeks 6-7)

Module 11:

Characters, Strings, and Reading Input (Weeks 7-8)

Module 100:

Structures and 2D Arrays (Weeks 8-9)

Module 101:

Dynamic Memory Management, Linked Lists, and Program Management (Weeks 10-11)

Module 110:

Abstract Data Types, Stacks, and Queues (Weeks 12-13)

Module 111:

Command-line Arguments and File I/O (Weeks 13-14)

5  Assignments and activities

Grinnell College defines a 4 credit class as an expected minimum of 12 hours of academic work per week (including class time).¹ With 4 hours of class per week, you should plan to spend approximately one hour per class on preparatory reading (5.1), perhaps another half hour finishing daily lab activities (5.2.1), with the remaining time (at least 4-5 hours) for collaborative projects (5.2.2) or individual homework (5.4). Your time may vary and, notably, may be more than this. If you are experiencing difficulties in keeping up with the course material in a timely fashion, please consult with the instructor as soon as possible.   This class is taught in a collaborative workshop style. Some days we will work through problems or concepts in an interactive fashion. Most days you'll work on laboratory problems on the computer with another student. To make class time most valuable for you, I do not plan to lecture on material that is covered in the reading. Instead, because experimenting and practicing is the best way to learn, you'll have the opportunity to answer and ask questions and then begin working collaboratively on the day's lab exercises with the instructor and a class mentor available to provide assistance. By studying the day's topics beforehand, we can concentrate the beginning of class on areas of confusion. Because our class time is limited, you must come prepared to each class, meaning you should:

1. Check the schedule for the day's meeting to find out the topic.
2. Study the assigned material before class.
3. Come to class on time, with your textbook, paper, and something to write with, ready to participate.

5.1  Reading

Nearly every day there will be brief readings assigned, some from the textbook and some from course web pages. No later than the night before each class, you should check the course schedule for the readings. Reading the material may entail the following:

Overview

You should look over each reading once to get an overview of the material to be covered (e.g., summary and section headers).

In-depth

Next, study the material more closely. Perhaps not everything will make sense at this point, but hopefully many or most things will.

Final notes

After your careful reading of the material, make a few notes to yourself about what you think are the most important concepts being covered, as well as any questions you have.

These important readings are often brief, but dense, introducing non-trivial ideas. You should plan to spend about an hour working on the material in the readings before class. At the end of the hour, you should be ready to discuss in class the material from the reading that is most important and most confusing to you. Thus, you should consider answering the following questions to be part of your daily preparation:

• Identify the section or concept from today's reading that you find most confusing.
• Briefly explain what you find confusing about it.

It is also extremely helpful to read the material no later than the night before class. Studies show that a little bit of forgetting between when you try to recall the information (like we will do at the beginning in class), can actually help you remember it better in the long run.²

5.2  Module Laboratories and Projects

Topics in this course are organized into eight modules and a few supplementary labs. Modules include laboratory sessions and conclude with an integrative project.

5.2.1  Laboratory sessions

Most class days will involve collaborative laboratory work; I randomly assign in-class lab partners that rotate every module. Labs introduce specific features of Grinnell's computing environment, highlight concepts and constructs introduced in class, allow instructor assistance in a "hands-on" setting, and supplement office hours. You likely will not complete all daily lab exercises during class; finishing additional exercises outside of class to be sure you are engaging in all the course material. Like playing an instrument or speaking a foreign language, the only way to become proficient is to practice, practice, practice! You should keep a careful record of your work as you progress through the lab. As a special incentive for mastering the laboratory exercises, 30-50% of the problems on each test and on the final exam will be taken from the laboratory exercises (with only slight editing).

5.2.2  Projects

Each of the eight modules concludes with a project involving a computer program and associated commentary. These projects require you to integrate the material from the module by meeting several somewhat open-ended specifications. Deadlines   Deadlines for the projects vary throughout the semester and are posted on the course schedule. Collaboration   Collaboration on projects is required. Grading   All projects are evaluated under the general rubric for the course; additional specific grading criteria are applied to each project individually.

5.3  Short Quizzes

At the end of each module, we will have a short quiz to help practice recall for the later exams and upcoming project. These will take ten minutes at the beginning of class and will be short answer or fill-in the blank questions designed to help reinforce your learning of important elements from the module. Quizzes count for relatively little in your overall grade and are not designed to cause significant stress. Rather, they should help you learn. Even if you don't ace every quiz, studies show that just taking it will help you learn more in the long run, which can improve your exam scores.³ Each module's learning outcomes (i.e., things you should know and/or be able to do) can help guide your preparation. The following are the tentative quiz dates, scheduled either the same day as the project or the class day before. No make up or advance quizzes will be scheduled (except for faculty-approved varsity athletics competitions). Instead, the two lowest quiz scores will be dropped (to accommodate for sickness, travel, or other incidents).

5.4  Homework assignments

Homework assignment problems extend the range of problems considered in the course and help sharpen problem-solving skills. To support this objective, all homework problems are to be done individually. You may ask the instructor about any part of the course (especially including any homework problem!) at any time. However, you must not discuss any homework problem with other students (e.g., students from the class, CS majors, or other students). For homeworks, allowable help from peer educators is limited. See Allowable Help from Peer Educators Deadlines   Deadlines for the homework vary throughout the semester and are posted on the course schedule. Collaboration   Collaboration on homework is forbidden. Students are encouraged to discuss homework questions with the instructor. Grading   All assignments are evaluated under the general rubric for the course; additional specific grading criteria are applied to each homework assignment individually.   Note that many aspects of the projects and homework assignment are verified automatically in Gradescope-they must compile without errors or warnings and run successfully. You should verify that your submission has been accepted for grading. Submissions are unlimited. In many cases, some aspects of the assignment will be autograded. While you may use this feedback to adjust your submissions, it is not a replacement for the testing requirements of each assignment.

5.5  Exams

As opportunities for you to demonstrate your grasp of the course material, there will be two midterm hour exams, and a final exam, all in-class. Written exams will have questions requiring short answers, reading code (e.g., to determine its outcome), and writing short, complete functions and/or programs. Exams are closed book and closed notes. Documentation for MyroC functions will be provided, but no other references will be provided. You are expected to write C programs using the basic C syntax and libraries learned thus far.⁴ You will not be penalized for simple syntax errors you could easily fix with a compiler, such as a missing semicolon or mismatched parentheses; grading emphasis will be on clear and correct logic, as well as a clear, demonstrated knowledge of the use of basic elements of C.

6  Electronic Devices

With the exception of collaborative computer-based lab activities that use the existing MathLAN computers, the use of electronic devices (smartwatches, phones, tablets, or laptops) will be prohibited during class. Why?

• Taking notes on laptops is generally less effective for learning than writing longhand, which forces you to consolidate information as you process it.⁵
• Distractions (email, messages, social media, etc.) hinder your own learning⁶⁷ as well as that of those around you.⁸
• Remarkably, even the mere presence of such devices impedes cognition.⁹

Oh, and you might even enjoy your time more.¹⁰ So: When class begins, please put your electronic devices away until after the conclusion of the class hour. Violations of this policy will negatively impact a student's grade. Exceptions to this policy can be made as an accommodation for those with documented disabilities. An exception may be made at the conclusion of some classes, when you may wish to trade contact information with your partner or establish a schedule to meet for out-of-class collaboration.

7  Partner responsibilities and Collegiality

Work on labs and projects in this course is often done collaboratively (in pairs, occasionally in a group of three). Many studies suggest substantial learning benefits with this type of group work.¹¹ However, successful collaboration requires partners to participate actively. Expectations for collegial behavior include:

• being present in class (physically and mentally)
• coming to class on time
• coming to class prepared
• asking questions when appropriate
• making positive contributions to class discussion by volunteering and when called upon
• staying on task during in-class lab exercises, and
• working effectively with your lab partner(s)

Students who regularly fail to meet these expectations should expect to have a conversation with the instructor. Egregious violations may result in a petition for dismissal from the course. As noted above, partners should actively participate throughout class sessions and make arrangements to meet in the lab outside of class to finish labs or projects, as needed. Each partner has an obligation to attend and contribute during planned out-of-class sessions. Failure to meet one's responsibilities to a group negatively impacts the individual and impedes the partner's education. Thus, except in exceptional circumstances (e.g., illness, family emergencies, serious injury), failure to follow through with one's responsibilities as a partner may have a significant impact on one's course grade and/or one's standing in the course. For more details and suggestions on how to be an effective pair programmer, please read the following article.

Wray, S. (2010). How pair programming really works. IEEE software, 27(1), 50-55.

8  Grading

My goal is for everyone taking this course to be able to demonstrate familiarity, fluency, and excellence with the course concepts. I would be very happy if you all met the goals above and received "A"s. The following weighting of individual activities will provide a basis for evaluation. Some work may be graded by someone other than the instructor. However, all questions or concerns about grading should only be directed to the instructor. The following is proposed as the grading scale for the course. To compensate for the unpredictability of learning exercises' outcomes, the brackets (left column) may be adjusted upward (but not downward).

9  Attendance

Because is a collaborative, discussion-based course, your presence is integral to your learning. Thus, 1.5% will be deducted from your overall grade for each unexcused absence. I know that sometimes "things happen." Therefore, you are granted one unexcused absence from class without penalty. However, this rebate is cancelled upon a second unexcused absence. Tardiness is indistinguishable from absence (until you arrive), and this causes problems in ensuring everyone has a partner for the day's work. Therefore, if you are more than five minutes late to class or arrive after we start the lab (when I will have needed to resituate your partner) you will be marked absent. Please plan to arrive by the start of class. If you will be absent, you must send a written notice (email is acceptable; explanation optional) before class starts except in the (rare) case of dire emergency. (Consider the analogy for phoning in sick for work; you wouldn't do it at 4 PM the day you missed work.)

If you know in advance that you will be absent for any reason, please notify me in writing (again, email is fine) at least 7 days beforehand to make appropriate arrangements for your absence. If you do miss a class for whatever reason, you must first talk to a classmate about any material that you may have missed. After that, you may follow up with me about any further questions or concerns. You should complete the lab assigned for any days you are absent and be sure you understand the material.

10  Academic honesty

As students, you are members of the academic community. Both the College and I expect the highest standards of academic honesty, as explained in the Grinnell College Catalog,
https://catalog.grinnell.edu/content.php?catoid=34&navoid=5483\#honest-academic.
Among other things, this means clearly distinguishing between work that is your own, and work that should be attributed to others. This includes ideas, examples, and code that you draw from labs and readings. It is expected that the collaboration policies given in this syllabus, on particular assignments, and in the allowable help from peer educators will be followed. In particular:

1. When you explicitly work as part of a group or team, you need not identify the work of each individual (unless I specify otherwise).
2. You may discuss concepts (algorithms, ideas, approaches, etc.) described in the readings, lab exercises, or during class with anyone.
3. You may only discuss homework assignments with the instructor.
4. You may only discuss project assignments (algorithms, solutions, write-ups, code, debugging, etc.) with your group members, computer science tutors, CSC 161 mentors, or the CSC 161 instructors.
5. All the work submitted (code, experimental data, write-ups, etc.) must be your own or that of your group. Code or documentation provided by the instructor must be attributed, including code that you copy and subsequently modify.
6. All non-syntax consultations (i.e., ideas about algorithms) from any source, including the readings, labs, provided code, and internal or external language references, require formal citation within the related program or write-up.
7. Any conceptual contributions by individuals not in your group must be acknowledged and attributed in your report. That is you must give specific attribution for any assistance you receive. (This includes assistance from tutors or mentors.) The suggested acknowledgment format is
   1. "[Person X] helped me to do [thing Y] by [explaining Z]."
8. Any program results or output must be faithfully recorded, not forged. (A thoughtful explanation of unexpected behavior can often be a worthwhile submission and is much better than the alternative.)
9. You are responsible for safeguarding your work from being copied by others. This requires you to take reasonable precautions with workstation logons, hard copy printouts as well as file system permissions. (Note that MathLAN's default permissions prevent others from viewing your files.)

As an instructor, I will meet my obligation to bring any work suspected to be in violation of the College's Academic Honesty Policy to the attention of the Committee on Academic Standing, after which there can be no recourse with me.

10.1  Policy on AI-based Tools

As Wray notes (see mechanism 1), conversations about course material can be exceedingly helpful. Fortunately, you have a wide variety of opportunities (7,12) to engage in such conversations for this class. We strongly encourage you to utilize those resources! However, you are not permitted to use OpenAI ChatGPT, GitHub Copilot, Google Gemini, Anthropic Claude, or any other generative AI or programming assistance tool that is trained on others' code for this course. If you aren't already aware, GitHub Copilot is a machine learning system that suggests code as you type in VSCode. Copilot is trained on code hosted by GitHub, and offers suggestions that go far beyond the usual API-based autocomplete suggestions many programming editors produce . There are major legal questions around Copilot's use of other code, particularly as it connects to open source licenses that place restrictions on the licensing of derivative works.¹² Importantly, while we hope you reach a point in your professional career where you have learned to "collaborate" appropriately with AI tools (perhaps similarly to the peer collaboration we'll use for now), it is pedagogically inappropriate to use or rely on them at this stage of your computing education.¹³ In addition to building (human) collaboration skills, our goals include developing (imperative) problem solving skills alongside the mechanics of a systems-level language. Doing AI-free work is a means of "embracing difficulty" that promotes deeper and more durable learning. ¹⁴ For the purposes of this class, there is no ambiguity about the use of AI tools:

requesting, reading, or submitting code suggested by AI tools (e.g., Copilot, ChatGPT, etc.) is a direct violation of honesty policy item #5 above.

If you happen to use your own computer for any programming in this class I ask that you do not install GitHub Copilot, or uninstall it if you have already done so. If you prefer not to remove Copilot from your personal computer, you are welcome to complete all work for this course on MathLAN machines, which will not have Copilot installed.

10.1.1  Minimal Exclusive Exception

Many search engines automatically produce responses to search queries generated by AI, including queries that may be valid for the purposes of the course. At present, such inadvertent use seems to be beyond your reasonable control in many search engines and will therefore not be considered a violation of the course policy prohibiting the intentional use of generative AI. In the event you utilize one of these AI-generated responses to a query, you are still responsible for citing the source material, just as you would for any web page you visit and use as a result of your search. Include the name of the search engine, the query, and the URL of the page containing the generated result that informs your work.

Important

You may not use this exception as back door to issue queries inappropriate to the task (i.e., "Hey Searchey, how would I write this function for my homework...") in the hopes that you'll get a response. Of course, the Academic Honesty policy dictates you must cite this, but then the course policy is that you would get a zero for the assignment (as for any inappropriate-but-cited collaboration).

11  Deadlines

Work is due at the time and date specified in the assignment. Unless otherwise specified, the deadline for a project or homework assignment is 10:30 PM on the date specified. Assignments due on days for which you have a prior excused absence must still be submitted by the deadline. A late penalty of approximately one letter grade will be deducted in each subsequent twenty-four hour period after the deadline.

Exceptions:

Deadlines may be adjusted under the following circumstances.

1. Deadlines for all MathLAN computer-based assignments will automatically be extended by at least one twenty-four hour period if MathLAN is down for an unscheduled period of three or more hours during the 72 hours preceding the assignment due date. Please notify me as soon as possible if you think this may be warranted.
2. Deadlines for Gradescope-submitted assignments will automatically be extended by at least one twenty-four hour period if Gradescope is down or significantly slowed for an unscheduled period of three or more hours during the 72 hours preceding the assignment due date.¹⁵

If you somehow miss a deadline on the night something is due, the assignment is already considered late. Thus, I typically recommend you stop working for the night, get the sleep you need, and continue the next day. You have another 24 hours to submit before grade penalties increase further. Absolute deadline: All work (except the final exam) must be submitted by Monday 15 December at 5 p.m. (Note that this is earlier than the institutional deadline).

12  Getting help

12.1  Peer educators

Our course mentors will hold weekly evening mentor sessions to go review, connect, and/or go further in-depth on the course topics. The Computer Science Department makes tutors for CSC 161 available for drop-in help in the open laboratory, SCI 3815 (Sunday-Thursday, 7-10 PM and Sundays 3-5). Come to chat with them or just hang out and do your work while they're around. Peer tutors may also be available for regular, more intensive one-on-one tutoring. As the course gets underway, please let me know if you believe you might benefit from regular weekly individual tutoring. I may also recommend individual tutoring.

12.2  Discussion with Piazza

For online class discussion or Q&A we will use Piazza, which is designed to get you help fast and efficiently from your classmates, mentors, and myself. Rather than emailing questions, please to post your questions on Piazza: https://piazza.com/grinnell/fall2025/csc161/home. You must ensure your queries and responses respect the academic honesty and collaboration policies for the course and specific problem. (In general, you should not post your code for projects or assignments!)

12.3  Office hours

Please come by during my office hours to discuss the course content, get any extra assistance, or just talk about how the course is going. Note that if multiple students have similar questions or issues, we may work together as a group. If you cannot attend a scheduled office hour, you may also email me to schedule an appointment; please include 3-4 possible meeting times so that I can pick one that works for me. Half of my office hours are open, "drop-in" times for students with quick or unplanned questions. If there is anyone waiting, I try to will limit our time to fifteen minutes to respect others' time. The other half may be reserved by signing up for a 15 minute slot. To promote greater access, please sign up for only one per day. (If any slots are unused, I will happily continue meeting on a drop-in basis.) I enjoy getting to know my students, but I prefer to reserve office hours for academic matters. If you would like to have a more informal conversation, I would be delighted to accept an invitation to eat lunch with you at the Marketplace.

12.4  Email

Email is also a reliable way to contact me, but please allow 24 hours for a response (except on weekends, when I do not regularly read email). You may also call me in my office (x9812) for more urgent matters (e.g., you will be missing a lab due to illness). Thanks to Janet Davis, Sam Rebelsky, and Henry Walker for many elements of this syllabus. Henry Walker wrote the original Textbook (3) section, the topics list of the Overview (2), and most of the Module Laboratories and Projects (5.2), and partner responsibilities (7) in CSC 161: Imperative Problem Solving and Data Structures under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License. The Title IX text was provided by the Grinnell College Academic Success Center.

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License.

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Footnotes:

¹Grinnell College. (2016) Grinnell College Semester Credit Definition. ²Soderstrom, N.C., & Bjork, R.A. Learning versus performance, in Dunn, D.S. (ed.). Oxford Bibliographies in Psychology (New York: Oxford University Press). ³Roediger, H.L., & Karpicke, J.D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological science 17(3), 249-25. ⁴This is an important and realistic skill. Your productivity as a software developer depends on your fluency in the language (consulting syntax references slows you down). Technical job interviews frequently require you to solve a complex problem with code on a whiteboard. ⁵Mueller, P. A., & Oppenheimer, D. M. (2014). The pen is mightier than the keyboard: Advantages of longhand over laptop note taking. Psychological science, 25(6), 1159-1168. ⁶Carter, S. P., Greenberg, K., & Walker, M. S. (2017). The impact of computer usage on academic performance: Evidence from a randomized trial at the United States Military Academy. Economics of Education Review, 56, 118-132. ⁷Glass, A. L., & Kang, M. (2018). Dividing attention in the classroom reduces exam performance. Educational Psychology, 1-14. ⁸Sana, F., Weston, T., & Cepeda, N. J. (2013). Laptop multitasking hinders classroom learning for both users and nearby peers. Computers & Education, 62, 24-31. ⁹Ward, A. F., Duke, K., Gneezy, A., & Bos, M. W. (2017). Brain drain: the mere presence of one's own smartphone reduces available cognitive capacity. Journal of the Association for Consumer Research, 2(2), 140-154. ¹⁰Dwyer, R. J., Kushlev, K., & Dunn, E. W. (2018). Smartphone use undermines enjoyment of face-to-face social interactions. Journal of Experimental Social Psychology, 78, 233-239. ¹¹Hanks, B., Fitzgerald, S., McCauley, R., Murphy, L., & Zander, C. (2011). Pair programming in education: A literature review. Computer Science Education, 21(2), 135-173. ¹²You can read more about these issues at https://www.plagiarismtoday.com/2021/07/08/github-copilot-and-the-copyright-around-ai/. ¹³Prather, J. et al. (2024) The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers International Conference on Computing Education Research, pp. 469-486. ¹⁴Brown, P. C., Roediger III, H. L., & McDaniel, M. A. (2014). Make it stick: The science of successful learning. Harvard University Press. ¹⁵The Gradescope status board https://gradescope.statuspage.io will be used to adjudicate the timing of the issue.