Table of Contents

1. Course Personnel & Materials
2. Overview
3. Learning Goals
4. Course Administration
5. Expectations
6. Getting Help
7. Evaluation
8. Doing Honest Work

Course Personnel & Materials

Instructor: Jean Salac (she/her)

Classroom: Weitz 235

Time: 2a (Mondays & Wednesdays 9:50-11am; Fridays 9:40-10:40am)

Course Schedule: You can find the course schedule and important deadlines here.

Required Course Materials:

• The Design of Everyday Things, Don Norman. See Moodle for PDF access instructions and Carleton library for the audiobook.
• Design Justice, Sasha Costanze-Chock. Open Access
• Additional readings linked through Moodle

I recognize the potential financial burden of additional course materials. If you need assistance to cover course expenses, please reach out to me by Friday of Week 1.

Student Drop-In Hours (also known as Office Hours):

• Jean (Olin 339): TBD
• Book an Appointment with Jean

Overview

There is not a single person on earth that is not impacted by technology, either directly (as consumers or producers) or indirectly (as targets, as victims). Technology can make life better: by making our daily tasks easier and more efficient, by connecting us to people literally all over the globe, by hastening breakthrough discoveries. Technology can also increase our annoyance, waste our time, and make us feel stupid. Technology can make life considerably brutal: by amplifying bias, by exploiting resources and workers, by hastening climate change.

Humans, and the human experience, are at the center of technology. Yet in computer science, we often focus on everything but the human: the code, the algorithm, the tools, the speed, the wow factor. In this course, we center the people in the technical loop, examining how people interact with technology and how technology can (and cannot) enhance humanity and society.

Learning Goals

My main goal for you in this course is to become more discerning creators and consumers of technology, by better understanding how we shape technology and how technology shapes us.There are five high-level learning goals in this course:

1. Cognition and info processing. Critically analyze and evaluate a variety of user interfaces in various contexts for various audiences.
2. Design lifecycle. Utilize and apply methods encompassing the full interface design lifecycle in a real-world design project.
3. Ethics. Reason about the ethical issues behind interface design, including accessibility issues.
4. Teamwork. Work effectively in teams, and provide constructive feedback on your own performance and your peers' performance in these environments.
5. Civic engagement. Collaborate effectively and ethically across difference with members of the community.

In addition, because this is an academic civic engagement (ACE) course, it has the following learning goals:

1. Understanding issues in their real world complexity
2. Recognizing and honoring different forms of knowledge that may reside in/with community partners
3. Awareness of your positionality, or who you are as you seek to do civic engagement efforts (such as gender, race, and/or socioeconomic background)
4. Doing — how can you take your course content and do something with it beyond the classroom while learning in the process
5. Developing leadership skills
6. Nurturing a commitment to life-long civic engagement

Course Administration

Moodle: All course related materials, assignments, projects, readings, and resources will be posted here.

GitHub: You will be developing a personal portfolio website hosted on GitHub Pages. Most of your individual assignments will be turned in as a page on this website.

Languages and Tools: HTML, CSS, Javascript for front-end development, R for behavioral statistics, Figma for front-end prototyping.

IDE: VSCode for HTML, CSS, and Javascript; RStudio for R

Default environment: I will default to Mac OSX in class for development.

I am committed to the principle of universal learning. This means that our physical and virtual spaces, our practices, and our interactions should be as inclusive as possible. Mutual respect, civility, and the ability to listen and observe others carefully are crucial to universal learning.

I strive to create an inclusive and respectful classroom that values diversity. Our individual differences enrich and enhance our understanding of one another and of the world around us. This class welcomes the perspectives of all ethnicities, genders, religions, ages, sexual orientations, disabilities, socioeconomic backgrounds, regions, and nationalities.

Expectations

Attendance: Attendance in class is expected. A quarter-long team project makes up the most of this class, and much of classtime will be spent working on deliverables that contribute to your team project. If you miss class, you will miss out on that opportunity.

Communication: I expect you to check Moodle every day for updates on activities and assignments. All out-of-class written communication will happen via the class announcement forum. Please make sure you are checking it regularly and/or have email or push notifications setup. Each class day will have preparation assignments for you to complete before class. All materials will be released at least 48 hours before they are due.

Technical Issues: If you experience significant technological problems that limit your ability to participate, please contact the ITS Helpdesk at 507-222-5999 or helpdesk@carleton.edu. For announcements of known technical issues, visit the Helpdesk portal.

Extenuating Circumstances: If your personal situation (due to COVID-19 illness or other circumstances) begins to impact your ability to engage with the course, please let me know and contact the Dean of Students Office.

Getting Help

Student Drop-In Hours: Student drop-in hours are a great time to clear up any lingering confusion you may have about certain concepts, ask about the daily readings and assignments, work through ideas, or just talk about something in the course or in computer science generally or in life that’s piqued your interest. These time slots are for you, so use them! If for whatever reason you cannot make it to my student drop-in hours, feel free to make an appointment to see me (info at the top of this page).

Lab Assistant Hours: Lab assistant hours are another opportunity to clear up any confusion or get help with debugging your code. Lab assistant hours are held in Olin 310 and you can get help for this class whenever there is a lab assistant who is comfortable with helping with CS344 material. The lab assistant schedule, along with the classes they can help with, is posted on the doors of Olin 310 and will be posted on the Moodle site for this class once available.

Quantitative Resource Center: We will be learning some behavioral statistics in this class to make sense of numerical data you may get from user studies. The Quantitative Resource Center offers statistics and R support through drop-in hours and appointments.

Accommodations for Students with Disabilities: Carleton College is committed to providing equitable access to learning opportunities for all students. The Office of Accessibility Resources (Henry House, 107 Union Street) is the campus office that collaborates with students who have disabilities to provide and/or arrange reasonable accommodations. If you have, or think you may have, a disability (e.g., mental health, attentional, learning, autism spectrum disorders, chronic health, traumatic brain injury and concussions, vision, hearing, mobility, or speech impairments), please contact OAR@carleton.edu or call Sam Thayer (’10), Director of the Office of Accessibility Resources (x4464), to arrange a confidential discussion regarding equitable access and reasonable accommodations.

Assistive Technologies: The Assistive Technologies program brings together academic and technological resources to complement student classroom and computing needs, particularly in support of students with physical or learning disabilities. Accessibility features include text-to-speech (Kurzweil), speech-to-text (Dragon) software, and audio recording Smartpens. If you would like to know more, contact aztechs@carleton.edu or visit go.carleton.edu/aztech.

Evaluation

One of my goals for you in this course is for you to continue to grow as an independent developer and designer. I’m much more interested in what skills and understanding you have mastered by the end of the course than the exact pace at which you master them. However, it isn’t healthy for you or me if you leave everything to the last minute. It also isn’t fair to your team if you haven’t prepared as much as they have. Therefore, my goal with the following evaluation metrics is to balance providing you flexibility to learn at your own pace while also making sure to spread your learning out over the entire term.

Towards that end, your performance in this class will be evaluated in three different ways according to the learning goals for the course:

• Team Deliverables: This course consists of a team project that lasts the majority of the course and has components that you will create and get feedback on along the way. Your work will be evaluated on the effectiveness of the process you and your team utilize, the product you attain (and how closely this product meets the project deliverable requirements), and the effectiveness of your team’s collaboration. This contributes to your final grade according to the specifications grading scale outlined below.
• Individual Deliverables: Many of the team project deliverables will also have an associated individual portion. The individual deliverables allow you to demonstrate individual mastery of the key learning objectives in this course – which will provide you with more targeted feedback on your performance. This contributes to your final grade according to the specifications grading scale outlined below.
• Preparation Assignments: Almost all class meetings will have an associated preparation assignment to be completed BEFORE class time to ensure you are ready for the material covered in class. These tasks will be assessed based on completion, not correctness.

Specifications Grading Scale

Each deliverable that you turn in will be evaluated against a checklist of specifications related to one or more of the course learning objectives. I will distribute the checklists I’ll use to assess each deliverable so that you know exactly what constitutes each of these levels. I will rank each learning objective, and the overall submission, according to a four-level scale:

1. Insufficient Evidence: The deliverable was not turned in or does not engage with the prompt in the intended manner.
2. Not Yet Proficient: The deliverable demonstrates progress towards the learning objective(s), but needs further work and engagement to reach proficiency.
3. Proficient: The deliverable demonstrates achievement of the learning objective(s).
4. Exemplary: The deliverable demonstrates achievement of the learning objective(s) at a higher level (e.g. more nuance, sophistication, etc).

Deadline Flexibility

In this course, we need to balance flexibility for individuals with structure for teams and the class as a whole. I also want to help you avoid procrastinating to the point that you can’t get everything submitted by the end of the term. Therefore, the late work/extension policy varies depending on the type of work.

All deliverables have a 1-hour grace period after their posted due date and time to account for slight technical delays in submission while allowing evaluation of submissions to start soon after the due date.

Preparation assignments are due 10 minutes after the start of the class meeting to account for slight delays in submission while enabling you to be prepared for the class it is associated with.

Late days: If you need to turn in a deliverable past the 1-hour grace period, you may use a late day. Each student in the class will get four (4) late days. Late days are used in 24-hour increments, so if you turn in a deliverable >1 and <=24 hours after the deadline, you would use 1 late day, if you turn in a deliverable >24 and <= 48 hours after the deadline, you would use 2 late days, etc. Since individual and team deliverables build on each other, you may use a maximum of two (2) late days per deliverable to ensure that you don't fall too far behind. If you need to turn in a team deliverable late, every teammate must have a sufficient number of late days and a late day will be deducted from the late day balance of every teammate, e.g. if each teammate has a balance of 3 late days and turn in a team deliverable >1 and <=24 hours after the deadline, each teammate will now have a balance of 2 late days. Late days can only be used on deliverables, not preparation assignments. Late days cannot be used on the final presentation and report, so that college-wide end-of-term deadlines can be met.

If you’re staring down a deadline that you know you can’t meet, or if you’ve fallen behind, get in touch with me immediately and we’ll make arrangements. While I need to put boundaries in place for my own health and wellness, and for fairness to everyone in the class, I also want to make sure you are progressing in your learning.

Per Carleton policy, all extensions for end of term work need to go through your class dean.

How Specifications Grading Translates to Course Grades

1. To earn a C: You must have 50% of deliverables at "Proficient" and have completed 75% of preparation assignments.
2. To earn a B: You must have 100% of deliverables at "Proficient" and have completed 85% of preparation assignments.
3. To earn an A: You must have at least 90% of deliverables at "Exemplary" and the remaining 10% at "Proficient", and have completed 95% of preparation assignments.
4. If you are between letter grades, +/- grades will be awarded based on which letter grade you are closer to (i.e. if you're closer to an A than a B, you get an A-; if you're closer to a B, you get a B+).
5. There will be approximately 11 deliverables and 24 preparation assignments throughout the term.
6. In the final determination of course grades, if you have any deliverables at "Not Yet Proficient", they can be balanced out with an equal number of deliverables at "Exemplary" to become "Proficients", e.g. one deliverable at "Not Yet Proficient" and one deliverable at "Exemplary" will balance out to two "Proficients".

Doing Honest Work

AI-Assistance

Only use AI assistance if the assigned work explicitly calls for it or allows it, and follow specific guidelines to disclose how you used it.

Teams

Computer Science is a collaborative discipline, & people who apply computer science concepts and skills both shape and are shaped by social, political, & cultural environments beyond themselves. You are encouraged to collaborate with your classmates to learn, but collaboration comes with additional repsonsibilities:

• As a member of a team, it is your responsibility to contribute equally and fully to the final product. For example, it is not acceptable to let a teammate “finish the project” without you and put your name on it before submitting it, or work on significant portions of the project without you, or any permutation of this. When pair programming, I expect you to actively work side-by-side with your partner the majority of the time.
• As a member of a team, it is your responsibility to be able to explain and/or reproduce your solution individually, if asked. If you do not understand the solution, then you did not contribute fully to the project, and this will hinder your learning of the course material.
• Whether you are working in a team or individually, it is your responsibility to follow the written and stated guidelines for any particular project. This includes guidelines from both me (e.g. tools and resources you can use) and your teammates (e.g. when to show up for team meetings).
• When you put your name on an assignment or deliverable, it is a contract between you, your teammates (if applicable), and me that this work represents your own work and not the work of someone else. It is never acceptable to turn in a program, or a problem solution, that you have found anywhere (on the Internet, in your textbook or another book, from a friend who took this course before) or commissioned as your own. Making cosmetic changes to someone else’s program or solution (such as changing the variable names or comments) is also not acceptable.
• Whether you are working in a team or individually, it is your responsibility to credit any assistance you receive. If you receive assistance from anyone (another book, a lab assistant, a classmate, me), you must properly credit your source. If a piece of your program utilizes outside assistance, you might include the following in your program’s comments:

# Method to decide how much financial aid students should receive
# I used this StackOverflow post to refresh on the syntax for writing conditional statements in Python: https://stackoverflow.com/link-to-post

Per Carleton policy, I am required to report cases of suspected academic dishonesty to the Dean's office.