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About CSE 373

CSE 373 will help you learn how to write code that runs efficiently and how to write code efficiently.

Table of contents

  1. Learning Cooperatively
  2. Receiving Help
  3. Effort, Participation, and Altruism
  4. Academic Honesty
    1. Permitted
    2. Permitted with Extreme Caution
    3. Forbidden
  5. Grading
  6. Lateness
  7. Midterm Clobber
  8. Access and Accommodations
  9. Extenuating Circumstances and Inclusiveness

The study of data structures and algorithms is more fundamentally about the process of iteratively refining our mental representations of problems. We learn this process by studying existing approaches for solving problems.

  1. First, we identify the sources of their limitations.
  2. Second, we evaluate their insights for opportunities to improve.
  3. Third, we combine small ideas to create solutions for big problems.

In this course, we will study several examples of this iterative refinement process and learn how they can be applied to solve important problems in computer science. By the end of the course, students should be able to:1

  1. Analyze runtime efficiency of algorithms related to data structure design.
  2. Select appropriate abstract data types for use in a given application.
  3. Compare data structure tradeoffs to select the appropriate implementation for an abstract data type.
  4. Design and modify data structures capable of insertion, deletion, search, and related operations.
  5. Trace through and predict the behavior of algorithms (including code) designed to implement data structure operations.
  6. Identify and remedy flaws in a data structure implementation that may cause its behavior to differ from the intended design.

A complete list of topics appears in the calendar.

Learning Cooperatively

Learning these ideas is challenging. With the obvious exception of exams, we encourage you to discuss course activities with your friends and classmates as you are working on them. You will definitely learn more in this class if you work with others than if you do not. Ask questions, answer questions, and share ideas liberally.

The philosophy of this course is that “there is no such thing as a free lunch.” In the real world, the problems we solve will not have clearly marked answers. Sometimes, there won’t even be any known solutions at all. Learning occurs through solving problems and reflecting on your problem solving process. Problems, oftentimes even more than solutions, are opportunities for learning.

As a result, learning cooperatively is different from sharing answers. You shouldn’t be showing your code to other students except to someone who has already submitted the assignment and is helping you finish. If you are helping another student, don’t just tell them the answer; they will learn very little and run into trouble on exams. Instead, try to guide them toward discovering the solution on their own.

Receiving Help

Piazza is our online discussion forum. For most questions about the course, Piazza is the right place to ask them. The course staff read it regularly, so you will get a quick answer. Furthermore, by posting online as opposed to emailing us directly, other students benefit by seeing the question and the answer.

The use of “Piazza Careers” is not required and you do not need to put any personal information in the website.

To meet with us, the best way is to come to office hours. Many of us are available at other times by appointment. In office hours, you can ask questions about the material, receive guidance on assignments, and work with peers and course staff in a small group setting.

Effort, Participation, and Altruism

To encourage cooperative learning, you can earn extra credit for effort, participation, and altruism.

Effort
Attending office hours, making progress on every homework, reading Piazza
Participation
Engaging in discussion in lecture or section, asking Piazza questions
Altruism
Helping other students, answering Piazza questions

EPA is optional and can provide a slight grade boost. Scoring is confidential (we’ll never tell you your EPA score and you shouldn’t ask), and is decided by the course staff.

Academic Honesty

The golden rule of academic honesty is that you should not claim to be responsible for work that is not yours.

This is open to some interpretation, and you’ll be getting some help from instructors, the internet, and other students throughout the course. This is permitted, and we hope that the class is an open, welcoming, collaborative environment where we can help each other build the highest possible understanding of the course material. To help (but not entirely define) the bounds of acceptable behavior, we have three important rules:

  1. By You Alone. All code that you submit (other than skeleton code) should be written by you alone, except for small snippets that solve tiny subproblems.
  2. Do Not Possess or Share Code. Before you’ve submitted your final work for a homework, you should never be in possession of solution code that you did not write.
  3. Cite Your Sources. When you receive significant assistance from someone else, you should cite that assistance somewhere in your source code with a comment.

For clarity, examples of specific activities are listed below.

Permitted

  • Discussion of approaches for solving a problem.
  • Giving away or receiving significant conceptual ideas towards a problem solution. Such help should be cited as comments in your code. For the sake of others’ learning experience, we ask that you try not to give away anything juicy, and instead try to lead people to such solutions.
  • Discussion of specific syntax issues and bugs in your code.
  • Using small snippets of code that you find online for solving tiny problems. For example, searching for “uppercase string java” may lead you to some sample code that you copy and paste into your solution. Such usages should be cited as comments!

Permitted with Extreme Caution

  • Looking at someone else’s homework code to assist with debugging. Typing or dictacting code into someone else’s computer is a violation of the “By You Alone” rule.
  • Looking at someone else’s homework code to understand a particular idea or part of a homework. This is strongly discouraged due to the danger of plagiarism, but not absolutely forbidden. We are very serious about the “By You Alone” rule!
  • Working on homework in close collaboration with another person or group of people. Your code should not substantially resemble anyone else’s!

Forbidden

  • Possessing another student’s homework code in any form before a final deadline, be it electronic or on paper. This includes the situation where you’re trying to help someone debug. Distributing such code is equally forbidden.
  • Possessing homework solution code that you did not write yourself before a final deadline. Distributing such code is equally forbidden.
  • Posting solution code to any assignment in a public place. This applies even after the course is over.
  • Working in lock-step with other students. Your workflow should not involve a group of people identifying, tackling, and effectively identically solving a sequence of subproblems.

Grading

Your course grade is computed using a point system with a total of 300 points.

CategoryWeightPoints
Lecture5%15
Homework50%150
Midterm15%45
Final30%90
Total100%300

There is no curve. Your final score will be rounded to the nearest integer before being converted to a grade based on the following point bins.

3.53.02.52.01.50.7
260225205185175160

The Lecture category is a catch-all for completing the following activities centered around lecture.

  1. Reading quizzes completed before lecture (about 25).
  2. Peer instruction questions completed during lecture (about 25).
  3. QuickCheck formative assessments completed most Fridays during lecture (8).

Each activity is worth 0.5 points so completing any combination of 30 activities is sufficient to receive full credit in the Lecture category. Completion of additional activities beyond the 30 required for full credit in the Lecture category will count towards your exam points up to a ceiling of 50% in the Midterm and Final exam categories. To clarify, if after the Midterm and the Final you have more than 67.5 points total in the exam categories, then the extra credit will not bump you up at all.

Lateness

The expected deadline for each homework is posted on the calendar. For each homework, you’ll automatically receive one day (24 hours) of slip time, allowing submissions up to one day after the expected deadline without needing to inform the staff. You may receive additional slip time by making a private post on Piazza.

  • Two additional days (48 hours) of slip time on any two homework assignments.
  • Four additional days (96 hours) of slip time on any one homework assignment.

Unused slip time does not rollover to later homework assignments nor do they earn points. This course moves quickly, so start homework as early as possible.

Midterm Clobber

The clobber policy allows you to override your Midterm score with the score of the Midterm subsection of the Final. We will only replace your Midterm score with the potential replacement score if it is better than your original score. In other words, this policy can only benefit you.

To account for differences in exam difficulty, we will standardize the potential replacement score.

/* Your score for the Midterm subsection of the Final. */
double subScore;
/* The class-wide mean for the subsection. */
double subMean;
/* The class-wide standard deviation for the subsection. */
double subStdDev;

/* Your standardized score for the subsection. */
double subZScore = (subScore - subMean) / subStdDev;

/* The class-wide mean for the Midterm. */
double midMean;
/* The class-wide standard deviation for the Midterm. */
double midStdDev;

/* Your potential replacement score. */
double potential = midMean + (midStdDev * subZScore);

Access and Accommodations

Your experience in this class is important to me. If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to me at your earliest convenience so we can discuss your needs in this course.

If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.

Extenuating Circumstances and Inclusiveness

We recognize that our students come from varied backgrounds and can have widely-varying circumstances. If you have any unforeseen or extenuating circumstance that arise during the course, please do not hesitate to contact the instructor in office hours, via email, or private Piazza post to discuss your situation. The sooner we are made aware, the more easily these situations can be resolved. Extenuating circumstances include work-school balance, familial responsibilities, religious observations, military duties, unexpected travel, or anything else beyond your control that may negatively impact your performance in the class.

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

Additionally, if at any point you are made to feel uncomfortable, disrespected, or excluded by a staff member or fellow student, please report the incident so that we may address the issue and maintain a supportive and inclusive learning environment. Should you feel uncomfortable bringing up an issue with a staff member directly, you may consider submitting anonymous feedback or contacting the Office of the Ombud.

  1. Leo Porter, Daniel Zingaro, Cynthia Lee, Cynthia Taylor, Kevin C. Webb, and Michael Clancy. 2018. Developing Course-Level Learning Goals for Basic Data Structures in CS2. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE ‘18). ACM, New York, NY, USA, 858-863. DOI: https://doi.org/10.1145/3159450.3159457