Syllabus

Prerequisites

Students are assumed to have taken introductory programming up to and including CSE 123, although this class will not use Java.  Some familiarity with formal logic (e.g. as taught in CSE 311) is heavily recommended, but not mandated.  Automata theory is not required in any way.  References to questions of computability and/or complexity may be included for students edification, but again are not required for successful completion of the course.

Grading (Sorry, but it's long and needs explanation)

Note: we will be experimenting with pop-quizes this quarter.  Because we have not done pop-quizzes in this class before, they will constitute a relatively small portion of your grade.

Because of the rise of AI in education, most students believe that their peers will use AI to cheat pervasively and without consequence.  Regardless of whether this is true, we must restore confidence that students who choose to follow all policies will not be at a competitive disadvantage to those who do cheat and get away with it.  For this reason, we are using an "exam ceiling" on your grade in this class.  It works as follows:

We will compute two different grades for each student and combine them.  The exam grade consists of a weighted average based solely on supervised, in-class work (exams, pop quizzes).  The summative grade consists of a weighted average of all work (homework, exams, pop quizzes, etc.).  The final grade is then computed as the minimum of the exam and summative grades.

This system has the following purpose and merits.  The exam grade acts as a ceiling/cap on the final grade.  A student who cheats (undetected) on take-home work, but fails the exams will fail the course.  Therefore it is wise for every student to use the homework as an opportunity to practice and learn.  However, exams cannot replace the benefit of the longer, sustained thinking involved in doing the homeworks.  Therefore the homeworks will be graded.  Unfortunately, this introduces a new problem...

One rational (but impermissible) strategy for students is to do the homework and then run all their answers by an AI (or human) checker to avoid losing any points.  This is cheating, however tempting it may be.  It is cheating in at least two substantial ways.  It cheats other students out of fair and meaningful grades (and therefore encourages them to cheat as well).  It also cheats the student doing it (and us as staff) out of a genuine opportunity to assess how well students are learning throughout the quarter.  This second sense of cheating is highly likely to produce lower exam scores and worse learning outcomes too.

To compensate for the above, we plan to count 85% correct homeworks as 100% for the purpose of grades.  The idea here is to remove the acute pressure to cheat on homeworks.  It is very unlikely that a student who regularly gets less than 85% on the homeworks will also get a perfect score on the exams.  So even if you score poorly on some homeworks, your final grade will likely not be impacted.  On the other hand, if you simply fail to do one or more homeworks, it will probably impact your final grade.  So there remains an incentive to make an honest attempt at each homework.

Exam Grade Percentages*

• 10% Pop Quizzes (we will discard the lowest 2 scores — we anticipate around 7 or 8 pop quizzes total)

• 40% Midterm Exam

• 50% Final Exam

Summative Grade Percentages*

• 50% Exam Grade as above

• 50% Homeworks (roughly equal weight, except HW0 at 1/2 weight)

Final Grade = min( Exam Grade, Summative Grade )

* Percentages are tenative.  We reserve the right to adjust these percentages and policies in the interest of achieving fair grades for students in this class.  We do not use a fixed mapping to a 4.0 scale, but also do not aim to achieve a particular grade distribution or "curve."  Participation (in class, office hours, etc.) will be considered favorably when determining final grades. 

Collaboration & Academic Integrity

This class will use the "no AI" version of the Allen School's new AI policies.  However, there is currently a lot of confusion among students and staff at universities about academic integrity.  This goes beyond simply concern about AI.  Therefore we have a separate academic integrity document for this class.  You are responsible for reading and abiding by those policies.  Where there is some conflict or disagreement between the class policy and more general policies, the class policy will take precedence.

Attendance

Both lecture and section are designed for synchronous participation.  You should plan to come to class.  We expect you to be there.  Lectures (but not section) will be recorded for the purposes of review.  The literature of controlled studies suggests that students who are highly engaged in class use recordings to review material and thus do better in class.  We would like to help such students.  This same literature suggests that recorded lectures discourage the least engaged students from attending.

In genuine cases of sickness or other hardship, recordings may also help mitigate the negative consequences of missing class.  However, we do not guarantee that every lecture will be recorded without mistake, error, or omission.

Based on many years of experience teaching this class, you will learn much more and enjoy the course much more if you attend every day and stay up-to-date on the material.  Searching around for the information you need to complete the homework minimally will set you up to do poorly on the exams and, more importantly, miss the opportunity to learn what this course is about — the material builds a "grand vision" of computer programs, not merely a pile of facts to be searched.  The pacing of 3 meetings per week and how they are scheduled with respect to homework due dates supports this approach — playing catch up will likely obfuscate the "big picture."