PSYC 408 Syllabus & Schedule

as of Fall 2023 (with some updates)

University of Illinois Urbana-Champaign

Lectures

Lectures are held Tuesdays and Thursdays from 9:30 am to 10:50 am.

Grading

Grades are assigned based on your final point total at the end of the semester.

The total possible number of points in this course is 125. Points come from two sources: weekly participation activities, including lecture attendance and weekly assignments (up to 5 points per week x 15 weeks = up to 75 points), and course project assignments leading to a term paper (up to 50 points from 8 assignments). There are no exams.

Points earned for weekly participation activities and larger course project assignments are scaled such that each point is expected to take about 1 to 1.5 hours of effort, including time to prepare (e.g. read, think) and complete (e.g. write, edit) the task. This is consistent with an expectation that a course takes about 3 times the number of credits in hours per week. As a 3-credit course (for undergrads), plan to spend an average of 9 hours per week working on this course, including attending lectures, weekly participation activities, and larger course project assignments.

You complete an activity, you earn a point. You attend lecture, you earn a point. You submit a course project assignment, you receive points as described on that assignment. Over the course of the semester, you collect your points. At the end of the semester, you exchange your points for a grade.

There are no +s or -s, only letter grades.

Points	Letter Grade
108 +	A
96 - 107.999	B
84 - 95.999	C
72 - 83.999	D
< 72	F

The total possible number of points in this course is 125 (75 weekly participation + 50 course project), although you may notice that the table for converting points into final grades is scaled like it’s out of 120 (eg. A = 90% = 108/120). The extra 5 points are intended to serve as an absence buffer that you are invited to take (i.e. skip/miss entirely) for any reason or no reason at all.

Final grades are based on total points; if Canvas shows a % calculation, ignore it.

Late Work Policy

I want to be as flexible as possible with due dates. My goal is for you to learn; the timeline along which that learning occurs is in many ways arbitrary.

But it’s also in many other ways not arbitrary. This course is designed to build knowledge sequentially. Also, I have to submit final grades by a very specific date and time, so we work backward from there.

All assignments are due by 5 pm on their due date. Why 5 pm? Because I’m not available at 11:59 pm to respond to technical problems. If you have a conflict with 5 pm deadlines, you are welcome to submit early; all assignments will be posted at least one week before they are due, most are posted much earlier.

Late penalties accrue automatically after 5 pm (5.01 pm is late).

There are two kinds of assignemnts in this course: weekly activities (up to 5 points per week) and course project assignments (50 points total across 8 assignments).

Lecture attendance counts as a synchronous weekly activity option. You earn 1 point for every lecture session you attend. Lecture attendance, of course, cannot be “submitted” late. However, every week there will be at least 5 asynchronous options to earn participation points, so if you are not able to attend lecture you are still able to earn your full participation activity points for that week.

Asynchronous weekly activities (such as commenting on a reading, completing an analysis, finding and evaluating scholarly and popular sources) are due by Friday at 5 pm the week they are assigned. Weekly activities may be submitted late by the following Monday at 5 pm for a 20% reduction in credit. Late activities will not be accepted after Monday at 5 pm unless prior arrangements have been made.

Course project assignments are due by Monday 5 pm following the week in which they are “due”. Course project assignments lose 10% credit per day late.

If you need an extension on an assignment, you must email me before the deadline. You are not required to tell me why you need an extension; however, I will ask that you (1) propose your own (reasonable) new deadline (subject to revision by me, if needed) and (2) keep to that proposed new deadline. Extensions on extensions will not be granted.

Exceptions to these policies are only made in exceptional circumstances (that is, those that would be documented by a letter from the Dean of Students.

Schedule


Week 1	Chapter 1. What We Know and What We Don’t	Participation activities are due every week by 5 pm on Friday
Week 2	Chapter 2. We’ve Been Wrong Before ; Chapter 3. A Very Brief History of Eugenics
Week 3	Chapter 4. Reading Behavior Genetics Research; Chapter 5.
Week 4	Chapter 6. Ancestry: What It Is & Isn’t	Topic & 5 Scholarly Sources due next Monday by 5 pm
Week 5	Chapter 7. Schizophrenia: Phenotyping & Process; Chapter 6. Autism: Heterogeneity & Disability Perspectives
Week 6	Chapter 9. Cognitive Ability, Educational Attainment, & Gene-Environment Correlation; Chapter 10. Internalizing, Stress, & Gene-Environment Interaction
Week 7	Chapter 11. Causal Reasoning
Week 8	Open work time	10 Scholarly Source Summaries due next Monday by 5 pm
Week 9	Chapter 12. Science Communication
Week 10	Chapter 13. Data Privacy
Week 11	Chapter 14. Genetic Engineering	Draft Paper due next Monday by 5 pm
Week 12	Chapter 15. At the Individual Level	Draft Popular Source due next Monday by 5 pm
	Fall Break - No Class
Week 13	Gattaca (1997)	Paper Peer Reviews and Popular Source Peer Reviews due next Monday by 5 pm
Week 14	Chapter 16. Genetics as a Social Construct	Final Popular Source due next Monday by 5 pm
Week 15	Wrap-up
Finals Week		Final Paper due TBA

Resources

Integrity and “AI” Policy

UIUC Student Code Academic Integrity Policy

Make good choices. You are ultimately responsible for the content of what you submit. The landscape of computer assistance for research and knowledge production is ever changing. A good guideline is that if you would be uncomfortable describing your process for producing work for this course (to me, to your parents, or announced on a roadside billboard), you should re-evaluate your approach. If you are in doubt, reach out to discuss options and approaches. I am a reasonable, practical person, and I can tell you from experience that the examples of (obviously) “AI” generated work that I have encountered have been terrible enough that they end up earning failing scores on their merits alone. We will work through some examples of how AI/large language models fail to reason competently in this area (true for most fields, once you get into advanced knowledge production and synthesis, as we do in college courses). If you would like to quickly see for yourself, try talking to any LLM about a topic you are very knowledgable about… it’ll say wrong things that sound good, except you know better. This is true for genetics as well (maybe even especially so, considering the amount of misinformation on the internet that these models have to draw from) - you will get a lot of confident nonsense that to an expert (like me) is obviously, hilariously, depressingly wrong.

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