Basic course information
| Quarter | Lecture | Lab | Location |
|---|---|---|---|
| Winter 2022 | Fridays 9:30AM-11:30AM | Fridays 11:30AM-12:20PM | Mandler Hall 1539 or Zoom |
| Name | Role | Office Hours | Office | |
|---|---|---|---|---|
| Prof. Judith Fan | Instructor | jefan at ucsd.edu | Mondays 9:15AM-10AM | McGill 5141 or Zoom |
| Keeshia Kamura | TA | skamura at ucsd.edu | Wednesdays 12PM-1PM | Zoom |
| Zoe Tait | TA | ztait at ucsd.edu | Thursdays 11AM-12PM | Zoom |
Why take this course?
It is impossible to understand the modern world without an understanding of statistics. From public opinion polls to clinical trials in medicine to online systems that recommend purchases to us, statistics play a role in nearly every aspect of our lives. The goal of this course is to provide an understanding of essential concepts in statistics — how to construct models to explain variation in data — as well as the skills to apply these concepts to real data.
What will you be doing in this course?
This is an advanced seminar and lab course on the science of learning as applied to statistical concepts & data science skills that are commonly used in modern psychological research. This course provides fast-paced engagement with core statistical concepts and the use of R, a widely used statistical programming language. As part of the lab assignments and final project, you will work in groups to analyze real-world data and communicate your findings using the tools you have learned in the course. You will also have the opportunity to engage with the research literature on statistics education.
Each week you will have come prepared by having completed the assigned CourseKata modules and/or posted thoughtful entries on the assigned research articles to the course Slack workspace. In class, we will generally spend our time working in small groups on lab assignments to practice the concepts/skills you learned in the CourseKata modules, as well as working towards your final research project milestones.
What kind of preparation is expected for this course?
There were no formal prerequisites listed for this course, but that was an oversight. It is generally expected that you have already taken PSYC 60: Introduction to Statistics (or equivalent) and PSYC 70: Research Methods (or equivalent). However, if you happened to have previously taken PSYC 60 with Dr. Fan in Spring 2021, please reach out to the Instructor for ideas about how to proceed.
No prior programming experience in R or any other programming language is necessary. Regardless of what level of previous experience you have with statistics, data science, and/or programming, you will likely have a more rewarding experience if you are coming to class ready to be challenged, a willingness to invest time to learn from one another, and are genuinely interested in data science and/or how people learn.
What happens after you finish this course?
By taking this course, you are also contributing to improvements in the way that introductory statistics and data science skills are taught in Psychology at UCSD. In addition, this course provides excellent preparation for an opportunity to serve as an Instructional Assistant in Dr. Fan’s PSYC 60 course in Spring 2022.
What do we expect from you?
We are looking forward to making this an awesome, positive, and supportive learning experience for everyone. These are the expectations we have of all students enrolled in this course, and your core responsibilities as a student:
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Show up. This means attending class each week. If you are unable to make it to class, please email both the Instructor and TA to let us know and provide enough context to let us know when you plan to make up missed coursework, and how we can be of help.
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Try. This means engaging sincerely with the material, even when — especially when — it’s hard. This means doing your best to figure things out on your own (e.g., Googling it, or checking the syllabus), then asking for help.
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Ask for help when you need it. This means letting us know when you are stuck, even after trying to figure things out on your own and consulting with your peers on Slack . This means coming to office hours and being prepared to describe your question, what you have already done to answer it, and what you are looking for from us.
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Be professional. This means being actively respectful, courteous, and thoughtful when communicating with one another in class, over Slack, and with members of the teaching team. This means proofreading your messages to all members of the teaching team, and ensuring that you have provided enough context for us to provide an informative response.
What tools will we be using in this class?
Canvas (for accessing CourseKata modules)
You will be assigned modules from a free online textbook called CourseKata to complete outside of class. Please see the CourseKata section below and the Schedule to understand when the due dates are for each module.
Slack (for class-wide communication)
Click this URL to join the course Slack workspace using your UCSD email address.
DataHub (for lab assignments)
You will be completing lab assignments using DataHub, a service hosted by UCSD to make it easier to use Jupyter notebooks without having to install any new software on your computer. These labs are available to download via our course GitHub page. Here are instructions for how to download these labs and upload them to Datahub to work on them during class.
Zoom (for remote classes)
While we will be meeting for class in person by defaultwhen campus COVID-19 guidelines permit, we may need to hold some classes remotely. Join the course Zoom room by clicking this URL. When class is remote, your experience will be enhanced by being able to use a laptop/desktop equipped with a microphone/camera, and being able to log in from a quiet place where you are able to speak & hear your classmates. If you have any concerns about being able to participate remotely, please let the Instructor know.
Website (you are already here!)
You can always find the latest information about this class on the course website: https://science-of-learning-data-science.github.io/.
What are the graded activities in this class?
CourseKata Modules
- Chapter sections will be assigned from “CourseKata Statistics and Data Science,” a free online and interactive textbook developed by Ji Son and James Stigler, together with their colleagues in the UCLA Teaching and Learning Lab.
- Unlike a traditional textbook, you will be asked questions throughout each chapter. To receive full credit for the CourseKata portion of your grade, you are responsible for making good-faith attempts to answer all of the questions embedded in the assigned chapter sections prior to each lecture as listed in the schedule.
- Your responses do not need to be correct to receive full credit – the purpose of working on the embedded questions/problems is to help you learn. Your responses to these questions will be reviewed when determining your final CourseKata grade.
- We advise that you budget approximately 4-6 hours per week working through these CourseKata chapters.
Lab Assignments
- Most of our synchronous class time will be spent working in small groups to work on lab assignments that allow you to practice the concepts and skills covered in the Coursekata reading that was assigned for that week.
- Most of our synchronous ‘lecture’ time will be spent working in small groups to work on lab assignments that allow you to practice the concepts and skills covered in the CourseKata modules assigned for that day.
- All parts of each lab must be submitted via Canvas (Lab 1) or via DataHub (Labs 2-5) by 11:59pm PT on the due date listed on the course schedule to receive full credit. Before submitting your lab, please make sure that your lab runs “top to bottom” without any errors.
- There will generally be no extensions for lab assignments. The reason for this policy is that grading late lab assignments imposes undue burdens on the teaching team. Out of respect for their time, please submit your lab assignments on time, even if they are not yet complete.
- On very rare occasions, excused late submissions may be accepted if there is a medical/family emergency or bona fide religious conflict. Requests to submit these assignments late without penalty must be accompanied by documentation sent via email to Dr. Fan as soon as you are aware of the emergency, rather than after the fact.
- Lab assignments will be graded for completion and accuracy; however, demonstration of effort during class and attending your TA’s office hours may positively impact your lab grade, even when there are inaccuracies.
- Honor Code: Unless otherwise stated, you can use any published resource you wish to complete the assignments (textbook, Internet, etc). You should also feel free to discuss the assignments with your classmates. However, you should not simply share solutions with your fellow students in person or electronically unless instructed to do so by the instructors; sharing answers (including computer code) will be viewed as a violation of the Honor Code.
Paper Discussions
- You will have the opportunity to read research articles that connect to the statistical concepts covered in this class.
- These articles have been grouped into four “themes”: graph comprehension, data-generating processes, model-based reasoning, and open/reproducible science.
- Each week that these papers are assigned, you will be asked to submit two 1-page response papers, one for each assigned article. These response papers should be submitted via Canvas before class.
- Each response paper should contain the following sections:
- Summary: Provide a high-level summary of the article in your own words. What was the key question the authors aimed to answer? What was their argument? Do not quote the article directly when writing your own summary.
- Relationship to SOLDS: How is this paper related to the concepts/skills covered in this class? What are some ideas/findings in the paper that go beyond what has been covered so far in this class?
- Relevance outside of SOLDS: How do the ideas/findings in this paper relate to life outside of class, either from your own experience or events in the wider world?
Theme 1: Graph comprehension (Jan. 21)
- Franconeri, S. L. (2021). Three Perceptual Tools for Seeing and Understanding Visualized Data. Current Directions in Psychological Science, 30(5), 367–375.
- Shah, P., & Hoeffner, J. (2002). Review of graph comprehension research: Implications for instruction. Educational Psychology Review, 14(1), 47–69.
Theme 2: Reasoning about data-generating processes (Feb. 4)
- Tintle, N., Chance, B., Cobb, G., Roy, S., Swanson, T., & VanderStoep, J. (2015). Combating anti-statistical thinking using simulation-based methods throughout the undergraduate curriculum. The American Statistician, 69(4), 362-370.
- Budgett, S., & Pfannkuch, M. (2019). Visualizing chance: tackling conditional probability misconceptions. In Topics and Trends in Current Statistics Education Research (pp. 3-25). Springer, Cham.
Theme 3: Teaching and learning model-based reasoning (Feb. 18)
- Son, J. Y., Blake, A. B., Fries, L., & Stigler, J. W. (2020). Modeling First: Applying Learning Science to the Teaching of Introductory Statistics. Journal of Statistics Education, 1-23.
- Rodgers, J. L. (2010). The epistemology of mathematical and statistical modeling: a quiet methodological revolution. American Psychologist, 65(1), 1.
Theme 4: Open and reproducible science (Mar. 4)
- Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251).
- Hardwicke, T. E., Wallach, J. D., Kidwell, M. C., Bendixen, T., Crüwell, S., & Ioannidis, J. P. (2020). An empirical assessment of transparency and reproducibility-related research practices in the social sciences (2014–2017). Royal Society Open Science, 7(2), 190806.
Final Project
- You will also have the opportunity to complete a group project in lieu of a final exam. You can expect to do much of this work during class.
- As part of this project, you will get to analyze real classroom data from a data science course, engage with research articles, and report your findings in a group presentation during finals week. Attendance and participation in the final project showcase is mandatory and will count toward the final project grade.
- LINK TO FINAL PROJECT POSTER TEMPLATE
Schedule
| CourseKata (before class) | Labs (in class) | Papers (before class) | Project (in class) | |
|---|---|---|---|---|
| Week 1 (Jan 7) REMOTE | Chapters 0, 1, 2 | Lab 1: Exploring data (due 1/14) | - | - |
| Week 2 (Jan 14) REMOTE | Chapters 3, 4 | Lab 2: Visualizing data (due 1/21) | - | - |
| Week 3 (Jan 21) REMOTE | - | - | Theme 1: Graph comprehension | Milestone 1: Explore & visualize data (due 1/28) |
| Week 4 (Jan 28) | Chapters 5, 6, 9 | Lab 3: Re-sampling data (due 2/4) | - | - |
| Week 5 (Feb 4) REMOTE | - | - | Theme 2: Reasoning about DGPs | Milestone 2: Study preregistration (due 2/11) |
| Week 6 (Feb 11) | Chapters 7, 8, 10 | Lab 4: Modeling data (due 2/18) | - | - |
| Week 7 (Feb 18) | - | - | Theme 3: Teaching model-based reasoning | Milestone 3: Fit model & visualize result (due 2/25) |
| Week 8 (Feb 25) | Chapters 11, 12 | Lab 5: Comparing models (due 3/4) | - | - |
| Week 9 (Mar 4) | - | - | Theme 4: Open and reproducible science | Milestone 4: Final project report (due 3/11) |
| Week 10 (Mar 11) | - | - | - | Milestone 5: Final project poster (due 3/16) |
| Finals Week | - | - | - | Final Project Showcase (Wed. 3/16 @ 8AM-11AM) |
Grading
Your grade will be calculated based on:
- CourseKata (20%)
- Labs (30%)
- Paper Discussions (20%)
- Final Project (30%)
Grading scale. The grading scale will be as follows:
- 97-100: A+
- 93-96: A
- 90-92: A-
- 87-89: B+
and so on (rounding to the nearest whole number). We may curve up at the bottom of the scale depending on the distribution, but I will not curve down (i.e. 87 will never be worse than B+).
Resources
- Keeshia Kamura’s Recommended Resources
- R Cheatsheet
- Chaining Operations using R
- LINK TO FINAL PROJECT POSTER TEMPLATE
What We Expect From Everyone
Values we share: We are genuinely committed to equality, diversity, and inclusion in this course. Consistent with the UC San Diego Principles of Community, we aim to provide an intellectual environment that is at once welcoming, nurturing and challenging, and that respects the full spectrum of human diversity in race, ethnicity, gender identity, age, socioeconomic status, national origin, sexual orientation, disability, and religion. We sincerely hope that you will share our commitment to actively creating and maintaining a safe environment founded on mutual respect and support. To be clear, this course affirms people of all gender expressions and gender identities. If you prefer to be called a different name than what is indicated on the class roster, please let us know. Feel free to correct us on your preferred gender pronoun. If you have any questions or concerns, please do not hesitate to contact any member of the teaching team.
Code of conduct: You are expected to treat the teaching team and your fellow students with courtesy and respect. This class should be a harassment-free learning experience for everyone regardless of gender, gender identity and expression, sexual orientation, disability, physical appearance, body size, race, age or religion. Harassment of any form will not be tolerated. For clear violations of course expectations for professional and respectful conduct in this course, whether in class or online, we may deduct points from the Attendance portion of a student’s grade, with the number of points proportional to the severity of the violation. If someone makes you or anyone else feel unsafe or unwelcome, please report it as soon as possible to a member of the teaching team. If you are not comfortable approaching the teaching team, you may also contact the UC San Diego Office of the Ombuds.
Acknowledgements
Many thanks to Ji Son, Jim Stigler, everyone in the UCLA Teaching and Learning Lab, Russ Poldrack, Tobi Gerstenberg, and Amy Fox for generously sharing their instructional materials.