Assessment
This course will be graded using a standards-based assessment system. In a more traditional grading system, your scores on a series of assignments are averaged over the course of the semester. In this course, rather than assessing and averaging your achievement on particular assignments, I will instead be assessing the development of your fluency in a set of pre-defined standards. You will have multiple opportunities over the course of the semester to showcase the depth of your understanding regarding these standards. A standards-based grading system carries the following benefits:
- Learning targets for the course are clearly defined from the outset, and almost every graded assignment that you receive will be directly tied to at least one standard. This should make it abundantly clear what skills and competencies I’m assessing on each assignment. There is no “busy work” with a standards-based system.
- No one assignment will make-or-break your grade. You have multiple opportunities to demonstrate fluency in a standard. This rewards students that take the time to practice and learn from their mistakes. It prioritizes student growth throughout the course of the semester and allows for us all to have off-days.
- Assessments in a standards-based system are much clearer than in a point-based grading system. Saying that I’ve become proficient in data wrangling, joining, and visualizing means more than saying that I earned a 92.5 in my Introduction to Data Science course. Further, when approaching me about how to improve your grade in the course, we can focus our conversation more on how to deepen certain skills and competencies rather than how to hit certain numeric benchmarks.
- A standards-based grading system makes it easier to monitor your progress towards a certain grade.
This is my second iteration of teaching a standards-based course, and I’ve made several revisions from the first iteration based on what I’ve learned from experience and student feedback. I’m excited to refine this system this semester as I believe it aligns with my overarching goals for the course. In this course, I find it far more important that you come away with an understanding of the concepts behind core data science strategies (along with an ability to find and interpret reference materials) than it is to demonstrate memorization of R syntax. Developing this understanding will empower you to learn and apply new data science languages on your own.
What are the standards I will be assessed on in this course?
How will I be assessed on the course standards?
Informal Assessments
Each week, you will be assigned a section of the course texts to read prior to class. I expect that you will come having read this section in order to prepare for in-class exercise and labs. You do not need to complete the exercises in the course texts but may choose to do so if you wish. Please note however that we won’t have time to go over the solutions in class, and I don’t have a solutions manual for these texts (though I’m happy to go over them in office hours). All course readings will be available in Perusall, and you can post questions and comments in the reading for myself or your classmates to answer.
Lab solutions will be posted as video lectures in Perusall. I expect that you will check your lab answers by reviewing these videos. You may leave comments in Perusall at certain timestamps as questions come up.
Formal Assessments
5PM (close of business day) will be the cut-off time for all assignments in this course. The reason I’ve set the deadlines to 5PM is that I’d like to discourage students from staying up late into the night to complete assignments. Note that there is a 24-hour grace period for submitting labs and project assignments. This means that you will still get full credit for these assignments as long as they are submitted by 5PM the day following the assignment due date. However, quizzes and course advancement assignments must be submitted by 5PM on the due date for credit.
In most weeks, you will be assigned a lab, which you will start in class and complete at home. There will be one lab per standard. Labs will be designed to help you practice applying the course standards towards the analysis of a dataset. You may work on labs in groups, but all group members should submit their own lab. Labs will be graded for completion, and you can earn 3 points per standard by completing the lab associated with that standard. All sections of the lab must be completed in good faith to earn these points.
There will be 3 projects, to be completed in groups of 3-4, assigned over the course of the semester. In each you will have an opportunity to demonstrate fluency in standards we have covered up to that point in the semester. I will provide prompts for each project, but you will have a lot of flexibility to demonstrate your own creativity and explore your own interests in designing a project around the prompt. You can earn up to 3 points towards 9 of the ten standards based on your project submission. If you don’t earn full credit on a standard for a project submission, you may improve your score on that standard in the next project. The only standard that won’t be covered in projects is Data Retrieval, which we will cover too late in the semester to work into projects. Projects will be graded for fluency.
There will be 3 quizzes administered throughout the semester - each assessing 3-4 course standards. There will be 3 questions per standard on each quiz, and you can earn up to 3 points towards each standard based on your quiz attempt: 1 point per question. In this sense, quizzes will be graded for fluency.
Quizzes will be taken at home, administered in Moodle, and are open book/open Internet. You may start a quiz at any time before its due date, but it must be completed by its due date in order to earn credit. Please note that extensions will not be granted for quizzes.
There are a series of very short assignments on the syllabus that are designed to ensure that you are prepared for individual and collaborative work. This includes things like reviewing the syllabus, developing a course study plan, developing group collaboration plans, and completing peer evaluations. In total, there are 13 course advancement assignments, and you can earn 1 point towards your final grade per assignment.
| Assignment | Points |
|---|---|
| Syllabus Quiz | 1 |
| CATME Survey | 1 |
| Problem Solving Lab | 2 |
| Study Plan | 2 |
| Study Plan Evaluation | 2 |
| Group Contract (x3) | 3 |
These assignments will be graded for completion. For the most part, you will get out of them, what you put into them. Because these assignments are designed to keep our course running smoothly, please note that extensions will not be granted for course advancement assignments.
This course will have an optional final exam. The final exam is an opportunity to reassess standards that you have not received full quiz credit on. If you choose to take the final exam, you only need to complete the sections of the exam associated with standards you wish to reassess, and the score you ultimately receive for a standard will be based on whichever is higher of your quiz score or your final exam score for that standard.
How will this system work?
If you’ve been adding, you may have figured out by this point that we have:
\((3 * 10) + (3 * 10) + (3 * 9) = 87\)
You can earn the remaining 13 points by completing the course advancement assignments, for a grand total of 100 points. At the end of the semester, I will sum your scores on all standards and other assignments and assign final grades accordingly:
| Letter Grade | Numeric Grade |
|---|---|
| A | ≥ 92.5 |
| A- | ≥ 90.0 |
| B+ | ≥ 87.5 |
| B | ≥ 82.5 |
| B- | ≥ 80.0 |
| C+ | ≥ 77.5 |
| C | ≥ 72.5 |
| C- | ≥ 70.0 |
| D+ | ≥ 67.5 |
| D | ≥ 62.5 |
| D- | ≥ 60.0 |
| E | < 60.0 |