Standards-based assessment in this course evaluates 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 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 are assessed on every single 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 visualization, wrangling, and mapping means more than saying that I earned a 92.5 in my Introduction to Data Science course. Further, when approaching me (the instructor) 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.
What Are The Standards I Will Be Assessed On?
Data Visualization
This dimension refers to the development of your ability to produce multiple types of compelling and well-designed visualizations from data.
Data Wrangling
This dimension refers to the development of your ability to transform datasets into new formats in order to prepare them for further analysis or visualization.
Data Science Workflow
This dimension refers to the development of your ability to apply data science workflow in your work.
Data Ethics
This dimension refers to the development of your ability to examine ethical implications of data science work critically, using published resources for guidance when needed.
How Will I Be Assessed On The Standards?
Informal Assessments
Textbook Readings: You are expected to read the assigned readings prior to coming to class in order to prepare for in-class exercises and discussions. You do not need to complete the exercises in the course texts but may choose to do so if you wish. The ModernDive textbook and the MDSR textbook are accessible on the navigational bar of this webpage.
Disability Inclusion Readings: You are also expected to read the assigned disability inclusion readings in order to understand the context of your mini-projects. You can access the disability inclusion readings on Perusall via our course Moodle page.
Engagement: It is difficult to explicitly codify what constitutes “an engaged student,” so instead I present the following rough principle I will follow: you’ll only get out of this class as much as you put in. That being said, here are multiple pathways for you to stay engaged in this class:
Asking and answering questions in class.
Coming to student hours.
Posting questions on Slack.
Even better: Responding to questions on Slack.
Formal Assessments
Labs: On most Fridays, you will be assigned a lab, which you will start in class and complete at home. You may work on labs in groups, but all group members should submit their own lab. Labs will be graded for completion. You can earn up to 2 points per standard towards the 12 standards based on your lab submission. All sections of the lab must be completed in good faith to earn these points.
Mini-Projects: We are planning to have 3 projects, to be completed in groups of 2, assigned over the course of the semester. Projects will be graded for fluency. In each project, 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 per standard towards the 12 standards based on your project submission.
Quizzes: There will be 3 quizzes administered throughout the semester, focusing on assessing your coding skills. Quiz 1 will assess VISUALIZATION AESTHETICS, VISUALIZATION CONTEXT, and COMPOSING & INTERPRETING PLOTS; Quiz 2 will assess TRANSFORMING DATA, JOINING DATASETS, TIDYING DATA, and PROGRAMMING IN R; Quiz 3 will assess MAPPING and reassess VISUALIZATION AESTHETICS, TRANSFORMING DATA, and TIDYING DATA. There will be 3 questions per standard on each quiz (except that in Quiz 3 there will be 6 questions on MAPPING): 1 point per question. You can earn up to 3 points per standard (again up to 6 points on MAPPING in Quiz 3) based on your quiz attempt. 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.
Course Advancement: There are a series of very short assignments on the syllabus and the group work that are designed to ensure that you are prepared for individual and collaborative work. This includes things like reviewing the syllabus and completing 3 mini-projects peer evaluations. In total, there are 4 course advancement assignments. You can earn 1 point per assignment towards your final grade. Because these assignments are designed to keep our course running smoothly, please note that extensions will not be granted for course advancement assignments.
How Will This System Work?
If you’ve been adding, you may have figured out that we have: (2*12)+(3*12)+(6*4+3*4)+4=100 total points possible. At the end of the semester, I will sum your scores on all standards and other assignments, and assign final grades accordingly: