Transitions To Success
Experiential Learning Video #1
Microaggressions Inventory + Visual
|Experiential Learning Video Title||Release/Availability Date||Due Date|
|ELV 1: Sharifa Wright, Manager of Equity, Diversity and Inclusion at Ontario Tech University||September 24||October 8|
Individual work – no collaborations
Data collection report, short application essay, and a visual that represents your data. Data collection can occur anytime between week 3 and 4.
|Time Management Estimates|
|Explanation of the workshop in class||15 minutes|
|Watch Video from Sharifa Wright, Manager OnTech, EID||10 minutes/viewing|
|Inventory & Data Collection||Daily up to 10 minutes X 7 days for total of about 70”|
|Organizing Data and Applying Video||About 1.5 hours|
|Creating Visual||About 1.5 hours|
|Total Time Estimate||About 4-5 hours|
Steps to Completion
There are six (6) steps to this assignment.
It is advised that you complete each step in the order presented. Some tasks can overlap. A rubric follows on the last page of this packet.
Step 1: WatchVideo
Watch Video by Sharifa Wright, Manager On Tech University for Equity, Inclusion, & Diversity. The video is posted in Canvas.
Step 2: Data Collection
For one week, document and record the instances of microaggressions you encounter. These may be those that happen in person, online, in mass media (e.g., news or TV). To do this assignment, you’ll have to figure out a few things:
- What constitutes a microaggression, in your mind? Does it have to be deliberate or can it be unintentional?
- How are you going to measure the microaggressions? Is one made in a national news cast equal to one made in a class discussion?
- How are you going to track your daily encounters? You may not want to sit down and try to remember it all each evening. Maybe it would work better to have some way of recording your observations as you go through your day.
- How much information do you feel comfortable providing? Will you document MA’s at the level of “Friday, 9:15 AM: in Dr. Smith’s Leadership class – had to listen to her use a racial stereotype about an Asian colleague being born to teach statistics.” Or will it be best to anonymize the data enough that it won’t get you in trouble down the road?
Focus on and record as many as you observe or produce.
- Microaggressions that are addressed directly towards you or a group with which you identify.
- These may be in person, overheard in passing, on TV or radio.
- These may also be on internet sources such as podcasts, Instagram or TicToc posts
- Microaggressions that you produce. This is the hard one.
- When did you do or say something that could be construed as a microaggression?
- Be sure to document time of day and context for the interaction.
- Be specific.
Step 3: Report
This is not an essay. In this step compile your data in a meaningful way. Organize it in a way that the instructors can understand. Excel Spreadsheets work really well here. Tables also work. The point is to be neat and complete.
You need this data as part of your Portfolio assignment so the better you do here, the better that assignment will be.
Step 4: Create Visual
You can collect and record your data and visualize it however you see fit. Just make sure the creative form clearly represents your data and others can understand it.
Data visualization can be included as part of your report (step 3) or uploaded as a separate document in Canvas.
For inspiration, you might wish to take a look at the Dear Data project. This site has a number of cool examples of lo-fi visualizations of data about everyday life. While I like these, feel free to be creative and display your data however you like.
Step 5: Write
Write 1-2 paragraph (approximately 1 page) on how the video influenced your report and visual. Include this with your report and visual.
Step 6: Submit
Submit Your completed ELV in Canvas on or before Thursday at 10 pm October 8.
That’s it – you’re done!
Grading and Rubric
|Experiential Learning Video #1||10% of final grade||Pass/Fail: To achieve a Pass for these assignments, you have to earn 70% or higher on the grading rubric.|
|Outstanding||Strong||Approaches Expectations||Below Expectations|
|PASS >90%||PASS 80-89%||PASS 70-79%||FAIL <70%|
|Visual Data Representation||Superior representation of data. Even a stranger would know what it represents.||Strong representation of data. Everyone in class will know what it represents.||Good, but not clearly representing data. You’d have to explain to your friends why it represents your data.||Did Not include &/or unclear how it represents data. Not very many folks would know what you are trying to represent.|
|Compilation||Complete, Clear & Concise data set that you can easily access and understand later in the semester.||Complete, Clear OR Concise data set that you may be able to access and understand later in the semester.||Attempted to complete this but missing important data that could help you later in the semester.||Sloppy and hard to understand. Not helpful for future use.|
|Making Connections||The ELV articulates multiple connections between this learning experience and content from this course, past learning, life experiences and/or future goals. Shows depth of understanding.||The ELV articulates connections between this learning experience and content from course, past learning experiences, and/or future goals. Shows understanding.||The ELV attempts to articulate connections between this learning experience and content from course, past learning experiences, or personal goals, but the connection is vague and/or unclear. Shows limited understanding.||The ELV does not articulate any connection to course materials or experiences or the assignment is incomplete.|
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