- Teaching Support and Services
- Guides to Teaching Writing
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- Teaching Writing with Chatbots
- List of GenAI Tools
- GenAI In The Writing Process
- GenAI Multimodal Projects
- Citation Conventions for GenAI and Chatbots
- Writing Genres and GenAI
- Writing Assignments in STEM
- GenAI and Writing in Engineering and Technical Communication
- Linguistic Justice and GenAI
- Sample U-M Syllabus Statements
- Using ChatGPT for Basic Research
- ChatGPT Response to Sample Essay Prompt
- Call for Test Cases
- Steps for ChatGPT Sample First-Year Writing Course Essay Test Case
- Assigning and Managing Collaborative Writing Projects
- Cultivating Reflection and Metacognition
- Giving Feedback on Student Writing
- Integrating Low-Stakes Writing Into Large Classes
- Motivating Students to Read and Write in All Disciplines
- Providing Feedback and Grades to Second Language Students
- Sequencing and Scaffolding Assignments
- Supporting Multimodal Literacy
- Teaching Argumentation
- Teaching Citation and Documentation Norms
- Teaching Multimodal Composition
- Teaching Project-based Assignments
- Teaching with ePortfolios
- Using Blogs in the Classroom
- Using Peer Review to Improve Student Writing
- OpenAI ChatGPT 3.5 vs UM-GPT: Test Case
- Support for FYWR Courses
- Support for ULWR Courses
- Fellows Seminar
- Writing Prize Nominating
Introduction to Multimodal Assessment
Since multimodal composition might be a new kind of assignment for many instructors, it is understandable that the task of assessing these compositions might seem daunting. This may especially be the case when an instructor is faced with multiple kinds of multimodal compositions for the same assignment. How do you develop assessment criteria that can address project that look quite different from one another? If you give your students the option of selecting their own medium, how do you use the same criteria for, say, a TV commercial and an infographic? How do you balance the multiple skills students are demonstrating in their compositions?
For an overview of best practices and considerations for giving feedback on student writing, we recommend reviewing Sweetland’s Giving Feedback on Student Writing resource. For further considerations specific to giving feedback on multimodal composition projects, see below:
Ground your assessment in rhetorical principles
You can use rhetorical principles to guide assessment criteria, just as you might with a more traditional print essay. This means, for example, considering elements of the rhetorical situation such as audience, purpose, and context. Some questions to ask include:
Does this multimodal composition effectively communicate its main message to its intended audience? Is it clear who the intended audience is for this particular composition?
How effectively is the purpose of this multimodal composition conveyed through the combination of modes present? Is the overall aim--to educate, entertain, persuade, etc.--clearly communicated?
Does the multimodal composition respond appropriately to the parameters of the assignment?
Explicitly acknowledge the relations between modes
Multimodal assessment needs to explicitly consider how effectively students have combined the multiple modes in their composition. For example, two common problems with multimodal compositions include:
Too much mode matching: The modes match so much that they are repetitive (song plays in the background while lyrics appear along with images)
The “it looks cool” factor: When students arbitrarily add things because they “look cool,” but it has little positive bearing or relevance to the meaning of the composition
Assessing the relationships between the modes in students’ projects can be helpful for addressing these and other common problems associated with multimodal projects.
Emphasize process over product, and ask students to reflect on the process as part of assessment:
Build components into your assessment criteria that emphasize gaining new skills over producing perfect products.
Well-constructed reflection prompts can encourage students to demonstrate knowledge of course concepts and explain rhetorical choices. Student reflection might be given more weight in multimodal assessment, so that students are motivated to take risks in new forms while also justifying their choices and evaluating their efforts in a more familiar form.
Make explicit to students the role their reflections will play into your assessment of their projects--for example, by reassuring them that a thoughtful reflection on a well-designed project is more important than an expert-level product.
Provide multiple opportunities for students to reflect on the rhetorical decisions they made at different stages of composition. Ask students a lot of "why" questions, which help suggest to students the choices they make should be purposeful and grounded in rhetorical principles.
Some questions you could consider asking: “Why did you narrow your topic the way you did in light of your other alternatives? Why did you include the visuals you did for this piece? Why did you design your navigation the way you did? Why did you include each piece of writing in this portfolio?” (Neal 87)
Ask students to back up their claims about their decision-making process and final artifact with evidence. The strength of the relationship between these claims and evidence can be a meaningful place to start assessment.
Developing assessment criteria:
Involving students in the process of developing assessment criteria can help you clearly articulate the goals of the assignment, foster transparency, and promote student buy-in. Once your students have had an opportunity to practice analyzing multimodal models in class, you might consider using small group and/or whole group discussion to collaborate with students in creating an assessment vocabulary for their projects. You can solicit students’ thoughts about the effective and ineffective models they have analyzed, and try to figure out together what a competent execution of the assignment would look like. This process can be a negotiation between instructor and students, one where everyone can propose values that are important to the final draft, and then see how they agree with or complement each other.
Once you and your students have created an agreed upon a set of assessment vocabulary for their projects, you might use these contributions to shape the rubric that you create.
The National Writing Project Multimodal Assessment Project identifies five areas that instructors should include in considering student multimodal compositions:
1) the piece itself as it circulates to audiences (the artifact)
2) attentiveness to rhetorical situation
3) depth and meaning of content
4) effective process management
5) habits of mind (ways of approaching problem-solving)
You can share these areas with students during this student-driven development of assessment criteria, and ask them to define, in the more localized context of your specific classroom, what these would look like in practice.
You might also consider asking students to differentiate between required elements (what has to be there for assignment to be complete) and features of a more polished effort.
Further Reading
McKee, Heidi A., and Dànielle Nicole DeVoss, editors. Digital Writing Assessment and Evaluation. Computers and Composition Digital Press/ Utah State University Press, 2013. Computers and Composition Digital Press, http://ccdigitalpress.org/book/dwae/index.html
Neal, Michael R. Writing Assessment and the Revolution in Digital Texts and Technologies. New York: Teachers College, 2011.
Shipka, Jody. “Negotiating Rhetorical, Material, Methodological, and Technological Difference: Evaluating Multimodal Designs.” CCC, vol. 61, no. 1, Sept. 2009, http://cccc.ncte.org/library/NCTEFiles/Resources/Journals/CCC/0611-sep09/CCC0611Negotiating.pdf
Sorapure, Madeleine. “Between Modes: Assessing Student New Media Compositions.” Kairos, vol. 10, no. 2, Winter 2006, http://kairos.technorhetoric.net/10.2/binder2.html?coverweb/sorapure/index.html
Whithaus, Carl, editor. Special issue on multimodal assessment. Computers and Composition, vol. 31, March 2014, https://www.sciencedirect.com/journal/computers-and-composition/vol/31/suppl/C