J. Elizabeth Clark, Digital Pedagogy in the Humanities
“What are students learning and how do we know?”
— J. Elizabeth Clark
This is the fundamental question for designing any assignment for any course.
Assessment
Clark — key questions her practices prompt
How do individual parts relate to the overall learning in a course?
Have you made the expectations, goals, and processes clear to students?
How are students building on prior learning?
Are students engaging actively in inquiry?
Would students benefit from integrative learning?
What is the role of digital tools?
Assessment
Clark — some examples and issues she surfaces
Amanda Starling Gould has students evaluate and annotate existing DH projects.
Hallie Scott emphasizes scaffolding from low-stakes assignments to high-stakes.
Madeleine Sorapure highlights mismatches between assignment and evaluation — e.g., using print criteria for multimodal work.
Backward Design
Or, why learning objectives and goals were so early in the semester
Philip Smith identifies three stages:
Clarify learning goals: “the core knowledge and skills students should acquire by the end of the course.”
Define assessments: what “will accurately measure students’ progress toward these objectives”?
Then “design learning activities to support the goals.”
Backward Design
Smith — on purpose
Smith advocates “ensuring that every task directly contributes to the learning goals. With this clarity, students have a clearer sense of purpose behind activities and are better able to engage with meaningful learning experiences.”
— Smith
That sense of purpose is particularly important when you don’t want students to offload some key part to AI.
Stage 1: Identify Desired Results
Smith — at every level of the course
“What students should know, understand, and be able to do by the end of your course, lesson, or module.”
— Smith
Stage 1: Identify Desired Results
Smith — the moves
Big Ideas: foundational concepts, broader intellectual frameworks, transferable knowledge.
Essential Questions based on big ideas: encourage debate, focus on key issues in a discipline, provoke thought.
Create learning objectives (hold that thought).
Prioritize content: basic familiarity vs. important knowledge/skills vs. take-aways for years later.
SMART Learning Objectives
Smith — an operational checklist
Specific: clearly define what students will achieve.
Measurable: include criteria for assessing progress.
Attainable: realistic within the course’s scope.
Relevant: aligned with the course goals.
Timely: include a timeframe for completion.
Stage 2: Determine Evidence for Results
Smith — summative vs. formative
Summative assessments “measure how well students have achieved course objectives by the end of a learning period. These are often high-stakes assessments that contribute significantly to final grades” — exams, portfolios, research papers.
— Smith
Formative assessments “focus on learning in progress. These assessments provide ongoing opportunities for students to practice, reflect, and integrate new knowledge while offering instructors insights into students’ development” — drafts, completion-credit homework, brief writing. Low stakes.
— Smith
Match Assessment Types to Learning Goals
Smith — the alignment check
How will I know if students have achieved the desired learning outcomes?
What evidence will demonstrate students’ understanding and proficiency?
Do the assessments align with and address the course’s learning objectives?
Are the assessments designed to measure both foundational knowledge and enduring understanding?
Rubric Example: Blogging (with AI)
Brian Croxall — a three-tier rubric that names AI explicitly
Posts count as Unsatisfactory, Satisfactory, or Excellent. Excellent builds on top of Satisfactory.
Satisfactory (all of):
Posted on time (9 a.m. day of discussion).
Descriptive title — sometimes LLM-generated.
Refers specifically to the day’s reading, often by direct quotation.
Meets the minimum word requirement.
Written clearly: less formal than a paper but still serious and evidence-based.
No more than 3 grammar or spelling errors.
Excellent (adds):
One illustrative media object (image, screenshot, GIF, embedded video or audio) with full credit — link and creator name.
Exceptional insight or analysis, connections beyond the day’s material, or compelling engagement with the LLM’s writing — Croxall’s shorthand: “do you make me raise an eyebrow in a good way?”
Stage 3: Plan Learning Experiences and Instruction
Smith — only now do we design the activities
What prior knowledge and skills will students need to achieve the learning goals?
What types of activities will help students build the necessary knowledge and skills?
What teaching strategies and instructional approaches will best support learning?
What resources and materials (texts, multimedia, interactive tools, real-world applications) will enhance understanding?
Cheating Was Already Common
Kies and Stanfill — the long view
“If your primary concern is that students will cheat, we’d argue that they would probably cheat even without generative AI. In the pre-digital age, students had underground markets to sell essays and exams to each other; they wrote notes on the soles of their shoes to look at during in-class exams. The internet enabled repositories of essays to copy or download. And there’s always the ‘grab quotes from a bunch of places on the internet and hope your professor doesn’t notice’ model we all know and loathe.
Generative AI doesn’t change much about students using unauthorized aid — or the Sisyphean task of catching them. Students cheat for many reasons: they don’t care about the class, they care about the grade and don’t think they can succeed otherwise, they’re overwhelmed and seek a shortcut.”
— Kies & Stanfill
Show Students the Purpose
Mollick — the assignments-feel-obsolete problem
“Students will want to understand why they are doing assignments that seem obsolete thanks to AI.”
— Ethan Mollick
Remember what we said about tasks that have a clear purpose? Show students why it matters. Convince them to do it themselves.
This is much more likely to work than trying to catch unauthorized AI use.
What Kinds of AI Use Might Be Authorized?
Mollick — the line isn’t obvious
“Does asking AI to provide a draft of an outline cheating? Requesting help with a sentence that someone is stuck on? Is asking for a list of references or an explainer about a topic cheating? AI can even act as an excellent writing mentor that can provide the kind of detailed feedback that teachers are hard-pressed to give.”
— Mollick
Students Offload Reading, Too
Why “just read it” isn’t a plan
There’s a lot of focus on offloading writing, but students often offload reading. Part of it is that they don’t care or can’t prioritize the course over other obligations. But it could also be:
We don’t explain the purpose.
They didn’t learn to read long texts in K–12, which focuses heavily on excerpts.
They literally never learned to read.
“The strategies that struggling readers use to get by — memorizing words, using context to guess words, skipping words they don’t know — are the strategies that many beginning readers are taught in school.”
— Emily Hanford
This Week
Discussion: Signature Assignment Draft — due Sunday, June 7.
Present a draft of the main assignment for your course: learning objectives, how it connects to your course goals through backward design, how it will be assessed, and how you have considered the role of AI in how students might complete it.
Readings: Clark, “Assessment”; Smith, “Backward Design”; Mollick, “The Homework Apocalypse.”
See weeks/week-04.md on Canvas for full reading links and the discussion prompt.