Introductory literature and drama courses at large universities can enroll well over a hundred students, and an assignment on Kaspar can produce a mountain of essays. The play rewards careful reading, but careful reading of a hundred papers is a significant time commitment. Instructors need a workflow that preserves quality while keeping the work manageable. The solution lies in planning before the essays arrive.

The first step is to narrow what you are assessing. An essay that tries to evaluate argument, research, style, and formatting at equal depth will take longer than one focused on two or three skills. For a first essay on Kaspar, you might emphasize thesis and close reading and treat other elements lightly. Clear priorities let you give detailed feedback where it counts most.

The second step is to build a rubric with descriptive levels rather than only points. When each level describes what the work looks like, much of your feedback is already written. Graders and teaching assistants can then point to the descriptor instead of composing a new explanation each time. This also makes scoring more consistent across multiple graders.

Coordinating Teaching Assistants

Large courses often rely on teaching assistants, and inconsistent grading among them is a frequent source of student complaints. A norming session before grading begins can address much of this. Have everyone grade the same three essays, compare scores, and discuss differences until the group converges. For Kaspar, pay special attention to how graders treat unusual interpretations of the prompters.

• Hold a norming session using anonymized sample essays
• Share a one-page guide to common errors in Kaspar essays
• Agree on how to handle essays that rely on outside historical research
• Check a random sample of each grader's work midway through
• Set a clear process for students who dispute a grade

Using Technology to Reduce Repetition

Much of the effort in grading large stacks goes into repeating similar comments, such as asking students to explain a quotation rather than leaving it alone. AI-assisted grading tools can generate these first-pass comments based on your rubric, which you can then approve or edit. This saves time without removing your judgment. The tool is most valuable when it is configured with your specific criteria for the assignment.

A useful practice is to review the first twenty AI-generated sets of comments carefully. Notice where the tool is accurate, where it overreaches, and where your own priorities are not reflected. Adjust the rubric accordingly, then run the remainder. This front-loaded effort pays off significantly over the rest of the stack.

Protecting Time for High-Value Feedback

Not every essay needs the same amount of attention. Students at the extremes, either very strong or struggling, often benefit most from personal comments. Mid-range essays may be well served by rubric-based feedback with one or two specific notes. Distributing your time this way ensures that your most careful reading goes where it will have the greatest impact.

Setting a time budget per essay also helps. If you plan to spend twelve minutes on each paper, you can plan the total hours and schedule accordingly. Without a budget, early essays tend to get lengthy comments and later ones get rushed. Consistent time allocation protects fairness across the whole stack.

Closing the Loop With Students

After returning essays, spend part of a class discussing patterns you saw across the stack. Mention the most common thesis problems and show anonymized examples of improvements. This kind of whole-class feedback addresses issues that many students share, reducing the need for repeated individual comments. It also signals that you read the essays carefully and see the class as a group of learners.

Finally, keep notes on what worked and what did not so that the next term starts from a stronger base. Record which rubric criteria caused confusion and which prompts produced the best essays. Over time, this documentation turns a stressful grading period into a refined process. Large classes will always demand effort, but a good workflow keeps that effort focused on teaching.