7 Assessing Student Learning and Providing Feedback

Most teaching assistants spend a considerable amount of their allocated hours of work grading student assignments. Prior to marking your first batch of assignments, it is helpful to familiarize yourself with the expectations of the course instructor and of McMaster more broadly, and to adopt strategies that will help your workflow.

McMaster Undergraduate Grading Scale

McMaster University operates with a 12-point undergraduate grade scale. You may be required to give grades as a percentage out of 100 or as a letter grade. Check with your course instructor before you begin grading to ensure that you are using the preferred method. Use this chart if you need to convert percentage grades to letter grades (or to see how the percentage or letter grade converts to a grade point):

Grade Equivalent Grade Point Equivalent Percentage
A+ 12 90-100
A 11 85-89
A- 10 80-84
B+ 9 77-79
B 8 73-76
B- 7 70-72
C+ 6 67-69
C 5 63-66
C- 4 60-62
D+ 3 57-59
D 2 53-56
D- 1 50-52
F 0 0-49 – Failure

Grading Consistently and Fairly

Grading is an important but often challenging part of teaching. Effective grading strategies help make the assessment process more transparent and consistent, time-efficient, and learning-focused. Click through the accordion below for strategies to help you streamline the grading process.

Want to learn more or practice with some examples? Check out the Marking Efficiently and Effectively module.

providing effective feedback

Providing feedback is an essential part of helping students learn. The most effective feedback is focused, clear, and considers motivation and learning, rather than justifying a grade. Click through the accordion below for strategies for providing efficient & effective student feedback.

Want to learn more or practice with some examples? Check out the Marking Efficiently and Effectively module.

Communicating with the instructor

The course instructor may require you to submit your grading for review; this may mean submitting all of the assignments you graded, or selecting representative examples of papers at different grade levels. This may be required for all assignments you mark in a course, or only for the first. The instructor or Lead TA might also hold a meeting to discuss grading standards or marking practices, either after the assignment is submitted but before you have begun marking or after the marking has been started or even completed. These meetings are a good opportunity to familiarize yourself with grading criteria or marking standards, and can help to ensure consistent marking and expectations across a course with multiple TAs.

Even if your course instructor does not require you to submit graded assignments for review or hold a meeting to discuss marking, you may still wish to communicate with them about the grading you have done. You can share a grading summary with the course instructor and/or Lead TA, which might include the average grade for your tutorial(s) and/or a list of common issues that you detected when grading. This will keep the instructor informed about common errors and challenges with an assignment, and the instructor might be able to mention these common errors during the next lecture.

Upholding Academic Integrity

McMaster University defines plagiarism as “to submit academic work that has been, entirely or in part, copied from or written by another person without proper acknowledgement, or, for which previous credit has been obtained” (s.18.a, Academic Integrity Policy). More broadly, academic dishonesty is defined as “to knowingly act or fail to act in a way that results or could result in unearned academic credit or advantage.” This can include, but is not limited to: plagiarism (submitting somebody else’s work as your own, failing to properly cite sources); submitting the same work in more than one course; using unauthorized aids during tests or exams (i.e., cheating); falsifying or forging documents; and helping another student to commit academic dishonesty.

Many instructors use software such as turnitin.com to check for plagiarism. While such software can be useful for flagging similarities across documents, they do not substitute for critical reading of students’ work. Be sure to read the originality reports generated by such software, if it is being used in your course, in order to understand the nature of textual matches, and do not automatically assume that an assignment that is not flagged is free of potential academic integrity issues. For example, automated similarity checkers do not catch students who may have paid someone to complete their assignment. Your knowledge of your students’ capabilities and your observation of their work habits can help you identify potential issues of concern. Also, a paper with a high similarity ranking on turnitin.com may not be plagiarized. In many cases, turnitin.com will report a high similarity ranking for a paper that is properly cited. This usually means that the student included a lot of citations, usually in the form of direct quotes. Always be sure to have a close and careful look at the similarity reports generated by turnitin.com

As a TA, your role in upholding academic integrity is to alert the instructor if you suspect a student has engaged in academic dishonesty. The instructor will review the case and usually call the student in for a meeting, which may or may not include you. You should not discuss the matter with the student yourself. If the instructor asks you to do so, you can reply that it is outside the scope of your duties and that you are not comfortable engaging with academic offences of this nature.

use of generative ai

According to the Provisional Guidelines on the Use of Generative AI in Teaching and Learning at McMaster, instructors decide if/to what extent generative AI is acceptable in their course. Make sure you’re aware how the use of generative AI is/isn’t permitted for each assessment you’re marking. 

Recognizing AI-generated writing while marking

Note: McMaster will be enabling Turnitin’s AI Detection Tool pending privacy impact and security assessments. However, care should be exercised when using such tools given issues with accuracy and reliability.  

 

Most AI writers use a large language model (LLM) algorithm. LLMs work by predicting which words are likely to be placed next to one another based on recurring patterns in the source data and context cues from the surrounding information. This can make it challenging to tell the difference between human and computer-written content. Here are some things to look out for.

Tone & style

Because LLMs use association to determine the probability of word placement, the output is often strung together, giving it a lack of transition words or varying tones and making it more uniform, almost robotic. AI generated outputs also often include a high frequency of keywords (e.g., words provided in the prompt), which can result in repetitive language. When people write, there are usually varying tones, styles, and language throughout the text as our thought patterns shift.

 

However, tools like undetectable.ai can check content for AI Detection and “Humanize” the output. Solely relying on tone/style also runs the risk of misclassifying non-native English writing as AI generated, which raises equity concerns.

 

It can be helpful to look for evidence of opinions or reference to personal experiences, though this may not always be relevant and will depend on the type of assessment.

Accuracy

“Hallucination” refers to AI-generated text that is not grounded in the training data or the input provided. It can occur when the model’s predictions are based on weak or incorrect patterns (e.g., reference to current events), which can lead to responses that seem plausible but are not accurate.

 

Recognizing hallucinations in AI-generated text can be challenging, especially when the writing sounds authoritative. Here are some tips to help you identify potential hallucinations:

  • Verify the information: Cross-check suspect information with reliable sources
  • Look for inconsistencies: Pay attention to inconsistencies in the response, such as contradictions or information that doesn’t align with your existing knowledge.
  • Assess the relevance: Evaluate whether the response is relevant to the question/assessment.
  • Be cautious with unfamiliar terms: If the writing uses unfamiliar terms or jargon, especially in technical contexts, take the time to research and verify their validity.
  • Seek expert advice: When in doubt, consult with the course instructor.

If you do suspect generative AI was used in a way that’s not permitted, contact the course instructor 

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McMaster Teaching Assistant Guide Copyright © 2023 by MacPherson Institute. All Rights Reserved.

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