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How AI Data Analysis can Help STEM Teachers with Grading

How AI Data Analysis can Help STEM Teachers with Grading

Grading tests can be a time-consuming task for educators, often taking away valuable instructional time. However, advancements in artificial intelligence have introduced new possibilities for streamlining the grading process. In this article, we will explore how STEM teachers can utilize Chat GPT to grade tests efficiently, saving time while providing valuable feedback to students.

Generally, there are many ways to implement Chat GPT into grading procedures. The primary step, however, should be to collect a substantial amount of test responses that cover a wide range of correct and incorrect answers. This data should be representative of the skills and knowledge being assessed. Next, the AI model needs to be trained with the collected data, with prompts highlighting exemplary answers versus those that lack fundamental understanding. After training the model, teachers can input sample student responses and tweak their prompts to obtain a desired result.

With Chat GPT prepared, teachers can now utilize it for test grading in a systematic and efficient manner. They can input the written responses of students into Chat GPT's interface, either individually or in batches. The model can then analyze these responses and provide a preliminary evaluation based on its training. It identifies common patterns, errors, and strengths, allowing it to provide constructive feedback to students. However, it is important for teachers to review and verify the grades assigned by the model. Human oversight ensures fairness and accuracy in the grading process, enabling adjustments where necessary. This step also offers an opportunity to customize and calibrate the model based on the discrepancies identified during the human verification process. It also may be worthwhile for students to be aware of the AI grading and have the ability to challenge the AI when necessary. Continuous feedback from both students and teachers will improve the quality of the AI grading.

Using Chat GPT for test grading offers several benefits. Firstly, it significantly reduces the time required for grading, allowing teachers to allocate more time to instruction and personalized support for students. Moreover, Chat GPT applies a consistent set of criteria to evaluate responses, reducing subjectivity in grading and ensuring fairness across students. Additionally, the model's ability to identify common errors and provide feedback can assist students in understanding their mistakes and improving their performance.

However, it is important for teachers to consider the limitations of Chat GPT. The model may encounter challenges in assessing nuanced or context-dependent answers. To mitigate this, clear instructions should be provided to minimize misunderstandings. Furthermore, ethical considerations must be taken into account. Transparency and fairness are crucial when using AI for grading. Educators must ensure that the technology is used responsibly and that human oversight is maintained.

With these points in mind, let’s looks at some pedagogical applications of text analysis:

Evaluating Open-Ended responses:

Certain exams may contain an essay component where a student must summarize or explain a scientific process. STEM teachers can use Chat GPT to expedite the evaluation process, inputting student responses into Chat GPT and evaluating the accuracy and completeness of the answer. The instructor can tailor the grading process to their needs, allowing for in-depth explanations by the AI on their evaluation technique.

For example, consider the following (slightly incorrect) explanation given by a college student on the topic of photosynthesis.

"Photosynthesis is the process where plants absorb sunlight, carbon dioxide, and nitrogen to produce energy. The energy is stored in the plant's roots, allowing them to grow and develop. During photosynthesis, plants release oxygen as a byproduct, which is essential for their survival. The process takes place in the mitochondria of plant cells, where chlorophyll captures sunlight and converts it into chemical energy. This energy is then used to convert carbon dioxide and nitrogen into glucose and other organic compounds through a series of enzymatic reactions."

With this simple prompt created on, teachers are able to evaluate this student response on a scale of 1 to 10. While my prompt places a larger emphasis on conceptual understanding in the grading process, STEM teachers can tailor this prompt to fit their own needs. Below is the AI’s response to the student’s explanation. Notice the thoroughness of the evaluation:

I would evaluate the student's response with a score of 8 out of 10. The response demonstrates a strong understanding of the basic concepts of photosynthesis, including the role of sunlight, carbon dioxide, and nitrogen in producing energy for plants. The mention of oxygen as a byproduct and its importance for plant survival is also accurate. The student correctly identifies the role of chlorophyll in capturing sunlight and converting it into chemical energy.

However, there are a few minor conceptual inaccuracies that could be addressed. First, photosynthesis does not occur in the mitochondria of plant cells; it occurs in the chloroplasts. Second, nitrogen is not directly involved in photosynthesis; rather, it is obtained by plants from the soil and used for various other metabolic processes. Finally, while glucose is indeed one of the organic compounds produced during photosynthesis, the statement that all organic compounds are produced from carbon dioxide and nitrogen is not entirely accurate.

Despite these minor inaccuracies, the student demonstrates a good understanding of the process of photosynthesis and provides a concise summary.

The AI provides detailed feedback in a timely manner. When returning graded tests to their students, STEM teachers can also link AI evaluations, allowing students to read through the AI’s notes and learn from their mistakes.

Step-by-Step Grading for Problem-Based Exams

Many STEM exams contain multi-step problems with intricate computations. These problems are often tedious to grade by hand, and would be better suited for an AI to grade. Furthermore, because these problems are objective and often have a finite number of solutions, teachers can ask the AI to generate a rubric after analyzing a small sample of responses. I asked Chat GPT to analyze my explanation of the monty-hall problem, generate a corresponding rubric, and score my response based on the rubric. Out of 20 points, here’s the breakdown I received:

  1. Correct Identification of Initial Probabilities (4 points):
    • The response correctly identifies the initial probabilities as 1/3 for choosing the car and 2/3 for selecting a goat. (4 points)
  2. Accurate Explanation of the Host's Action (2 points):
    • The response acknowledges the host's role in revealing a goat but does not fully explain its effect on the probabilities. (2 points)
  3. Correct Analysis of the Switching Strategy (4 points):
    • The response mentions the concept of switching increasing the probability of winning to 2/3 but does not provide a comprehensive analysis or explanation. (4 points)
  4. Clear and Coherent Explanation (3 points):
    • The response presents the explanation but lacks clarity and coherence in connecting the key points. (3 points)
  5. Concise and Effective Communication (2 points):
    • The response communicates the explanation but could be more concise and focused. (2 points)

Once again, STEM teachers can utilize AI grading as a learning tool for students. Just like with a human teacher, students can ask ChatGPT about a specific criteria of the rubric for clarification. Automated grading systems can process and evaluate a large volume of student responses in a significantly shorter time compared to manual grading. This frees up valuable time for teachers to focus on other important aspects of their teaching responsibilities. AI grading systems can help maintain consistency in grading by applying predefined criteria consistently across all student responses. This reduces potential bias or variations that may arise from manual grading, ensuring a more standardized assessment process.

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