Grading Massive Open Online Courses Using Large Language Models

• URL: http://arxiv.org/abs/2406.11102v2
• Date: Mon, 16 Dec 2024 06:50:20 GMT
• Title: Grading Massive Open Online Courses Using Large Language Models
• Authors: Shahriar Golchin, Nikhil Garuda, Christopher Impey, Matthew Wenger,
• Abstract summary: Massive open online courses (MOOCs) offer free education globally. Peer grading, often guided by a straightforward rubric, is the method of choice. We explore the feasibility of using large language models (LLMs) to replace peer grading in MOOCs.
• Score: 3.0936354370614607
• License:
• Abstract: Massive open online courses (MOOCs) offer free education globally. Despite this democratization of learning, the massive enrollment in these courses makes it impractical for an instructor to assess every student's writing assignment. As a result, peer grading, often guided by a straightforward rubric, is the method of choice. While convenient, peer grading often falls short in terms of reliability and validity. In this study, we explore the feasibility of using large language models (LLMs) to replace peer grading in MOOCs. To this end, we adapt the zero-shot chain-of-thought (ZCoT) prompting technique to automate the feedback process once the LLM assigns a score to an assignment. Specifically, to instruct LLMs for grading, we use three distinct prompts based on ZCoT: (1) ZCoT with instructor-provided correct answers, (2) ZCoT with both instructor-provided correct answers and rubrics, and (3) ZCoT with instructor-provided correct answers and LLM-generated rubrics. We tested these prompts in 18 different scenarios using two LLMs, GPT-4 and GPT-3.5, across three MOOCs: Introductory Astronomy, Astrobiology, and the History and Philosophy of Astronomy. Our results show that ZCoT, when augmented with instructor-provided correct answers and rubrics, produces grades that are more aligned with those assigned by instructors compared to peer grading. Finally, our findings indicate a promising potential for automated grading systems in MOOCs, especially in subjects with well-defined rubrics, to improve the learning experience for millions of online learners worldwide.

Related papers

• Using Large Language Models for Automated Grading of Student Writing about Science [2.883578416080909]
  AI has introduced the possibility of using large language models (LLMs) to evaluate student writing. An experiment was conducted using GPT-4 to determine if machine learning methods based on LLMs can match or exceed the reliability of instructor grading. The results should also be applicable to non-science majors in university settings, where the content and modes of evaluation are similar.
  arXiv  Detail & Related papers  (2024-12-25T00:31:53Z)
• Understanding the Dark Side of LLMs' Intrinsic Self-Correction [55.51468462722138]
  Intrinsic self-correction was proposed to improve LLMs' responses via feedback prompts solely based on their inherent capability. Recent works show that LLMs' intrinsic self-correction fails without oracle labels as feedback prompts. We identify intrinsic self-correction can cause LLMs to waver both intermedia and final answers and lead to prompt bias on simple factual questions.
  arXiv  Detail & Related papers  (2024-12-19T15:39:31Z)
• Grade Like a Human: Rethinking Automated Assessment with Large Language Models [11.442433408767583]
  Large language models (LLMs) have been used for automated grading, but they have not yet achieved the same level of performance as humans. We propose an LLM-based grading system that addresses the entire grading procedure, including the following key components.
  arXiv  Detail & Related papers  (2024-05-30T05:08:15Z)
• LLMs as Meta-Reviewers' Assistants: A Case Study [4.345138609587135]
  Large Language Models (LLMs) can be used to generate a controlled multi-perspective summary (MPS) of experts opinions. This paper performs a case study with three popular LLMs, i.e., GPT-3.5, LLaMA2, and PaLM2, to assist meta-reviewers in better comprehending experts perspectives.
  arXiv  Detail & Related papers  (2024-02-23T20:14:16Z)
• AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails [43.19453208130667]
  Large Language Models (LLMs) have found several use cases in education, ranging from automatic question generation to essay evaluation. In this paper, we explore the potential of using Large Language Models (LLMs) to author Intelligent Tutoring Systems. We create a sample end-to-end tutoring system named MWPTutor, which uses LLMs to fill in the state space of a pre-defined finite state transducer.
  arXiv  Detail & Related papers  (2024-02-14T14:53:56Z)
• Large Language Models As MOOCs Graders [3.379574469735166]
  We explore the feasibility of leveraging large language models (LLMs) to replace peer grading in MOOCs. To instruct LLMs, we use three different prompts based on a variant of the zero-shot chain-of-thought prompting technique. Our results show that Zero-shot-CoT, when integrated with instructor-provided answers and rubrics, produces grades that are more aligned with those assigned by instructors.
  arXiv  Detail & Related papers  (2024-02-06T07:43:07Z)
• The Earth is Flat? Unveiling Factual Errors in Large Language Models [89.94270049334479]
  Large Language Models (LLMs) like ChatGPT are in various applications due to their extensive knowledge from pre-training and fine-tuning. Despite this, they are prone to generating factual and commonsense errors, raising concerns in critical areas like healthcare, journalism, and education. We introduce a novel, automatic testing framework, FactChecker, aimed at uncovering factual inaccuracies in LLMs.
  arXiv  Detail & Related papers  (2024-01-01T14:02:27Z)
• AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations [52.43593893122206]
  Alignedcot is an in-context learning technique for invoking Large Language Models. It achieves consistent and correct step-wise prompts in zero-shot scenarios. We conduct experiments on mathematical reasoning and commonsense reasoning.
  arXiv  Detail & Related papers  (2023-11-22T17:24:21Z)
• Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves [57.974103113675795]
  We present a method named Rephrase and Respond' (RaR) which allows Large Language Models to rephrase and expand questions posed by humans. RaR serves as a simple yet effective prompting method for improving performance. We show that RaR is complementary to the popular Chain-of-Thought (CoT) methods, both theoretically and empirically.
  arXiv  Detail & Related papers  (2023-11-07T18:43:34Z)
• Tuna: Instruction Tuning using Feedback from Large Language Models [74.04950416204551]
  We propose finetuning an instruction-tuned large language model using our novel textitprobabilistic ranking and textitcontextual ranking approaches. Probabilistic ranking enables the instruction-tuned model to inherit the relative rankings of high-quality and low-quality responses from the teacher LLM. On the other hand, learning with contextual ranking allows the model to refine its own response distribution using the contextual understanding ability of stronger LLMs.
  arXiv  Detail & Related papers  (2023-10-20T09:55:06Z)
• FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation [92.43001160060376]
  We study the factuality of large language models (LLMs) in the context of answering questions that test current world knowledge. We introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types. We benchmark a diverse array of both closed and open-source LLMs under a two-mode evaluation procedure that allows us to measure both correctness and hallucination. Motivated by these results, we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA.
  arXiv  Detail & Related papers  (2023-10-05T00:04:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.