mlsanityData to 7 Oct 2026

Analyzing Large Language Models for Classroom Discussion Assessment

arXiv 2406.08680 ↗First posted 12 Jun 2024cs.CLcited by 4

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Abstract (arXiv)

Automatically assessing classroom discussion quality is becoming increasingly feasible with the help of new NLP advancements such as large language models (LLMs). In this work, we examine how the assessment performance of 2 LLMs interacts with 3 factors that may affect performance: task formulation, context length, and few-shot examples. We also explore the computational efficiency and predictive consistency of the 2 LLMs. Our results suggest that the 3 aforementioned factors do affect the performance of the tested LLMs and there is a relation between consistency and performance. We recommend a LLM-based assessment approach that has a good balance in terms of predictive performance, computational efficiency, and consistency.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 36 references that are in the map. First posted on arXiv 12 Jun 2024.

Builds on

24 of 36 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  5. 2023Mistral 7Bcited by 3,065
  6. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  7. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  8. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  9. 2020Contrastive Learning with Hard Negative Samplescited by 619
  10. 2020How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answeringcited by 439
  11. 2023Fact-Checking Complex Claims with Program-Guided Reasoningcited by 146
  12. 2021Do Long-Range Language Models Actually Use Long-Range Context?cited by 92
12 more
  1. 2023TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Taskscited by 48
  2. 2021Measuring Conversational Uptake: A Case Study on Student-Teacher Interactionscited by 44
  3. 2023Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instructioncited by 37
  4. 2022Computationally Identifying Funneling and Focusing Questions in Classroom Discoursecited by 15
  5. 2021Using Transformers to Provide Teachers with Personalized Feedback on their Classroom Discourse: The TalkMoves Applicationcited by 10
  6. 2024The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Educationcited by 9
  7. 2023Automated Evaluation of Classroom Instructional Support with LLMs and BoWs: Connecting Global Predictions to Specific Feedbackcited by 7
  8. 2019Argument Component Classification for Classroom Discussionscited by 5
  9. 2019Annotating Student Talk in Text-based Classroom Discussionscited by 4
  10. 2023Measuring Five Accountable Talk Moves to Improve Instruction at Scalecited by 4
  11. 2021Contextual Argument Component Classification for Class Discussionscited by 3
  12. 2023Utilizing Natural Language Processing for Automated Assessment of Classroom Discussioncited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Multimodal Assessment of Classroom Discourse Quality: A Text-Centered Attention-Based Multi-Task Learning Approachcited by 2
  2. 2026LLM Reasoning Predicts When Models Are Right: Evidence from Coding Classroom Discoursecited by 2
  3. 2026From Tool to Teammate: LLM Coding Agents as Collaborative Partners for Behavioral Labeling in Educational Dialogue Analysiscited by 0
  4. 2026Mitigating LLM biases toward spurious social contexts using direct preference optimizationcited by 0