NLP and Education: using semantic similarity to evaluate filled gaps in a large-scale Cloze test in the classroom

• URL: http://arxiv.org/abs/2411.01280v1
• Date: Sat, 02 Nov 2024 15:22:26 GMT
• Title: NLP and Education: using semantic similarity to evaluate filled gaps in a large-scale Cloze test in the classroom
• Authors: Túlio Sousa de Gois, Flávia Oliveira Freitas, Julian Tejada, Raquel Meister Ko. Freitag,
• Abstract summary: Using data from Cloze tests administered to students in Brazil, WE models for Brazilian Portuguese (PT-BR) were employed to measure semantic similarity. A comparative analysis between the WE models' scores and the judges' evaluations revealed that GloVe was the most effective model.
• Score: 0.0
• License:
• Abstract: This study examines the applicability of the Cloze test, a widely used tool for assessing text comprehension proficiency, while highlighting its challenges in large-scale implementation. To address these limitations, an automated correction approach was proposed, utilizing Natural Language Processing (NLP) techniques, particularly word embeddings (WE) models, to assess semantic similarity between expected and provided answers. Using data from Cloze tests administered to students in Brazil, WE models for Brazilian Portuguese (PT-BR) were employed to measure the semantic similarity of the responses. The results were validated through an experimental setup involving twelve judges who classified the students' answers. A comparative analysis between the WE models' scores and the judges' evaluations revealed that GloVe was the most effective model, demonstrating the highest correlation with the judges' assessments. This study underscores the utility of WE models in evaluating semantic similarity and their potential to enhance large-scale Cloze test assessments. Furthermore, it contributes to educational assessment methodologies by offering a more efficient approach to evaluating reading proficiency.

Related papers

• An Automatic and Cost-Efficient Peer-Review Framework for Language Generation Evaluation [29.81362106367831]
  Existing evaluation methods often suffer from high costs, limited test formats, the need of human references, and systematic evaluation biases. In contrast to previous studies that rely on human annotations, Auto-PRE selects evaluators automatically based on their inherent traits. Experimental results indicate our Auto-PRE achieves state-of-the-art performance at a lower cost.
  arXiv  Detail & Related papers  (2024-10-16T06:06:06Z)
• Single Ground Truth Is Not Enough: Add Linguistic Variability to Aspect-based Sentiment Analysis Evaluation [41.66053021998106]
  Aspect-based sentiment analysis (ABSA) is the challenging task of extracting sentiment along with its corresponding aspects and opinions from human language. Current evaluation methods for this task often restrict answers to a single ground truth, penalizing semantically equivalent predictions that differ in surface form. We propose a novel, fully automated pipeline that augments existing test sets with alternative valid responses for aspect and opinion terms.
  arXiv  Detail & Related papers  (2024-10-13T11:48:09Z)
• A Comparative Study of Quality Evaluation Methods for Text Summarization [0.5512295869673147]
  This paper proposes a novel method based on large language models (LLMs) for evaluating text summarization. Our results show that LLMs evaluation aligns closely with human evaluation, while widely-used automatic metrics such as ROUGE-2, BERTScore, and SummaC do not and also lack consistency.
  arXiv  Detail & Related papers  (2024-06-30T16:12:37Z)
• Lessons from the Trenches on Reproducible Evaluation of Language Models [60.522749986793094]
  We draw on three years of experience in evaluating large language models to provide guidance and lessons for researchers. We present the Language Model Evaluation Harness (lm-eval), an open source library for independent, reproducible, and evaluation of language models.
  arXiv  Detail & Related papers  (2024-05-23T16:50:49Z)
• Auditing the Use of Language Models to Guide Hiring Decisions [2.949890760187898]
  Regulatory efforts to protect against algorithmic bias have taken on increased urgency with rapid advances in large language models. Current regulations -- as well as the scientific literature -- provide little guidance on how to conduct these assessments. Here we propose and investigate one approach for auditing algorithms: correspondence experiments.
  arXiv  Detail & Related papers  (2024-04-03T22:01:26Z)
• How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey [23.757740341834126]
  We show that H-Score generally performs well with superiorities in effectiveness and efficiency. We also outline the difficulties of consideration of training details, applicability to text generation, and consistency to certain metrics which shed light on future directions.
  arXiv  Detail & Related papers  (2023-12-08T01:17:28Z)
• FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets [69.91340332545094]
  We introduce FLASK, a fine-grained evaluation protocol for both human-based and model-based evaluation. We experimentally observe that the fine-graininess of evaluation is crucial for attaining a holistic view of model performance.
  arXiv  Detail & Related papers  (2023-07-20T14:56:35Z)
• From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
  We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• Multi-Dimensional Evaluation of Text Summarization with In-Context Learning [79.02280189976562]
  In this paper, we study the efficacy of large language models as multi-dimensional evaluators using in-context learning. Our experiments show that in-context learning-based evaluators are competitive with learned evaluation frameworks for the task of text summarization. We then analyze the effects of factors such as the selection and number of in-context examples on performance.
  arXiv  Detail & Related papers  (2023-06-01T23:27:49Z)
• Evaluating the Performance of Large Language Models on GAOKAO Benchmark [53.663757126289795]
  This paper introduces GAOKAO-Bench, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples. With human evaluation, we obtain the converted total score of LLMs, including GPT-4, ChatGPT and ERNIE-Bot. We also use LLMs to grade the subjective questions, and find that model scores achieve a moderate level of consistency with human scores.
  arXiv  Detail & Related papers  (2023-05-21T14:39:28Z)
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach [84.02388020258141]
  We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
  arXiv  Detail & Related papers  (2021-02-20T03:29:20Z)

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.