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<p>Automated Essay Scoring (AES) systems are computer-based tools to evaluate students’ responses. While ensemble and hybrid models have demonstrated effectiveness in various Natural Language Processing (NLP) tasks, their specific applications within AES remain underexplored. This study addresses this gap through a systematic literature review. Guided by the Kitchenham methodology, we formulated five research questions and conducted a structured search and evaluation process. Twenty-five (25) articles published between 2004 and 2024 were included based on predefined inclusion and quality criteria. The review identifies two types of ensemble models and outlines diverse hybrid model techniques used in AES. It highlights the models’ effectiveness, limitations, dataset types, evaluation metrics, and the best-performing approaches. This review provides researchers with insights into trends, challenges, and opportunities in applying ensemble and hybrid techniques for AES, thereby supporting the development of performance-oriented AES systems.</p>

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Ensemble and Hybrid Models in Automated Essay Scoring: A Literature Review

  • 期刊论文

  • 发表日期：

    2025年8月8日

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  • Vidasha Ramnarain-Seetohul,
  • Yasmine Rosunally,
  • Vandana Bassoo

摘要

Automated Essay Scoring (AES) systems are computer-based tools to evaluate students’ responses. While ensemble and hybrid models have demonstrated effectiveness in various Natural Language Processing (NLP) tasks, their specific applications within AES remain underexplored. This study addresses this gap through a systematic literature review. Guided by the Kitchenham methodology, we formulated five research questions and conducted a structured search and evaluation process. Twenty-five (25) articles published between 2004 and 2024 were included based on predefined inclusion and quality criteria. The review identifies two types of ensemble models and outlines diverse hybrid model techniques used in AES. It highlights the models’ effectiveness, limitations, dataset types, evaluation metrics, and the best-performing approaches. This review provides researchers with insights into trends, challenges, and opportunities in applying ensemble and hybrid techniques for AES, thereby supporting the development of performance-oriented AES systems.