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Arabic Automatic Essay Scoring Systems: An Overview Study

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Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

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Abstract

Assessing a large number of student essays is a challenging task for teachers. It requires a lot of time and effort. Therefore, the automation of this work has become essential. For a decade, Automatic Essay Scoring (AES) systems have represented a very important and complicated research axis in the field of natural language processing. In literature, various methods of automatic essay scoring (AES) have been proposed. However, most of them have concentrated on European languages, while works dealing with Arabic language are very limited.

This paper presents an overview of Arabic Automatic Essay Scoring systems. The purpose of this study is to present a state of the art of the existing methods and models of Automatic Essay Scoring (AES) systems in Arabic language. We describe some models then we compare and discuss the achieved results. Finally, we give our perspectives and challenges to propose our future work in this context.

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Notes

  1. 1.

    Correlation is a metric for measuring the effectiveness of AES systems compared to human scoring. It is the rate of correspondence between the human score and the automatic score provided by the system [20].

  2. 2.

    LCS is a character-based similarity algorithm that determines the similarity between two strings to the length of the longest sub-string that appears in the two sentences.

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Author information

Authors and Affiliations

  1. National School of Computer Science, Manouba University, Manouba, Tunisia

    Rim Aroua Machhout, Chiraz Ben Othmane Zribi & Saoussen Mathlouthi Bouzid

Authors
  1. Rim Aroua Machhout
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  2. Chiraz Ben Othmane Zribi
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  3. Saoussen Mathlouthi Bouzid
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Corresponding author

Correspondence to Rim Aroua Machhout .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Weiping Ding

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Machhout, R.A., Zribi, C.B.O., Bouzid, S.M. (2022). Arabic Automatic Essay Scoring Systems: An Overview Study. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_108

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