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Semantic-Based Feedback Recommendation for Automatic Essay Evaluation

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

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Abstract

With the development of sophisticated e-learning platforms, educational recommender systems and automatic essay evaluation are becoming an important feature in e-learning systems. Most of the works in educational recommendation techniques are focused on recommending learning materials or learning activities to the learners. In this paper, we proposed and implemented a semantic-based feedback recommendation approach for automatic essay evaluation, which will allow assessors to interact with automatic essay evaluation systems, give feedback on learner’s essay solution in the form of textual comments and provide recommendation to other similar essay solution based on the solution which the assessor has given textual feedback. To compute the semantic similarity and to provide feedback recommendation, we used neural word embedding and relaxed word mover’s similarity. The proposed approach achieves high-performance accuracy, compared to the state-of-the-art methods, according to our experimental results.

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Notes

  1. 1.

    http://etestsupport.com/.

  2. 2.

    https://www.kaggle.com/c/asap-sas.

  3. 3.

    https://code.google.com/archive/p/word2vec/.

  4. 4.

    http://scikit-learn.org/.

  5. 5.

    http://www.numpy.org/.

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Acknowledgments

The research has been supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.2–16-2017-00013).

Supported by Telekom Innovation Laboratories (T-Labs), the Research and Development unit of Deutsche Telekom.

Author information

Authors and Affiliations

  1. Faculty of Informatics, Department of Data Science and Engineering, Telekom Innovation Laboratories, ELTE-Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, 1117, Hungary

    Tsegaye Misikir Tashu & Tomáš Horváth

  2. Faculty of Informatics, 3in Research Group, ELTE-Eötvös Loránd University, Martonvásár, Hungary

    Tsegaye Misikir Tashu

  3. Faculty of Science, Institute of Computer Science, Pavol Jozef Šafárik University, Košice, Slovakia

    Tomáš Horváth

Authors
  1. Tsegaye Misikir Tashu
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  2. Tomáš Horváth
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Corresponding author

Correspondence to Tsegaye Misikir Tashu .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Tashu, T.M., Horváth, T. (2020). Semantic-Based Feedback Recommendation for Automatic Essay Evaluation. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_24

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