Educational Natural Language Processing (EduNLP)

The junior research group "EduNLP" is part of the research center CATALPA.

Automatic evaluation of free text answers and automated feedback for learners and teachers - to make this work very reliably in the future, our junior research group investigates how language processing methods can be used here.


Goals and research questions

This project investigates how natural language processing methods can be used to automatically score free-text answers and provide learners and teachers with automatic feedback.

A core research question of the project is how learners can be provided with formative feedback about their essays. This can cover different aspects of writing such as syntactic or lexical variance, structure and argumentation, coherence, thematic fit with the topic or use of figurative language.

A number of sub-questions arise from this overall goal, such as:

  • What is the performance of existing scoring algorithms for a certain phenomenon and how can they be adapted for a specific use-case?
  • What are properties of useful formative feedback from the learners’ perspective and how can datasets with such feedback messages be collected?
  • How can we automatize such feedback, for example by training a decision tree to select an appropriate feedback message from a pool of human-created messages or by using natural language generation methods to produce the feedback?
  • How do humans judge such feedback, e.g. in terms of understandability and naturalness and how does the feedback influence the learning outcome?
  • Dr. Andrea Horbach

    • Yuning Ding (Doctoral Researcher, start date: 01.01.2022)
    • Viet Nguyen (student Assistant, start date: 01.02.2022)
    • Finn Brodmann (student Assistant, start date: 01.02.2022)
    • Joey Pehlke (student Assistant, start date: 01.02.2022)
  • December 2021 – November 2024

    • Ding, Y., Bexte, M., & Horbach, A. (2022). Don’t Drop the Topic - The Role of the Prompt in Argument Identification in Student Writing. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications.
    • Bexte, M., Horbach, A., & Zesch, T. (2022). Similarity-based Content Scoring - How to Make S-BERT Keep up with BERT. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications.
    • Laarmann-Quante, R., Schwarz, L., Horbach, A., & Zesch, T. (2022). Meet me at the ribary’ – Acceptability of spelling variants in free-text answers to listening comprehension prompts. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications.
    • Bexte, M., Laarmann-Quante, R., Horbach, A., & Zesch, T. (2022). LeSpell - A Multi-Lingual Benchmark Corpus of Spelling Errors to Develop Spellchecking Methods for Learner Language. In Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC-2022).
    • Bexte, M., Horbach, A., & Zesch, T. (2021). Implicit Phenomena in Short-answer Scoring Data. In Proceedings of the First Workshop on Understanding Implicit and Underspecified Language. https://aclanthology.org/2021.unimplicit-1.2/
    • Horbach, A., Aldabe, I., Bexte, M., Lopez de Lacalle, O., & Maritxalar, M. (2020). Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions. In Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC-2020). https://aclanthology.org/2020.lrec-1.217/
    • Ding, Y., Riordan, B., Horbach, A., Cahill, A., & Zesch, T. (2020). Don’t take “nswvtnvakgxpm” for an answer - The surprising vulnerability of automatic content scoring systems to adversarial input. In Proceedings of the 28th International Conference on Computational Linguistics(COLING 2020). https://aclanthology.org/2020.coling-main.76/
    • Ding, Y., Horbach, A., Wang, H., Song, X., & Zesch, T. (2020). Chinese Content Scoring: Open-Access Datasets and Features on Different Segmentation Levels. In Proceedings of the 1st conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing(AACL-IJCNLP 2020). https://aclanthology.org/2020.aacl-main.37/
    • Horbach, A., & Zesch, T. (2019). The Influence of Variance in Learner Answers on Automatic Content Scoring. Frontiers in Education, 4, 28. https://duepublico2.uni-due.de/servlets/MCRFileNodeServlet/duepublico_derivate_00047459/Horbach_Zesch_Influence_Variance.pdf
    • Horbach, A., Stennmanns, S., & Zesch, T. (2018). Cross-lingual Content Scoring. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications (pp. 410–419). New Orleans, LA, USA: Association for Computational Linguistics. http://www.aclweb.org/anthology/W18-0550
    • Horbach, A., & Pinkal, M. (2018). Semi-Supervised Clustering for Short Answer Scoring. In LREC. Miyazaki, Japan. http://www.lrec-conf.org/proceedings/lrec2018/pdf/427.pdf
    • Zesch, T., & Horbach, A. (2018). ESCRITO - An NLP-Enhanced Educational Scoring Toolkit. In Proceedings of the Language Resources and Evaluation Conference (LREC). Miyazaki, Japan: European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2018/pdf/590.pdf
    • Horbach, A., Ding, Y., & Zesch, T. (2017). The Influence of Spelling Errors on Content Scoring Performance. In Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017) (pp. 45–53). Taipei, Taiwan: Asian Federation of Natural Language Processing. https://www.aclweb.org/anthology/W17-5908
    • Horbach, A., Scholten-Akoun, D., Ding, Y., & Zesch, T. (2017). Fine-grained essay scoring of a complex writing task for native speakers. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications (pp. 357–366). Copenhagen, Denmark: Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-5040
    • Riordan, B., Horbach, A., Cahill, A., Zesch, T., & Lee, C. M. (2017). Investigating neural architectures for short answer scoring. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications (pp. 159–168). Copenhagen, Denmark: Association for Computational Linguistics. https://aclanthology.org/W17-5017/