Sebastian Gombert

I started to work as a doctoral researcher at the Educational Technologies group, DIPF, in 2021. Before this, I studied Linguistic and Literary Computing at TU Darmstadt. I finished with a thesis on detecting semantic uncertainty in scientific writing using pre-trained transformer language models and conditional random fields. Besides this, I worked as a teaching assistant, software developer and junior research assistant in various contexts and was active in academic self-government. When I am not busy researching, I like producing electronic music, playing the drums and hiking.

I am primarily interested in processing linguistic and behavioural learner data using methods from data science, machine learning, natural language/speech processing, corpus linguistics and computational social science, an endeavour often referred to as learning analytics. Within the field of learning analytics, my main interest is to build systems that analyse learners’ performance and interactions to support them with adaptive individualized feedback. Moreover, I am interested in research infrastructures, research software engineering and cognitive and post-structuralist approaches to linguistics.

• Master of Arts in Linguistic and Literary Computing, 2021, TU Darmstadt
• Bachelor of Arts in Digital Philology and Computer Science, 2019, TU Darmstadt

• 2014 – Developer @ Sanofi-Aventis Deutschland GmbH
• 2015-2017 – Developer @ Abius GmbH
• 2015-2020 – Co-founder @ mediacollective UG
• 2017 – IT Administrator @ Student Council FB2, TU Darmstadt
• 2016-2021 Teaching- and Junior Research Assistant @ Corpus- and Computational Linguistics, TU Darmstadt
• 2020 Teaching Assistant @ Ubiquitous Knowledge Processing Lab, TU Darmstadt
• 2020-2021 Developer @ AskAlbert / studiumdigitale, Goethe Universität Frankfurt

• Gombert, S., Di Mitri, D., Karademir, O., Kubsch, M., Kolbe, H., Tautz, S., Grimm, A., Bohm, I., Neumann, K., & Drachsler, H. (2022). Coding energy knowledge in constructed responses with explainable NLP models. In Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12767
• Gombert, S. (2022). Methods and perspectives for the automated analytic assessment of free-text responses in formative scenarios. In Proceedings of the Doctoral Consortium at the Seventeenth European Conference on Technology Enhanced Learning (EC-TEL 2022), Toulouse, France, September 12th-16th, 2022
• Daniele Di Mitri, Sebastian Gombert, & Onur Karademir (2022). Reflecting on the Actionable Components of a Model for Augmented Feedback. In Proceedings of the Second International Workshop on Multimodal Immersive Learning Systems (MILeS 2022) At the Seventeenth European Conference on Technology Enhanced Learning (EC-TEL 2022), Toulouse, France, September 12th-16th, 2022 (pp. 45–50). CEUR-WS.org.
• Böttger, F., Cetinkaya, U., Di Mitri, D., Gombert, S., Shingjergji, K., Iren, D., & Klemke, R. (2022). Privacy-Preserving and Scalable Affect Detection in Online Synchronous Learning. In Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption. EC-TEL 2022. Lecture Notes in Computer Science, vol 13450. Springer International Publishing. https://doi.org/10.1007/978-3-031-16290-9_4
• Menzel, L., Gombert, S., Di Mitri, D., & Drachsler, H. (2022). Superpowers in the Classroom: Hyperchalk is an Online Whiteboard for Learning Analytics Data Collection. In Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption. EC-TEL 2022. Lecture Notes in Computer Science, vol 13450. Springer International Publishing. https://doi.org/10.1007/978-3-031-16290-9_37
• Gombert, S. (2021). Twin BERT Contextualized Sentence Embedding Space Learning and Gradient-Boosted Decision Tree Ensembles for Scene Segmentation in German Literature. In Proceedings of the Shared Task on Scene Segmentation co-located with the 17th Conference on Natural Language Processing (KONVENS 2021), Düsseldorf, Germany, September 6th, 2021 (Vol. 3001, pp. 42–48). CEUR-WS.org.
• Gombert, S., & Bartsch, S. (2021). TUDA-CCL at SemEval-2021 Task 1: Using Gradient-boosted Regression Tree Ensembles Trained on a Heterogeneous Feature Set for Predicting Lexical Complexity. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021) (pp. 130-137). Association for Computational Linguistics.
• Gombert, S. & Bartsch, S. (2020). MultiVitaminBooster at PARSEME Shared Task 2020: Combining Window- and Dependency-Based Features with Multilingual Contextualised Word Embeddings for VMWE Detection. In Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons (pp. 149–155). Association for Computational Linguistics.

• Fink, A., Frey, A., Liu, T., & Gombert, S. (2022, September). Nutzung von Natural Language Processing zur automatisierten Kodierung von Essays in digital gestützten Großveranstaltungen an Hochschulen. In 52nd Conference of the German Psychological Society (DGPs).
• Gombert, S. (2022). Workshop: Assessing constructed responses with explainable natural language processing. In Sixteenth EATEL Summer School on Technology Enhanced Learning.
• Gombert, S., Bhattacharya, S., Di Mitri, D., Drachsler, H. (2022). Interpretierbare maschinelle Sprachverarbeitung für die Identifikation konzeptionellen Wissens in energiephysikbezogenen deutschsprachigen Kurzantworten. In Komplexität nicht nur bewältigen, sondern zu Nutze machen: Log- und Textdaten im Educational Assessment. 9. Tagung der Gesellschaft für Empirische Bildungsforschung.