Maxim Konca

Total: 5

2023 (1)

• A. Mehler, M. Bagci, A. Henlein, G. Abrami, C. Spiekermann, P. Schrottenbacher, M. Konca, A. Lücking, J. Engel, M. Quintino, J. Schreiber, K. Saukel, and O. Zlatkin-Troitschanskaia, “A Multimodal Data Model for Simulation-based Learning with Va.Si.Li-Lab,” in Proceedings of HCI International 2023, 2023. accpeted
  [BibTeX]

  @inproceedings{Mehler:et:al:2023:a,
      author    = {Alexander Mehler and Mevlüt Bagci and Alexander Henlein and Giuseppe Abrami and Christian Spiekermann and Patrick Schrottenbacher and Maxim Konca and Andy Lücking and Juliane Engel and Marc Quintino and Jakob Schreiber and Kevin Saukel and Olga Zlatkin-Troitschanskaia},
      title     = {A Multimodal Data Model for Simulation-based Learning with {Va.Si.Li-Lab}},
      booktitle = {Proceedings of {HCI} International 2023},
      series    = {Lecture Notes in Computer Science},
      publisher = {Springer},
      year      = {2023},
      note      = {accpeted},
  }

2022 (2)

• M. Konca, A. Lücking, A. Mehler, M. Nagel, and O. Zlatkin-Troitschanskaia, Computational educational linguistics for `Critical Online Reasoning’ among young professionals in medicine, law and teaching, 2022.
  [BibTeX]

  @misc{Konca:et:al:2022,
    author = {Konca, Maxim and L{\"u}cking, Andy and Mehler, Alexander and Nagel, Marie-Theres and Zlatkin-Troitschanskaia, Olga},
    howpublished = {Presentation given at the AERA annual meeting, 21.-26.04. 2022, WERA symposium},
    month = 04,
    title = {Computational educational linguistics for `Critical Online Reasoning' among young professionals in medicine, law and teaching},
    year = 2022,
    pdf = {https://www.texttechnologylab.org/wp-content/uploads/2022/10/BRIDGE_WERA_AERA-2022_reduce.pdf}
  }

• A. Mehler, M. Konca, M. Nagel, A. Lücking, and O. Zlatkin-Troitschanskaia, On latent domain-specific textual preferences in solving Internet-based generic tasks among graduates/young professionals from three domains, 2022.
  [Abstract] [BibTeX]

  Although Critical Online Reasoning (COR) is often viewed as a general competency (e.g. Alexander et al. 2016), studies have found evidence supporting their domain-specificity (Toplak et al. 2002). To investigate this assumption, we focus on commonalities and differences in textual preferences in solving COR-related tasks between graduates/young professionals from three domains. For this reason, we collected data by requiring participants to solve domain-specific (DOM-COR) and generic (GEN-COR) tasks in an authentic Internet-based COR performance assessment (CORA), allowing us to disentangle the assumed components of COR abilities. Here, we focus on GEN-COR to distinguish between different groups of graduates from the three disciplines in the context of generic COR tasks. We present a computational model for educationally relevant texts that combines features at multiple levels (lexical, syntactic, semantic). We use machine learning to predict domain-specific group membership based on documents consulted during task solving. A major contribution of our analyses is a multi-part text classification system that contrasts human annotation and rating of the documents used with a semi-automatic classification to predict the document type of web pages. That is, we work with competing classifications to support our findings. In this way, we develop a computational linguistic model that correlates GEN-COR abilities with properties of documents consulted for solving the GEN-COR tasks. Results show that participants from different domains indeed inquire different sets of online sources for the same task. Machine learning-based classifications show that the distributional differences can be reproduced by computational linguistic models.

  @misc{Mehler:et:al:2022,
  author = {Mehler, Alexander and Konca, Maxim and Nagel, Marie-Theres and L\"{u}cking, Andy and Zlatkin-Troitschanskaia, Olga},
  year = {2022},
  month = {03},
  howpublished = {Presentation at BEBF 2022},
  title = {On latent domain-specific textual preferences in solving Internet-based generic tasks among graduates/young professionals from three domains},
  abstract={Although Critical Online Reasoning (COR) is often viewed as a general competency (e.g. Alexander et al. 2016), studies have found evidence supporting their domain-specificity (Toplak et al. 2002). To investigate this assumption, we focus on commonalities and differences in textual preferences in solving COR-related tasks between graduates/young professionals from three domains. For this reason, we collected data by requiring participants to solve domain-specific (DOM-COR) and generic (GEN-COR) tasks in an authentic Internet-based COR performance assessment (CORA), allowing us to disentangle the assumed components of COR abilities. Here, we focus on GEN-COR to distinguish between different groups of graduates from the three disciplines in the context of generic COR tasks. We present a computational model for educationally relevant texts that combines features at multiple levels (lexical, syntactic, semantic). We use machine learning to predict domain-specific group membership based on documents consulted during task solving. A major contribution of our analyses is a multi-part text classification system that contrasts human annotation and rating of the documents used with a semi-automatic classification to predict the document type of web pages. That is, we work with competing classifications to support our findings. In this way, we develop a computational linguistic model that correlates GEN-COR abilities with properties of documents consulted for solving the GEN-COR tasks. Results show that participants from different domains indeed inquire different sets of online sources for the same task. Machine learning-based classifications show that the distributional differences can be reproduced by computational linguistic models.},
  pdf = {https://www.texttechnologylab.org/wp-content/uploads/2022/04/On_latent_domain-specific_textual_preferences_in_solving_Internet-based_generic_tasks_among_graduates__young_professionals_from_three_domains.pdf}
  }

2021 (1)

• M. Konca, A. Mehler, D. Baumartz, and W. Hemati, “From distinguishability to informativity. A quantitative text model for detecting random texts.,” Language and Text: Data, models, information and applications, vol. 356, pp. 145-162, 2021.
  [BibTeX]

  @article{Konca:et:al:2021,
    title={From distinguishability to informativity. A quantitative text model for detecting random texts.},
    author={Konca, Maxim and Mehler, Alexander and Baumartz, Daniel and Hemati, Wahed},
    journal={Language and Text: Data, models, information and applications},
    volume={356},
    pages={145--162},
    year={2021},
    editor={Adam Paw{\l}owski, Jan Ma{\v{c}}utek, Sheila Embleton and George Mikros},
    publisher={John Benjamins Publishing Company},
    doi={10.1075/cilt.356.10kon}
  }

2020 (1)

• A. Mehler, W. Hemati, P. Welke, M. Konca, and T. Uslu, “Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks,” Frontiers in Education, vol. 5, p. 206, 2020.
  [Abstract] [BibTeX]

  We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Controlling the size factor, we investigate this hypothesis for a number of 25 subject areas. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article therefore deals with the question of whether Wikipedia exhibits this kind of linguistic relativity or not. From the perspective of educational science, the article develops a computational model of the information landscape from which multiple texts are drawn as typical input of web-based reading. For this purpose, it develops a hybrid model of intra- and intertextual similarity of different parts of the information landscape and tests this model on the example of 35 languages and corresponding Wikipedias. In the way it measures the similarities of hypertexts, the article goes beyond existing approaches by examining their structural and semantic aspects intra- and intertextually. In this way it builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.

  @article{Mehler:Hemati:Welke:Konca:Uslu:2020,
    abstract = {We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Controlling the size factor, we investigate this hypothesis for a number of 25 subject areas. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article therefore deals with the question of whether Wikipedia exhibits this kind of linguistic relativity or not. From the perspective of educational science, the article develops a computational model of the information landscape from which multiple texts are drawn as typical input of web-based reading. For this purpose, it develops a hybrid model of intra- and intertextual similarity of different parts of the information landscape and tests this model on the example of 35 languages and corresponding Wikipedias. In the way it measures the similarities of hypertexts, the article goes beyond existing approaches by examining their structural and semantic aspects intra- and intertextually. In this way it builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.},
    author = {Mehler, Alexander and Hemati, Wahed and Welke, Pascal and Konca, Maxim and Uslu, Tolga},
    doi = {10.3389/feduc.2020.562670},
    issn = {2504-284X},
    journal = {Frontiers in Education},
    pages = {206},
    title = {Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks},
    url = {https://www.frontiersin.org/article/10.3389/feduc.2020.562670},
          pdf = {https://www.frontiersin.org/articles/10.3389/feduc.2020.562670/pdf},
    volume = {5},
    year = {2020}
  }