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(Schlagwörter: "Data Mining")

A short history, emerging challenges and co-operation structures for Artificial Intelligence in […] Mavrikis, Manolis; Cukurova, Mutlu; Di Mitri, Daniele; Schneider, Jan; Drachsler, Hendrik Journal Article | In: Bildung und Erziehung | 2021 41559 Endnote: Author(s): Mavrikis, Manolis; Cukurova, Mutlu; Di Mitri, Daniele; Schneider, Jan; Drachsler, Hendrik
Title: A short history, emerging challenges and co-operation structures for Artificial Intelligence in education
In: Bildung und Erziehung, (2021) 74:3, S. 249-263
DOI: 10.13109/buer.2021.74.3.249
URL: https://doi.org/10.13109/buer.2021.74.3.249
Publication Type: 3a. Beiträge in begutachteten Zeitschriften; Bibliografien/Rezensionen u.ä. (z.B. Linktipps)
Language: Englisch
Keywords: Künstliche Intelligenz; Digitalisierung; Bildung; Ethik; Geschichte <Histor>; Kooperation; Lernprozess; Datenanalyse; Feedback; Automatisierung; Digitale Medien; Medieneinsatz; Data Mining; Lernforschung; Lehrer; Roboter; Implementierung; Vertrauen; Akzeptanz
Abstract: Der vorliegende Beitrag präsentiert für das Themenheft über Künstliche Intelligenz und Pädagogik eine kurze Geschichte der Forschung auf diesem Gebiet und fasst aktuelle Herausforderungen zusammen. Der Artikel fokussiert auf mögliche Paradigmenwechsel auf dem Forschungsgebiet und betont die Notwendigkeit der Betrachtung von Theorie und Praxis unter Beachtung ethischer Grundsätze. Abschließend wird auf internationale Kooperationsstrukturen in diesem Bereich hingewiesen, welche interdisziplinäre Perspektiven und methodische Vorgehen unterstützen können, die für die Forschung in diesem Bereich erforderlich sind. (DIPF/Orig.)
Abstract (english): To accompany the special issue in Artificial Intelligence and Education, this article presents a short history of research in the field and summarises emerging challenges. We highlight key paradigm shifts that are becoming possible but also the need to pay attention to theory, implementation and pedagogy while adhering to ethical principles. We conclude by drawing attention to international co-operation structures in the field that can support the interdiscipniary perspectives and methods required to undertake research in the area. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Künstliche Intelligenz. Ihr Potenzial und der Mythos des Lehrkraft-Bots Zehner, Fabian Journal Article | In: Schulmanagement-Handbuch | 2019 39057 Endnote: Author(s): Zehner, Fabian
Title: Künstliche Intelligenz. Ihr Potenzial und der Mythos des Lehrkraft-Bots
In: Schulmanagement-Handbuch, (2019) 169, S. 6-30
URN: urn:nbn:de:0111-pedocs-175610
URL: http://nbn-resolving.org/urn:nbn:de:0111-pedocs-175610
Publication Type: 3b. Beiträge in weiteren Zeitschriften; praxisorientiert
Language: Deutsch
Keywords: Künstliche Intelligenz; Begriff; Computer; Spracherkennung; Data Mining; Codierung; Technologie; Innovation; Unterricht; Lernen; Unterstützung; Testauswertung; E-Learning; Bildungsforschung
Abstract: [In diesem] Kapitel legt der Autor dar, was Künstliche Intelligenz ausmacht, in welchen Bereichen wir bereits mit Künstlicher Intelligenz konfrontiert sind und wie sie schon heute in unseren Alltag integriert sind. Darauffolgend wird erläutert, wie Künstliche Intelligenz im Bildungsbereich gewinnbringend eingesetzt werden kann. (DIPF/Orig.)
DIPF-Departments: Bildungsqualität und Evaluation

Argumentation mining in user-generated web discourse Habernal, Ivan; Gurevych, Iryna Journal Article | In: Computational Linguistics Journal | 2017 36233 Endnote: Author(s): Habernal, Ivan; Gurevych, Iryna
Title: Argumentation mining in user-generated web discourse
In: Computational Linguistics Journal, 43 (2017) 1, S. 125-179
DOI: 10.1162/COLI_a_00276
URL: http://www.mitpressjournals.org/doi/abs/10.1162/COLI_a_00276#.WIDIonpp-nU
Publication Type: 3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Language: Englisch
Keywords: Argumentation; Automatisierung; Computerlinguistik; Data Mining; Diskurs; Erziehungswissenschaft; Information Retrieval; Modell; Reliabilität; Soziale Software; Textanalyse; World wide web 2.0
Abstract: The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

What is the essence of a claim? Cross-domain claim identification Daxenberger, Johannes; Habernal, Ivan; Stab, Christian; Gurevych, Iryna Book Chapter | Aus: Association for Computational Linguistics (Hrsg.): The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017): Proceedings of the conference, September 9-11, 2017, Copenhagen, Denmark | Stroudsburg; PA: Association for Computational Linguistics | 2017 37872 Endnote: Author(s): Daxenberger, Johannes; Habernal, Ivan; Stab, Christian; Gurevych, Iryna
Title: What is the essence of a claim? Cross-domain claim identification
In: Association for Computational Linguistics (Hrsg.): The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017): Proceedings of the conference, September 9-11, 2017, Copenhagen, Denmark, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 2045-2056
URL: http://www.aclweb.org/anthology/D/D17/D17-1217.pdf
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Argumentation; Computerlinguistik; Data Mining; Qualitative Forschung; Sprachanalyse; Text; Textanalyse
Abstract: Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent conceptualization of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Neural end-to-end learning for computational argumentation mining Eger, Steffen; Daxenberger, Johannes; Gurevych, Iryna Book Chapter | Aus: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 1 (long papers), July 30 - August 4, 2017, Vancouver, Canada | Stroudsburg; PA: Association for Computational Linguistics | 2017 37878 Endnote: Author(s): Eger, Steffen; Daxenberger, Johannes; Gurevych, Iryna
Title: Neural end-to-end learning for computational argumentation mining
In: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 1 (long papers), July 30 - August 4, 2017, Vancouver, Canada, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 11-22
DOI: 10.18653/v1/P17-1002
URL: https://aclanthology.info/pdf/P/P17/P17-1002.pdf
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Argumentation; Automatisierung; Computerlinguistik; Data Mining; Klassifikation; Rhetorik; Semantik; Textanalyse
Abstract: We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiL-STMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning 'natural' subtasks, in a multi-task learning setup, improves performance. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Visualization for text mining in the digital humanities. Empowering researchers to use advanced […] Hocker, Julian Book Chapter | Aus: Gäda, Maria;Trkulja, Violeta;Petras, Vivien (Hrsg.): Everything changes, everything stays the same? Understanding information spaces: Proceedings of the 15th International Symposium of Information Science (ISI 2017), Berlin, Germany, 13th - 15th March 2017 | Glückstadt: Hülsbusch | 2017 37200 Endnote: Author(s): Hocker, Julian
Title: Visualization for text mining in the digital humanities. Empowering researchers to use advanced tools for text mining
In: Gäda, Maria;Trkulja, Violeta;Petras, Vivien (Hrsg.): Everything changes, everything stays the same? Understanding information spaces: Proceedings of the 15th International Symposium of Information Science (ISI 2017), Berlin, Germany, 13th - 15th March 2017, Glückstadt: Hülsbusch, 2017 (Schriften zur Informationswissenschaft, 70), S. 308-314
URL: http://isi2017.ib.hu-berlin.de/ISI_17_ONLINE_FINAL.pdf#page=309
Publication Type: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Benutzerfreundlichkeit; Visualisierung; Data Mining; Geisteswissenschaften; Digitalisierung; Forscher; Qualitative Forschung; Textanalyse; Computerunterstütztes Verfahren; Tool; Konzeption
Abstract: In this PhD thesis, a visual interface for text analysis and text mining in the digital humanities (DH) will be developed. Text analysis is a crucial task in the DH, but advanced text mining technologies like topic modeling or clustering are difficult to use for most researchers. My work bridges this gap using visualizations. To ensure an adequate usability of visualizations for epistemological practices, the visualizations will be realized with researchers in an agile and participatory approach. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Educational process mining. New possibilities for understanding students' problem-solving skills Tóth, Krisztina; Rölke, Heiko; Goldhammer, Frank; Barkow, Ingo Book Chapter | Aus: Csapó, Benő;Funke, Joachim (Hrsg.): The nature of problem solving: Using research to inspire 21st century learning | Paris: OECD Publishing | 2017 37734 Endnote: Author(s): Tóth, Krisztina; Rölke, Heiko; Goldhammer, Frank; Barkow, Ingo
Title: Educational process mining. New possibilities for understanding students' problem-solving skills
In: Csapó, Benő;Funke, Joachim (Hrsg.): The nature of problem solving: Using research to inspire 21st century learning, Paris: OECD Publishing, 2017 , S. 193-209
DOI: 10.1787/9789264273955-14-en
Publication Type: 4. Beiträge in Sammelwerken; Sammelband (keine besondere Kategorie)
Language: Englisch
Keywords: Computer; Problemlösen; Schüler; Kompetenz; Schülerleistung; Leistungsmessung; Technologiebasiertes Testen; Datenanalyse; Data Mining; Logdatei; Informationssystem; Datenbank; Visualisierung; Interaktion; Verhalten
Abstract: The assessment of problem-solving skills heavily relies on computer-based assessment (CBA). In CBA, all student interactions with the assessment system are automatically stored. Using the accumulated data, the individual test-taking processes can be reproduced at any time. Going one step further, recorded processes can even be used to extend the problem-solving assessment itself: the test-taking process-related data gives us the opportunity to 1) examine human-computer interactions via traces left in the log file; 2) map students' response processes to find distinguishable problem-solving strategies; and 3) discover relationships between students' activities and task performance. This chapter describes how to extract process-related information from event logs, how to use these data in problem-solving assessments and describes methods which help discover novel, useful information based on individual problem-solving behaviour. (DIPF/Orig.)
DIPF-Departments: Bildungsqualität und Evaluation; Informationszentrum Bildung

Mass collaboration on the web. Textual content analysis by means of natural language processing Habernal, Ivan; Daxenberger, Johannes; Gurevych, Iryna Book Chapter | Aus: Cress, Ulrike;Moskaliuk, Johannes;Jeong, Heisawn (Hrsg.): Mass collaboration and education | Cham: Springer | 2016 35504 Endnote: Author(s): Habernal, Ivan; Daxenberger, Johannes; Gurevych, Iryna
Title: Mass collaboration on the web. Textual content analysis by means of natural language processing
In: Cress, Ulrike;Moskaliuk, Johannes;Jeong, Heisawn (Hrsg.): Mass collaboration and education, Cham: Springer, 2016 , S. 367-390
DOI: 10.1007/978-3-319-13536-6_18
Publication Type: 4. Beiträge in Sammelwerken; Sammelband (keine besondere Kategorie)
Language: Englisch
Keywords: Argumentation; Computerlinguistik; Data Mining; Daten; Inhaltsanalyse; Text; Web log; Wiki; Wissen
Abstract: This chapter describes perspectives for utilizing natural language processing (NLP) to analyze artifacts arising from mass collaboration on the web. In recent years, the amount of user-generated content on the web has grown drastically. This content is typically noisy and un- or at best semi-structured, so that traditional analysis tools cannot properly handle it. To discover linguistic structures in this data, manual analysis is not feasible due to the large quantities of data. In this chapter, we explain and analyze web-based resources of mass collaboration, namely, wikis, web forums, debate platforms, and blog comments. We introduce recent advances and ongoing efforts to analyze textual content in two of these resources with the help of NLP. This includes an approach to discover flows of knowledge in online mass collaboration as well as methods to mine argumentative structures in natural language text. Finally, we outline application scenarios of the previously discussed techniques and resources within the domain of education. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Domain-specific corpus expansion with focused webcrawling Remus, Steffen; Biemann, Chris Book Chapter | Aus: European Language Resources Association (Hrsg.): Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) | Portoroz: European Language Resources Association | 2016 37066 Endnote: Author(s): Remus, Steffen; Biemann, Chris
Title: Domain-specific corpus expansion with focused webcrawling
In: European Language Resources Association (Hrsg.): Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016), Portoroz: European Language Resources Association, 2016 , S. 3607-3611
URL: http://www.lrec-conf.org/proceedings/lrec2016/pdf/316_Paper.pdf
Publication Type: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Algorithmus; Automatisierung; Bildung; Computerlinguistik; Data Mining; Hypertext; Modell; Sprache; Text; Textanalyse
Abstract: This work presents a straightforward method for extending or creating in-domain web corpora by focused webcrawling. The focused webcrawler uses statistical N-gram language models to estimate the relatedness of documents and weblinks and needs as input only N-grams or plain texts of a predefined domain and seed URLs as starting points. Two experiments demonstrate that our focused crawler is able to stay focused in domain and language. The first experiment shows that the crawler stays in a focused domain, the second experiment demonstrates that language models trained on focused crawls obtain better perplexity scores on in-domain corpora. We distribute the focused crawler as open source software. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Crowdsourcing a large dataset of domain-specific context-sensitive semantic verb relations Sukhareva, Maria; Eckle-Kohler, Judith; Habernal, Ivan; Gurevych, Iryna Book Chapter | Aus: European Language Resources Association (Hrsg.): Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) | Portoroz: European Language Resources Association | 2016 36972 Endnote: Author(s): Sukhareva, Maria; Eckle-Kohler, Judith; Habernal, Ivan; Gurevych, Iryna
Title: Crowdsourcing a large dataset of domain-specific context-sensitive semantic verb relations
In: European Language Resources Association (Hrsg.): Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016), Portoroz: European Language Resources Association, 2016 , S. 2131-2137
URL: https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/publikationen/2016/lrec2016_sukhareva.pdf
Publication Type: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Automatisierung; Computerlinguistik; Data Mining; Klassifikation; Semantik; Textanalyse
Abstract (english): We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing. These relations capture fine-grained semantic information between verb-centric propositions, such as temporal or entailment relations. We propose a novel semantic verb relation scheme and design a multi-step annotation approach for scaling-up the annotations using crowdsourcing. We employ several quality measures and report on agreement scores. The resulting dataset is available under a permissive CreativeCommons license. It represents a valuable resource for various applications, such as automatic information consolidation or automatic summarization. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung