Literaturnachweis - Detailanzeige
| Autor/inn/en | Xue, Linting; Lynch, Collin F.; Chi, Min |
|---|---|
| Titel | Unnatural Feature Engineering: Evolving Augmented Graph Grammars for Argument Diagrams [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016). |
| Quelle | (2016), (8 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Graphs; Persuasive Discourse; College Students; Expertise; Artificial Intelligence; Automation; Computation; Data Analysis; Standards; Pennsylvania (Pittsburgh) |
| Abstract | Graph data such as argument diagrams has become increasingly common in EDM. Augmented Graph Grammars are a robust rule formalism for graphs. Prior research has shown that hand-authored graph grammars can be used to automatically grade student-produced argument diagrams. But hand-authored rules can be time consuming and expensive to produce, and they may not generalize well to novel contexts. We applied Evolutionary Computation to automatically induce empirically-valid graph grammars for argument diagrams that can be used for automatic grading or provide the basis for hints. Our results show that our approach can generate more relevant rules than experts or other state of the art algorithms, and that these evolved rules outperform the alternatives. [For the full proceedings, see ED592609.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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