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Autor/inn/en | Banawan, Michelle P.; Shin, Jinnie; Arner, Tracy; Balyan, Renu; Leite, Walter L.; McNamara, Danielle S. |
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Titel | Shared Language: Linguistic Similarity in an Algebra Discussion Forum |
Quelle | 12 (2023), Artikel 53 (20 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
Zusatzinformation | ORCID (Banawan, Michelle P.) ORCID (Arner, Tracy) Weitere Informationen |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
Schlagwörter | Algebra; Discourse Analysis; Semantics; Syntax; Classification; Models; Communities of Practice; Language Usage; Natural Language Processing; Artificial Intelligence; Computational Linguistics; Computer Mediated Communication; Language Role; Group Dynamics; Intelligent Tutoring Systems; Mathematics Instruction; Mentors; Middle School Students; State Standards; Grade 7; Grade 8; Grade 9; School Districts; Scores; Language Variation; Florida Diskursanalyse; Semantik; Classification system; Klassifikation; Klassifikationssystem; Analogiemodell; Community; Sprachgebrauch; Natürliche Sprache; Künstliche Intelligenz; Linguistics; Computerlinguistik; Computerkonferenz; Gruppendynamik; Intelligentes Tutorsystem; Mathematics lessons; Mathematikunterricht; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; School year 07; 7. Schuljahr; Schuljahr 07; School year 08; 8. Schuljahr; Schuljahr 08; School year 09; 9. Schuljahr; Schuljahr 09; School district; Schulbezirk; Sprachenvielfalt |
Abstract | Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the extent to which discourse reveals "shared language" among its participants that can promote inclusion or affinity. Shared language is characterized in terms of linguistic features and lexical, syntactical, and semantic similarities. We leverage a multi-method approach, including (1) feature engineering using state-of-the-art natural language processing techniques to select the most appropriate features, (2) the bag-of-words classification model to predict linguistic similarity, (3) explainable AI using the local interpretable model-agnostic explanations to explain the model, and (4) a two-step cluster analysis to extract innate groupings between linguistic similarity and emotion. We found that linguistic similarity within and between the threaded discussions was significantly varied, revealing the dynamic and unconstrained nature of the discourse. Further, word choice moderately predicted linguistic similarity between posts within threaded discussions (accuracy = 0.73; F1-score = 0.67), revealing that discourse participants' lexical choices effectively discriminate between posts in terms of similarity. Lastly, cluster analysis reveals profiles that are distinctly characterized in terms of linguistic similarity, trust, and affect. Our findings demonstrate the potential role of linguistic similarity in supporting social cohesion and affinity within online discourse communities. (As Provided). |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2024/1/01 |