Literaturnachweis - Detailanzeige
| Autor/in | Olney, Andrew M. |
|---|---|
| Titel | Generating Response-Specific Elaborated Feedback Using Long-Form Neural Question Answering [Konferenzbericht] Paper presented at ACM Conference on Learning @ Scale (8th, Online, Jun 22-25, 2021). |
| Quelle | (2021), (10 Seiten)
PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) |
| Zusatzinformation | Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| DOI | 10.1145/3430895.3460131 |
| Schlagwörter | Feedback (Response); Error Patterns; Artificial Intelligence; Test Format; Natural Language Processing; Information Retrieval; Community Colleges; Two Year College Students; Mathematics; Definitions; Tennessee |
| Abstract | In contrast to simple feedback, which provides students with the correct answer, elaborated feedback provides an explanation of the correct answer with respect to the student's error. Elaborated feedback is thus a challenge for AI in education systems because it requires dynamic explanations, which traditionally require logical reasoning and knowledge engineering to generate. This study presents an alternative approach that formulates elaborated feedback in terms of long-form question answering (LFQA). An off-the-shelf LFQA system was evaluated by human raters in a 2x2x2x2 ablation design that manipulated the context documents given to the LFQA model and the post-processing of model output. Results indicate that context manipulations improve performance but that postprocessing can have detrimental results. [This paper was published in: "Proceedings of the Eighth ACM Conference on Learning @ Scale," 2021, pp. 27-36.] (As Provided). |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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