Literaturnachweis - Detailanzeige
| Autor/inn/en | Li, Chenglu; Xing, Wanli; Leite, Walter |
|---|---|
| Titel | Building Socially Responsible Conversational Agents Using Big Data to Support Online Learning: A Case with Algebra Nation |
| Quelle | (2022), (28 Seiten)
PDF als Volltext |
| Zusatzinformation | ORCID (Li, Chenglu) ORCID (Xing, Wanli) ORCID (Leite, Walter) Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Social Responsibility; Computer Mediated Communication; Group Discussion; Artificial Intelligence; Safety; Learning Analytics; Benchmarking; Online Systems; Algebra; Mathematics Instruction; Computer Software; Social Support Groups; Models; Cooperative Learning; Integrated Learning Systems; Intuition; Discourse Analysis; Antisocial Behavior; Language Usage |
| Abstract | A discussion forum is a valuable tool to support student learning in online contexts. However, interactions in online discussion forums are sparse, leading to other issues such as low engagement and dropping out. Recent educational studies have examined the affordances of conversational agents (CA) powered by artificial intelligence (AI) to automatically support student participation in discussion forums. However, few studies have paid attention to the safety of CAs. This study aimed to address the safety challenges of CAs constructed with educational big data to support learning. Specifically, we proposed a safety-aware CA model, benchmarked with two state-of-the-art (SOTA) models, to support high school student learning in an online algebra learning platform. We applied automatic text analysis to evaluate the safety and socio-emotional support levels of CA-generated and human-generated texts. A large dataset was used to train and evaluate the CA models, which consisted of all discussion post-reply pairs (n = 2,097,139) by 71,918 online math learners from 2015 to 2021. Results show that while SOTA models can generate supportive texts, their safety is compromised. Meanwhile, our proposed model can effectively enhance the safety of generated texts while providing comparable support. [This is the online version of an article published in "British Journal of Educational Technology."] (As Provided). |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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