Automatic feedback in online learning environments: A systematic literature review

By A. P. Cavalcanti, A. Barbosa, Ruan Carvalho, F. Freitas, Yi-Shan Tsai, Dragan Gasevic, R. F. Mello. Computers and Education: Artificial Intelligence, 2021

384 citations recorded by the source corpus.

Subfields: Computer Science Applications, Education

View Paper: https://www.semanticscholar.org/paper/48167e4f65ff0fb7d2de503f4ca3b540777dd627

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Jiahong Su
2. Davy Tsz Kit Ng
3. S. K. Chu
4. Jac Ka Lok Leung
5. R. Ng
6. Lixiang Yan
7. Dragan Gasevic

In Education

1. S. K. Chu
2. Lixiang Yan
3. Davy Tsz Kit Ng
4. Dragan Gasevic

Citations in the corpus

• Practical and ethical challenges of large language models in education: A systematic scoping review (2023, background): “…punctual, time-consuming tasks (Carroll et al., 2022) such as question generation (Kurdi et al., 2020; Bulut and Yildirim-Erbasli, 2022; Oleny, 2023), feedback provision (Cavalcanti et al., 2021; Nye et al., 2023), scoring essays (Ramesh and Sanam-pudi, 2022) and short answers (Zeng et al., 2023).”
• Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world (2023, background): “, parents, colleagues), enhance personalized learning to understand students’ learning progress and needs, and conduct various tasks such as offering automatic feedback, self-diagnosing, and promoting online collaboration among learners (Cavalcanti et al., 2021).”
• Impact of AI assistance on student agency (2023, background): “…systems suggest personalised learning resources (Bodily & Verbert, 2017), automated feedback systems provide real-time feedback on students ’ work (Cavalcanti et al., 2021; Deeva, Bogdanova, Serral, Snoeck, & De Weerdt, 2021), personalised nudging systems remind students of upcoming deadlines…”