Using scaffolded feedforward and peer feedback to improve problem-based learning in large classes. (June 2022)

• Record Type: Journal Article
• Authors: Rodríguez, María Fernanda; Nussbaum, Miguel; Yunis, Leyla; Reyes, Tomás; Alvares, Danilo; Joublan, Jean; Navarrete, Patricio
• Is Part Of: Computers & education. Volume 182(2022)
• Publication Date: 2022-06
• Subjects: Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2022.104446
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: The growing demand for access to higher education has seen institutions turn increasingly towards large classes. Implementing active, problem-based learning in this context can be difficult as it requires the lecturer to attend to every student's individual needs. Given the lack of tools for providing personalized feedback, this represents a significant challenge. The aim of this study is to see how best to support lecturers in giving timely feedback to students in a large class during problem-based learning. To meet this goal, we propose a model that combines feedforward, scaffolded using an automated summarization tool, with peer feedback. In this sense, the lecturer first provides feedforward through a series of general comments before an anonymous peer gives personalized feedback. The results show that, despite not giving personalized feedback, the lecturer is able to provide enriched formative feedforward thanks to the summary generated by the automated system. Furthermore, in more qualitative terms, the students show that they appreciate the opportunity to both give and receive feedback. Finally, the students' critical thinking skills are also shown to improve progressively from one activity to the next. Given the research gap regarding how lecturers use the reports generated by automated summarization tools, our study contributes to the literature by proposing a strategy for lecturers to use such reports to provide feedforward. Additionally, this study also contributes to the literature by proposing a model that can be fully integrated in both synchronous and asynchronous online learning. Highlights: Providing feedforward to large classes during problem-based learning is a challenge for the lecturer. Automated summarization tools support the lecturer's feedforward, by helping them provide general comments. In-class, peer feedback, guided by the lecturer's feedforward can power personalized feedback.