Rafael A. Calvo is a senior lecturer in the School of Electrical and Information Engineering at the University of Sydney specializing in learning systems and director of the Learning and Affect Technologies Engineering (Latte) group. He has a Ph.D. in Artificial Intelligence applied to automatic document classification and has worked at Carnegie Mellon University, and Universidad Nacional de Rosario, and as a consultant for projects worldwide. Rafael is author of numerous publications in the areas of Web engineering and learning systems, recipient of four teaching awards, Senior Member of IEEE and Associate Editor of IEEE Transactions on Affective Computing.
Students' Conceptions of Tutor and Automated Feedback in Professional Writing
Article first published online: 2 JAN 2013
2010 American Society for Engineering Education
Journal of Engineering Education
Volume 99, Issue 4, pages 427–438, October 2010
How to Cite
Calvo, R. A. and Ellis, R. A. (2010), Students' Conceptions of Tutor and Automated Feedback in Professional Writing. Journal of Engineering Education, 99: 427–438. doi: 10.1002/j.2168-9830.2010.tb01072.x
- Issue published online: 2 JAN 2013
- Article first published online: 2 JAN 2013
- automated feedback;
- professional writing
Professional writing is an essential outcome for engineering graduates and hence a vital part of engineering education. To provide a successful learning experience for students engaged in writing activities, timely feedback is necessary. Providing this feedback to increasing numbers of students poses a major challenge for instructors. New automated systems work towards providing both timely and appropriate writing feedback, but students' views on automated feedback, and feedback in general, are not well understood.
To contribute to a deeper understanding of students' conceptions of feedback from tutors and an automated system called Glosser, and how these conceptions are related to achievement.
Students in an engineering course worked in pairs to write an engineering report on e-business. The design of the study involves in-depth interviews and the analysis employs an approach in which student conceptions of automated feedback are investigated in relation to related feedback from their tutor, perceptions of automated feedback in general, and their academic achievement.
Students' conceptions of feedback vary and can be grouped into cohesive and fragmented, which is consistent with other theoretical models. Close associations were found between more cohesive conceptions of feedback and better academic performance.
A student's conception of traditional and automated feedback is similar, being either cohesive or fragmented. Changing one may change the other. Deep learners see feedback as a way of learning about the topic whereas shallow learners see them as a way to improve the communication aspects of writing. Design considerations based on these results are discussed.