@inproceedings{e11ce50323a74ecc9c06373292010b07,
title = "The impact of the peer review process evolution on learner performance in e-learning environments",
abstract = "Student performance over a course of an academic program can be significantly affected and positively influenced through a series of feedback processes by peers and tutors. Ideally, this feedback is structured and incremental, and as a consequence, data presents at large scale even in relatively small classes. In this paper, we investigate the effect of such processes as we analyze assessment data collected from online courses. We plan to fully analyze the massive dataset of over three and a half million granular data points generated to make the case for the scalability of these kinds of learning analytics. This could shed crucial light on assessment mechanism in MOOCs, as we continue to refine our processes in an effort to strike a balance of emphasis on formative in addition to summative assessment.",
keywords = "Elearning, Peer-Reviews, Recursive Feedback, Student Performance",
author = "Matthew Montebello and Petrilson Pinheiro and Bill Cope and Mary Kalantzis and Tabassum Amina and Duane Searsmith and Dungyun Cao",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery. All rights reserved.; 5th Annual ACM Conference on Learning at Scale, L at S 2018 ; Conference date: 26-06-2018 Through 28-06-2018",
year = "2018",
month = jun,
day = "26",
doi = "10.1145/3231644.3231693",
language = "English (US)",
series = "Proceedings of the 5th Annual ACM Conference on Learning at Scale, L at S 2018",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of the 5th Annual ACM Conference on Learning at Scale, L at S 2018",
address = "United States",
}